Fermi LAT detected blazars

Bright AGN Source List from the First Three Months of the Fermi Large Area Telescope All-Sky Survey


The first three months of sky-survey operation with the Fermi Gamma Ray Space Telescope (Fermi) Large Area Telescope (LAT) reveals 132 bright sources at b10 with test statistic greater than 100 (corresponding to about 10). Two methods, based on the CGRaBS, CRATES and BZCat catalogs, indicate high-confidence associations of 106 of these sources with known AGNs. This sample is referred to as the LAT Bright AGN Sample (LBAS). It contains two radio galaxies, namely Centaurus A and NGC 1275, and 104 blazars consisting of 57 flat spectrum radio quasars (FSRQs), 42 BL Lac objects, and 5 blazars with uncertain classification. Four new blazars were discovered on the basis of the LAT detections. Remarkably, the LBAS includes 10 high-energy peaked BL Lacs (HBLs), sources which were so far hard to detect in the GeV range. Another 10 lower-confidence associations are found. Only thirty three of the sources, plus two at b10, were previously detected with EGRET, probably due to the variable nature of these sources. The analysis of the gamma-ray properties of the LBAS sources reveals that the average GeV spectra of BL Lac objects are significantly harder than the spectra of FSRQs. No significant correlation between radio and peak gamma-ray fluxes is observed. Blazar log N - log S and luminosity functions are constructed to investigate the evolution of the different blazar classes, with positive evolution indicated for FSRQs but none for BLLacs. The contribution of LAT-blazars to the total extragalactic -ray intensity is estimated.

gamma rays: observations — galaxies: active — galaxies: jets — BL Lacertae objects: general

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1 Introduction

The Gamma ray Large Area Space Telescope (GLAST ) was launched on 11 June 2008, and renamed the Fermi Gamma Ray Space Telescope shortly after entering its scientific operating mission, which began on 11 August, 2008. The Large Area Telescope (LAT) on Fermi provides an increase in sensitivity by more than an order-of-magnitude over its predecessor EGRET, the Energetic Gamma Ray Experiment Telescope on the Compton Gamma Ray Observatory (Thompson et al., 1993), and the Italian Space Agency Satellite AGILE (Astro-rivelatore Gamma a Immagini Leggero; Tavani et al., 2008). In sky survey mode, the LAT observes all parts of the sky every 3 hours, providing effectively uniform exposure on longer timescales.

One of the major scientific goals of the Fermi Gamma Ray Space Telescope is to provide new data about -ray activity of AGNs. Rapidly varying fluxes and large luminosities of extragalactic -ray sources are best explained if the rays are emitted from collimated jets of charged particles moving at relativistic speeds (Blandford & Rees, 1978; Maraschi et al., 1992). Fermi-LAT observations will help determine how these particles are accelerated, where the gamma rays are emitted, what the energy and power budgets of the supermassive black-hole engines are, what this says for the fueling and growth of black holes, and the reasons for the differences between radio-loud and radio-quiet AGNs, and FSRQs and BL Lac objects. These are just a few of the questions that -ray AGN studies with the Fermi-LAT are helping to answer (see Atwood et al., 2009, for more discussion of these goals).

In a companion publication (Abdo et al., 2009c), 132 bright sources at with test statistic (TS) are found in the preliminary three month Fermi all-sky survey. As expected from the EGRET legacy, a large fraction of these sources are AGNs. Detailed results of the subset of the Fermi bright source list that are associated with AGNs are presented here.

Sixty-six high-confidence blazars are listed in the Third EGRET catalog of high-energy gamma-ray sources (3EG catalog; Hartman et al., 1999), with the majority of them, , identified as flat spectrum radio quasars (FSRQs), and the remaining identified as belonging to the BL Lac class.57 The recently released catalog of high-confidence AGILE gamma-ray sources58 (Pittori et al., 2008) shows a somewhat higher percentage of BL Lacs. Unlike AGN surveys at optical or X-ray energies, in which the majority of AGNs are radio quiet (e.g., della Ceca et al., 1994; Ivezić et al., 2002), all AGNs detected at MeV energies are also significant radio sources. This includes the 3EG and AGILE blazars, which are so far identified with flat spectrum (radio spectral index at GHz frequencies) radio-loud AGNs, and most show superluminal motion (Jorstad et al., 2001; Kellermann et al., 2004). Moreover, the redshift distribution is broad, with the largest redshift AGN known in the 3EG catalog at .

Here we present a source list of bright AGNs found in the set of the 132 bright LAT sources at b10. Identification of variable -ray sources with blazars depends on the statistical likelihood of positional association and correlated variability of the -ray emissions with lower-frequency radiations (e.g. Sowards-Emmerd et al., 2003). The 106 sources having high-confidence associations with known blazars and radio-galaxies constitute the LAT Bright AGN Sample (LBAS). Included in this list are mean fluxes, weekly peak fluxes, spectral indices, locations, and variability information. Only sources with confidence levels greater than are retained in the LBAS. This list is not, however, complete, as we already know of many more sources at lower significance. The limiting flux depends on both the source sky location and the spectral hardness.

In Section 2, observations with the LAT, analysis methods, and the source detection procedure are presented. Section 3 describes the association method and gives the list of bright Fermi-LAT detected blazars. Key properties of the LBAS, including flux and spectral index, are presented in Section 4. The LBAS is compared with EGRET blazars in Section 5. Section 6 considers the radio/gamma-ray connection. Population studies, including source types and redshifts, are presented in Section 7, where the N - S flux distributions and luminosity functions of the LBAS are constructed. The results are discussed in Section 8, including implications of the results for blazar evolution. We summarize in Section 9.

In the following we use a CDM cosmology with values given within 1 of the WMAP results (Komatsu et al., 2008), namely , and . Here the Hubble constant km s Mpc is used.

2 Observations with the Large Area Telescope

The Fermi-LAT is a pair-conversion gamma-ray telescope sensitive to photon energies greater than 20 MeV. It is made of a tracker (composed of two sections, front and back, with different capabilities), a calorimeter and an anticoincidence system to reject the charged-particle background. The LAT has a large peak effective area ( cm for 1 GeV photons in the event class considered here), viewing sr of the full sky with excellent angular resolution (68% containment radius at GeV for the front section of the tracker and about a factor of 2 larger for the back section). A full description of the LAT instrument and its predicted performance are reported in Atwood et al. (2009). During the first year, the telescope operates in sky-survey mode observing the whole sky every 3 hours. The overall coverage of the sky is fairly uniform, with variations of around 15% around the mean value.

The LAT data used here were collected during the first 3-month all-sky survey, from August 4 to October 30 2008. We refer to the companion paper (Abdo et al., 2009c) for a full description of the data selection and analysis. In order to avoid background contamination from the bright Earth limb, time intervals where the Earth entered the LAT Field-of-View (FoV) were excluded from this study (corresponding to a rocking angle  47 deg). In addition, events that were reconstructed within 8 of the Earth limb were excluded from the analysis (corresponding to a zenith angle cut of 105). Due to uncertainties in the current calibration, only photons belonging to the ”Diffuse” class with energies above 100 MeV were retained. These photons provide the purest gamma-ray dataset. The energy range was even more restricted in the source detection and spectral fitting analyses described below, where only photons with E 200 MeV were selected. The list of sources reported in Tables 1 and 2 was obtained as the result of the source detection, localization and significance estimate analyses described in detail in Abdo et al. (2009c).

The source detection step made use of two wavelet algorithms, (mr_filter) (Starck & Pierre, 1998) and (PGWAVE) (Ciprini et al., 2007). The algorithms were run independently for different energy bands associated with different localization power and the results were cross-checked. The positions of the sources for which the detection significance was above threshold (4) were then refined using (pointfit), a simplified likelihood method (see Abdo et al. (2009c)). This algorithm uses photons with E500 MeV and returns the optimized sky position as well as an estimate of the error radius for most detected sources. As discussed in Abdo et al. (2009c), the final error in the source position was estimated by multiplying the error radius returned by the algorithm by a factor close to 1.4 and adding 0.04 in quadrature (estimated from the residuals between the estimated and expected position of Vela). The 95% confidence error radius was then evaluated assuming a 2-D normal distribution.

To better estimate the source significance, we used the maximum likelihood algorithm implemented in (gtlike) a tool that is part of the standard Fermi-LAT ScienceTools software package59. The flux, photon index and test statistic (TS) of each source in the energy range 0.2-100 GeV were determined by analyzing regions of interest (ROI) typically 15 in radius. The model of the ROI used to fit the data was built taking into account of all the sources detected within a given ROI. The isotropic background and Galactic Diffuse background models used in the fit are discussed in Abdo et al. (2009c). Each source was modeled with a simple power law (k) for photons E 200 MeV. The flux [E100 MeV] (F), which is conventionally reported, was then calculated with the fitted parameters. This flux will be used throughout this paper. The spectral energy distributions of some bright sources show clear evidence for a break or curvature. A fit with a single power law function is certainly not the most appropriate choice for these sources but the resulting photon index does reflect the spectral hardness. A more detailed spectral analysis of the LBAS sources is beyond the scope of this paper. The source fluxes were also estimated by fitting independent power law functions in two energy bands (0.1-1 GeV) and (1-100 GeV) and summing up the two obtained fluxes. These fluxes (F in Table 3) are the same as those reported in the Fermi bright source list paper (Abdo et al., 2009c). For most sources, the fluxes obtained by the two methods are consistent within 30%.

The same procedure was applied to generate weekly light curves (spanning a 12-week period). From those, the weekly peak flux as well as a variability index (corresponding to a simple criterion) were derived. The variability tag reported in this paper is set for sources associated with a probability of being constant lower than 1%. A few representative light curves are displayed in Fig. 1.

This analysis was performed with the preflight instrument response functions (P6_V1). In flight, the presence of pile-up signals in the LAT tracker and calorimeter left by earlier particles was revealed in periodic-trigger events. This feature leads to a reduction of the real acceptance as compared to the predicted one as fewer events pass the rejection cuts, most notably for low-energy photons. The magnitude of this reduction is still under investigation, but the fluxes reported here may be lower than the true ones by as much as 30% and the photon indices greater than the true ones by as much as 0.1 (true spectra could be softer by 0.1 unit in the photon index). Because of the current uncertainty, no correction has been applied to the results. This uncertainty applies uniformly to all sources. Our relative errors are much smaller (about 3% on the flux, Abdo et al., 2009c). With the acceptance used in this analysis, the measured fluxes of the 3 bright pulsars, Vela, Geminga and Crab (Abdo et al., 2009c) are found to be compatible within 11% with those reported in the 3EG catalog.

Fig. 2 shows the 3-month flux sensitivity for TS=100 and a photon index=2.2 as a function of the sky position, calculated by a semi-analytical, maximum likelihood estimate of the significance. This estimate takes the actual exposure, the PSF and the different backgrounds (galactic diffuse, extragalactic diffuse and instrumental) into account. The limiting flux is higher at low galactic latitude due to a higher galactic diffuse background and close to the celestial south pole (l , b ) where the exposure is lower.

The final result of the detection analysis is a list of 205 sources with a (, ), composing the LAT Bright Source (0FGL) list (see Table 6. in Abdo et al., 2009c). For comparison, 31 sources detected by EGRET have a significance greater than in the 3EG (Hartman et al., 1999) and EGR (Casandjian & Grenier, 2008) catalogs. Of these, only 13 were detected at 10. In the 0FGL, a total of 132 sources, including 7 pulsars, are present at 10. We have explored the possibility of associating AGNs with the 125 remaining sources.

3 Source association

Any source association procedure primarily relies on spatial coincidence. Fig. 3 shows the 95% error radius vs () for the sources considered here. This radius depends on both the flux and the photon index, with a mean of 0.14. For comparison, the average corresponding radius for the blazars in the 18 month EGRET sky survey is 0.62. Of the 186 3EG sources, 66 (35%) had “high” (but unspecified) confidence positional associations with blazars in the 3EG catalog. Another 27 positional coincidences were noted at lower significance. Although subsequent work (e.g. Mattox et al., 2001; Sowards-Emmerd et al., 2003) did find additional associations, 40% of the high-latitude 3EG sources remained unidentified.

Although the LAT localization accuracy is much better than those of previous gamma-ray telescopes, it is not good enough to enable a firm identification of a LAT source based solely on spatial coincidence. For the LAT, a firm identification is assumed only if correlated variability is observed at different wavelengths. In order to find associations between LAT sources and AGNs, two different approaches were pursued. The first method is based on a procedure similar to that developed by Sowards-Emmerd et al. (2003) for associating EGRET blazars with radio counterparts using an observational figure of merit (FoM). The second one is based on the calculation of source association probabilities following a Bayesian approach (de Ruiter et al., 1977; Sutherland & Saunders, 1992), similar to that used by Mattox et al. (2001) to associate EGRET sources with radio sources. This method is described in Abdo et al. (2009c).

Several catalogs were used by the two association methods, the most important ones being the Combined Radio All-Sky Targeted Eight GHz Survey (CRATES; Healey et al., 2007) catalog and the Roma-BZCAT60 (Massaro et al., 2007). The CRATES catalog contains precise positions, 8.4 GHz flux densities, and radio spectral indices for more than 11,000 flat-spectrum sources over the entire sky. The Roma-BZCAT is a master list of blazars based on an accurate examination of literature data and presently includes about 2700 sources, all observed at radio and optical frequencies and showing proper characteristics of blazars. Sources are classified as BL Lacertae objects (BZB), flat spectrum radio quasars (BZQ) or as blazars of uncertain type (BZU).

3.1 The Figure-of-Merit Method

The figure of merit (FoM) approach requires a large, uniform all-sky sample of radio sources from which to draw; for this purpose, we use the Combined Radio All-Sky Targeted Eight GHz Survey (CRATES; Healey et al., 2007) catalog. In order to quantify the correlation between CRATES sources and LAT detections, we compare the average number of positional coincidences between LAT sources and CRATES sources to the number of positional coincidences between LAT sources and sources drawn from 1,000 randomized simulations of the radio sky. We count as a positional coincidence any occurrence of a radio source (real or simulated) within twice the 95% error radius of a LAT source, and we generate the simulated radio skies by scrambling the Galactic coordinates of the CRATES sources while keeping their radio flux densities, spectral indices, and counterpart RASS fluxes intact.

We define the excess fractional source density of radio/-ray matches as and we compute this quantity in bins of radio flux density at 8.4 GHz, radio spectral index , and X-ray flux from the ROSAT All-Sky Survey (RASS; Voges et al., 1999). These functions—, , and — constitute the counterpart spectral energy distribution (SED) components of the FoM. The final component is the dependence on the offset between the radio position and the LAT position, which we model simply as , where CL is the confidence limit of the LAT localization contour passing through the radio position. The FoM is then given by . To evaluate the significance of the FoM, we again generated, in the manner described above, 1,000 random simulations of the radio sky and computed the average distribution of FoM. We compared this to the distribution of FoM for the real CRATES sky by again computing the excess fractional source density as a function of FoM. This fractional excess can be directly interpreted as a probability of radio/-ray association for source , giving an immediate mapping from FoM to association probability for each individual source (i.e., is the probability of a false positive association). We find that 1,000 simulated skies result in sufficient statistics in each FoM bin to ensure that the mapping is robust. Very similar results are obtained with 10,000 simulations.

The results of this association procedure are shown in Table 1 and Table 2. Most of the associated radio sources are in the Candidate Gamma-Ray Blazar Survey (CGRaBS; Healey et al., 2008), an optical survey of the 1,625 CRATES sources that were most similar in their radio and X-ray properties to the 3EG blazars. Optical spectroscopy of the sources with unknown redshifts is ongoing. We also considered the possibility of an association with a non-CRATES radio source when no CRATES association was found. Indeed, a FoM can be computed for any object for which the necessary radio data are available. Thus, for those LAT sources without CRATES associations, we drew candidate counterparts from the 1.4 GHz NRAO VLA Sky Survey (NVSS; Condon et al., 1998) or the 843 MHz Sydney University Molonglo Sky Survey (SUMSS; Mauch et al., 2003), searched NED for archival 8.4 GHz data, and calculated the FoM for each candidate. These procedures find high-confidence () associations for 101 of the 125 non-pulsar sources in the 0FGL list with for an association rate of 81%. We also find low-confidence FoM associations () for 14 more sources, bringing the total association rate to 92%. Thus, the radio-bright blazar population continues to dominate the extragalactic sky.

The individual association probabilities can be used to estimate the number of false positives in a given sample: if the probabilities are sorted from highest to lowest, then the number of false positives in a sample of sources is . Among the high-confidence associations, there are 3 false positives, and less than one of the 74 most probable associations should be false.

We also studied the power of the FoM analysis to reject a blazar association for a LAT source. We considered NVSS/SUMSS sources in the direction of the unassociated LAT sources and computed the FoM that each source would have if (A) it were as bright as the 4.85 GHz flux density upper limit from the Green Bank 6 cm survey (GB6; Gregory et al., 1996) or the Parkes-MIT-NRAO survey (PMN; Griffith & Wright, 1993) (unless the source had an actual GB6/PMN detection, in which case we used the measured flux density) or (B) its radio spectrum were as severely inverted as between 1.4 GHz and 4.85 GHz, whichever constraint was tighter. From the low-frequency radio spectrum (or upper limits), we extrapolated the implied 8.4 GHz flux density. If the resulting FoM indicated that the source could conceivably be a flat-spectrum blazar, then we drew no conclusion, but if we found that the “best-case” association probability were 0%, then we concluded that the LAT source was not associated with any typical member of the population of flat-spectrum blazars, and we refer to such cases as “anti-associations.” Note that the spectral index is an extremely conservative cutoff. The most inverted radio spectrum for any actual association has . We are able to secure anti-associations for 10 sources. In fact, five of these turn out to be high-latitude LAT pulsars and pulsar candidates. This shows that, given a reliable LAT error circle, the FoM analysis is capable of indicating definitively that a source is not a blazar

3.2 Summary of association results

The combination of the FoM (described above) and positional association methods yields a number of 106 high-confidence ( 0.90) associations (constituting the LBAS) and 11 low-confidence (0.400.90) associations listed in Table 1 and 2 respectively. Simple extrapolation of these numbers implies that the LAT should be detecting some 20-25 blazars through the Galactic plane at . Indeed, several have already been located, e.g. 0FGL J0036.7+5951 (1ES 0033+595), 0FGL J0730.41142 (PKS 072711), 0FGL J0826.02228 (PKS 0823223), 0FGL J1802.63939 (PMN J18023940), 0FGL J1833.42106 (PKS 1830211). A more complete search for Galactic background blazars, incorporating spectrum, variability and multiwavelength properties is in progress.

Tables 1 and 2 report, for each source, the LAT name, the name of the associated source based on the FoM method, the value of the FoM parameter and its probability, the name of the positionally associated source and its probability, the redshift and the AGN class. Fig. 4 shows the sky location of the LBAS AGNs.

One source, 0FGL J10340+6051 reported in Table 1, merits special comment. Two radio associations were found by the FoM method for this -ray source, one with very high probability and one with lower, but still significant, probability reported in Table 2. Although the high-probability source likely dominates the -ray emission, it is entirely plausible that the low-probability source contributes non-negligibly to the total -ray flux. We believe that as the LAT detects more sources and confusion of the -ray sky increases, the power of the FoM formalism will become increasingly important to the identification of multiple lower-energy counterparts of complex -ray sources.

Fig. 5 shows the overall, normalized angular separation distributions for both sets of sources (i.e. high- and low-confidence associations). The solid curve corresponds to the expected distribution ( distribution with 2 d.o.f. ) for real associations, the dashed one for accidental associations. This figure provides confidence that most associations are real. From this figure, it appears that the 1.4 correction factor applied to the error radius is somewhat overestimated. This overly conservative factor will be significantly reduced with additional analysis updates.

Four new blazars were discovered. Two of these, CRATES J1012+2439, and CRATES J1032+6051 were classified as FSRQ blazars while CRATES J0144+2705 is a BL Lac. The classification of these three sources was made on the basis of the broad lines observed in their optical spectrum obtained after the LAT detection (Shaw et al., in preparation). The forth new LAT detected blazar is CLASS J1054+2210. Its classification as a BL Lac object was made possible by the analysis of its optical spectrum available at the SDSS on-line archive. As discussed above, CRATES J1032+6051 is the source which has a low probability to be associated with 0FGL J10340+6051.

The other sources listed in Table 1 and 2 were classified as FSRQ or BLLac following the Roma-BZCat and CRATES/CGRaBS catalogs. Some sources, which cannot be properly classified because of the scarcity of available data or which show optical spectra intermediate between those of BL Lacs, FSRQs or radio galaxies, were assigned to the “uncertain class” ( “Unc” label in the tables).

Based on this classification, the LBAS comprises 57 FSRQs, 42 BL Lac objects, 5 blazars of uncertain type, and 2 radio-galaxies (RGs). The relevant EGRET sample of reference corresponds to that of the 18 month EGRET all-sky survey during Phase 1 of the CGRO mission (Fichtel et al., 1994; Dermer, 2007). This survey had relatively uniform exposure, and contained 60 sources, 46 FSRQs, 14 BL Lacs. BL Lacs make up 40% of the LBAS blazars, a fraction significantly higher than found with EGRET (23%). The detection of hard sources (BL Lac objects, see below) by the LAT is intrinsically favored over soft ones (FSRQs). This is partly due to the strongly energy-dependent PSF. The larger bandpass and higher energy for the peak sensitivity (in the 1-5 GeV range) of the LAT as compared to EGRET adds to this effect.

Eleven LBAS sources are associated with blazars already detected in the TeV energy range by the ground based imaging air Cherenkov telescopes. Among these, 7 are classified as high-frequency peaked BL Lacs (HBLs): 1ES 1011+496, Mrk 421, PG 1553+11, Mrk 501, 1ES 1959+650, PKS 2005489 and PKS 2155304; 3 are low-frequency peaked BL Lacs (LBLs): 3C 66A, W Com and BL Lac and one is a FSRQ: 3C 279. These 11 sources represent more than 50% of the TeV blazars detected so far (21). The results of simultaneous observations that cover the optical, X-ray, and high energy gamma-ray bands (LAT and H.E.S.S.) of PKS 2155-304 are reported in Aharonian et al. (2009). Another three HBLs in the LBAS are not yet detected in the TeV range: KUV003111938, 1ES0502+675, B3 0133+388. A total of 10 HBLs are thus present in the LBAS, a remarkable feature given that sources in this class were difficult to detect in the GeV range. Many of these sources were not particularly flaring at other wavelengths during the period of observation.

We compared the broad-band (radio, optical, X-ray) properties of our sample of Fermi-LAT detected blazars with those of the known blazars listed in the Roma-BZCat catalog and found that the broadband properties of the Fermi-LAT detected BL Lacs and FSRQs are consistent with the parent population of FSRQs and BL Lacs. This is illustrated in Fig. 6 displaying the soft X-ray flux vs radio flux density (at 1.4 GHz) diagram for the Fermi-LAT blazars and the full blazar catalog.

The LBAS includes 13 sources (10 FSRQs and 3 BL Lacs) that were detected in a flaring state promptly announced to the community through Astronomical Telegrams. Among these, 0FGL J2254.0+1609, associated with 3C 454.3, is the brightest gamma-ray extra-galactic source observed in the 3-month Fermi-LAT survey and is studied in detail in Abdo et al. (2009a).

The Fermi-LAT has discovered gamma-ray emission from a source having an high-confidence association with NGC 1275, the supergiant elliptical galaxy at the center of the Perseus galaxy cluster. EGRET observations yielded only an upper limit to the NGC 1275 gamma-ray emission. All the details about the gamma-ray properties of this source will be reported in Abdo et al. (2009b).

Cen A is the nearest radio galaxy to us and it was one of the few radio galaxies associated with a 3EG source (J13244314; Sreekumar et al., 1999). It is included in the LBAS and the position of its nucleus is well inside the 95% confidence error radius of the source 0FGL J1310.64301. The measured Fermi flux is F ph cm s, about a factor of 2 greater than that measured by EGRET (Sreekumar et al., 1999).

Recently, two more sources reported in the 3EG catalog were tentatively associated with radio galaxies, 3C 111 (Hartman et al. 2008), and possibly NGC 6251 (Mukherjee et al., 2002; Foschini et al., 2005). These objects are not LBAS sources but the number of radio-galaxies detected at high-energy is expected to increase in the near future as more data accumulate.

Table 4 lists the 33 sources associated with 3EG sources (two more located at 10 were also incorporated). Three bright EGRET blazars associated with 0827+243, PKS 1622297 and 1730130 (NRAO 530), whose average EGRET fluxes are in the range of (25 - 47) ph MeV) cm s do not appear in the LBAS. Presumably, these blazars are simply in a lower flux state than when EGRET was in operation. These 3 sources are also among the 22 sources in the pre-launch LAT monitored list61. Of these 22 sources, 17 have high-significance LAT detections in the first 3-months of data. The remaining two monitored sources (H 1426+428, 1ES 2344+514) did not have previous 3EG detections and thus were not expected to be very bright GeV sources.

We note that the LBAS object B2 0218+35 is a well-known gravitational lens. The source PMN J0948+0022, associated with 0FGL J0948.3+0019, has a flat radio spectrum, but shows an optical spectrum with only narrow emission lines, making it an “uncertain”-type object in the Roma-BZCat.

4 Gamma-ray properties of the LBAS

4.1 Introduction

Table 3 lists the key properties of the 116 sources associated with AGNs (sources with low-confidence associations are in italics): the name, equatorial and galactic coordinates, the parameter measuring the significance of the detection, the photon index (), the photon flux F, the weekly peak flux, the photon flux F and the variability flag. The uncertainties are statistical only. From Table 3 (last column), 40 FSRQs (70%), 12 BL Lacs (29%) and 1 Uncertain blazar (0FGL J0714.2+1934) present in the LBAS show evidence for variability. The observed variability for FSRQs is thus higher than for BL Lacs. One must be careful in interpreting this result as the flux distributions are different for the two classes (see Fig. 7), making the detection of variability easier for FSRQs. An in-depth variability analysis of the LBAS is beyond the scope of this paper.

Table 4 gives similar parameters for the subset of 35 sources (including both high-confidence and low-confidence associations, plus two at b) corresponding to 3EG sources. This subset will be discussed in more detail in section 5.

The source photon index is plotted as a function of the flux in Fig. 7. It is already visible in this figure that the photon indices of BL Lac objects (open circles) and FSRQs (closed circles) are quite distinct. The flux sensitivity (calculated in the same way as for the map shown in Fig. 2 and depicted as solid lines for two different galactic latitudes) is fairly strongly dependent on the photon index. The upper envelope in the spectral index - flux (100 MeV) plot reflects that the peak sensitivity of the LAT is at energies much higher than 100 MeV. These ranges of spectral index and apparent flux limits translate to approximately constant limits above 1 GeV. For a photon index of 2.2, the 10 flux sensitivity F 10 ph cm s, about 3 times lower than that of the Third EGRET catalog.

4.2 Flux

It makes sense to compare the LBAS fluxes with those reported in the Third EGRET Catalog for the EGRET sample. As several analyses (e.g. Mücke & Pohl, 2000; Dermer, 2007) used the peak flux (maximum flux in all EGRET viewing periods), instead of the mean flux because of the fairly non-uniform coverage in the EGRET Catalog, comparisons will be performed both for the mean and peak flux distributions. For EGRET, both distributions are biased as observations were preferentially made of sources known to be highly variable in the gamma-ray band, and some of the observations were triggered by ToO requests when an object was brightly flaring in other wavebands. No such bias exists for the LAT.

Fig. 8a compares the mean flux distribution measured in the LBAS with that measured in the EGRET sample. The high-flux ends of these distributions look similar. This observation points to a nearly constant global gamma-ray luminosity of detectable blazars at a given time, as can naively be expected. In stark contrast, the weekly peak flux distributions (Fig. 8b) look different, the peak fluxes being significantly higher in the EGRET sample. This feature probably arises from the shorter sampling period for the Fermi-LAT as compared to EGRET. In the 3-month period considered here, a given source had much less opportunity to explore very different states than in the 4.5 years over which the EGRET observations were conducted. Another illustration of this effect is given in Fig. 8c,d where the peak flux vs the mean flux and the peak flux/mean flux ratio distributions are shown respectively. The inference that the gamma-ray blazars have characteristic variability timescales of months to years is well confirmed by the observation that only 30% of the EGRET blazars are still detected by the LAT at a comparable flux.

4.3 Photon index

The photon index distribution gives insight into the emission and acceleration processes acting within the AGN jets, as it enables some of the physical parameters involved in these processes to be constrained. Moreover, it can be used to test whether the BL Lac and FSRQ populations have different ray emission properties.

Fig. 9 top displays the photon index distribution for all the LBAS sources. This distribution looks fairly similar to that observed for the EGRET sample (Nandikotkur et al., 2007): it is roughly symmetric and centered at = 2.25. The corresponding distributions for FSRQs and BL Lacs are shown in Fig. 9 middle and bottom respectively. These distributions appear clearly distinct, with little overlap between them. This is a remarkable feature, given that the statistical uncertainty typically amounts to 0.1 for most sources. The distributions have (mean, rms)=(1.99, 0.22) for BL Lacs and (2.40, 0.17) for FSRQs. We used a Kolmogorov-Smirnov (KS) test to test the null hypothesis that both index samples are drawn from the same underlying distribution and found a probability of 2 10 62. Although indications for the existence of two spectrally distinct populations (BL Lacs and FSRQs) in the EGRET blazar sample were mentioned in the literature (Pohl et al., 1997; Venters & Pavlidou, 2007), this is the first time that the distinction appears so clearly. The mean photon index of the 10 HBLs included in the LBAS is 1.76, i.e significantly lower (sources are harder) than the mean of the whole BL Lac subset as expected for these high-energy peaked sources.

To infer physical properties of the blazar populations from the observed photon index distributions, possible instrumental and/or statistical effects have to be assessed. A systematic bias may indeed arise in the likelihood analysis of sources with low photon statistics. To quantify this possible bias we performed a simulation study with the gtobssim tool which is part of the ScienceTools. This tool allows observations to be simulated using the instrument response functions and the real orbit/attitude parameters. Both instrumental and diffuse backgrounds were modeled on the basis of the real backgrounds observed by the LAT.

  1. Samples of sources (100 FSRQs and 100 BL Lacs) with random positions in the 10 sky were simulated.

  2. The real spacecraft orbit and attitude profiles spanning 94 days starting from Aug 4 2008 were used.

  3. The sources were assumed to have a power-law energy distributions. The photon index was drawn from a gaussian distribution with (mean, sigma)= (2.0,0.3) for BL Lacs and (2.3,0.3) for FSRQs. These distributions are referred to as ”input” probability distribution functions (pdfs).

  4. Fluxes were generated according to a lognormal distribution with and

  5. A likelihood analysis was performed for all sources. The pdfs of the spectral indices and fluxes were built for sources with TS100 (“like” pdfs). The TS cut was also applied to the ”input” pdfs.

Possible bias arising from the likelihood analysis as well as the robustness of the separation between BL Lac and FSRQ “like “ pdfs were studied by means of KS tests. ”Input” and ”like” pdfs were found to be consistent with a probability of , for BLLacs and FSRQs respectively, excluding any sizeable bias coming from the likelihood analysis. The TS cut was observed to only affect the distribution tails. Concerning the separation between BL Lacs and FSRQs, the KS test returned that the probability for the two distributions to result from the same parent distribution is .

5 Sources already detected by Egret

After an elapsed time of about 10 years, it is interesting to look at the fraction of the AGNs that were active in the EGRET era and are detected again by the LAT with a comparable flux. Out of 116 sources in the Fermi-LAT sample, 3 sources have positions compatible with sources in the Third EGRET Catalog. Two additional sources, 0FGL J1802.63939 and 0FGL J1833.42106 located at b10 fulfills this condition as well. The 35 sources are listed in Table 4, along with the mean fluxes and photon indices measured by the Fermi-LAT and EGRET as well as the AGN class. These 35 AGNs are composed of 20 FSRQs, 11 BL Lacs, 3 of uncertain type and 1 AGN (Cen A). The BL Lacs are again overrepresented (with a fraction of 31%) as compared to the 1st year sky survey EGRET sample (14 out of 60, i.e. 23%). The (non-simultaneous) fluxes and indices measured by both intruments are compared in Fig. 12. The large scatter observed when comparing the fluxes (Fig. 12 left) can be expected from the variable nature of the blazar emission. The scatter observed when comparing the photon indices is more moderate, as could be expected from the fairly strong correlation between photon index and blazar class mentioned above. For many sources, and most especially for BL Lacs, the indices are measured by the Fermi-LAT with a much better accuracy.

6 Radio gamma-ray connection

With 116/125 high , non-pulsar LAT bright sources associated with radio sources in the CRATES/CGRaBS and the Roma-BZCAT lists, we confirm the findings of the 3EG catalog. In particular, 98/106 () of our high confidence associations have flux density above 100 mJy at 8.4 GHz. In terms of the radio luminosity , the sources in the present sample with a measured redshift span the range erg s. As shown by the histogram in Fig. 13, BL Lacs and FSRQ are not uniformly distributed in this interval, with the former on average at lower radio luminosities (Log  [erg s]) than the latter (Log  [erg s]). Blazars of uncertain type generally lack a redshift. Of the two radio galaxies associated with objects in the LBAS, NGC 1275 is similar to BL Lacs ( erg s), while Cen A lies at the very lower end of the radio power distribution, with erg s.

Cen A, the source associated with 0FGL J1325.44303, is also the only source showing a significant amount of extended radio emission at low frequency (). For all other sources with a low frequency (typically, 365 MHz from the Texas survey, 325 MHz from the WENSS, or 408 MHz from the B2) and a high frequency, high resolution (typically at 8.4 GHz from CRATES) flux density measurement, we find little or no evidence of significant deviation from . Therefore, we find not only that all the sources in our sample are radio emitters, but that they also possess compact cores with flat radio spectral index and much higher luminosity than those of radio galaxies of similar or larger power (Giovannini et al., 1988).

Thanks to the comparatively large number of LBAS sources, it is worthwhile to perform a statistical comparison of their properties in the gamma-ray and radio bands. Previous studies based on EGRET data for 38 extragalactic point sources have been reported (Mücke et al., 1997), which did not support claims of correlations between radio and gamma-ray luminosities. In particular, the analysis of possible correlations needs to be treated with care, because of the many biases that can arise, e.g. from the common redshift dependence when one considers luminosities, or from the reduced dynamical range when one considers mean flux densities, just to name a few.

We have therefore looked at several possible pairs of observables, and we summarize our results in Table 5. In general, we apply the K-correction to the luminosities but not to the fluxes, since this would introduce a bias for the sources without a known redshift. We show in Fig. 14 (left panel) the peak gamma-ray flux vs the radio flux density from CRATES (or NED, in the few cases in which the source is not in the CRATES list). In general, BL Lacs tend to populate the low flux region, and FSRQs the high flux region. Such a constellation is prone to create correlations artificially from purely combining both populations. Given their different redshift distributions, this would be even more apparent in the luminosity plane. For this reason, it is necessary to consider the two populations separately (see Table 5). Indeed, the results of our analysis show the significance of a radio-to-gamma-ray connection to be marginal at most on the basis of the present data, in particular for the FSRQs. Clearly, there is need for a deeper analysis on an enlarged sample regarding this issue, including Monte-Carlo simulations, which we defer to a forthcoming paper.

Finally, we show in the right panel of Fig. 14 the radio luminosity vs. gamma-ray spectral index plane. Thanks to the large LAT energy range, the separation between BL Lacs and FSRQs is readily seen, showing a trend of softer spectral indices for more luminous radio sources. Moreover, this plot seems quite effective at finding sources of a different nature, such as the radio galaxy Cen A, whose gamma-ray index is much softer than that of other low power radio sources. For instance, 0FGL J001740503 is a FSRQ at (Healey et al., 2008) with index = 2.71 and radio luminosity erg s, which could then be a rare case of low-energy peak and low radio luminosity blazar. The other source with large photon index (2.60) and comparatively low radio luminosity ( erg s) is associated with the peculiar source PMN J0948+0022.

7 Population Studies

As described before, the LBAS includes 57 FSRQs, 42 BL Lac objects, 5 blazars of uncertain type and 2 radio galaxies. Ten other sources have lower confidence associations with known blazars. This sample is already comparable with that provided by EGRET and can be used to derive some early results about the redshift and source count distributions and the luminosity function of blazars.

7.1 Redshifts

Fig. 15 and Fig. 16 display the redshift distributions for FSRQs and BL Lac objects, respectively, and their comparison with those of the parent distributions in the BZCat catalog. Please note that 12 of 42 BL Lacs have no measured redshifts. BL Lac objects are generally found at low, , redshift, whereas the peak of the FSRQ redshift distribution is around . Similar distributions were observed for the EGRET blazars (Mukherjee et al., 1997). In the future, as fainter sources become visible, detection of additional nearby radio-galaxies will enhance the number of very low redshift objects in the AGN redshift distributions measured with the Fermi LAT.

Fig. 17 shows the luminosities of the detected sources plotted as a function of their redshifts. The isotropic gamma-ray luminosity was derived using:


Here, is the -ray energy flux (E 100 MeV), is the energy index and is the luminosity distance. A beaming factor was assumed. The solid curve corresponds to a flux limit of F = ph cms.

7.2 log N - log S

Monte Carlo Simulations

Proper population studies must rely on a thorough understanding of the properties of the survey where these objects have been detected. In order to properly estimate the source-detection efficiency and biases, we performed detailed Monte Carlo simulations. The method we adopted is the one developed for ROSAT analysis (Hasinger et al., 1993) and lately used (Cappelluti et al., 2007) for the analysis of the XMM-COSMOS data. For each source population (blazars, FSRQs and BL Lacs) we created a set of 20 LAT all-sky images with background patterns resembling as close as possible the observed ones. The simulations were performed using a similar method as that described in section 4.3. An extragalactic population of pointlike sources was added to each simulated observation. The coordinates of each source were randomly drawn in order to produce an isotropic distribution on the sky. Source fluxes were randomly drawn from a standard log –log distribution with parameters similar to the one observed by LAT (see next section). Even though the method we adopt to derive the survey sensitivity does not depend on the normalization or the slope of the input log –log , using the real distribution allows us to produce simulated observations which closely resemble the real LAT sky. The photon index of each source was also drawn from a Gaussian distribution with observed mean and 1  width consistent with the real population. Thus, for the three simulation sets we adopted the following photon indices similar to the measured ones:

  • 2.240.25 for the total blazar population;

  • 2.410.17 for the FSRQ population;

  • 1.980.22 for the BL Lac population.

More than 30000 sources were simulated for each population. The mock observations were processed applying the same filtering criteria used for real in-flight data. Source detection was performed on E200 MeV photons with a simplified version of the detection algorithm63. For every pair of input-output sources, we computed the quantity:


where , and are the source coordinates and flux of the detected sources while , and are the corresponding values of the input sources and , , the associated statistical uncertainties. We then flagged those with the minimum value of R as the most likely associations. Only sources at b are retained.

The goal of these simulations is to derive the probability of detecting a source (with given mean properties, e.g. photon index and flux) in the LAT survey as a function of source flux. This can be computed from the simulations reported above as the ratio between the number of detected and input sources in a given flux bin. The detection efficiencies for the three source populations are reported in Fig. 18. A few things can be noted readily. First, the bias of the LAT survey against soft sources (i.e. FSRQs) is apparent. Second, the LAT b10 survey becomes complete for F(100 MeV)2 ph cm s irrespective of the source photon index or its location in the sky. Multiplying these functions by the solid angle of the survey (34089.45 deg in case of a b cut) yields the so called sky coverage which is used for the statistical studies reported in the next sections.

Incompleteness of the Extragalactic Sample

We report in Table  6 the composition of the b10 sample. The number of sources with high-confidence associations is 106. Of these 57 are FSRQs and 42 are BL Lacs. As already shown in the previous sections, FSRQs and BL Lacs are represented in almost equal fractions in the LAT survey. The 5 blazars with uncertain classifications are likely split between these two categories as the redshift-luminosity plane (Fig. 17) shows. The incompleteness factor varies as a function of the sample under study. When considering the non-pulsar part of the high-confidence sample, the incompleteness is given by low confidence and unassociated objects. This turns out to be 11 %. However, when considering the FSRQ and BL Lac samples separately one must also include the sources with uncertain classifications. Thus the incompleteness factor of the FSRQ and BL Lac samples rises to 15 %. A reasonable and simple hypothesis is one which assumes that these sources reflect the composition of the identified portion of the sample. This would mean that there are an additional 9 FSRQs and 7 BL Lacs are hiding among the unidentified/unassociated/low-confidence sources. These uncertainties will be used in the next sections.

Since the uncertainty due to the incompleteness is relatively large, we will use a flux-limited sample to verify the results derived from the main sample. Indeed, for F ph cm s the number of uncertain, unassociated, and low-confidence sources falls to 2 (and 2 are anti-associated). Above this flux limit, the sample contains 44 sources of which 29 and 9 are FSRQs and BL Lacs respectively, while 2 are Radio galaxies. Moreover, all but one BL Lac have a measured redshift. Thus, while low numbers penalize this flux-limited sample, its incompleteness is 5 %.

Source Counts Distributions

The source counts distribution, also known as the log –log , flux, or size distribution, is readily computed once the sky coverage is known through the expression:


where is the total number of detected sources with fluxes greater than , and (i.e. Fig. 18 multiplied by the solid angle) is solid angle associated with the flux of the source. The variance of the source number counts is defined as


In building the source counts distributions, we used the source flux averaged over the three month timescale. The log –log of the entire extragalactic sample (excluding pulsars) is shown in Fig. 19.

We fitted the source counts distribution with a power-law model of the type:


A common practice in this case (e.g., see Ajello et al., 2008) is to fit the unbinned dataset employing a maximum likelihood (ML) algorithm. For this purpose the ML estimator can be written as


where runs over the detected sources. The 1  error associated to the fitted parameters (in this case ) is computed by varying the parameter of interest, while the others are allowed to float, until an increment of =1 is achieved. This gives an estimate of the 68 % confidence region for the parameter of interest (Avni, 1976). In this formulation of the ML function, the normalization is not a parameter of the problem. Once the slope is determined, the normalization is derived as the value which reproduces the number of observed sources. An estimate of its statistical error is given by the Poisson error of sources used to build the log –log .

Since the sky coverage is somewhat uncertain at very low fluxes, the fit is performed above F(100 MeV) ph cm s even though all the data are displayed. The results of the best fits to the different source counts distributions are summarized in Table  7. It is clear that all distributions are compatible, within their errors, with a Euclidean distribution (). In order to check the stability of our results we have shifted the sky coverage of Fig. 18 by 20 % on either side. Taking the whole extragalactic population as an example (see first line of Table 7) we get that the best fit values of the slope are 2.47 and 2.62 for the  % and +20 % case respectively. These values are consistent within the error (e.g. 2.59), showing that at bright fluxes our analysis does not suffer from major systematic uncertainties in the sky coverage. The same result holds for the other log –log distributions reported in Table 7.

The log – log distributions for FSRQs and BL Lacs are shown in Fig. 20 and 21. We do not find any indication of a break in the source counts distributions of the two populations. As the fitting results of Table 7 show, there might be an intrinsic difference between the log –log of both populations, with the source counts distribution of BL Lacs being flatter than that of of FSRQs. However, both of them are compatible within 1  errors with the Euclidean value of 2.5. Moreover, the analysis of the flux-limited sample (see bottom part of Table 7) confirms the results of the main sample, showing that incompleteness is not a main issue in this study.

For the EGRET sample, a surface density for F ph cm s of FSRQs and BL Lacs of 3.31 sr and 0.83 sr, respectively, is reported (Mücke & Pohl, 2000). From LAT we derive that the surface density (above F ph cm s) of FSRQs and BL Lacs is 4.41 sr and 1.01 sr respectively. Thus the LAT results are in good agreement with EGRET.

A measurement of the number fluence using the average three-month fluxes of bright Fermi blazars of different classes is readily obtained from the log –log distributions through the expression:


Unless otherwise stated, we adopt a value for of 4 ph cm s. To compare with the EGRET results, the upper limit of integration cannot be set to infinity. Indeed, all point sources detected above F ph cm s in the Second EGRET Catalog (2EG; Thompson et al., 1995) were subtracted in the measurement of the extragalactic diffuse -ray background (EDGB) (Sreekumar et al., 1998). Thus, we set to  ph cm s. The integral in Eq. 7 yields a total flux of 1.06() ph cm s sr This can be compared with the intensity of the EDGB, as measured by EGRET, of 1.45 ph cm s sr. Already in this small flux range, LAT is resolving into pointlike sources 7 % of the EGRET EDGB. Preliminary analysis of the log –log distributions shows that LAT is expected to resolve a much larger fraction of the EDGB within the next few months of observation.

7.3 Evolution of Blazars

Evolutionary Test

A simple and robust test of evolution is the test Schmidt (1968). The quantity is the ratio between the (comoving) volume within which the source has been detected and the maximum comoving volume available for its detection. For a given source, is expected to be uniformly distributed between 0 and 1. For a population uniformly distributed in Euclidean space (and with constant properties with ) and non-evolving, the average should be consistent with a value of 0.5. The error on the average value is for sources. A value of indicates positive evolution (more sources or brighter sources at earlier times), and the opposite indicates negative evolution.

The comoving volume for a ith source is given by


where is the comoving volume element per unit redshift and unit solid angle (see e.g. Hogg, 1999) and is the aforementioned sky coverage for the source with rest-frame luminosity at redshift . We note that the definition of the reported in Eq. 8 encompasses also the definition of the test (Avni & Bahcall, 1980), which for the purposes here are formally equivalent.

We computed the average for FSRQs, BL Lacs and all sources in the high-confidence sample with measured redshift (these includes the sources with uncertain classification). The results are summarized in Table 8. All 57 FSRQs present in the extragalactic sample (see Table 6) have a measured redshift. The shows that the population of FSRQs detected by LAT evolves positively (i.e. there were more FSRQs in the past or they were more luminous) at the 3  level. This result is also confirmed by the analysis of the 29 FSRQs which constitute a flux-limited sample (see lower part of Table 6).

Only 31 out of the 42 BL Lac objects have a measured redshift. The test is compatible within with no evolution. Assigning the mean redshift value of the BL Lac sample (i.e. =0.38) to those objects without a without a redshift produces a value of =0.472. The result does not change if the redshift is drawn from a Gaussian distribution with mean and dispersion consistent with the observed redshift distribution of BL Lacs. However, it is difficult to assess the validity of both these hypotheses. Indeed, the fact that these objects show a featureless continuum might suggest that their redshifts could be the largest in the sample (Padovani et al., 2007). In this case, their true redshift would produce a larger value of the statistic. The of all the objects with a measured redshift in the high-confidence sample is compatible with no evolution.

Luminosity Function of FSRQs

We estimate the gamma-ray luminosity function (GLF) in fixed redshift bins using the method (equivalent in our formalism to the method). For each bin of redshift the GLF can be expressed as


where is the maximum comoving volume associated with the source (see Eq. 8). The cumulative and differential luminosity functions of FSRQs, in three redshift bins, are reported in Fig. 22. One thing is readily apparent from this figure. FSRQs are strongly evolving. A non-evolving population would have GLFs which are continuous across different redshift bins. In the case of FSRQs we note a change in space density (or luminosity) with redshift. Also, one can see that the space density of intermediate-luminosity FSRQs (e.g. L10 erg s) is increasing with redshift. On the other hand, the most luminous FSRQs have an almost constant space density with redshift. This might be a sign of a cut-off in the evolution of FSRQs. A decline in the space density of luminous FSRQs has also been determined at radio and X-ray energies (e.g., Wall et al., 2005; Padovani et al., 2007; Ajello et al., 2009). We derive from the GLF that the space density of FSRQs with L erg s is 1.05 Gpc.

We made a Maximum Likelihood fit to the three unbinned datasets using a simple GLF model defined as


The ML estimator can be expressed similarly to Eq. 6 by the expression


The results of the ML fits to the GLF of FSRQs are summarized in Table 9. For z , the GLF can be successfully parametrized by a single power-law model. The slope is compatible with the canonical value of 2.5–2.8 determined for X–ray selected samples of radio-quiet AGNs (Ueda et al., 2003; Hasinger et al., 2005; Silverman et al., 2008). This indicates that at high redshifts the Fermi-LAT is sampling the bright end of the luminosity distribution of FSRQs. For , the best-fit value of the slope is 1.56, compatible with luminosity function slopes found in radio/X-ray selected samples (Padovani et al., 2007). This is much flatter than the canonical value of . As the cumulative GLF shows (left panel of Fig. 22), there might be a hint of a break with respect to a simple power-law model in the GLF. A more detailed analysis, comparing different methods to derive the GLF and its evolution, will be considered in future publications.

Luminosity Function of BL Lacs

The luminosity function of BL Lacs, reported in Fig. 23, is in agreement with the results of the test. Indeed, sub-dividing the entire BL Lac sample in two bins of redshift produces two GLFs which connect smoothly to each other. A simple power-law GLF describes the entire dataset well. The GLF slope is and is well in agreement with the value of 2.12 reported for a radio/X–ray selected sample of BL Lacs (Padovani et al., 2007). The GLF of 12 EGRET BL Lac objects in a recent study (Bhattacharya et al., 2008) was found to show no significant evidence for evolution, with a GLF slope . Past claims (e.g., Rector et al., 2000; Beckmann et al., 2003) of negative evolution of BL Lac objects, selected mainly in the X–ray band, are not confirmed by our data. The dynamical range of the LAT GLF samples 4 decades in luminosity and nearly 8 in space density. From our GLF we derive that the density of BL Lac objects with L3 erg s is 1.9() Mpc.

Above a luminosity of Lerg s, the cumulative density of BL Lacs and FSRQs is comparable, with BL Lacs being times less numerous than FSRQs. However, given the fact that they reach lower luminosities, BL Lacs are times more abundant than FSRQs above their respective limiting luminosities.

8 Discussion

The value TS defining the detection significance for bright sources corresponds to significance, or a limiting flux over the entire high-latitude sky of (3 – ph( MeV) cm s during the three-month sky survey. In comparison, EGRET reached a high-confidence on-axis flux limit of ph( MeV) cm s for a two-week pointing over sr of the sky, only becoming complete at ph (cm s (Dermer, 2007). Of the 66 high-confidence and 27 lower-confidence AGN associations in the 3EG catalog (Hartman et al., 1999), 32 sources in the Fermi-LAT sample were also detected with EGRET. An additional source is detected at b 10. Many of the other high-confidence EGRET sources are detected with Fermi-LAT at , reflecting the rapid variability and periods of activity of -ray blazars on timescales of years or longer.

During the 18-month EGRET all-sky survey when exposure to all parts of the sky was relatively uniform compared to the remainder of the mission, 60 high-confidence blazars consisting of 14 BL Lacs and 46 FSRQs were found (Fichtel et al., 1994). Compared with % of EGRET blazars being BL Lac objects, nearly % of the Fermi-LAT blazars are BL Lac objects. The larger fraction of BL Lac objects in the Fermi bright AGN sample is partly a consequence of the good sensitivity to high-energy emission by Fermi-LAT, whereas self-vetoing in EGRET reduced its effective area to photons with energies GeV(Thompson et al., 1993). Consequently, dim hard-spectrum sources are favored to be detected with the Fermi-LAT compared to EGRET.

A clear separation between the spectral indices of FSRQs and BL Lacs is found in the Fermi-LAT data (Fig. 9), with mean photon indices of (rms) for FSRQs and (rms) for BL Lac objects. A KS test gives a probability of 2 10 for the two index samples to be drawn from the same parent distribution. Moreover, the SEDs of bright flaring blazars in the cases of 3C 454.3 and AO 0235+164 show a spectral softening at GeV. If this behavior persists in weaker FSRQs, then an even greater fraction of BL Lac objects will be found in Fermi-LAT analyses over longer times, because signal-to-noise detection significance for weak hard-spectrum sources becomes better than for weak soft-spectrum sources due to the reduced background at higher photon energies.

Another reason for the larger fraction of BL Lac objects in the Fermi-LAT blazars could be related to the redshift distribution of the bright AGNs. The BL Lac objects are dominated by low-redshift, blazars, with a tail extending to , whereas the FSRQs have a broad distribution peaking at and extending to (see Fig. 15 and Fig. 16). These distributions are similar to the distribution of EGRET blazars (Mukherjee et al., 1997). Because the peak of the EGRET FSRQ redshift distribution is already at , detection of higher redshift FSRQs with the more sensitive Fermi-LAT would be impeded by cosmological factors that strongly reduce the received fluxes. Moreover, the period of dominant AGN activity was probably at or 2. The increased sensitivity for the BL Lac objects with Fermi-LAT, on the other hand, allows it to probe beyond the low-redshift population of BL Lac objects detected with EGRET where the detectable volume is still rapidly increasing with . The likelihood of detecting BL Lac objects does depend, however, on their evolution.

The simplest index of population evolution is the test. We found for the BL Lac objects with redshift in the LBAS (Table 8), so that the BL Lac objects are within of showing no evidence for evolution. For the FSRQs in the LBAS, by contrast, we found , so that the FSRQs exhibit strong positive evolution. The strong positive evolution of FSRQs and weakly negative or no evolution of BL Lac objects in the LBAS is contrary, however, to our reasoning that population evolution of the lower redshift BL Lac objects explains the larger fraction of BL Lacs in the LBAS compared with the BL Lac fraction observed with EGRET. As indicated by the indices of the (eq. 6 and Table 7), which show much weaker evidence for evolution than given by the test, the actual situation may be more complicated and depend on both density and luminosity evolution.

The BL Lac objects are found to display systematically harder spectra, with spectra rising at GeV energies, compared to the powerful FSRQs where the peak of the spectrum is at photon energies MeV – GeV. This is generally attributed to a different dominant radiation process; self-Compton scattering of the jet electron’s synchrotron emission in the case of BL Lac objects, and Compton scattering of external radiation fields in the case of FSRQs if leptonic processes dominate the radiation output (recently reviewed in Böttcher, 2007). The excellent sensitivity and full-sky coverage of the Fermi LAT is, for the first time, giving us broadband evolving SEDs from the radio to the -ray regime in sources like 3C 454.3, PKS 2155-304, and others that will require detailed spectral modeling to assess the relative importance of self-Compton and external Compton scattering processes in the different blazar classes.

Such results will be important to determine whether FSRQs and BL Lac objects may have a direct evolutionary relationship, or instead represent separate unrelated tracks of supermassive black hole fueling and growth. A scenario whereby BL Lac objects are the late stages of FSRQs, as the gas and dust produced in a galaxy merger or tidal interaction fuels the supermassive black hole (Böttcher & Dermer, 2002; Cavaliere & D’Elia, 2002), provides a framework to understand the blazar phenomenology and makes definite predictions about the relative black hole masses in the two classes. The more abundant scattered radiation and fueling in the evolution from FSRQ to BL Lac object would then lead to a blazar sequence like behavior (Fossati et al., 1998; Ghisellini et al., 1998) if the amount of accreting matter controls black hole power and the surrounding radiation field.

It is still premature to compare the number of blazars in this bright source list with prelaunch predictions (Mücke & Pohl, 2000; Stecker & Salamon, 2001; Narumoto & Totani, 2006; Dermer, 2007; Inoue & Totani, 2008) made on the basis of differing assumptions, to sensitivities rather than , and over different spans of time. Nevertheless, nearly complete surveys with far more sources than detected with EGRET are now available for calculating luminosity and number evolution, with implications that can be compared with results from the EGRET era.

This study can be used to examine the observational basis for assuming an underlying radio/-ray connection used to calculate the blazar contribution to the -ray background (Stecker & Salamon, 1996; Giommi et al., 2006; Narumoto & Totani, 2006). Figure 14 shows that except for a (at most) weak correlation of the brightest -ray blazars with the most radio-bright blazars, the -ray and radio fluxes display a large amount of scatter. Whether a stronger correlation can be found by comparing mean -ray fluxes with radio fluxes will require further study. But even at this early stage of the Fermi mission, we find that the bright sources can already comprise about % of the diffuse extragalactic -ray background flux measured with EGRET (Sreekumar et al., 1998).

We conclude this study by noting that the Fermi-LAT results imply the non-thermal luminosity density of AGNs on various size scales. A -ray blazar makes a contribution to the non-thermal emissivity in terms of -ray luminosity and injection volume derived from redshift. The Fermi-LAT results from Table 2 show that BL Lac objects provide local emissivities W Mpc, whereas FSRQs have W Mpc. Cen A, because of its proximity at Mpc, dominates the non-thermal luminosity, with W Mpc (Dermer et al., 2008). Sources of UHECRs must have a luminosity density within the GZK radius, Mpc, of W Mpc or ergs Mpc yr (Waxman & Bahcall, 1999). To have sufficient emissivity within the GZK radius, if AGNs are the sources of the UHECRs (The Pierre AUGER Collaboration et al., 2007), the Fermi-LAT results would therefore seem to favor BL Lac objects over FSRQs as the source of the UHECRs.

9 Summary

We have presented a list of 116 bright, sources at taken from the list of bright sources (Abdo et al., 2009c) observed with the Fermi-LAT in its initial three-month observing period extending from August 4 to October 30 of 2008. Of these sources, 106 are associated with blazars with high confidence and compose the LBAS. The number of low-confidence AGN associations is 11 (one source having two possible associations - one high and one low confidence). At , 5 sources out of a total of 125 non-pulsar sources remain unidentified. Two of the AGNs are associated with radio galaxies. The purpose of this work is to present the key properties of the AGN population of this bright GeV source list. The main results are summarized as follows:

  1. With a success rate from correlating the bright gamma-ray source list with AGN radio catalogs (CRATES/CGRABS, BZCAT) the bright extragalactic gamma-ray sky continues to be dominated by radio-bright AGNs.

  2. The number of HBLs in the LBAS detected at GeV energies (even when not flaring) has risen to at least 10 (out of 42 BL Lacs) as compared to one (out of 14 BL Lacs) detected by EGRET. Seven LBAS HBLs are known TeV-blazars.

  3. Only of the bright Fermi AGN list were also detected by EGRET. This may be a consequence of the duty cycle and variability behavior of GeV blazars.

  4. BL Lac objects make up almost half of the bright Fermi AGN sample (consisting of 57 FSRQs, 42 BL Lac objects, 2 radio galaxies, and only 5 AGN remain unclassified), while the BL Lac fraction in the 3EG catalog was only . This feature most probably arises from the different instrument responses of the LAT and EGRET.

  5. The mean flux distribution of the Fermi AGN remains similar to the corresponding one based on the EGRET sample, while the peak flux distributions differ appreciably.

  6. We find a spectral separation between BL Lacs and FSRQs in the GeV gamma-ray band with FSRQs having significantly softer spectra than BL Lac objects. This confirms earlier indications for the existence of spectrally distinct populations in the EGRET blazar sample. The average photon index is (rms) for BL Lacs, with a tendency of HBLs displaying even harder spectra, and (rms) for FSRQs. A KS test gives a probability of 2 10 for the two index samples to be drawn from the same parent distribution.

  7. Fermi FSRQs in the bright source list are on average more luminous and more distant than the Fermi-detected BL Lac objects in that list. I.e., FSRQs exhibit a broad redshift distribution, starting with (3C 273), peaking at and extending up to while BL Lacs are mostly found in the redshift bin with a tail extending up to . No significant relation between the gamma-ray photon index and redshift is found within each source class, in agreement with corresponding studies based on the EGRET AGN samples.

  8. The peak gamma-ray flux is at best only weakly related to the 8.4 GHz radio flux, with the brightest gamma-ray AGNs having the largest radio flux densities.

  9. Using mean fluxes the Log N-Log S distribution of all the bright sources (except the pulsars) appears compatible with an Euclidean distribution without any breaks. This is also true within for the source counts distributions of the FSRQ and BL Lac sample separately. Surface densities of sr and sr (ph cm s) for FSRQs and BL Lacs, respectively, are reached.

  10. The combined emission in the flux range ph cm s observed from these individually resolved AGN during this three-month period already corresponds to of the EGRET detected extragalactic diffuse gamma-ray background.

  11. A analysis shows positive evolution at the 3 level for the bright Fermi-detected FSRQs with the most luminous FSRQs having an almost constant space density with redshift, while for the Fermi-detected BL Lacs no evolution within one is apparent.

  12. The gamma-ray luminosity function of bright FSRQs can be described by a single power-law with index and for the high () and low () redshift range, respectively, while the BL Lac gamma-ray luminosity function follows a power law with index . The space density of gamma-ray emitting BL Lacs of Gpc above their limiting luminosity, erg s, is a factor larger than for the Fermi-detected FSRQ population above their limiting luminosity, erg s. Thus, within the Fermi bright AGN list BL Lacs are intrinsically more numerous than FSRQs. Bright Fermi detected BL Lacs and FSRQs display comparable cumulative number counts above erg s, with BL Lacs being times more numerous than FSRQs.

These early results from the first three months of the science mission of the Fermi Gamma ray Space Telescope demonstrate its exceptional capabilities to provide important new knowledge about -ray emission from active galactic nuclei and blazars. As the Fermi-LAT data accumulate, many more AGNs at lower flux levels will likely be detected- as well as flaring AGNs at brighter fluxes than yet observed - helping to refine these results and improve our understanding of supermassive black holes.

10 Acknowledgments

The Fermi LAT Collaboration acknowledges the generous support of a number of agencies and institutes that have supported the LAT Collaboration. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase from the following agencies is also gratefully acknowledged: the Istituto Nazionale di Astrofisica in Italy and the K. A. Wallenberg Foundation in Sweden for providing a grant in support of a Royal Swedish Academy of Sciences Research fellowship for JC. MA acknowledges N. Cappelluti for extensive discussion about the sky coverage. Facilities: Fermi LAT.
Figure 1: Examples of weekly light curves for five bright blazars detected by Fermi-LAT and the Vela light curve for comparison (flux unit: photons cm s, please note the different scales). The dashed line is the average value and the grey area shows the 3% systematic error we have adopted. Different flux variability amplitudes and timescales are clearly visible in the blazar light curves.
Figure 2: Flux limit [E100 MeV] (ph cms) as a function of sky location (in galactic coordinates), for a photon index=2.2
Figure 3: 95% error radius as a function of TS for the sources presented in this paper. FSRQs: closed circles, BL Lacs: open circles, Uncertain type: closed triangles, Radio galaxies: open stars.
Figure 4: Location of the LBAS sources. FSRQs: closed circles, BL Lacs: open circles, Uncertain type: closed triangles, RG: open stars.
Figure 5: Normalized angular separation between the Fermi-LAT location and that of the counterpart. The solid (dashed) histogram corresponds to the sources with high-(low-) confidence associations. The solid curve corresponds to the expected distribution ( distribution with 2 d.o.f.) for real associations, the dashed one for accidental associations.
Figure 6: The -ray (0.5 - 2.0 keV) vs radio flux density (1.4 GHz) plot of all -ray detected blazars in the BZCAT catalog (small dots) and the Fermi-LAT detected blazars (BL Lacs: open circles, FSRQs: filled circles, blazars of uncertain type: triangles, radio galaxies: stars).
Figure 7: Flux [E100 MeV] vs photon index for the 116 sources. FSRQs: closed circles, BL Lacs: open circles, Uncertain type: closed triangles, Radio galaxies: open stars. The solid curves represent the TS=100 limit estimated for two galactic latitudes b=20 and b=80 . The dashed curve represents the TS=100 limit for b=80 and 0.2 E 3 GeV.
Figure 8: a) Comparison of mean flux distribution for blazars detected by Fermi-LAT (solid) and EGRET (dashed). b) same of as a), for the peak flux distribution. c) Peak flux as a function of mean flux, for the Fermi-LAT (closed circles) and EGRET (open circles) AGNs. d) same as a), for the peak/mean flux ratio.
Figure 9: Photon index distributions for the LBAS blazars. Top: All sources. Middle: FSRQs. Bottom: BL Lacs.
Figure 10: Gamma-ray SED of 3 bright blazars calculated in five energy bands, compared with the power law fitted over the whole energy range. Left: 3C454.3 (FSRQ), middle: AO 0235+164 (IBL), right: Mkn 501 (HBL)
Figure 11: Left: LBAS photon index as a function of redshift. Same symbols as before. Right: same as left, for the EGRET sample.
Figure 12: Left: Fermi-LAT vs EGRET mean flux for the 33 AGNs present in both samples FSRQs: closed circles, BL Lacs: open circles, Uncertain type: closed triangles. Right: same as left, for photon index.
Figure 13: Histogram of the radio power distribution for LBAS sources, for all sources (upper panel), FSRQs (middle), and BL Lacs (bottom) only.

Figure 14: Radio vs. gamma-ray properties. Left: peak gamma-ray flux vs. radio flux density at 8.4 GHz; the dashed lines show the CRATES flux density limit and the typical LAT detection threshold. Right: gamma-ray photon index vs. radio luminosity.
Figure 15: Redshift distribution for the FSRQs in the LBAS (solid) and in the BZCat catalog (dashed).
Figure 16: Redshift distribution for the BL Lacs in the LBAS (solid) and in the BZCat catalog (dashed).
Figure 17: Gamma-ray Luminosity vs redshift for the LBAS. The solid line was drawn using a and a photon index of 2.2.
Figure 18: Detection efficiencies in the LAT b10 survey as a function of flux. The solid line is for the entire blazar population while the dashed and long-dashed are for the FSRQs and BL Lacs respectively. The errors on the detection efficiency are due to the counting statistics in our Monte Carlo simulations.
Figure 19: Source count distribution for the whole extragalactic population (excluding the pulsars). The dashed line is the best power-law fit to the F(100 MeV) ph cm s data. The inset shows the differential distribution.
Figure 20: Source count distribution for FSRQs. The dashed line is the best power-law fit to the F(100 MeV) ph cm s data. The inset shows the differential distribution.
Figure 21: Source count distribution for BL Lacs. The dashed line is the best power-law fit to the F(100 MeV) ph cm s data. The inset shows the differential distribution.
Figure 22: Luminosity functions of FSRQs in bins of redshift. The cumulative and differential distributions are shown, respectively, on the left and on the right panel. The (color-coded) solid lines are the ML fits to the 3 different datasets using a simple power law to model the GLF.
Figure 23: Luminosity functions of BL Lacs in bins of redshift. The cumulative and differential distributions are shown, respectively, on the left and on the right panel. The (color-coded) solid lines are the ML fits to the 2 different datasets using a simple power law to model the GLF.
FoM gtsrcid
LAT Name Source Name FoM Prob. Source Name Prob. Other Names Class
0FGL J0017.40503 CGRaBS J00170512 16.20 0.93 CGRaBS J00170512 0.92 0.227 FSRQ
0FGL J0033.61921 BZB J00331921 0.99 KUV 003111938 0.610 BLLac
0FGL J0050.50928 CGRaBS J00500929 61.01 1.00 CRATES J00500929 0.99 PKS 0048097 BLLac
0FGL J0051.10647 CGRaBS J00510650 42.95 0.98 CRATES J00510650 0.99 PKS 0048071 1.975 FSRQ
0FGL J0112.1+2247 CGRaBS J0112+2244 48.96 0.98 S2 0109+22 1.00 S2 0109+22 0.265 BLLac
0FGL J0118.72139 CGRaBS J01182141 35.02 0.97 CGRaBS J01182141 0.99 PKS 0116219 1.165 FSRQ
0FGL J0120.52703 CGRaBS J01202701 60.25 1.00 PKS 0118272 1.00 PKS 0118272 0.557 BLLac
0FGL J0136.6+3903 BZB J0136+3905 12.45 0.91 B3 0133+388 1.00 B3 0133+388 BLLac
0FGL J0137.1+4751 CGRaBS J0136+4751 52.21 0.99 CGRaBS J0136+4751 0.99 DA 55 0.859 FSRQ
0FGL J0144.5+2709 CRATES J0144+2705 30.93 0.96 CRATES J0144+2705 0.58 TXS 0141+268 BLLac
0FGL J0145.12728 CGRaBS J01452733 37.41 0.97 CGRaBS J01452733 0.96 PKS 0142278 1.148 FSRQ
0FGL J0204.81704 CGRaBS J02041701 55.22 0.99 CGRaBS J02041701 0.96 PKS 020217 1.740 FSRQ
0FGL J0210.85100 CGRaBS J02105101 69.87 1.00 PKS 0208512 1.00 PKS 0208512 1.003 FSRQ
0FGL J0217.8+0146 CGRaBS J0217+0144 52.67 0.99 CGRaBS J0217+0144 1.00 PKS 0215+015 1.715 FSRQ
0FGL J0220.9+3607 CGRaBS J0221+3556 7.66 0.89 CGRaBS J0221+3556 0.95 B2 0218+35 0.944 Unc64
0FGL J0222.6+4302 BZB J0222+4302 23.18 0.95 3C 66A 1.00 3C 66A 0.444 BLLac
0FGL J0229.53640 BZQ J02293643 29.20 0.96 BZQJ02293643 0.94 PKS 0227369 2.115 FSRQ
0FGL J0238.6+1636 CGRaBS J0238+1636 60.54 1.00 CGRaBS J0238+1636 1.00 AO 0235+164 0.940 BLLac
0FGL J0245.64656 CRATES J02464651 23.23 0.95 CRATES J02464651 0.54 PKS 0244470 Unc65
0FGL J0303.72410 CRATES J03032407 9.32 0.90 PKS 0301243 1.00 PKS 0301243 0.260 BLLac
0FGL J0320.0+4131 CGRaBS J0319+4130 33.67 0.97 0316+413 1.00 NGC 1275 0.018 RG
0FGL J0334.14006 CGRaBS J03344008 63.24 1.00 PKS 0332403 1.00 PKS 0332403 BLLac
0FGL J0349.82102 CGRaBS J03492102 47.40 0.98 CGRaBS J03492102 0.99 PKS 0347211 2.944 FSRQ
0FGL J0428.73755 CGRaBS J04283756 54.09 0.99 CGRaBS J04283756 1.00 PKS 0426380 1.112 BLLac
0FGL J0449.74348 CRATES J04494350 5.52 0.81 PKS 0447439 1.00 PKS 0447439 0.205 BLLac
0FGL J0457.12325 CGRaBS J04572324 35.74 0.97 CGRaBS J04572324 1.00 PKS 0454234 1.003 FSRQ
0FGL J0507.9+6739 BZB J0507+6737 4.74 0.76 1ES 0502+675 1.00 1ES 0502+675 0.416 BLLac
0FGL J0516.26200 CGRaBS J05166207 12.04 0.91 CGRaBS J05166207 0.94 PKS 0516621 Unc66
0FGL J0531.0+1331 CGRaBS J0530+1331 65.48 1.00 CRATES J0530+1331 1.00 PKS 0528+134 2.070 FSRQ
0FGL J0538.84403 CRATES J05384405 53.80 0.99 BZBJ05384405 0.99 PKS 0537441 0.892 BLLac
0FGL J0654.3+4513 CGRaBS J0654+4514 42.13 0.98 CGRaBS J0654+4514 1.00 B3 0650+453 0.933 FSRQ
0FGL J0654.3+5042 CGRaBS J0654+5042 49.98 0.99 CGRaBS J0654+5042 1.00 Unc67
0FGL J0700.06611 CRATES J07006610 33.82 0.97 CRATES J07006610 0.64 PKS 0700661 Unc68
0FGL J0712.9+5034 CGRaBS J0712+5033 44.20 0.98 CGRaBS J0712+5033 0.99 BLLac
0FGL J0714.2+1934 CLASS J0713+1935 20.54 0.94 0.534 FSRQ
0FGL J0719.4+3302 CRATES J0719+3307 14.33 0.92 BZUJ0719+3307 0.89 TXS 0716+332 0.779 FSRQ
0FGL J0722.0+7120 CGRaBS J0721+7120 66.40 1.00 CRATES J0721+7120 1.00 S5 0716+71 0.310 BLLac
0FGL J0738.2+1738 CGRaBS J0738+1742 25.45 0.95 PKS 0735+17 1.00 PKS 0735+178 0.424 BLLac
0FGL J0818.3+4222 CGRaBS J0818+4222 61.26 1.00 OJ 425 1.00 OJ 425 0.530 BLLac
0FGL J0824.9+5551 CGRaBS J0824+5552 57.80 0.99 CGRaBS J0824+5552 0.98 TXS 0820+560 1.417 FSRQ
0FGL J0855.4+2009 CGRaBS J0854+2006 8.67 0.90 OJ 287 0.99 OJ 287 0.306 BLLac
0FGL J0921.2+4437 CGRaBS J0920+4441 13.49 0.92 CGRaBS J0920+4441 0.95 RGB J0920+446 2.190 FSRQ
0FGL J0948.3+0019 CGRaBS J0948+0022 18.64 0.93 CGRaBS J0948+0022 0.94 PMN J0948+0022 0.585 FSRQ
0FGL J0957.6+5522 CRATES J0957+5522 50.91 0.99 BZQJ0957+5522 0.96 4C +55.17 0.896 FSRQ
0FGL J1012.9+2435 CRATES J1012+2439 13.63 0.92 1.805 FSRQ
0FGL J1015.2+4927 CGRaBS J1015+4926 18.06 0.93 1ES 1011+496 1.00 1ES 1011+496 0.212 BLLac
0FGL J1015.9+0515 CRATES J1016+0513 28.86 0.96 CRATES J1016+0513 0.78 PMN J1016+0512 1.713 FSRQ
0FGL J1034.0+6051 CGRaBS J1033+6051 52.57 0.99 CGRaBS J1033+6051 0.98 S4 1030+61 1.401 FSRQ
0FGL J1053.7+4926 BZB J1053+4929 11.55 0.91 MS 1050.7+4946 1.00 MS 1050.7+4946 0.140 BLLac
0FGL J1054.5+2212 CLASS J1054+2210 16.20 0.93 BLLac
0FGL J1057.8+0138 CGRaBS J1058+0133 3.71 0.68 CGRaBS J1058+0133 0.93 PKS 1055+018 0.888 FSRQ
0FGL J1058.9+5629 CGRaBS J1058+5628 24.66 0.95 RXS J10586+5628 1.00 RXS J10586+5628 0.143 BLLac
0FGL J1100.28000 CGRaBS J10588003 53.65 0.99 CGRaBS J10588003 0.99 PKS 105779 BLLac
0FGL J1104.5+3811 CGRaBS J1104+3812 35.10 0.97 Mrk 421 1.00 Mrk 421 0.030 BLLac
0FGL J1129.81443 CRATES J11301449 27.54 0.96 BZQ J11301449 0.84 PKS 112714 1.184 FSRQ
0FGL J1146.73808 CGRaBS J11473812 45.04 0.98 CGRaBS J11473812 0.99 PKS 1144379 1.048 FSRQ
0FGL J1159.2+2912 CGRaBS J1159+2914 39.38 0.97 CGRaBS J1159+2914 0.98 4C 29.45 0.729 FSRQ
0FGL J1218.0+3006 CGRaBS J1217+3007 31.80 0.96 B2 1215+30 1.00 B2 1215+30 0.130 BLLac
0FGL J1221.7+2814 CGRaBS J1221+2813 36.82 0.97 W Com 1.00 W Com 0.102 BLLac
0FGL J1229.1+0202 CGRaBS J1229+0203 73.53 1.00 3C 273 1.00 3C 273 0.158 FSRQ
0FGL J1246.62544 CGRaBS J12462547 43.45 0.98 CGRaBS J12462547 0.99 PKS 1244255 0.635 FSRQ
0FGL J1253.4+5300 CRATES J1253+5301 43.34 0.98 S4 1250+53 1.00 S4 1250+53 BLLac
0FGL J1256.10547 CGRaBS J12560547 71.21 1.00 3C279 1.00 3C 279 0.536 FSRQ
0FGL J1310.6+3220 CGRaBS J1310+3220 55.91 0.99 CGRaBS J1310+3220 0.99 B2 1308+32 0.997 FSRQ
0FGL J1325.44303 BZU J13254301 75.23 1.00 NGC 5128 1.00 NGC 5128, Cen A 0.002 RG
0FGL J1331.70506 CGRaBS J13320509 44.64 0.98 CGRaBS J13320509 0.93 PKS 1329049 2.150 FSRQ
0FGL J1333.3+5058 CLASS J1333+5057 21.52 0.94 1.362 FSRQ
0FGL J1355.01044 CRATES J13541041 22.52 0.94 BZUJ13541041 0.84 PKS 1352104 0.330 FSRQ
0FGL J1427.1+2347 CRATES J1427+2347 19.69 0.94 PKS 1424+240 1.00 PKS 1424+240 BLLac
0FGL J1457.63538 CGRaBS J14573539 26.03 0.95 CGRaBS J14573539 0.99 PKS 1454354 1.424 FSRQ
0FGL J1504.4+1030 CGRaBS J1504+1029 48.85 0.98 CGRaBS J1504+1029 1.00 PKS 1502+106 1.839 FSRQ
0FGL J1511.20536 PKS 150805 10.27 0.90 BZQJ15100543 0.73 PKS 150805 1.185 FSRQ
0FGL J1512.70905 PKS 151008 74.49 1.00 BZQJ15120905 0.98 PKS 151008 0.360 FSRQ
0FGL J1517.92423 CGRaBS J15172422 19.18 0.94 AP Lib 1.00 AP Lib 0.048 BLLac
0FGL J1522.2+3143 CGRaBS J1522+3144 51.06 0.99 CGRaBS J1522+3144 1.00 TXS 1520+319 1.487 FSRQ
0FGL J1543.1+6130 CRATES J1542+6129 45.22 0.98 RXS J15429+6129 1.00 RXS J15429+6129 BLLac
0FGL J1553.4+1255 CRATES J1553+1256 26.38 0.95 PKS 1551+130 0.85 PKS 1551+130 1.308 FSRQ
0FGL J1555.8+1110 CGRaBS J1555+1111 44.23 0.98 PG 1553+11 1.00 PG 1553+11 0.360 BLLac
0FGL J1625.82527 CGRaBS J16252527 56.82 0.99 PKS 1622253 0.99 PKS 1622253 0.786 FSRQ
0FGL J1635.2+3809 CGRaBS J1635+3808 54.10 0.99 CRATESJ1635+3808 0.99 4C +38.41 1.814 FSRQ
0FGL J1653.9+3946 CGRaBS J1653+3945 59.08 0.99 Mrk 501 1.00 Mrk 501 0.033 BLLac
0FGL J1719.3+1746 CGRaBS J1719+1745 40.87 0.98 PKS 1717+177 1.00 PKS 1717+177 0.137 BLLac
0FGL J1751.5+0935 CGRaBS J1751+0939 19.73 0.94 CGRaBS J1751+0939 0.99 OT 081 0.322 BLLac
0FGL J1802.2+7827 CGRaBS J1800+7828 28.07 0.96 CGRaBS J1800+7828 0.99 S5 1803+78 0.680 BLLac
0FGL J1847.8+3223 CGRaBS J1848+3219 12.76 0.92 CGRaBS J1848+3219 0.94 TXS 1846+322 0.798 FSRQ
0FGL J1849.4+6706 CGRaBS J1849+6705 53.89 0.99 CGRaBS J1849+6705 1.00 S4 1849+67 0.657 FSRQ
0FGL J1911.22011 CGRaBS J19112006 23.51 0.95 CGRaBS J19112006 0.97 PKS 1908201 1.119 FSRQ
0FGL J1923.32101 CGRaBS J19232104 37.72 0.97 CGRaBS J19232104 0.97 TXS 1920211 0.874 FSRQ
0FGL J2000.2+6506 CGRaBS J1959+6508 19.12 0.94 1ES 1959+650 1.00 1ES 1959+650 0.047 BLLac
0FGL J2009.44850 CGRaBS J20094849 72.13 1.00 PKS 2005489 1.00 PKS 2005489 0.071 BLLac
0FGL J2025.60736 CRATES J20250735 42.71 0.98 BZQJ20250735 0.98 PKS 202207 1.388 FSRQ
0FGL J2056.14715 CGRaBS J20564714 67.00 1.00 CRATES J20554716 1.00 PKS 205247 1.491 FSRQ
0FGL J2139.44238 CRATES J21394235 13.48 0.92 MH 2136428 1.00 MH 2136428 BLLac
0FGL J2143.2+1741 CGRaBS J2143+1743 36.88 0.97 CGRaBS J2143+1743 0.96 OX 169 0.213 FSRQ
0FGL J2147.1+0931 CGRaBS J2147+0929 53.97 0.99 CGRaBS J2147+0929 0.99 PKS 2144+092 1.113 FSRQ
0FGL J2157.5+3125 CGRaBS J2157+3127 54.48 0.99 CGRaBS J2157+3127 0.97 B2 2155+31 1.486 FSRQ
0FGL J2158.83014 CGRaBS J21583013 54.87 0.99 CGRaBS J21583013 1.00 PKS 2155304 0.116 BLLac
0FGL J2202.4+4217 BZB J2202+4216 45.62 0.98 BZB J21394239 1.00 BL Lacertae 0.069 BLLac
0FGL J2203.2+1731 CGRaBS J2203+1725 23.91 0.95 CGRaBS J2203+1725 0.93 PKS 2201+171 1.076 FSRQ
0FGL J2207.05347 CGRaBS J22075346 39.56 0.97 CGRaBS J22075346 0.99 PKS 220454 1.215 FSRQ
0FGL J2229.80829 CGRaBS J22290832 42.99 0.98 CGRaBS J22290832 0.99 PHL 5225 1.560 FSRQ
0FGL J2232.4+1141 BZQ J2232+1143 45.97 0.98 BZQ J2232+1143 1.00 CTA 102 1.037 FSRQ
0FGL J2254.0+1609 CGRaBS J2253+1608 70.34 1.00 CGRaBS J2253+1608 1.00 3C 454.3 0.859 FSRQ
0FGL J2325.3+3959 CRATES J2325+3957 29.25 0.96 B3 2322+396 1.00 B3 2322+396 BLLac
0FGL J2327.3+0947 CGRaBS J2327+0940 21.12 0.94 CGRaBS J2327+0940 0.93 PKS 2325+093 1.843 FSRQ
0FGL J2345.51559 CGRaBS J23451555 30.19 0.96 CGRaBS J23451555 0.93 PMN J23451555 0.621 FSRQ
Table 1: The high-confidence association Bright AGN List
FoM gtsrcid
LAT Name Source Name FoM Prob. Source Name Prob. Other Names Class
0FGL J0100.2+0750 CRATES J0100+0745 5.12 0.78 0.000 Unc69
0FGL J0238.4+2855 CGRaBS J0237+2848 7.67 0.89 CGRaBS J0237+2848 0.88 B2 0234+28 1.213 FSRQ
0FGL J0407.63829 CRATES J04063826 3.00 0.61 PKS 0405385 1.285 Unc70
0FGL J0412.95341 CRATES J04135332 1.92 0.46 0.00 Unc71
0FGL J0423.10112 CGRaBS J04230120 4.26 0.72 CRATESJ04230120 0.84 PKS 0420014 0.915 FSRQ
0FGL J0909.7+0145 CGRaBS J0909+0200 4.16 0.71 PKS 0907+022 0.87 PKS 0907+022 BLLac
0FGL J1034.0+6051 CRATES J1032+6051 5.22 0.79 1.064 FSRQ
0FGL J1248.7+5811 PG 1246+586 0.86 BLLac
0FGL J1625.92423 CRATES J16272426 2.33 0.53 Unc72
0FGL J1641.4+3939 CLASS J1641+3935 6.22 0.85 0.539 FSRQ
0FGL J2017.2+0602 CLASS J2017+0603 7.03 0.88 Unc73
Table 2: The low-confidence association Bright AGN List
LAT Name R.A. Dec 74 75 76 77 Var.
0FGL J0017.4-0503 4.358 5.054 101.273 66.485 14.7 2.71  0.14 13.9  2.4 34.8  6.5 12.1  1.4 T
0FGL J0033.6-1921 8.401 19.360 94.215 81.220 10.7 1.70  0.14 1.6  0.4 2.9  1.3 0.4  0.178
0FGL J0050.5-0928 12.637 9.470 122.209 72.341 20.5 2.15  0.08 10.2  1.4 19.0  4.0 8.8  1.3 T
0FGL J0051.1-0647 12.796 6.794 122.751 69.666 15.7 2.22  0.11 8.5  1.5 19.7  4.4 7.2  1.4 T
0FGL J0100.2+0750 15.051 7.844 126.716 54.963 11.1 1.80  0.16 1.9  0.7 3.9  1.7 0.3  0.179
0FGL J0112.1+2247 18.034 22.789 129.148 39.832 17.6 2.10  0.09 7.4  1.2 12.6  2.7 6.0  0.7
0FGL J0118.7-2139 19.676 21.656 172.990 81.728 17.8 2.32  0.10 9.6  1.4 21.4  4.5 7.6  1.1 T
0FGL J0120.5-2703 20.128 27.056 213.951 83.529 11.8 1.99  0.14 3.2  0.8 6.7  2.3 2.6  0.8
0FGL J0136.6+3903 24.163 39.066 132.446 22.969 12.5 1.65  0.13 1.8  0.5 4.7  1.5 0.5  0.180
0FGL J0137.1+4751 24.285 47.854 130.818 14.317 18.8 2.20  0.09 10.9  1.7 18.6  4.5 10.8  1.6 T
0FGL J0144.5+2709 26.142 27.159 137.248 34.231 10.4 2.22  0.14 5.4  1.3 12.7  3.8 2.0  0.5
0FGL J0145.1-2728 26.289 27.478 217.694 78.067 13.4 2.55  0.14 9.2  1.7 26.3  5.4 9.4  1.3 T
0FGL J0204.8-1704 31.219 17.068 186.072 70.274 16.6 2.48  0.11 11.1  1.7 18.9  3.9 10.7  1.3
0FGL J0210.8-5100 32.706 51.013 276.083 61.776 34.1 2.28  0.06 24.4  2.0 76.2  6.9 22.8  1.2 T
0FGL J0217.8+0146 34.467 1.768 162.139 54.389 21.7 2.13  0.08 10.2  1.3 16.5  3.8 9.8  1.2 T
0FGL J0220.9+3607 35.243 36.121 142.504 23.325 12.3 2.61  0.16 11.0  2.4 22.5  6.1 10.9  1.3
0FGL J0222.6+4302 35.653 43.043 140.132 16.763 47.4 1.97  0.04 25.9  1.6 49.6  4.8 26.6  1.4 T
0FGL J0229.5-3640 37.375 36.681 243.801 67.189 19.2 2.57  0.11 15.8  2.1 34.1  6.2 14.1  1.5 T
0FGL J0238.4+2855 39.600 28.923 149.521 28.368 10.9 2.49  0.15 9.0  2.0 24.7  5.9 8.6  1.6
0FGL J0238.6+1636 39.663 16.613 156.775 39.112 85.7 2.05  0.02 72.6  2.5 104.8  7.1 67.6  2.2 T
0FGL J0245.6-4656 41.423 46.934 262.019 60.098 11.4 2.34  0.15 6.2  1.5 12.4  4.0 5.6  0.8
0FGL J0303.7-2410 45.940 24.176 214.764 60.119 12.3 2.01  0.13 3.8  0.9 8.0  2.8 2.9  0.9
0FGL J0320.0+4131 50.000 41.524 150.601 13.230 29.7 2.17  0.06 22.1  1.9 35.9  5.3 18.2  1.4 T
0FGL J0334.1-4006 53.546 40.107 244.710 54.088 13.2 2.15  0.12 5.3  1.1 11.2  3.1 4.9  1.4
0FGL J0349.8-2102 57.465 21.046 214.385 49.035 21.2 2.55  0.09 19.2  2.3 27.8  5.0 17.3  1.6
0FGL J0407.6-3829 61.923 38.491 241.360 47.751 13.5 2.31  0.13 7.5  1.5 22.2  4.1 6.9  1.3 T
0FGL J0412.9-5341 63.230 53.686 263.001 44.716 10.7 2.30  0.15 5.4  1.3 12.3  3.8 6.0  1.3
0FGL J0423.1-0112 65.785 1.204 195.131 33.092 11.5 2.38  0.16 8.1  2.2 13.4  4.0 10.5  3.1
0FGL J0428.7-3755 67.193 37.923 240.689 43.597 39.6 2.14  0.05 24.5  1.8 31.5  4.7 23.1  1.6
0FGL J0449.7-4348 72.435 43.815 248.780 39.859 28.4 2.01  0.06