PACS photometry of the Herschel Reference Survey

PACS photometry of the Herschel Reference Survey – Far-infrared/sub-millimeter colours as tracers of dust properties in nearby galaxies1

Abstract

We present Herschel/PACS 100 and 160 m integrated photometry for the 323 galaxies in the Herschel Reference Survey (HRS), a K-band-, volume-limited sample of galaxies in the local Universe. Once combined with the Herschel/SPIRE observations already available, these data make the HRS the largest representative sample of nearby galaxies with homogeneous coverage across the 100-500 m wavelength range. In this paper, we take advantage of this unique dataset to investigate the properties and shape of the far-infrared/sub-millimeter spectral energy distribution in nearby galaxies. We show that, in the stellar mass range covered by the HRS (812), the far-infrared/sub-millimeter colours are inconsistent with a single modified black-body having the same dust emissivity index for all galaxies. In particular, either decreases, or multiple temperature components are needed, when moving from metal-rich/gas-poor to metal-poor/gas-rich galaxies. We thus investigate how the dust temperature and mass obtained from a single modified black-body depend on the assumptions made on . We show that, while the correlations between dust temperature, galaxy structure and star formation rate are strongly model dependent, the dust mass scaling relations are much more reliable, and variations of only change the strength of the observed trends.

keywords:
galaxies: fundamental parameters – galaxies: ISM – infrared: galaxies

1 Introduction

It is now well established that approximately half of the radiative energy produced by galaxies is absorbed by dust grains and re-emitted in the infrared regime (Hauser & Dwek, 2001; Boselli et al., 2003; Dole et al., 2006; Dale et al., 2007; Burgarella et al., 2013). Thus, observations in the 10-1000 m wavelength range provide us with a unique opportunity not only to quantify half of the bolometric luminosity of galaxies, but also to characterise the properties of cosmic dust. Moreover, since dust grains are crucial for the star formation cycle (Hollenbach & Salpeter, 1971), such information can give us important insights into the physical processes regulating galaxy evolution (e.g., Dunne et al., 2011).

Unfortunately, despite its paramount importance, we are still missing a complete and coherent picture of dust properties in galaxies across the Hubble sequence, and of the exact role played by grains in regulating star formation (McKee & Krumholz, 2010). Indeed, we know very little about the dust composition in galaxies outside our own Local Group (Draine & Li, 2007; Compiègne et al., 2011) and if/how it is regulated by the physical conditions experienced by grains in the inter-stellar medium (ISM). Hence, our estimates of dust masses in galaxies are still highly uncertain (Finkbeiner et al., 1999; Dupac et al., 2003; Gordon et al., 2010; Paradis et al., 2010; Planck Collaboration et al., 2011b).

Luckily, the last decade has seen the start of a golden age for observational far-infrared (FIR) and sub-millimeter (submm) astronomy, providing a new boost to the refinement of theoretical dust models (Meny et al., 2007; Draine & Li, 2007; Hoang et al., 2010; Compiègne et al., 2011; Steinacker et al., 2013). In particular, the Spitzer (Werner et al., 2004), and more recently Herschel (Pilbratt et al., 2010) and Planck (Planck Collaboration et al., 2011a) space telescopes are finally gathering a wealth of information on the dust emission from thousands of galaxies up to 2. Particularly important for a proper characterisation of dust in galaxies is the radiation emitted at wavelengths 100-200 m. In this regime, the integrated emission from galaxies originates predominantly from dust in thermal equilibrium, heated by the diffuse interstellar radiation field (ISRF), which represents the bulk of the dust mass in a galaxy (e.g., Sodroski et al., 1989; Sauvage & Thuan, 1992; Calzetti et al., 1995; Walterbos & Greenawalt, 1996; Bendo et al., 2010; Boquien et al., 2011; Bendo et al., 2012). Thus, by characterising the dust emission in the 100 m wavelength domain, we have a unique opportunity to provide strong constraints to theoretical models, and to refine our census of the dust budget in galaxies.

The first natural step in this direction is to quantify how the shape of the dust spectral energy distribution (SED) varies with galaxy properties across a wide range of morphological type, star formation activity, cold gas mass and metal content. This is necessary to determine if the amount of radiation emitted at each wavelength is simply regulated by the intensity of the ISRF responsible for the dust heating, or whether it retains an imprint of the chemical composition of the grains. Indeed, only after a careful characterisation of the physical parameters regulating the dust SED, will it be possible to properly convert observables into physical quantities such as dust temperatures and dust masses.

Many recent works (Gordon et al., 2010; Skibba et al., 2011; Davies et al., 2012; Planck Collaboration et al., 2011b; Galametz et al., 2012; Auld et al., 2013) have shown that, above 100 m, the dust SED is very well approximated by a simple modified black-body (but see also Bendo et al., 2012):

(1)

where is the flux density emitted at the frequency , is the dust mass absorption coefficient at the frequency , gives its variation as a function of frequency, is the galaxy distance and is the Planck function. Mounting evidence is emerging that is not the same in all galaxies (e.g., Rémy-Ruyer et al., 2013), and may also vary within galaxies (e.g., Galametz et al., 2012; Smith et al., 2012).

Modified black-bodies are simple models and cannot properly reproduce real dust properties (e.g., Draine & Li, 2007; Shetty et al., 2009; Bernard et al., 2010). Several dust components at various temperatures contribute to the total emission along the lines-of-sight. This implies the presence of temperature mixing that can cause variations of the infrared slope, and thus in the apparent emissivity index . Nevertheless, parameterization of the dust SEDs through modified black-body fitting is a powerful tool to help understand variations of dust properties with other galaxy characteristics, especially in case of sparse sampling of the FIR/sub-mm wavelength range (e.g., high-redshift galaxies Magdis et al., 2011; Symeonidis et al., 2013). Therefore, it is extremely important to determine in which cases a single modified back-body can be used, and how temperature and dust mass estimates are affected by the assumptions made on .

In order to ascertain the dust properties of galaxies in the local Universe, and to provide new constraints to theoretical models, we have carried out the Herschel Reference Survey (HRS, Boselli et al., 2010b), a Herschel guaranteed time project focused on the study of the interplay between dust, gas and star formation in a statistically significant sample of 300 galaxies spanning a wide range of morphologies, stellar masses (8log(M/M12), cold gas contents (-3log(M/M)1), metallicities (8.212+log(O/H) 8.9), and specific star formation rates (-12 log(SFR/M)-9). The combination of Herschel/SPIRE (Griffin et al., 2010) observations with the multi-wavelength dataset we have been assembling (Ciesla et al., 2012; Cortese et al., 2012a; Boselli et al., 2013; Hughes et al., 2013), has already allowed us to have a first glimpse at how the dust content and shape of the dust SED vary with internal galaxy properties (Boselli et al., 2010a, 2012; Cortese et al., 2012b). In particular, Boselli et al. (2010a, 2012) have shown that the slope of the dust SED in the 200-500 m interval decreases from 2 to 1 when moving from metal-rich to metal-poor galaxies. However, our analyses have so far been limited by the lack of data in the 100-200 m wavelength range for the entire sample.

Thus, in this paper we present integrated Herschel/PACS (Poglitsch et al., 2010) 100 and 160 m flux densities for all the HRS sample and take advantage of our multiwavelength dataset to perform a first analysis of the properties of the dust SED across our entire sample. Corresponding to the peak of the dust SED, the 100-200 m wavelength interval is crucial not only to properly quantify the shape of the SED, but also to accurately determine the average dust temperature and total dust mass in galaxies. These data make the HRS the largest representative sample of nearby galaxies with homogeneous coverage across the 100-500 m wavelength range. In addition to releasing our dataset to the community, our primary goals are 1) to investigate how the shape of the dust SED varies with internal galaxy properties, and 2) to determine whether the integrated dust SED of HRS galaxies can always be reduced to a single modified black-body with a constant value of and, if not, what are the possible biases introduced by this assumption. The results of SED fitting with the dust models of Draine et al. (2007) will be presented in a forthcoming paper (Ciesla et al., submitted.).

This paper is organized as follows. In Sect. 2 we describe the Herschel observations, data reduction, flux density estimates and comparison with the literature. In Sect. 3 we use the PACS and SPIRE colours to investigate how the shape of the dust SED varies with internal galaxy properties. In Sec. 4, we show how the dust temperature and mass obtained from fitting a single modified black-body to the Herschel data depend on the assumptions made on . Finally, the summary and implications of our results are presented in Sec. 5.

Figure 1: Comparison of the quality of our PACS images with the Sloan Digital Sky Survey optical and SPIRE 250 m images. We show three types of objects: an early-type with dust lanes (top row), an unperturbed late-type spiral and an un-detected elliptical and its spiral companion. The size of the PACS and SPIRE beams is shown in the bottom left corner of each panel.

2 The data

2.1 The Herschel Reference Survey

The HRS is a volume-limited sample (i.e., 1525 Mpc) including all late-type galaxies (261 Sa and later) with 2MASS (Skrutskie et al., 2006) K-band magnitude K 12 mag and all early-type galaxies (62 S0a and earlier) with K 8.7 mag2. Additional selection criteria are high galactic latitude ( +55) and low Galactic extinction ( 0.2 mag, Schlegel et al., 1998), to minimize Galactic cirrus contamination. More details on the original selection can be found in Boselli et al. (2010b), while the most recent morphological classifications and distance estimates are presented in Cortese et al. (2012a).

2.2 PACS observations and data reduction

The Herschel/PACS 100 and 160 m observations of HRS galaxies presented in this work have been obtained as part of various open-time Herschel projects.

The vast majority of the data (228 out of 323 galaxies) comes from our own Herschel cycle 1 open time proposal (OT1_lcortese1). Each galaxy was observed in scan mode, along two perpendicular axes, at the medium scan speed of 20″/sec. Two repetitions were done in each scan direction. The size of each map was chosen to match the size of our SPIRE images (see Ciesla et al., 2012), making sure to have homogeneous coverage across the entire 100-500 m range.

Maps for additional 83 HRS galaxies have been obtained as part of the Herschel Virgo Cluster Survey (HeViCS, Davies et al., 2010). HeViCS mapped the Virgo cluster with both PACS and SPIRE simultaneously at the fast scan speed of 60″/sec. The observing strategy consists of scanning each 44 deg field in two orthogonal directions, and repeating each scan four times (Auld et al., 2013). The faster scan speed of the Herschel parallel mode with respect to the scan map mode, used for our observations, is compensated by the higher number of repetitions performed in the Virgo cluster, making the two datasets highly comparable (i.e., within 30%) in terms of their final noise.

PACS observations for the remaining 12 HRS galaxies have been retrieved from the Herschel public archive, and come from various projects (i.e., Kennicutt et al., 2011, KPGT_esturm_1, OT1_acrocker_1, OT2_emurph01_3, GT1_lspinogl_2, OT2_aalonsoh_2). All data have been obtained in scan mode at the medium scan speed of 20″/sec and they reach a noise level similar or lower than our own observations. For one galaxy (HRS3) only 160 m observations are available as the object lies at the edge of the 100 m map, making the data not suitable for accurate photometry. Thus, in summary, all 323 galaxies in the HRS have been observed at 160 m, whereas 100 m data are available for 322 objects.

All raw PACS data were processed from Level-0 to Level-1 within HIPE (v10.0.0, Ott, 2010) using the calibration file v48. This pre-processing includes, among the other tasks, pixel flagging, flux density conversion and coordinate assignment. To remove the 1/ noise which, at this point, still dominates the timelines, the Level-1 data were fed into Scanamorphos (version 21, Roussel, 2013), an IDL algorithm which performs an optimal correction by exploiting the redundancy in the observations of each sky pixel. No noise modelling is hence needed. The pixel size of the final maps was chosen to sample at the best the point-spread-function, at the respective wavelengths, typical of the data taken at medium scan speed: 1.7 and 2.85 arcsec pixel at 100 and 160 m, respectively (i.e., FWHM/4). The typical pixel-by-pixel noise in the map varies between 0.1 and 0.25 mJy pixel at 160 m and between 0.04 and 0.1 mJy pixel at 100 m.

In order to show the data quality of the new observations presented here, in Fig. 1 we compare the PACS images for three of our targets with the RGB Sloan Digital Sky Survey (Abazajian et al., 2009) optical and SPIRE 250 m (Ciesla et al., 2012) images. We show an example of an early-type galaxy with dust lanes (HRS45, top row), late-type galaxy (HRS48, middle row) and un-detected elliptical and its spiral companion (HRS244/245, bottom row).

2.3 PACS 100 and 160 m integrated photometry

Integrated 100 and 160 m photometry has been performed following very closely the technique used by Ciesla et al. (2012) for the SPIRE data of HRS galaxies. This is crucial to properly combine the two datasets, and to characterise the shape of the SED across the entire 100-500 m wavelength range. Thus, whenever possible, we determined integrated flux densities within the same apertures adopted in Ciesla et al. (2012). The aperture sizes are adapted to include the entire extent of the FIR emission from the galaxies, and they correspond to 1.4, 0.7 and 0.3 times the optical diameter for late-type, lenticular and elliptical galaxies, respectively. Only for 36 galaxies (11% of the sample) we choose different sizes than those used for SPIRE. There are three different reasons why we did so: a) For 23 galaxies (HRS6, 14, 22, 32, 67, 71, 75, 158, 209, 223, 225, 238, 243, 249, 255, 257, 261, 264, 286, 300, 315, 317, 322) the 100 and 160 m emission is significantly less extended than the size of the aperture used by Ciesla et al. (2012). Although this does not affect the estimate of the integrated flux density, it artificially boosts the error associated with our measurements to values always above 50%, and sometimes even higher than 100%. Thus, for these objects, we reduced the size of the aperture (on average by 26%) to obtain more realistic error estimates. We note that the size chosen is still larger than the extent of the FIR emission (so that aperture corrections are not necessary), and that the flux density estimated within these new apertures is consistent with the value obtained using Ciesla et al. (2012) apertures. b) 10 galaxies (HRS7, 68, 129, 138, 161, 174, 210, 231, 258, 308) were not spatially resolved in the SPIRE bands, and SPIRE photometry was carried out directly on the time-line data. For these cases, which are generally resolved by PACS, we chose new apertures which include all the emission from the target. c) For 3 galaxies (HRS4, 122, 263), the PACS maps available from the archive were slightly smaller than our SPIRE maps. While these maps are large enough to include the entire aperture used in Ciesla et al. (2012), no space is left to properly estimate the background. Thus, the aperture has been reduced in order to allow a more accurate background estimate, and still encompass all the emission from the galaxy.

Sky background was determined in fifteen to thirty regions, depending on the size of the target, around the chosen aperture. The use of various regions instead of just a circular annulus makes it easier to estimate the large scale variations in the background and to avoid background/foreground sources around the target. The mean sky value was then subtracted from each map before performing the flux density extraction. Since cirrus contamination is significantly less of an issue than in SPIRE images, we did not find necessary to perform a more complex modelling of the background. However, as discussed below, the effect of any residual large scale gradient is included in our error estimates.

Errors on integrated flux densities have been estimated following the guidelines described in Roussel (2013), which are consistent with what is done in Ciesla et al. (2012) for HRS SPIRE data. Briefly, there are three sources of errors that affect our measurements:

(2)

where is the flux calibration uncertainty (here assumed to be 5%; Balog et al., 2013), is the instrumental noise which depends on the number of scans crossing a pixel, and is obtained by summing in quadrature the values on the error map within the chosen aperture, and is the error on the sky measurement. As discussed in Roussel (2013), the sky uncertainty results from the combination of the uncorrelated error on the mean value of the sky ( i.e., the pixel-to-pixel variation across the image), and the correlated noise due to long time-scale drift residuals responsible for the large scale structures present in the image background ( i.e., the standard deviation of the mean value of the sky measured in different apertures around the galaxy; see also Boselli et al., 2003; Gil de Paz & Madore, 2005). In detail,

(3)

where is the number of pixels in the aperture used to integrate the galaxy flux density. As expected, for the vast majority of our objects the dominant source of error is the correlated uncertainty on the large-scale structure of the background. The average total uncertainties are 16% and 12% at 100 and 160 m, respectively.

Out of the 323 galaxies observed, 282 have been detected in both bands (284 at 160 m only). This matches the HRS detection fraction in the SPIRE bands (i.e., 284 galaxies detected at 250 m), allowing us to characterise the shape of the FIR/sub-mm SED across the entire 100-500 m range for almost 300 galaxies. In case of non detections, upper limits have been estimated as 3, using the same apertures as in Ciesla et al. (2012).

Figure 2: Comparison between our 160 m (left) and 100 m (right) flux density estimates and those presented in the literature. The bottom panels show the difference (this work (T.W.) -literature) in percentage for each dataset. For each PACS channel, the left panel shows the comparison with literature estimates based on PACS data, while in the right panel the comparison with Spitzer/MIPS and IRAS observations is presented. The dotted lines indicate the one-to-one relation, and the dashed lines the average uncertainty in our flux density estimates.

The results of our photometry are presented in Table 1. The columns are as follows:

Columns 1-6: HRS (Boselli et al., 2010b), CGCG (Zwicky et al., 1961), VCC (Binggeli et al., 1985), UGC (Nilson, 1973), NGC (Dreyer, 1888) and IC (Dreyer, 1895) names.

Columns 7-8: the J2000 right ascension and declination.

Column 9: Morphological type, taken from Cortese et al. (2012a): -2=dE/dS0, 0=E-E/S0, 1=S0, 2=S0a-S0/Sa, 3=Sa, 4=Sab, 5=Sb, 6=Sbc, 7=Sc, 8=Scd, 9=Sd, 10=Sdm-Sd/Sm, 11=Sm, 12=Im, 13=Pec, 14=S/BCD, 15=Sm/BCD, 16=Im/BCD, 17=BCD.

Column 10: 100 m flux density measurement flag. Non detections=0, Detections=1, Confused (i.e., flux density estimate significantly contaminated by the presence of another object)=2. For confused galaxies, flux densities should be considered as an upper limit to the real value.

Column 11: Integrated 100 m flux density, or upper limit in Jy.

Column 12: Total uncertainty on the 100 m flux density measurement in Jy.

Column 13: 160 m flux density measurement flag.

Column 14: Integrated 160 m flux density, or upper limit in Jy.

Column 15: Total uncertainty on the 160 m flux density measurement in Jy.

Columns 16-18: Major, minor semi-axis (in arcseconds) and position angle (in degrees) of the aperture used for the photometry.

Column 19: Herschel Proposal ID.

This table, as well as all the reduced PACS maps, are publicly available on the Herschel Database in Marseille (HeDaM, http://hedam.oamp.fr/).

2.4 Comparison with the literature

In order to check the reliability of the PACS flux density measurements presented here, we compare our far-infrared integrated flux densities with the values presented in the literature, which are based on PACS, Spitzer/MIPS or IRAS observations. The results of these comparisons are shown in Fig. 2.

The difference between our flux density estimates and those presented in Dale et al. (2012) is +6% (standard deviation of 2-3%), with our flux densities being brighter, although the number statistics is very small (6 galaxies in total). This difference is within the quoted uncertainties, and is mainly due to the different technique used to estimate flux densities (i.e., different background apertures and the use of aperture corrections not adopted in this work).

Auld et al. (2013) recently published PACS flux density measurements for all the VCC galaxies in the HeViCS footprint. A comparison between the flux density estimates for the 65 detected galaxies in common reveals a nice correlation between the two estimates with a standard deviation of just 12% and 7% at 100 and 160 m, respectively. However, Auld et al. (2013) measurements are systematically 12% and 15% lower than ours.

After various tests, we concluded that there are two main reasons for this discrepancy. First, a different flux density estimate technique. Auld et al. (2013) used apertures on average significantly smaller than ours (e.g., see their Fig. 3), and then applied aperture corrections. Indeed, by using our own apertures on the Auld et al. (2013) dataset, we find no systematic offset with our 100 m data, whereas at 160 m there is still a difference of 12%.

Second, a different data reduction technique. Auld et al. (2013) used the naive projection task photProject in HIPE to reduce PACS images. This requires the use of a high-pass filter to correct for 1/ noise, and such procedure could remove diffuse emission associated to extended objects. By using the same apertures on the HeViCS maps reduced with both photProject and Scanamorphos, we find that photProject maps provide flux densities 10% lower than those obtained with Scanamorphos, while no difference is seen at 100 m. Thus, the remaining difference at 160 m is due to the use of photProject instead of Scanamorphos. Indeed, as mentioned above, this is likely due to the use of high-pass filtering which removes diffuse emission, much more commonly present at 160 m than at 100 m (see also Rémy-Ruyer et al., 2013).

We also compared our measurements to those presented by Davies et al. (2012) for the 49 galaxies in common. These are based on an early HeViCS data release and are measured on apertures much more similar to the ones we used. Our flux density measurements agree very well with these estimates (+222% and +214% at 100 and 160 m, respectively). The scatter is larger than in the case of Auld et al. (2013), but consistent with the typical uncertainty given in Davies et al. (2012). It is likely that, in this case, the different calibration between the two datasets compensates for the intrinsic differences between photProject and Scanamorphos, providing a set of measurements consistent with our own.

Spitzer/MIPS 160 m flux densities for 103 galaxies in the HRS have been published by Bendo et al. (2012). In order to perform a proper comparison with our data, we removed those galaxies which were flagged as problematic due to incomplete coverage, or simply being confused with other nearby galaxies of similar surface brightness in Bendo et al. (2012). For the remaining 65 objects in common our flux densities are 8% brighter than those of MIPS one, with quite a large scatter (22%). This large scatter is mainly due to two galaxies (which fall outside the residual plot in Fig. 2): HRS129, 258. A comparison between the PACS, SPIRE and MIPS data for these galaxies shows that the MIPS data suffer from background confusion effects, making it difficult to separate emission from the target and background sources. Moreover, the MIPS observations for these galaxies were performed in photometry mode, which produces compact maps where it is difficult to measure the background. Once these are removed from the sample, the difference between MIPS and PACS measurements becomes +1014%. Conversely, the comparison with the Spitzer/MIPS 160 m flux densities presented in Dale et al. (2007) for the 6 SINGS galaxies in our sample shows an average difference of -511%. All these values are within the 12% flux calibration uncertainty in MIPS data (Stansberry et al., 2007). A PACS-to-MIPS 160 m flux density ratio systematically higher than 1 has also been found by comparing pixel-by-pixel photometry of nearby galaxies (Aniano et al., 2012; Draine et al., 2013).

We can thus conclude that our 160 m PACS flux density measurements are consistent with those of Spitzer/MIPS within 20%, in agreement with the results obtained by the PACS Team (Paladini et al., 2012).

Figure 3: From top to bottom: 100-to-250 m, 100-to-500 m, 160-to-500 m and 250-to-500 m as a function of the 100-to-160 m flux density ratio. The first column shows the entire HRS sample, while in the following three columns points are colour-coded according to morphological type (open circles=E+S0, filled circles=Sa and later), Hi gas fraction (open circles=log()-1, filled circles=log()-1) and gas phase metallicity (open circles=12+log(O/H)8.65, filled circles=12+log(O/H)8.65). The Pearson correlation coefficients () for the whole sample are shown in the top left corner of each panel. The solid and dashed lines represent the expected colours for a modified black body with =2 and 1, respectively. We consider a temperature range between 10 and 40 K. Typical errorbars are shown on the bottom right corner of each panel.
Table 2: The Pearson correlation coefficients () and scatter () of the best-fitting bisector linear fit for each sample shown in the colour-colour relations of Fig. 3.

Finally, we compared our PACS 100 m flux density estimates with those presented in the IRAS Faint Source Catalogue (164 galaxies after exclusion of confused/contaminated objects), finding an average difference of +715% (see also Ali, 2011).

We remind the reader that, although the central wavelengths of MIPS and IRAS correspond to those of PACS, the bandpasses are not identical and part of the offsets shown above are certainly due to the different filter responses of the three instruments.

3 Far-infrared/sub-millimeter colours as a proxy for the shape of the dust SED

In the last few years, several studies have shown how infrared colours can be used as a proxy of dust properties (e.g., Boselli et al., 2010a, 2012; Dale et al., 2012; Bendo et al., 2010, 2012; Galametz et al., 2010; Boquien et al., 2011; Rémy-Ruyer et al., 2013). The novelty of the present work is that, for the first time, we cover the 100-500 m domain for a representative sample of galaxies spanning a large range in stellar mass, star formation activity, cold gas and metal content. For example, compared to the work presented in Boselli et al. (2012), which focused on Hi-normal spiral galaxies only, this analysis takes advantage of a more complete coverage at wavelengths shorter than 250 m, and includes the entire HRS sample detected by Herschel (282 versus 146 objects). Similarly, the number of HRS galaxies detected at all PACS and SPIRE wavelengths is significantly larger (i.e., 282 versus 195) than that of Auld et al. (2013), which focuses on Virgo cluster galaxies only.

Particularly interesting is to quantify how well the shapes of the dust SED at the short and long wavelength-ends correlate among each other. Indeed if, in the 100-500 m wavelength range, the dust SED can be well approximated by a single modified black-body with fixed (i.e., the variation of the dust emissivity with frequency described by ), all FIR/sub-mm colours should be strongly correlated.

The SPIRE flux densities are obtained from Ciesla et al. (2012), but we applied several corrections to these flux estimates. We multiplied their values by 1.0253, 1.0250 and 1.0125 at 250, 350 and 500 m to take into account the new SPIRE calibration (v.11), and then by 0.9097, 0.9136 and 0.8976 at 250, 350 and 500 m, to correct for the new beam areas (Bendo et al., 2013; Herschel Space Observatory, 2013). We did not make any attempt to include variations of the beam size as a function of the shape of the SED (Herschel Space Observatory, 2013), as these are generally within the measurement errors (10%). Moreover, such correction would mainly result in a systematic offset in the flux densities, whereas the relative variation between the SPIRE bands would be 3% for the ranges of investigated here. Thus, we are confident that this does not affect our conclusions.

Figure 4: Same as Fig. 3, but with the predictions for two temperatures modified black-body SEDs with =2 overplotted on the data points. In each plot, isotherms for the cold (=10, 15 and 20 K) and warm (=20, 25 and 30K) dust components are indicated by the dotted and dashed lines, respectively. Cold-to-warm dust mass ratios are 1, 2, 5 and 10 from left to right.

In Fig. 3 we plot the 100-to-160 m flux density ratio, which usually embraces the peak of the dust SED, as a function of various flux density ratios (i.e., from top to bottom: 100-to-250 m, 100-to-500 m, 160-to-500 m and 250-to-500 m) sensible to the shape of the SED at increasingly longer wavelengths3. Similar results are found if additional colours (e.g., including the 350m flux density) are used.

Figure 5: The left panel shows the distribution of reduced () for the best-fitting single modified black-body with =free (solid line) and =2 (dashed). The ratio of obtained for the two cases as a function of gas-phase metallicity and Hi gas fraction are presented in the central and right panel, respectively. Empty circles show galaxies with Hi deficiency greater than 0.5. We show only those galaxies for which at least one of the two corresponds to a probability 95%.

It is clear that the farther away in wavelength two colours are, the weaker their correlation is, as already noted by Boselli et al. (2012). Indeed, the Pearson correlation coefficient () decreases from 0.8 to 0.2 when moving from the 100-to-250 m to the 250-to-500 m flux density ratios (see first column of Fig. 3). Intriguingly, the increase of a factor of 3 in scatter ()4 observed when moving from the top to the third panel appears to be due to a population of galaxies that detaches from the main relation. To see if this is indeed the case, in Fig. 3 we highlight galaxies according to (from left to right) their morphological type, the ratio of their atomic cold gas (Hi) to stellar mass content and gas-phase metallicity. Hi measurements have mainly been obtained from Haynes et al. (2011) and Springob et al. (2005), and are presented in Boselli et al. (2014)5. Stellar masses are from Cortese et al. (2012a), and gas-phase metallicities (i.e., oxygen abundances) converted into the Pettini & Pagel (2004) O3N2 base metallicity are taken from Hughes et al. (2013). We use 12+log(O/H)=8.65 (above which the stellar vs. mass metallicity relation starts flattening, Kewley & Ellison, 2008) and 0.1 (below which the stellar mass vs. Hi fraction relation is no longer linear, Cortese et al., 2011; Bothwell et al., 2009) to divide gas-rich/metal-poor from gas-poor/metal-rich galaxies. The Pearson correlation coefficients and scatter around the best-fitting bisector linear fit are indicated in Table 2.

Gas-rich/metal-poor galaxies seem to be responsible for the significant increase in scatter when moving from the 100-to-250 m to the 160-to-500 m colour-colour plots. If we consider gas-poor/metal-rich galaxies only, the scatter in the three bottom panels of Fig. 3 decreases by at least a factor 2. Indeed, performing a Kolmogorov-Smirnov test, we found that there is only a 4% chance that the 160-to-500 m colour distributions of metal-poor (12+log(O/H)8.65) and metal-rich (12+log(O/H)8.65) galaxies are drawn from the same population, as already demonstrated by Boselli et al. (2012). We note that some galaxies do not appear in the third and fourth column of Fig. 3. This is because for some objects Hi and metallicity information is not available.

Our findings suggest that, in the 100-500 m regime, the shape of the dust SED for galaxies with stellar mass 10M/M10 cannot be reproduced by simply varying the value of the average dust temperature. In other words, either must also vary (Boselli et al., 2012; Smith et al., 2012; Rémy-Ruyer et al., 2013) or multiple temperatures components are required (Shetty et al., 2009; Dunne & Eales, 2001; Boquien et al., 2011; Bendo et al., 2012; Clemens et al., 2013).

In order to visually illustrate this result, we plot in Fig. 3 and 4 the colours expected for these two different scenarios. In Fig. 3 we show the flux density ratios derived from single modified black-bodies with temperatures ranging from 10 and 40 K and values fixed to 2 (solid line) and 1 (dashed line). In Fig. 4, we show a combination of two modified black-bodies with =2. We vary the cold dust temperature () from 10 to 20 K, and the warm dust temperature () from 20 to 30 K. The four columns show different mass ratios increasing from 1 (left) to 10 (right).

It is clear that, while the temperature is the main driver of the trends observed in each colour-colour plot, only a variation in , or an additional temperature component, can explain the increasing scatter when moving from the 100-to-250 m to 160-to-500 m colours. Interestingly, the two temperature components scenario is able to reproduce the observed range of colours only if the warm component contributes negligibly to the total dust budget of the galaxy (i.e., 5; Vlahakis et al., 2005). This is easy to understand if we consider the fact that, at fixed dust mass, the flux density emitted by a black-body in the FIR/submm wavelength range increases with temperature. Thus, if the warm and cold components have the same dust mass, the warm dust dominates the total emission, and the shape of the SED is very close to that of a single black-body. Only if the cold dust component dominates the mass budget, the shape of the combined SED deviates significantly from a single black-body.

Unfortunately, with our current data it is impossible to discriminate between a varying and a multiple temperature component scenario. Our lack of coverage below 100 m makes it meaningless to perform a two temperatures fit, as the warm component is not constrained. Thus, in the rest of this paper we will focus on the single modified black-body case only, and investigate how different assumptions on can affect the interpretation of Herschel observations. A detailed comparison with the predictions of the Draine et al. (2007) dust models will be presented in a forthcoming paper (Ciesla et al., submitted.).

4 Fitting the dust SED with a single modified black-body

4.1 How well do colours trace the average dust temperature?

The results presented in the previous section show that FIR/sub-mm colours may not always represent a proxy for the average underlying dust temperature. In order to investigate this issue in more detail, it is interesting to quantify how the FIR/sub-mm colours correlate with the parameters obtained from a single modified black-body fitting. We assume either a constant value of =2, or keep this as a free parameter. The model functions were convolved with the PACS and SPIRE filter response functions and fitted to the relative spectral responsivity function-weighted flux density measurements. Best-fit parameters and their 1 uncertainties are determined via a minimisation using the Python version of the minimisation library MINUIT (James & Roos, 1975). We choose =2 simply because this seems to correctly reproduce the shape of the SED for massive, metal-rich spiral galaxies in the local Universe (Davies et al., 2012; Boselli et al., 2012; Draine et al., 2013). However, our results do not qualitatively change if a different (but fixed) value of is used. In the rest of the paper, we consider only those objects detected in all 5 PACS/SPIRE bands, and for which the reduced () corresponds to a probability 95%: i.e., 2.6 (203 galaxies) and 3 (242 galaxies) for a fixed and variable , respectively. The best-fit dust masses and temperatures for these galaxies, as well as their distance, are provided in Table 3. This guarantees that we are not contaminated by objects whose FIR/submm emission is dominated by synchrotron emission (Baes et al., 2010).

A comparison between the reduced obtained for the =free and =2 cases is shown in Fig. 5. Not surprisingly, leaving free provides on average better fits. Moreover, as shown in the central and right panel of Fig. 5, the difference between the two techniques increases when moving towards metal-poor/gas-rich systems. This is even more evident when Hi-deficient galaxies (i.e., 0.5, empty points in Fig. 5), for which the gas content is no longer a good indicator of enrichment history (Cortese & Hughes, 2009; Hughes et al., 2013), are excluded (0.38 and 0.54 for all galaxies and Hi-normal systems only, respectively).

In Fig. 6, we show how the FIR/sub-mm colours correlate with the best-fit parameters obtained from our SED fitting. Not surprisingly, all SPIRE and PACS colours strongly correlate with dust temperature if is kept fixed (we note that these results do not qualitatively change if we fix to a different value). It is also expected that the lowest scatter is observed for the colour spanning the largest wavelength range (i.e., the 100-to-500 m flux density ratio), as the variation in colour is larger, and less affected by measurement errors.

Figure 6: The modified black-body best-fitting parameters as a function of far-infrared/sub-millimeter colours (from left to right: 100-to-160 m, 100-to-250 m, 100-to-500 m and 250-to-500 m flux density ratio). The bottom row shows the dust temperature obtained by keeping fixed to 2, while the middle and top rows show the best-fitting values for and obtained by varying both parameters freely. The Pearson correlation coefficients are indicated in each panel. In the bottom row, the dotted lines show the expected relations between temperature and colour for a single modified black-body with =2, while the dashed line indicates the 1-to-1 relation.

More interesting is the case when is treated as a free parameter. In this case, there is a clear difference in the colours behaviour when crossing a of 200 m. At shorter wavelengths, there is still a strong correlation of colour with temperature (0.7), while only a very weak trend is seen with (-0.15). Moving to longer wavelengths, the trends with temperature become weaker, and reverse for the 250-to-500 m colour (-0.3), whereas the correlation with becomes gradually stronger. The best relation is found with the 250-to-500 m flux density ratio (0.9), which appears to be mainly tracing variations of and not dust temperature, as also shown in Fig. 3. These results are likely a direct consequence of the fact that the FIR/submm SED for our sample peaks at 200 m, and while the PACS colours trace the peak of the dust SED, any variations in the emissivity of the grains will predominantly affect the SPIRE colours. The average value of for HRS galaxies is 1.80.5, a value consistent with what is found in the Milky Way and in other nearby galaxies (Planck Collaboration et al., 2013; Galametz et al., 2012; Boselli et al., 2012; Smith et al., 2012, 2013).

Figure 7: From top to bottom: the 250-to-500 m flux density ratio, the best-fitting value of , the 100-to-160 m flux density ratio, the best fitting temperature assuming =free and 2 as a function of stellar mass, stellar mass surface density (), specific star formation rate (), Hi gas fraction () and gas phase metallicity (12+log(O/H). Filled and open circles show late- and early-type galaxies, respectively. The Pearson correlation coefficients for the whole sample are shown in each panel.

An important issue affecting any modified black-body fitting with and as free parameters is the known anti-correlation between them, which is clearly shown in the right column of Fig. 6. While it is still debated whether part of this anti-correlation has a physical origin (Shetty et al., 2009; Galametz et al., 2012; Smith et al., 2012; Juvela & Ysard, 2012; Juvela et al., 2013; Rémy-Ruyer et al., 2013; Tabatabaei et al., 2013), there is no doubt that it is mainly due to the fitting technique (Shetty et al., 2009). Indeed, in the 2D vs. plane, the region corresponding to the absolute minimum of depends on both quantities, giving rise to an anti-correlation between and . This is clearly visible by just looking at the 2D confidence levels for any modified black-body fit. Since in the first and third columns of Fig. 6 temperature and show opposite trends with colour, it is very likely that they are affected by this degeneracy. However, the significant difference in scatter between the various relations suggests that the 100-to-160 m colour vs. and 250-to-500 m colour vs. are less contaminated than the other correlations. As mentioned above, this is because the PACS colours mainly trace the peak of the dust SED, whereas the SPIRE ones are mostly sensitive to variations in the dust emissivity.

4.2 The relation between dust temperature, and integrated galaxy properties

In this section we investigate further how the variation of , necessary to reproduce the observed colours of HRS galaxies in a single modified black-body scenario, is mirrored by a variation in galaxy properties. For comparison, we will also show the results obtained by keeping fixed, since we consider this an instructive exercise to illustrate how the model assumptions influence the parameters we derive. In Fig. 7, we show how the best-fitting dust parameters, as well as the 100-to-160 m and 250-to-500 m flux density ratios, are related to gas-phase metallicities, Hi gas fractions, specific star formation rate (), stellar mass surface density [= where is the radius containing 50% of the total -band light] and stellar mass. Star formation rates are determined by combining WISE 22m (Ciesla et al., submitted.) and NUV photometry (Cortese et al., 2012a) using the recipes presented in Hao et al. (2011) as described in Cortese (2012).

By comparing the two bottom rows of Fig. 7, it is clear that the assumptions made on significantly influence the correlations between temperature and integrated galaxy properties. For fixed to 2, the strongest correlation is found with stellar mass surface density (0.45). A weak anti-correlation is visible with gas-fraction (-0.3), while no correlation is found with specific star formation rate, stellar mass or metallicity ( 0.2). Quite different results are obtained if is left free. In this case, the temperature anti-correlates very weakly with (-0.3), while it is strongly correlated with (see also Clemens et al., 2013), Hi gas fraction, metallicity and stellar mass (0.5). Even more importantly, some of the correlations show opposite trends. For a fixed value of , the temperature increases with metallicity and stellar mass surface densities, whereas it decreases for =free. The ‘reversal’ of these correlations is driven exclusively by metal-poor/gas-rich galaxies, and it is simply a consequence of the fact that, for these objects, the best-fitting value of is significantly lower than 2. Thus, many of the correlations shown in Fig. 7 depend on the assumptions made about the dust SED, and may not be physical (Magnelli et al., 2012; Roseboom et al., 2013).

In particular, we have shown (see Fig. 6) that the 100-to-160 m and 250-to-500 m flux density ratios are the best proxies for and , respectively. If all the trends observed in Fig. 7 are physical, we should find similar correlations when and are replaced by the flux density ratios. However, this is not always the case. The 100-to-160 m flux density ratio correlates only with (0.5), while the 250-to-500 m ratio correlates weakly with (-0.2), but varies strongly with stellar mass, stellar mass surface density, Hi gas fraction and gas-phase metallicity (0.6-0.7). Thus, the vs. Hi gas fraction and vs. trends might be spurious.

In summary, our analysis confirms that the typical dust temperature of a galaxies as measured from a single modified black-body is mainly related to specific star formation rate, while varies more with the degree of metal enrichment of the ISM. As discussed in the previous section, at this stage it is impossible to determine whether the variation of across the HRS indicates a variation in the dust properties/composition, or it simply highlights the need of multiple temperature components for gas-rich/metal-poor/low-mass galaxies.

Figure 8: Left panel: Comparison between the dust masses obtained from a black-body SED fitting with 2 and free. Right panel: Dust masses obtained from a black-body SED fitting with 2 as a function of those obtained using the empirical recipes of Cortese et al. (2012b), which are based on SPIRE colours only. Filled and empty circles indicate gas-rich and gas-poor galaxies, respectively (see also Fig. 3).

4.3 Dust mass estimates

It is interesting to investigate how the variation of across the HRS for a single modified black-body affects the estimate of the dust mass reservoir. Thus, in the left panel of Fig. 8, we compare the dust masses obtained for =free and =2. Dust masses have been calculated from Eq. 1 assuming 856.5 GHz (i.e., 350 m) and 0.192 m kg (Draine, 2003). It is evident that dust masses are significantly less affected than dust temperatures by the assumptions made on . The average difference between the two measurements is 0.08 dex, with a standard deviation of 0.15 dex, which is consistent with the typical statistical error obtained from the SED fitting: 0.05 and 0.1 dex for =2 and =free. Not surprisingly, the largest difference is observed in gas-rich galaxies (filled circles, =0.140.14 dex), while the two techniques give consistent results for gas-poor systems (empty circles, =-0.020.11 dex).

This result implies that correlations involving dust masses are quite robust against the assumptions made on the shape of the SED. Different assumptions can certainly affect the exact slope of the dust scaling relations, but they are not able to produce the same dramatic inversion of some correlations observed for the dust temperature (see Fig. 7).

This conclusion is reinforced by the fact that the differences, already quite small, between the two cases might be overestimated, as we varied , by keeping fixed the value of dust opacity used to determine the dust mass. As recently shown by Bianchi (2013), this is not entirely correct because the value of is calibrated on a dust model with a well defined value of . Thus, if changes, should change as well. Unfortunately, varying along with is far from trivial, and it is only possible by either having a consistent dust model for each value of , or by comparing dust mass estimates obtained from SED fitting with the ones obtained from other independent methods: e.g., using the amount of cold gas and metals, as proposed by James et al. (2002).

Finally, it is interesting to compare the dust masses estimated by fitting a single modified black-body with =2, to those obtained by using the empirical recipes developed by Cortese et al. (2012b), which assume =2 but are based on SPIRE data only. In this way we can quantify the benefit provided by inclusion of the PACS data in the dust mass estimates. As shown in the right panel of Fig. 8, the two estimates show a good agreement with a mean difference of -0.07 dex and a standard deviation of 0.14 dex, lower than the typical uncertainty of 0.2 dex in the recipes by Cortese et al. (2012b). Even in this case, the largest offset (-0.120.11 dex) is found for gas-rich galaxies. This is a natural consequence of the fact that, for these objects, the shape of the dust SED is no longer perfectly consistent with =2.

Thus, while dust mass estimates based on SPIRE colours are a reliable tool for estimating dust masses within 0.2 dex, only a complete coverage of the 100-500 m wavelength range can provide us with accurate (within 0.1dex) dust mass estimates necessary to quantify in great detail the correlation between dust mass and other galaxy properties.

5 Summary & Conclusions

In this paper we presented PACS 100 and 160 m integrated photometry for the Herschel Reference Survey. We have combined these data with SPIRE observations to investigate how the shape of dust SED varies across the Hubble sequence. Being the largest representative sample of nearby galaxies with homogeneous coverage in the 100-500 m wavelength domain, the HRS is ideal to quantify if and how dust emission varies across the local galaxy population. Our main results are as follows.

  • The shape of the dust SED is not well described by a single modified black-body having just the dust temperature as a free parameter. Instead, there is a clear need to vary the dependence of the dust emissivity () on wavelength, or to invoke multiple temperature components in order to reproduce the colours observed in our sample. This is particularly important as the HRS does not include very metal-poor dwarf galaxies, for which we already knew that the dust SED is significantly different from the one of metal-rich, massive galaxies (Galliano et al., 2005; Galliano et al., 2011; Engelbracht et al., 2008; Galametz et al., 2009; Rémy-Ruyer et al., 2013). Our results suggest that the difference in FIR/sub-mm colours between giant and dwarf galaxies (Draine & Li, 2007) may not be the result of a dramatic transition in dust properties, but just the consequence of the gradual variation that we observe as a function of metal and gas content.

  • The variation in the slope of the dust SED strongly affects dust temperature estimates from single modified black-bodies fits. In particular, the correlations between galaxy properties and dust temperatures strongly depend on the assumptions made on : i.e., trends can disappear or even reverse. Conversely, dust mass estimates are more robust, and variations in do not produce the same dramatic inversion of some correlations observed for the dust temperature.

  • We confirm that the temperature of a single modified black-body is mainly related to specific star formation rate, while varies more with the degree of metal enrichment of the ISM.

The results presented in this paper may appear in contradiction with several recent works showing that the dust SED is very well reproduced by a simple modified black-body with 2 (Davies et al., 2012; Auld et al., 2013). However, all these works were focused on massive, metal-rich and relative gas-poor galaxies, for which we also find that a constant value of provides a good fit to our data. It is when we move to the gas-rich/metal-poor regime that the shape of the SED starts to change (Boselli et al., 2010a, 2012; Rémy-Ruyer et al., 2013).

Our findings overall reinforce the results already presented in Boselli et al. (2010a, 2012). However, it is important to note that the discovery of a clear variation in the shape of the SED across the HRS has only been possible thanks to the large wavelength coverage obtained by combining both PACS and SPIRE data. Indeed, with SPIRE or PACS data only, it would be not only much more difficult to show under which conditions a simple modified black-body approach does not work, but it would also be nearly impossible to quantify how model assumptions can affect the correlation of dust temperature with star formation, galaxy structure and chemical enrichment.

Acknowledgments

We thank an anonymous referee for his/her very useful comments and suggestions which have significantly improved this manuscript. LC thanks B. Draine for useful discussions, and B. Catinella for comments on this manuscript. We thank all the people involved in the construction and the launch of Herschel.

The research leading to these results has received funding from the European CommunityÕs Seventh Framework Programme (/FP7/2007-2013/) under grant agreement No 229517, and was supported under Australian Research Council’s Discovery Projects funding scheme (project number 130100664). IDL is a postdoctoral researcher of the FWO-Vlaanderen (Belgium).

PACS has been developed by a consortium of institutes led by MPE (Germany) and including UVIE (Austria); KU Leuven, CSL, IMEC (Belgium); CEA, LAM (France); MPIA (Germany); INAF-IFSI/OAA/OAP/OAT, LENS, SISSA (Italy); IAC (Spain). This development has been supported by the funding agencies BMVIT (Austria), ESA-PRODEX (Belgium), CEA/CNES (France), DLR (Germany), ASI/INAF (Italy), and CICYT/MCYT (Spain). SPIRE has been developed by a consortium of institutes led by Cardiff University (UK) and including Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC (UK); and NASA (USA).

Part of the HRS data was accessed through the Herschel Database in Marseille (HeDaM - http://hedam.lam.fr) operated by CeSAM and hosted by the Laboratoire d’Astrophysique de Marseille.

We acknowledge the use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

HRS CGCG VCC UGC NGC IC R.A. (J.2000) Dec (J.2000) Type Flag F Flag F a b P.A. Proposal ID

hh:mm:ss.ss dd:mm:ss.s Jy Jy Jy Jy arcsec arcsec degree


1
123-035 0 0 0 0 10:17:39.66 +22:48:35.9 13 1 0.748 0.169 1 0.932 0.079 48. 40. -10. OT1_lcortese_1
2 124-004 0 5588 0 0 10:20:57.13 +25:21:53.4 5 1 2.439 0.227 1 2.808 0.179 47. 45. 40. OT1_lcortese_1
3 94-026 0 5617 3226 0 10:23:27.01 +19:53:54.7 0 0 0.0 0.0 2 0.846 0.087 39. 39. 15. GT1_lspinogl_2
4 94-028 0 5620 3227 0 10:23:30.58 +19:51:54.2 3 2 17.589 1.104 2 22.675 1.165 100. 84. -25. GT1_lspinogl_2/OT2_aalonsoh_2
5 94-052 0 0 0 610 10:26:28.37 +20:13:41.5 7 1 4.502 0.331 1 5.563 0.528 81. 48. 28. OT1_lcortese_1
6 154-016 0 5662 0 0 10:27:01.16 +28:38:21.9 5 1 0.275 0.14 1 0.483 0.085 62. 22. -30. OT1_lcortese_1
7 154-017 0 5663 3245 0 10:27:18.39 +28:30:26.6 1 1 3.472 0.206 1 2.843 0.151 48. 30. -3. OT1_lcortese_1
8 154-020 0 5685 3254 0 10:29:19.92 +29:29:29.2 6 1 2.878 1.092 1 4.641 1.041 210. 66. 46. OT1_lcortese_1
9 154-026 0 5731 3277 0 10:32:55.45 +28:30:42.2 4 1 1.948 0.397 1 3.037 0.523 82. 73. 25. OT1_lcortese_1
10 183-028 0 5738 0 0 10:34:29.82 +35:15:24.4 5 1 1.168 0.238 1 0.788 0.094 56. 43. 30. OT1_lcortese_1
11 124-038 0 5742 3287 0 10:34:47.31 +21:38:54.0 9 1 5.192 0.3 1 6.148 0.409 88. 41. 20. OT1_lcortese_1
12 124-041 0 0 0 0 10:35:42.07 +26:07:33.7 17 1 0.613 0.054 1 0.492 0.05 29. 22. 20. OT1_lcortese_1
13 183-030 0 5753 3294 0 10:36:16.25 +37:19:28.9 7 1 19.809 1.322 1 25.224 1.717 149. 76. -65. OT1_lcortese_1
14 124-045 0 5767 3301 0 10:36:56.04 +21:52:55.7 2 1 0.477 0.078 1 0.372 0.106 35. 35. 55. OT1_lcortese_1
15 65-087 0 5826 3338 0 10:42:07.54 +13:44:49.2 7 1 13.12 2.907 1 20.386 2.277 247. 152. -80. OT1_lcortese_1
16 94-116 0 5842 3346 0 10:43:38.91 +14:52:18.7 8 1 5.688 0.695 1 10.294 0.785 113. 98. -72. OT1_lcortese_1
17 95-019 0 5887 3370 0 10:47:04.05 +17:16:25.3 7 1 10.209 0.872 1 12.793 0.832 133. 75. -30. OT1_lcortese_1
18 155-015 0 5906 3380 0 10:48:12.17 +28:36:06.5 3 1 1.465 0.248 1 2.015 0.241 71. 57. 20. OT1_lcortese_1
19 184-016 0 5909 3381 0 10:48:24.82 +34:42:41.1 13 1 4.335 0.525 1 4.371 0.375 86. 78. 55. OT1_lcortese_1
20 184-018 0 5931 3395 2613 10:49:50.11 +32:58:58.3 8 2 16.137 1.402 2 16.068 0.871 84. 63. 50. KPGT_esturm_1
21 155-028 0 5958 0 0 10:51:15.81 +27:50:54.9 6 1 0.625 0.255 1 1.084 0.148 78. 45. 0. OT1_lcortese_1
22 155-029 0 5959 3414 0 10:51:16.23 +27:58:30.0 1 1 0.618 0.072 1 0.685 0.118 40. 36. 10. OT1_lcortese_1
23 184-028 0 5972 3424 0 10:51:46.33 +32:54:02.7 5 1 18.098 0.981 1 19.636 1.076 109. 59. -70. OT1_lcortese_1
24 184-029 0 5982 3430 0 10:52:11.41 +32:57:01.5 7 1 10.909 1.182 1 16.037 1.577 167. 94. 35. OT1_lcortese_1
25 125-013 0 5995 3437 0 10:52:35.75 +22:56:02.9 7 1 21.647 1.187 1 21.174 1.142 101. 67. -63. OT1_lcortese_1
26 184-031 0 5990 0 0 10:52:38.34 +34:28:59.3 4 1 0.744 0.098 1 0.864 0.106 58. 35. 15. OT1_lcortese_1
27 184-034 0 6001 3442 0 10:53:08.11 +33:54:37.3 3 1 3.148 0.286 1 3.173 0.253 56. 48. -2. OT1_lcortese_1
28 155-035 0 6023 3451 0 10:54:20.86 +27:14:22.9 9 1 3.569 0.232 1 5.184 0.321 80. 48. 50. OT1_lcortese_1
29 95-060 0 6026 3454 0 10:54:29.45 +17:20:38.3 7 1 2.403 0.292 1 3.422 0.326 90. 51. -65. OT1_lcortese_1
30 95-062 0 6028 3455 0 10:54:31.07 +17:17:04.7 5 1 2.87 0.545 1 3.859 0.37 100. 62. 70. OT1_lcortese_1
31 267-027 0 6024 3448 0 10:54:39.24 +54:18:18.8 13 1 12.17 1.183 1 10.63 1.579 236. 75. 65. OT1_lcortese_1
32 95-065 0 6030 3457 0 10:54:48.63 +17:37:16.3 5 1 0.042 0.024 1 0.182 0.034 20. 20. 90. OT1_lcortese_1
33 95-085 0 6077 3485 0 11:00:02.38 +14:50:29.7 5 1 5.138 0.368 1 6.83 0.532 88. 77. 60. OT1_lcortese_1
34 95-097 0 6116 3501 0 11:02:47.32 +17:59:22.2 8 1 5.002 0.64 1 8.619 0.832 167. 52. 30. OT1_lcortese_1
35 267-037 0 6115 3499 0 11:03:11.03 +56:13:18.2 13 1 0.24 0.057 1 0.273 0.081 34. 30. 20. OT1_lcortese_1
36 155-049 0 6118 3504 0 11:03:11.21 +27:58:21.0 4 1 35.557 1.977 1 31.358 1.651 113. 88. -30. OT1_lcortese_1
37 155-051 0 6128 3512 0 11:04:02.98 +28:02:12.5 7 1 4.532 0.346 1 5.328 0.513 68. 63. -42. OT1_lcortese_1
38 38-129 0 6167 3526 0 11:06:56.63 +07:10:26.1 7 1 1.818 0.291 1 1.886 0.286 83. 45. 55. OT1_lcortese_1
39 66-115 0 6169 0 0 11:07:03.35 +12:03:36.2 5 1 0.915 0.21 1 1.199 0.333 81. 39. 0. OT1_lcortese_1
40 67-019 0 6209 3547 0 11:09:55.94 +10:43:15.0 5 1 4.478 0.305 1 4.51 0.363 80. 39. 7. OT1_lcortese_1
41 96-011 0 6267 3592 0 11:14:27.25 +17:15:36.5 7 1 1.418 0.278 1 1.584 0.144 74. 41. -63. OT1_lcortese_1
42 96-013 0 6277 3596 0 11:15:06.21 +14:47:13.5 7 1 12.111 2.072 1 18.583 1.787 171. 163. 0. OT1_lcortese_1
43 96-022 0 6299 3608 0 11:16:58.96 +18:08:54.9 0 0 0.119 0.0 0 0.187 0.0 28. 28. 0. OT1_lcortese_1
44 96-026 0 6320 0 0 11:18:17.24 +18:50:49.0 5 1 2.521 0.197 1 1.982 0.208 54. 51. -73. OT1_lcortese_1
45 291-054 0 6330 3619 0 11:19:21.60 +57:45:27.8 1 1 1.782 0.393 1 2.72 0.439 78. 75. -65. OT1_lcortese_1
46 96-029 0 6343 3626 0 11:20:03.80 +18:21:24.5 1 1 4.995 0.272 1 4.9 0.284 59. 54. -20. OT1_acrocker_1
47 156-064 0 6352 3629 0 11:20:31.82 +26:57:48.2 8 1 2.652 0.465 1 3.163 0.509 96. 68. 65. OT1_lcortese_1
48 268-021 0 6360 3631 0 11:21:02.85 +53:10:11.0 7 1 29.87 3.057 1 38.272 3.115 210. 201. -62. OT1_lcortese_1
49 39-130 0 6368 3640 0 11:21:06.85 +03:14:05.4 0 0 0.261 0.0 0 0.231 0.0 35. 35. 90. OT1_lcortese_1
50 96-037 0 6396 3655 0 11:22:54.62 +16:35:24.5 7 1 20.97 1.076 1 22.078 1.141 76. 53. 30. OT1_lcortese_1
51 96-038 0 6405 3659 0 11:23:45.49 +17:49:06.8 11 1 4.542 0.423 1 4.996 0.582 87. 62. 55. OT1_lcortese_1
52 268-030 0 6406 3657 0 11:23:55.57 +52:55:15.5 7 1 0.783 0.274 1 1.305 0.237 61. 54. -20. OT1_lcortese_1
53 67-071 0 6420 3666 0 11:24:26.07 +11:20:32.0 7 1 8.762 0.75 1 10.815 0.829 184. 50. -85. OT1_lcortese_1
54 96-045 0 6445 3681 0 11:26:29.80 +16:51:47.5 6 1 2.819 0.577 1 3.076 0.537 94. 75. -15. OT1_lcortese_1
55 96-047 0 6453 3684 0 11:27:11.18 +17:01:49.0 6 1 7.742 0.768 1 11.416 0.769 121. 84. -55. OT1_lcortese_1
56 291-072 0 6458 3683 0 11:27:31.85 +56:52:37.4 7 1 28.93 1.498 1 30.107 1.604 82. 65. -52. OT1_lcortese_1
57 96-049 0 6460 3686 0 11:27:43.95 +17:13:26.8 6 1 12.431 1.106 1 17.983 1.255 134. 104. 25. OT1_lcortese_1
58 96-050 0 6464 3691 0 11:28:09.41 +16:55:13.7 5 1 2.098 0.194 1 2.449 0.155 53. 42. 30. OT1_lcortese_1
59 67-084 0 6474 3692 0 11:28:24.01 +09:24:27.5 5 1 3.801 0.395 1 5.622 0.431 132. 48. -85. OT1_lcortese_1
60 268-051 0 6547 3729 0 11:33:49.34 +53:07:31.8 3 1 7.738 0.915 1 10.477 0.772 118. 80. 15. OT1_lcortese_1
61 292-009 0 6575 0 0 11:36:26.47 +58:11:29.0 8 1 0.7 0.121 1 0.937 0.15 83. 25. -9. OT1_lcortese_1
62 186-012 0 6577 3755 0 11:36:33.37 +36:24:37.2 7 1 2.73 0.498 1 3.527 0.528 133. 58. -65. OT1_lcortese_1
63 268-063 0 6579 3756 0 11:36:48.02 +54:17:36.8 6 1 6.471 1.15 1 12.252 1.339 175. 88. -1. OT1_lcortese_1
64 292-017 0 6629 3795 0 11:40:06.84 +58:36:47.2 7 1 1.333 0.326 1 1.682 0.29 92. 48. 53. OT1_lcortese_1
65 292-019 0 6640 3794 0 11:40:53.42 +56:12:07.3 9 1 1.909 0.476 1 2.082 0.426 94. 61. -60. OT1_lcortese_1
66 186-024 0 6651 3813 0 11:41:18.65 +36:32:48.3 5 1 21.54 1.151 1 23.821 1.255 94. 46. 85. OT1_lcortese_1
67 268-076 0 6706 0 0 11:44:14.83 +55:02:05.9 11 1 0.841 0.181 1 1.209 0.279 67. 45. 60. OT1_lcortese_1
68 186-045 0 0 0 0 11:46:25.96 +34:51:09.2 5 1 1.518 0.1 1 1.531 0.092 40. 40. 0. OT1_lcortese_1
69 268-088 0 6787 3898 0 11:49:15.37 +56:05:03.7 4 1 3.46 1.115 1 4.318 1.203 184. 108. -72. OT1_lcortese_1
70 0 0 0 0 2969 11:52:31.27 -03:52:20.1 6 1 1.605 0.2 1 2.018 0.161 61. 51. -75. OT1_lcortese_1
71 292-042 0 6860 3945 0 11:53:13.73 +60:40:32.0 1 1 1.567 0.957 1 4.253 0.685 170. 112. -15. OT1_acrocker_1
72 0 0 0 3952 2972 11:53:40.63 -03:59:47.5 12 1 2.403 0.261 1 1.972 0.308 81. 48. 79. OT1_lcortese_1
73 269-013 0 6870 3953 0 11:53:48.92 +52:19:36.4 6 1 28.168 2.638 1 49.52 4.004 243. 129. 13. OT1_lcortese_1
74 269-019 0 6918 3982 0 11:56:28.10 +55:07:30.6 5 1 16.493 1.023 1 17.392 0.978 98. 86. 38. OT1_lcortese_1
75 269-020 0 6919 0 0 11:56:37.51 +55:37:59.5 10 1 0.148 0.071 1 0.37 0.062 30. 24. 90. OT1_lcortese_1
76 269-022 0 6923 0 0 11:56:49.43 +53:09:37.3 12 1 0.886 0.176 1 0.909 0.172 84. 35. -10. OT1_lcortese_1
77 13-033 0 6993 4030 0 12:00:23.64 -01:06:00.0 6 1 58.47 3.251 1 73.761 4.064 175. 127. 31. OT1_lcortese_1
78 98-019 0 6995 4032 0 12:00:32.82 +20:04:26.0 12 1 2.04 0.223 1 2.802 0.442 78. 76. -4. OT1_lcortese_1
79 69-024 0 7001 4019 755 12:01:10.39 +14:06:16.2 5 1 0.986 0.192 1 1.583 0.428 108. 52. -35. OT1_lcortese_1
80 69-027 0 7002 4037 0 12:01:23.67 +13:24:03.7 5 1 1.479 0.57 1 2.875 0.68 105. 86. 15. OT1_lcortese_1
81 13-046 0 7021 4045 0 12:02:42.26 +01:58:36.4 3 1 15.308 0.97 1 17.08 1.267 126. 93. 5. OT1_lcortese_1
82 98-037 0 0 0 0 12:03:35.94 +16:03:20.0 4 1 1.89 0.293 1 1.683 0.158 58. 54. -75. OT1_lcortese_1
83 41-031 0 7035 0 0 12:03:40.14 +02:38:28.4 3 1 0.674 0.137 1 0.48 0.109 47. 45. -30. OT1_lcortese_1
84 69-036 0 7048 4067 0 12:04:11.55 +10:51:15.8 5 1 1.791 0.191 1 2.595 0.179 50. 37. 45. OT1_lcortese_1
85 243-044 0 7095 4100 0 12:06:08.60 +49:34:56.3 6 1 23.375 2.26 1 30.458 1.966 226. 75. -17. OT1_lcortese_1
86 41-041 0 7111 4116 0 12:07:36.82 +02:41:32.0 10 1 6.575 1.5 1 9.4 1.357 160. 101. -17. OT1_lcortese_1
87 69-058 0 7117 4124 0 12:08:09.64 +10:22:43.4 1 1 1.703 0.293 1 1.831 0.266 68. 51. -30. OT1_lcortese_1
88 41-042 0 7116 4123 0 12:08:11.11 +02:52:41.8 7 1 12.906 2.2 1 14.472 2.443 210. 163. -75. OT1_lcortese_1
89 69-088 66 7215 4178 0 12:12:46.45 +10:51:57.5 10 1 10.83 1.126 1 15.005 1.774 225. 79. 30. OT1_lcortese_1
90 13-104 0 7214 4179 0 12:12:52.11 +01:17:58.9 1 0 1.653 0.0 0 2.248 0.0 91. 91. 0. OT1_lcortese_1
91 98-108 92 7231 4192 0 12:13:48.29 +14:54:01.2 4 1 28.035 2.497 1 45.141 2.893 411. 109. -25. KPOT_jdavie01
92 69-101 131 7255 0 3061 12:15:04.44 +14:01:44.3 7 1 1.686 0.23 1 2.382 0.199 102. 51. -60. KPOT_jdavie01
93 187-029 0 7256 4203 0 12:15:05.06 +33:11:50.4 1 1 2.028 0.348 1 2.87 0.42 104. 85. 0. OT1_acrocker_1
94 69-104 145 7260 4206 0 12:15:16.81 +13:01:26.3 6 1 3.312 0.404 1 5.238 0.536 206. 51. 0. KPOT_jdavie01
95 69-107 152 7268 4207 0 12:15:30.50 +09:35:05.6 8 1 7.801 0.45 1 8.137 0.444 82. 37. -60. OT1_lcortese_1
96 69-110 157 7275 4212 0 12:15:39.36 +13:54:05.4 7 1 20.43 1.226 1 25.125 1.338 151. 84. 75. KPOT_jdavie01
97 69-112 167 7284 4216 0 12:15:54.44 +13:08:57.8 5 1 18.247 1.559 1 33.198 2.559 383. 91. 19. KPOT_jdavie01
98 69-119 187 7291 4222 0 12:16:22.52 +13:18:25.5 7 1 3.222 0.323 1 5.373 0.362 144. 47. 55. KPOT_jdavie01
99 69-123 213 7305 0 3094 12:16:56.00 +13:37:31.0 5 1 1.077 0.067 1 1.304 0.079 39. 30. -88. KPOT_jdavie01
100 98-130 226 7315 4237 0 12:17:11.42 +15:19:26.3 6 1 10.131 0.529 1 14.106 0.736 84. 49. -75. KPOT_jdavie01
101 158-060 0 7338 4251 0 12:18:08.31 +28:10:31.1 1 0 1.447 0.0 0 1.831 0.0 87. 87. 0. OT1_lcortese_1
102 98-144 307 7345 4254 0 12:18:49.63 +14:24:59.4 7 1 111.145 6.099 1 141.58 7.52 258. 235. 60. KPOT_jdavie01
103 42-015 341 7361 4260 0 12:19:22.24 +06:05:55.2 3 1 0.859 0.125 1 1.039 0.113 68. 36. 45. KPOT_jdavie01
104 99-015 0 7366 0 0 12:19:28.66 +17:13:49.4 5 0 0.544 0.0 0 0.385 0.0 50. 50. 0. OT1_lcortese_1
105 99-014 355 7365 4262 0 12:19:30.58 +14:52:39.8 1 0 0.283 0.0 0 0.256 0.0 44. 44. 0. KPOT_jdavie01
106 42-032 393 7385 4276 0 12:20:07.50 +07:41:31.2 7 1 2.069 0.504 1 2.238 0.466 88. 88. 3. OT1_lcortese_1
107 42-033 404 7387 0 0 12:20:17.35 +04:12:05.1 9 1 1.021 0.143 1 1.499 0.115 81. 39. 15. KPOT_jdavie01
108 42-037 434 0 4287 0 12:20:48.49 +05:38:23.5 7 1 1.011 0.167 1 1.313 0.132 77. 34. 70. KPOT_jdavie01
109 42-038 449 7403 4289 0 12:21:02.25 +03:43:19.7 8 1 2.676 0.347 1 3.376 0.391 181. 37. 1. KPOT_jdavie01
110 70-024 465 7407 4294 0 12:21:17.79 +11:30:40.0 8 1 6.137 0.495 1 7.821 0.501 166. 52. -20. KPOT_jdavie01
111 99-024 483 7412 4298 0 12:21:32.76 +14:36:22.2 7 1 14.297 0.758 1 22.151 1.142 101. 75. -40. KPOT_jdavie01
112 42-044 492 7413 4300 0 12:21:41.47 +05:23:05.4 3 1 1.059 0.149 1 1.002 0.139 91. 30. 40. KPOT_jdavie01
113 99-027 497 7418 4302 0 12:21:42.48 +14:35:53.9 7 1 17.551 1.098 1 30.598 1.62 271. 59. -3. KPOT_jdavie01
114 42-045 508 7420 4303 0 12:21:54.90 +04:28:25.1 6 1 102.907 5.799 1 118.6 6.85 277. 225. -18. KPOT_jdavie01
115 42-047 517 7422 0 0 12:22:01.30 +05:06:00.2 4 1 1.012 0.095 1 1.014 0.086 57. 36. -10. KPOT_jdavie01
116 70-031 522 7432 4305 0 12:22:03.60 +12:44:27.3 3 0 1.045 0.0 0 1.366 0.0 109. 109. 0. KPOT_jdavie01
117 70-029 524 7431 4307 0 12:22:05.63 +09:02:36.8 5 1 4.524 0.534 1 7.711 0.477 161. 47. 25. KPOT_jdavie01
118 42-053 552 7439 0 0 12:22:27.25 +04:33:58.7 8 1 1.385 0.287 1 1.611 0.269 79. 60. -10. KPOT_jdavie01
119 99-029 559 7442 4312 0 12:22:31.36 +15:32:16.5 4 1 6.626 0.487 1 8.675 0.606 214. 52. -10. KPOT_jdavie01
120 70-034 570 7445 4313 0 12:22:38.55 +11:48:03.4 4 1 4.293 0.454 1 7.038 0.533 214. 49. -37. KPOT_jdavie01
121 70-035 576 7447 4316 0 12:22:42.24 +09:19:56.9 6 1 5.544 0.322 1 8.633 0.506 107. 46. -70. KPOT_jdavie01
122 99-030 596 7450 4321 0 12:22:54.90 +15:49:20.6 6 1 87.905 6.335 1 123.549 7.21 330. 293. 30. KPOT_rkennicu_1
123 42-063 613 7451 4324 0 12:23:06.18 +05:15:01.5 1 1 1.473 0.397 1 2.938 0.336 148. 42. 53. KPOT_jdavie01
124 70-039 630 7456 4330 0 12:23:17.25 +11:22:04.7 8 1 3.122 0.66 1 6.081 0.82 246. 61. 64. KPOT_jdavie01
125 42-068 648 7461 4339 0 12:23:34.94 +06:04:54.2 0 0 0.111 0.0 0 0.109 0.0 22. 22. 0. KPOT_jdavie01
126 99-036 654 7467 4340 0 12:23:35.31 +16:43:19.9 1 0 0.957 0.0 0 1.896 0.0 86. 86. 0. OT1_lcortese_1
127 42-070 656 7465 4343 0 12:23:38.70 +06:57:14.7 5 1 4.339 0.267 1 6.305 0.35 109. 44. -50. KPOT_jdavie01
128 42-072 667 7469 0 3259 12:23:48.52 +07:11:12.6 10 1 0.595 0.115 1 1.331 0.109 79. 39. 15. KPOT_jdavie01
129 99-038 685 7473 4350 0 12:23:57.81 +16:41:36.1 1 1 0.853 0.114 1 0.502 0.123 40. 40. -0. OT1_lcortese_1
130 70-045 692 7476 4351 0 12:24:01.56 +12:12:18.1 4 1 2.01 0.309 1 2.918 0.29 84. 62. 70. KPOT_jdavie01
131 42-079 697 7474 0 3267 12:24:05.53 +07:02:28.6 8 1 0.552 0.19 1 1.263 0.136 65. 65. 30. KPOT_jdavie01
132 42-080 699 7477 0 3268 12:24:07.44 +06:36:26.9 13 1 1.695 0.259 1 1.396 0.158 82. 58. 22. KPOT_jdavie01
133 158-099 0 7483 4359 0 12:24:11.06 +31:31:17.8 7 1 1.661 0.504 1 2.545 0.371 150. 49. -75. OT1_lcortese_1
134 70-048 713 7482 4356 0 12:24:14.53 +08:32:08.9 7 1 1.602 0.289 1 2.763 0.274 136. 36. 40. KPOT_jdavie01
135 42-083 731 7488 4365 0 12:24:28.23 +07:19:03.1 0 0 0.703 0.0 0 0.434 0.0 78. 78. 0. KPOT_jdavie01
136 42-089 758 7492 4370 0 12:24:54.93 +07:26:40.4 3 1 3.107 0.198 1 3.891 0.22 74. 37. 80. KPOT_jdavie01
137 70-057 759 7493 4371 0 12:24:55.43 +11:42:15.4 1 0 2.143 0.0 0 2.027 0.0 122. 122. 0. KPOT_jdavie01
138 70-058 763 7494 4374 0 12:25:03.78 +12:53:13.1 0 1 1.014 0.074 1 0.896 0.067 40. 39. 90. KPOT_jdavie01
139 42-093 787 7498 4376 0 12:25:18.06 +05:44:28.3 12 1 1.801 0.195 1 2.078 0.25 77. 45. -45. KPOT_jdavie01
140 42-092 785 7497 4378 0 12:25:18.09 +04:55:30.2 3 1 1.92 0.534 1 3.115 0.833 129. 104. -20. KPOT_jdavie01
141 70-061 792 7503 4380 0 12:25:22.17 +10:01:00.5 5 1 3.723 0.391 1 7.037 0.498 148. 74. -25. KPOT_jdavie01
142 99-044 801 7507 4383 0 12:25:25.50 +16:28:12.0 3 1 12.722 0.736 1 11.722 0.674 109. 54. 20. OT1_lcortese_1
143 42-095 827 7513 0 0 12:25:42.63 +07:13:00.1 8 1 5.804 0.429 1 8.478 0.449 157. 44. -25. KPOT_jdavie01
144 70-068 836 7520 4388 0 12:25:46.82 +12:39:43.5 5 1 18.998 1.033 1 19.916 1.193 214. 52. 90. KPOT_jdavie01
145 70-067 849 7519 4390 0 12:25:50.67 +10:27:32.6 6 1 2.168 0.348 1 3.319 0.346 92. 76. -55. KPOT_jdavie01
146 42-098 851 7518 0 3322 12:25:54.12 +07:33:17.4 8 1 2.383 0.221 1 2.886 0.191 91. 39. -20. KPOT_jdavie01
147 42-099 859 7522 0 0 12:25:58.30 +03:25:47.3 9 1 2.257 0.363 1 3.973 0.552 121. 41. -50. OT1_lcortese_1
148 99-049 865 7526 4396 0 12:25:58.80 +15:40:17.3 9 1 3.96 0.53 1 6.539 0.826 142. 58. -55. OT1_lcortese_1
149 70-071 873 7528 4402 0 12:26:07.56 +13:06:46.0 5 1 18.263 0.974 1 26.98 1.385 166. 49. 90. KPOT_jdavie01
150 70-072 881 7532 4406 0 12:26:11.74 +12:56:46.4 0 0 2.154 0.0 1 0.579 0.356 130. 114. -50. KPOT_jdavie01
151 70-076 912 7538 4413 0 12:26:32.25 +12:36:39.5 4 1 3.781 0.552 1 4.395 0.543 123. 74. 15. KPOT_jdavie01
152 42-104 921 7536 4412 0 12:26:36.10 +03:57:52.7 5 1 6.307 0.405 1 6.358 0.416 79. 66. 76. OT1_lcortese_1
153 42-105 938 7541 4416 0 12:26:46.72 +07:55:08.4 8 1 3.24 0.376 1 4.547 0.465 91. 85. -35. KPOT_jdavie01
154 70-082 939 7546 0 0 12:26:47.23 +08:53:04.6 8 1 2.224 0.417 1 2.702 0.448 103. 98. -15. KPOT_jdavie01
155 70-080 944 7542 4417 0 12:26:50.62 +09:35:03.0 1 0 0.704 0.0 0 0.545 0.0 86. 86. 0. KPOT_jdavie01
156 99-054 958 7551 4419 0 12:26:56.43 +15:02:50.7 3 1 17.548 1.121 1 18.421 0.992 148. 58. -47. OT1_lcortese_1
157 42-106 957 7549 4420 0 12:26:58.48 +02:29:39.7 6 1 7.145 0.436 1 9.352 0.608 86. 51. 8. OT1_lcortese_1
158 42-107 971 7556 4423 0 12:27:08.97 +05:52:48.6 10 1 0.928 0.178 1 1.4 0.347 107. 42. 20. OT1_lcortese_1
159 70-090 979 7561 4424 0 12:27:11.59 +09:25:14.0 3 1 6.7 0.438 1 6.132 0.381 85. 80. -80. KPOT_jdavie01
160 42-111 1002 7566 4430 0 12:27:26.41 +06:15:46.0 5 1 4.205 0.583 1 7.142 0.79 94. 90. -60. OT1_lcortese_1
161 70-093 1003 7568 4429 0 12:27:26.56 +11:06:27.1 1 1 4.848 1.025 1 5.509 0.593 169. 78. -81. KPOT_jdavie01
162 70-098 1030 7575 4435 0 12:27:40.49 +13:04:44.2 1 1 4.72 0.304 1 4.484 0.302 83. 77. 10. KPOT_jdavie01
163 70-097 1043 7574 4438 0 12:27:45.59 +13:00:31.8 5 1 11.949 0.863 1 15.454 1.09 134. 118. 27. KPOT_jdavie01
164 70-099 1047 7581 4440 0 12:27:53.57 +12:17:35.6 3 0 0.992 0.0 0 0.972 0.0 84. 84. 0. KPOT_jdavie01
165 42-117 1048 7579 0 0 12:27:55.39 +05:43:16.4 10 1 0.905 0.208 1 1.342 0.223 78. 36. -50. OT1_lcortese_1
166 70-100 1062 7583 4442 0 12:28:03.89 +09:48:13.0 1 0 1.685 0.0 0 1.393 0.0 121. 121. 0. KPOT_jdavie01
167 70-104 1086 7587 4445 0 12:28:15.94 +09:26:10.7 4 1 1.215 0.139 1 2.378 0.181 93. 33. -75. KPOT_jdavie01
168 70-108 1091 7590 0 0 12:28:18.77 +08:43:46.1 6 1 0.935 0.088 1 0.916 0.12 58. 37. -5. KPOT_jdavie01
169 99-063 0 7595 0 3391 12:28:27.28 +18:24:55.1 8 1 1.39 0.212 1 2.187 0.32 64. 50. -85. OT1_lcortese_1
170 99-062 1110 7594 4450 0 12:28:29.63 +17:05:05.8 4 1 9.713 3.046 1 14.263 3.336 258. 170. -9. OT1_lcortese_1
171 70-111 1118 7600 4451 0 12:28:40.55 +09:15:32.2 6 1 4.509 0.257 1 4.894 0.269 82. 41. -10. KPOT_jdavie01
172 99-065 1126 7602 0 3392 12:28:43.26 +14:59:58.2 5 1 4.083 0.632 1 5.039 0.426 123. 49. 40. OT1_lcortese_1
173 42-124 1145 7609 4457 0 12:28:59.01 +03:34:14.2 5 1 10.527 0.664 1 10.627 0.834 95. 95. -15. OT1_lcortese_1
174 70-116 1154 7614 4459 0 12:29:00.03 +13:58:42.9 1 1 4.355 0.25 1 4.049 0.226 40. 40. 0. OT1_lcortese_1
175 70-115 1158 7613 4461 0 12:29:03.01 +13:11:01.5 1 0 1.165 0.0 0 0.869 0.0 84. 84. 0. KPOT_jdavie01
176 70-121 1190 7622 4469 0 12:29:28.03 +08:44:59.7 2 1 3.158 0.282 1 4.171 0.323 85. 48. 85. KPOT_jdavie01
177 42-132 1205 7627 4470 0 12:29:37.78 +07:49:27.1 3 1 4.592 0.246 1 4.981 0.308 77. 48. 0. KPOT_jdavie01
178 42-134 1226 7629 4472 0 12:29:46.76 +08:00:01.7 0 0 1.317 0.0 0 1.438 0.0 92. 92. 0. KPOT_jdavie01
179 70-125 1231 7631 4473 0 12:29:48.87 +13:25:45.7 0 0 0.214 0.0 0 0.179 0.0 36. 36. 0. KPOT_jdavie01
180 70-129 1253 7638 4477 0 12:30:02.17 +13:38:11.2 1 1 1.221 0.101 1 1.285 0.127 56. 44. 35. KPOT_jdavie01
181 70-133 1279 7645 4478 0 12:30:17.42 +12:19:42.8 0 0 0.127 0.0 0 0.115 0.0 22. 22. 0. KPOT_jdavie01
182 42-139 1290 7647 4480 0 12:30:26.78 +04:14:47.3 7 1 4.369 0.319 1 5.391 0.353 84. 60. -10. OT1_lcortese_1
183 70-139 1316 7654 4486 0 12:30:49.42 +12:23:28.0 0 1 0.693 0.182 1 0.82 0.156 58. 57. -21. KPOT_jdavie01
184 70-140 1326 7657 4491 0 12:30:57.13 +11:29:00.8 3 1 2.644 0.217 1 2.265 0.196 79. 39. -32. KPOT_jdavie01
185 42-141 1330 7656 4492 0 12:30:59.74 +08:04:40.6 3 1 1.181 0.276 1 1.979 0.283 82. 82. 75. KPOT_jdavie01
186 129-005 0 7662 4494 0 12:31:24.03 +25:46:29.9 0 1 0.37 0.037 1 0.313 0.046 26. 26. 0. OT1_lcortese_1
187 42-144 1375 7668 4505 0 12:31:39.21 +03:56:22.1 11 2 12.509 2.008 2 15.141 3.227 200. 158. 70. OT1_lcortese_1
188 99-075 1379 7669 4498 0 12:31:39.57 +16:51:10.1 9 1 4.308 0.549 1 6.629 0.617 120. 64. -55. OT1_lcortese_1
189 99-077 1393 7676 0 797 12:31:54.76 +15:07:26.2 7 1 2.238 0.304 1 2.67 0.262 71. 47. -72. OT1_lcortese_1
190 99-076 1401 7675 4501 0 12:31:59.22 +14:25:13.5 5 1 74.118 5.57 1 104.85 5.821 304. 162. -40. OT1_lcortese_1
191 99-078 1410 7677 4502 0 12:32:03.35 +16:41:15.8 8 1 0.633 0.163 1 0.854 0.212 62. 33. 40. OT1_lcortese_1
192 70-152 1419 7682 4506 0 12:32:10.53 +13:25:10.6 3 1 0.403 0.049 1 0.583 0.062 36. 29. -75. KPOT_jdavie01
193 70-157 1450 7695 0 3476 12:32:41.88 +14:03:01.8 12 1 3.138 0.314 1 3.928 0.363 95. 73. 30. KPOT_jdavie01
194 14-063 0 7694 4517 0 12:32:45.59 +00:06:54.1 8 1 28.254 1.875 1 48.955 2.661 462. 86. 80. OT2_emurph01
195 99-087 1479 7703 4516 0 12:33:07.56 +14:34:29.8 4 0 1.184 0.0 0 1.282 0.0 90. 90. 0. OT1_lcortese_1
196 70-167 1508 7709 4519 0 12:33:30.25 +08:39:17.1 9 1 8.617 0.893 1 10.184 0.763 151. 109. -28. KPOT_jdavie01
197 70-168 1516 7711 4522 0 12:33:39.66 +09:10:29.5 8 1 4.664 0.381 1 6.095 0.44 170. 42. 33. KPOT_jdavie01
198 159-016 0 7714 4525 0 12:33:51.19 +30:16:39.1 8 1 1.015 0.487 1 2.011 0.377 126. 67. 65. OT1_lcortese_1
199 99-090 1532 7716 0 800 12:33:56.66 +15:21:17.4 7 1 1.256 0.546 1 1.533 0.226 82. 60. -30. OT1_lcortese_1
200 42-155 1535 7718 4526 0 12:34:03.03 +07:41:56.9 1 1 15.632 0.827 1 17.282 0.88 71. 68. -17. KPOT_jdavie01
201 42-156 1540 7721 4527 0 12:34:08.50 +02:39:13.7 6 1 75.281 3.999 1 93.527 4.864 246. 79. 67. OT1_lcortese_1
202 70-173 1549 7728 0 3510 12:34:14.79 +11:04:17.7 -2 0 0.116 0.0 0 0.114 0.0 22. 22. 0. KPOT_jdavie01
203 42-158 1554 7726 4532 0 12:34:19.33 +06:28:03.7 12 1 15.489 0.835 1 15.638 0.953 112. 51. -14. OT1_lcortese_1
204 42-159 1555 7727 4535 0 12:34:20.31 +08:11:51.9 7 1 34.851 3.113 1 61.656 3.907 270. 232. 0. KPOT_jdavie01
205 14-068 1562 7732 4536 0 12:34:27.13 +02:11:16.4 6 1 56.393 3.262 1 58.539 3.082 304. 138. -40. KPOT_rkennicu_1
206 42-162 1575 7736 0 3521 12:34:39.42 +07:09:36.0 15 1 2.733 0.481 1 3.017 0.367 84. 59. 18. OT1_lcortese_1
207 99-093 1588 7742 4540 0 12:34:50.87 +15:33:05.2 8 1 5.468 0.398 1 6.779 0.528 79. 67. -35. OT1_lcortese_1
208 99-096 1615 7753 4548 0 12:35:26.43 +14:29:46.8 5 1 13.601 5.576 1 24.022 3.148 252. 210. -30. OT1_lcortese_1
209 0 0 0 4546 0 12:35:29.51 -03:47:35.5 1 1 0.513 0.132 1 0.632 0.121 57. 37. 80. OT1_lcortese_1
210 70-182 1619 7757 4550 0 12:35:30.61 +12:13:15.4 1 1 0.516 0.096 1 0.486 0.125 83. 23. -2. KPOT_jdavie01
211 70-184 1632 7760 4552 0 12:35:39.88 +12:33:21.7 0 0 0.724 0.0 0 0.498 0.0 65. 65. 0. KPOT_jdavie01
212 99-098 0 7768 4561 0 12:36:08.14 +19:19:21.4 10 1 2.505 0.299 1 2.426 0.293 77. 63. -70. OT1_lcortese_1
213 129-010 0 7772 4565 0 12:36:20.78 +25:59:15.6 5 1 55.365 4.659 1 90.833 5.143 596. 80. -45. OT1_lcortese_1
214 70-186 1664 7773 4564 0 12:36:26.99 +11:26:21.5 0 0 0.291 0.0 0 0.268 0.0 38. 38. 0. KPOT_jdavie01
215 70-189 1673 7777 4567 0 12:36:32.71 +11:15:28.8 6 2 14.15 0.743 2 19.184 0.976 87. 41. 75. KPOT_jdavie01
216 70-188 1676 7776 4568 0 12:36:34.26 +11:14:20.0 6 2 47.991 2.419 2 59.217 2.974 93. 53. 33. KPOT_jdavie01
217 70-192 1690 7786 4569 0 12:36:49.80 +13:09:46.3 4 1 31.332 2.268 1 42.761 2.608 259. 157. 23. KPOT_rkennicu_1
218 42-178 1692 7785 4570 0 12:36:53.40 +07:14:48.0 1 0 1.339 0.0 0 2.675 0.0 84. 84. 0. OT1_lcortese_1
219 70-195 1720 7793 4578 0 12:37:30.55 +09:33:18.4 1 0 1.607 0.0 0 0.94 0.0 90. 90. 0. OT1_lcortese_1
220 70-197 1727 7796 4579 0 12:37:43.52 +11:49:05.5 5 1 25.583 3.355 1 38.766 2.589 264. 205. -80. KPOT_rkennicu_1
221 42-183 1730 7794 4580 0 12:37:48.40 +05:22:06.4 3 1 5.049 0.335 1 7.3 0.501 83. 73. -20. OT1_lcortese_1
222 70-199 1757 7803 4584 0 12:38:17.89 +13:06:35.5 3 1 0.523 0.228 1 1.259 0.201 82. 44. 20. OT1_lcortese_1
223 42-186 1758 7802 0 0 12:38:20.82 +07:53:28.7 10 1 0.346 0.139 1 0.612 0.145 64. 33. 55. OT1_lcortese_1
224 42-187 1760 7804 4586 0 12:38:28.44 +04:19:08.8 3 1 2.206 0.53 1 2.806 0.514 182. 49. -65. OT1_lcortese_1
225 70-202 1778 7817 0 3611 12:39:04.14 +13:21:48.7 5 1 0.044 0.025 1 0.194 0.038 25. 20. -65. OT1_lcortese_1
226 42-191 1780 7821 4591 0 12:39:12.44 +06:00:44.3 5 1 1.717 0.217 1 2.468 0.226 82. 37. 40. OT1_lcortese_1
227 14-091 0 7819 4592 0 12:39:18.74 -00:31:55.2 10 1 5.65 0.713 1 6.237 0.875 242. 63. -85. OT1_lcortese_1
228 0 0 0 0 0 12:39:22.26 -05:39:53.3 13 0 0.111 0.0 0 0.113 0.0 22. 22. 0. OT1_lcortese_1
229 70-204 1809 7825 0 3631 12:39:48.02 +12:58:26.1 5 0 0.368 0.0 0 0.357 0.0 46. 46. 0. OT1_lcortese_1
230 99-106 1811 7826 4595 0 12:39:51.91 +15:17:52.1 5 1 3.515 0.327 1 3.768 0.323 91. 60. -70. OT1_lcortese_1
231 70-206 1813 7828 4596 0 12:39:55.94 +10:10:33.9 1 1 1.102 0.113 1 0.897 0.125 40. 40. 0. OT1_lcortese_1
232 70-213 1859 7839 4606 0 12:40:57.56 +11:54:43.6 3 1 2.524 0.289 1 2.9 0.28 66. 57. 33. OT1_lcortese_1
233 70-216 1868 7843 4607 0 12:41:12.41 +11:53:11.9 5 1 8.108 0.768 1 10.674 0.752 164. 57. 4. OT1_lcortese_1
234 70-214 1869 7842 4608 0 12:41:13.29 +10:09:20.9 1 0 2.29 0.0 0 1.576 0.0 103. 103. 0. OT1_lcortese_1
235 42-205 1883 7850 4612 0 12:41:32.76 +07:18:53.2 1 0 0.343 0.0 0 0.579 0.0 51. 51. 0. OT1_lcortese_1
236 70-223 1903 7858 4621 0 12:42:02.32 +11:38:48.9 0 0 0.877 0.0 0 0.512 0.0 69. 69. 0. OT1_lcortese_1
237 42-208 1923 7871 4630 0 12:42:31.15 +03:57:37.3 12 1 5.408 0.703 1 6.54 0.611 97. 67. 10. OT1_lcortese_1
238 14-109 0 7869 4629 0 12:42:32.67 -01:21:02.4 11 1 0.695 0.179 1 0.581 0.234 50. 45. 80. OT1_lcortese_1
239 99-112 1932 7875 4634 0 12:42:40.96 +14:17:45.0 8 1 11.903 0.702 1 13.602 0.771 119. 51. -24. OT1_lcortese_1
240 70-229 1938 7880 4638 0 12:42:47.43 +11:26:32.9 1 0 0.642 0.0 0 0.369 0.0 48. 48. 0. OT1_lcortese_1
241 43-002 1939 7878 4636 0 12:42:49.87 +02:41:16.0 0 1 0.31 0.07 1 0.32 0.066 37. 35. 0. OT1_lcortese_1
242 70-230 1943 7884 4639 0 12:42:52.37 +13:15:26.9 6 1 6.587 0.759 1 7.273 0.849 134. 84. -57. OT1_lcortese_1
243 15-008 0 7895 4643 0 12:43:20.14 +01:58:42.1 2 1 0.673 1.005 1 3.354 0.916 107. 104. -45. OT1_lcortese_1
244 71-015 1972 7896 4647 0 12:43:32.45 +11:34:57.4 7 1 17.159 1.265 1 24.35 1.408 109. 91. -75. OT1_lcortese_1
245 71-016 1978 7898 4649 0 12:43:40.01 +11:33:09.4 0 0 0.243 0.0 0 0.605 0.0 45. 45. 0. OT1_lcortese_1
246 100-004 0 7901 4651 0 12:43:42.63 +16:23:36.2 7 1 17.873 1.553 1 22.95 1.833 164. 116. 80. OT1_lcortese_1
247