Bright Debris Disk Candidates Observed with AKARI/Far-Infrared Surveyor (FIS)

# Bright Debris Disk Candidates Observed with AKARI/Far-Infrared Surveyor (FIS)

## Abstract

We cross-correlate  main-sequence star catalog with  catalog, and identify 136 stars (at % reliability) with far-infrared detections at least in one band. After rejecting 51 stars classified as young stellar objects, Be stars, other type stars with known dust disks or with potential contaminations and 2 stars without infrared excess emission, we obtain a sample of 83 candidate stars with debris disks. Stars in our sample cover spectral types from B to K-types with most being early types. This represents an unique sample of luminous debris disks that derived uniformly from all sky survey with a spatial resolution a factor of two better than the previous such survey by . Moreover, by collecting the infrared photometric data from other public archives, 85% of them have infrared excesses in more than one bands, allowing the estimate of the dust temperatures. We fit the blackbody model to the broad band spectral energy distribution of these stars to derive the statistical distribution of the disk parameters. 7 stars require an additional warm component of temperature around 200 K. While a substantial fraction of our sample(58 stars) have weak 12 µm excess indicating that a warm dust component maybe common among these bright debris disk systems.

main sequence stars — infrared excess: stars — circumstellar dust

## 1 Introduction

Our solar system is a typical debris system with the asteroid belt at 2 - 3.5 AU and the Kuiper belt at 30 - 48 AU (Kim et al., 2005). Debris disks have been detected in extra-solar stellar systems as well, commonly referred to as “The Vega Phenomenon” (Silverstone, 2000). The stars with debris disks are generally much older than 10 Myr (Krivov, 2010), which is much longer than the typical time scale of collisional destruction of dust grain or of spiraling inward due to Poynting-Robertson effect. Thus dust grains have to be continuously replenished by collisions and/or evaporation of planetesimals (Backman & Paresce, 1993; Wyatt, 2008). The studies of debris systems are significative to a better understanding of the formation and evolution of planetesimal belts and planetary systems (Zuckerman & Song, 2004; Moór et al., 2011; Raymond et al., 2011, 2012).

The first extra-solar debris disk was detected by the Infrared Astronomical Satellite () around Vega in 1983 (Auman et al., 1984). Up to now, nearly a thousand debris disks have been detected. Most of these systems were discovered through the detection of infrared excess over the stellar photospheric emission. The infrared excess is produced by dust reradiation of the absorbed starlight. At the Spitzer/MIPS sensitivity level, the incidences of debris disks around main-sequence stars is about 15% (Krivov, 2010). Due to their small sizes, only dozens debris disks around nearby stars were identified through direct imaging in optical, mid-infrared, and submillimeter band (e.g., Schneider et al. 2001; Greaves et al. 2005; Wyatt et al. 2005; Kalas et al. 2006; Su et al. 2008; Lagrange et al. 2012; also see4).

The observed debris disks display diverse properties. Most debris disks have relative low dust temperatures between 30-120 K, corresponding to a disk size from several tens to a hundred AU for type A to K stars (Chen et al., 2006; Moór et al., 2011; Plavchan et al., 2009; Rhee et al., 2007). A small subset of warm debris disks have been discovered recently with ,  and  (Fujiwara & Onaka et al., 2010; Fujiwara & Ishihara et al., 2010, 2013; Olofsson et al., 2012; Ribas et al., 2012), and the incidence of such disk drops very rapidly with the age of the stars (Urban et al, 2012). More recently, Herschel revealed a population of cold debris disk extending to more than one hundred AU with its good sensitivity to the long infrared wavelength (Eiroa et al., 2011). A single temperature dust model usually provides a good fit to the mid-infrared spectrum, suggesting that grains distribute over a relative narrow annulus (Schutz et al., 2005; Chen et al., 2006). The relative narrow width has been confirmed by direct imaging in the infrared and sub-mm band (Booth et al., 2013).

The incidence of debris disks as a function of other stellar parameters is of great interest as it gives further clue to its origin. It appears that the frequency of stars with debris disk is larger among earlier type of stars, and decreases with the increase of stellar age (Rhee et al., 2007; Wyatt, 2008). The rate appears also correlated with the presence of planets, but not the metallicity of the host stars (Maldonado et al., 2012). The general trend with stellar age reflects the consumption of planetesimals during the system evolution. However, interpretation of the correlation with stellar types may be more complex as early type stars have much shorter life-time than late type stars and the correlation may be entirely caused by age-dependence of incidence. In addition, the detected debris disks displayed a wide range of infrared excess from up to of stellar bolometric luminosity. Over such a wide range, different mechanism of debris disk may operate, thus it would be interesting to examine the incidence at a certain fraction of infrared excesses. To explore large parameter space, a large unbiased sample of debris disks with known host parameters are required.

Up to now, debris disks are discovered mostly based on the infrared data from three satellites:  (Mannings & Barlow, 1998; Rhee et al., 2007), Infrared Space Observatory () (Kessler et al., 1996; Oudmaijer et al., 1992; Abraham et al., 1999; Habing et al., 1999; Fajardo et al., 1999; Spangler et al., 2001; Decin et al., 2003) and Spitzer Space Telescope (Beichman et al., 2006; Bryden et al., 2006; Chen et al., 2005; Kim et al., 2005; Moór et al., 2006, 2011; Rebull et al., 2008; Rieke et al., 2005; Siegler et al., 2007; Su et al, 2006; Wu et al., 2012).  contains a cryogenically cooled telescope orbiting above the Earth’s atmosphere to make an unbiased all-sky survey at 12, 25, 60, and 100 (Neugebauer et al., 1984) at a relatively poor spatial resolution and low sensitivity. The survey results an uniform sample of 146 infrared bright debris disk candidates.  and  surveys covered much smaller sky at mid and far-infrared bands but with much better spatial resolution and sensitivity. Thus the latter missions discover mostly faint debris disks. The latter satellites also carried pointed observations of the nearby bright stars that is sensitive to the infrared excess down to of host star luminosity.

In this paper, we search systematically the debris systems around main sequence stars by cross correlation of  catalog (Perryman et al., 1997) with AKARI/Far-Infrared Surveyor(kawada et al. 2007) All-Sky Survey Bright Source Catalogue (AKARIBSC, Yamamura et al. 2010).  surveyed all sky at mid and far-infrared with a spatial resolution better than  and at a sensitivity comparable to . The higher resolution will significantly reduce the false contamination in comparison with . The same with  mission, our survey also focuses on infrared bright debris disks (Fukagawa et al., 2009), that complements the deep surveys from  and . Our primary motivation is to search the FIR excess stars by  and discuss the fundamental parameters of the disks such as dust temperatures, fractional luminosity and dust locations. These parameters can be estimated from spectral energy distribution (SED) of the dust emission. Fortunately, 82 stars in our 83 IR excess sample have  detection which leads to better wavelength coverage than many previous searches. As Moor et al. [2011] shown, the interpretation of SED is ambiguous (e.g. considering the radial location of the dust) but by handling a debris disk sample as an ensemble, one can obtain a meaningful picture about the basic characteristics of the parent planetesimal belt(s) and about the evolutionary trends. The paper is arranged as follows. We will describe the data sets and methods used in the construction of the debris disk sample in Sect. 2; and present an analysis of the properties of the disks as well as their host stars in Sect. 3; In Sect. 4, we discuss the sample comparison; Finally, we present the conclusion in Sect. 5.

## 2 The Method and the Sample

### 2.1 Matches between Hipparcos catalog and AKARIBSC

The primary star catalog used in this work is the  catalog, which contains over 110,000 stars with precise photometry as well as astrometry of unprecedented accuracy for the nearby stars (Bessell, 2000). In Fig. 1, we compile a Hertzsprung-Russell (H-R) diagram for all the cataloged stars by extracting the colors (B V) and parallaxes from  database. The main sequence (MS) stars are selected using the criterion (Rhee et al., 2007). This results in a catalog of 67,186  MS stars.

We then cross-correlate the catalog with the AKARIBSC to identify the  MS stars detected in the  bands. Since the AKARIBSC has much worse position precision than the  catalog, we determine the matching radius based on the performance of  only. In high density areas, false positive matches are very severe, while in low density region, false match is not an issue even with a larger matching radius. In order to identify as many reliable matches as possible, we choose the matching radius adaptively according to the local surface density of star field. The radius is so chosen that the reliability of the matched source is at a statistic confident level better than 90%. The bottom panel of Fig. 2 presents the selected matching radius in areas with different densities. The cross-correlation results in 136 matching pairs with % statistic confidence and the corresponding numbers in different density areas are shown in the middle panel of Fig. 2. The upper panel shows the  MS stars numbers with corresponding density areas.

In some young and massive main-sequence stars, significant IR excess may arise from gas free-free emission instead of from the dusty disk. O stars generate strong ionized winds that produce strong infrared and radio excesses. 5 stars are excluded from our sample. Besides, We checked the left sources from SIMBAD, and reject 12 Be stars. Be star is a B-type star with prominent hydrogen emission lines in its spectrum. Observational characteristics include optical linear polarization and infrared excess (Porter et al., 2003). Both emission lines and excessive infrared emission are formed in the circumstellar disk, that is most likely ejected or stripped from the stars themselves. We also removed 4 stars rejected by Rhee et al.[2007] and some special stars including a star with not reliable flux density of , a quasar and a Post-AGB stars. This results in a sample of 112  detected stars down to a flux limit of 0.2 Jy. All these rejected stars are listed in Table 1. We then cross-match our star sample with other infrared catalogs, Two Micron All Sky Survey () All-Sky Point Source Catalog (Skrutskie et al., 2006) and Wide-field Infrared Survey Explorer () All-Sky Source Catalog (Wright et al., 2010).  has mapped the whole sky in four infrared bands W1, W2, W3 and W4 centered at 3.4, 4.6, 12 & 22 with 5 point source sensitivities better than 0.08, 0.11, 1 and 6 mJy, respectively. The angular resolutions are 61, 64, 65 & 120 at corresponding bands, and the astrometric precision for high SNR sources is better than 015 (Wright et al., 2010). The high sensitivity and angular resolution will be used to remove the confusion source and to further constrain the disk properties in the SED fitting. A search radius of 6″ was adopted to reflect the positional error. All stars except HIP  746 have matching pairs. We check the  image of these sources in order to avoid false matches and contamination by nearby infrared sources and reject 7 stars from the sample leaving 105 stars for further discussion.

### 2.2 Infrared Stellar Photospheric Emission

Obtaining the stellar photospheric flux densities is essential for identifying and measuring the strength of any IR excess (Bryden et al., 2006). We collected the optical to near-infrared (NIR) absolute photometry of stars in our sample to construct the spectral energy distribution (SED). Visible magnitudes in and are derived from the  satellite measurements. NIR photometry are extracted from Two Micron All Sky Survey (2MASS) catalogs (Skrutskie et al., 2006). The observed magnitudes were converted into flux density(Janskys) using the zero magnitudes in Cox(2000).

The stellar SEDs are fitted with the latest Kurucz’ models (ATLAS9) 5 (Castelli et al., 2004). The grids cover a wide range in these four parameters: temperature, surface gravity, metallicity, and projected rotational velocity. We select relevant model spectra from ATLAS9 for different spectral type stars according to Allen’s astrophysical quantities (Cox et al., 2000). For B- type stars, the effective temperature is in 500 K increments from 10000 to 20,000 K, the surface gravity log  cm s values are 4.0. For A- type and later types stars, the effective temperature is in 250 K increments from 3,500 to 10,000 K, the surface gravity log  cm s values are 4.0, 4.5. We assume microturbulent velocity =2 km s and metallicity values (solar metallicity) for all cases.

We fit the model spectra to the observed SED from optical to near-infrared of each object in order to find the best matched stellar models. During the fit, the stellar spectra are reddened and convolved with the response of each filter to yield the model flux density at each band. This method gives the model flux densities more accurate than adopting a constant magnitude to flux conversion factor, especially when the passband includes significant spectral features such as the Balmer jump (Rhee et al., 2007). The best-fitted stellar models were obtained by minimizing with the extinction and normalization constant as free parameters.

Using the best-fit Kurucz model, we estimate the stellar photospheric flux densities at the  and  infrared bands. The uncertainties are estimated by considering the distributions of models around its minimum , and all models within (at 90% confidence level for a single parameter) are considered to be acceptable. The predicted IR flux densities are plotted in black error bars. They are used for determining the infrared excess of our sample stars. From the Fig. 3, we can see the  3.4   and 4.6   flux density fits the model spectrum very well. It illustrates that our fitting is reliable.

Our goal is searching for the IR excess from debris disks, while debris disk is not the only source of the infrared excess of stars. Young stellar objects (YSOs) are thought to harbor protoplanetary disks (Moór et al., 2006), and often display IR excesses. They are classified observationally according to the shape of their SED in the infrared between the K band (at 2.2 ) and the N band (at 10 ) defined as (Armitage 2007),

 αIR=Δlog(λFλ)Δlogλ, (1)

where is a strong indication for a YSO. In our sample, several stars have the YSOs’ SED features as: Class I (approximately flat or rising SED into mid-IR ()) and Class II (falling SED into mid-IR ()). Class I YSOs are typically younger and possess more massive disks than Class II objects. In principle, YSOs should have been removed from our selection of main sequence stars using H-R diagram. However, stars will cross the main sequence belt on the H-R diagram when they evolve from pre-main-sequence to the zero-age main sequence stars. Most these stars are very close to the main sequence stars, and only a small fraction may have massive planetary disk. According to our SED fitting, we reject 20 YSOs in total and list them in the Table 1.

### 2.3 Identification of Debris Disk Candidates

In order to assess whether there is an excess infrared emission in the rest of the sample, we calculate the significance of the excess to the stellar photospheric emission model in each  band using following formula (Beichman et al., 2006; Moór et al., 2006) :

 χ=[FIR−Fphot]/σtot (2)

where is the measured flux density, is the predicted photospheric flux density. And is the quadratic sum of the uncertainty of the measured flux density and the uncertainty of the predicted flux density in the specific band as follows:

 σtot=√σ2IR+σ2phot (3)

An object is considered as an excess candidate star when (Su et al, 2006) either in 65, 90, 140 or 160 bands. Applying this criterion, we identified in total 83 candidate stars in the  database. Due to the shallow  flux limit, only 2 brightest stars (HIP 97649, HIP 101983) in the infrared detected sample does not show far-infrared excess.

The mid-infrared excess from WISE 22 m are estimated in the same way. Among 83 objects, 70 stars show excesses at the 22 m at more than 3 level. We have checked the distribution of difference between the model flux and the observed 22m flux in the negative value part to evaluate the accuracy of estimate. It is consistent with our uncertainty estimation.

The relevant photometric data for these 83 objects are presented in Table 2. The  identification is given in columns (1). The star and magnitude and  photometry are listed in columns (2) to (6). The  photometry magnitudes are listed in (7) to (10). The  90 µm flux density and its flux quality are listed in column (11) and (12) respectively. The last column is the matching offset in arcsec.

## 3 Properties of Debris Disks and Host Stars

We have identified a sample of 83 stars with debris disks. In this section, we will study the properties of host stars and debris disks. The stellar properties includes magnitudes, color, their location on the H-R diagram as well as these derived from SED fitting in the previous section. The properties of debris disks are derived by combing the parameters obtained in modeling the infrared excesses in  and  data.

Previous studies suggested that debris disks are optically thin and usually consists of a narrow ring (Backman & Paresce, 1993) in the thermal equilibrium with the stellar radiation field. Therefore the IR excess is usually modeled as a single-temperature black body (Kim et al., 2005; Bryden et al., 2006; Rhee et al., 2007). There are two free parameters in the fit, black body temperature and its normalization. To fully determine the model parameters, excesses in at least two bands are needed, while with more data points, we can get a best fit by minimizing . Therefore, according to the number of bands with detected far and mid-infrared excesses ( 4 bands and  12 µm and 22 µm), we further divide the IR excess sample into two groups: infrared excess in a single band (Group I) and excesses in two or more bands (Group II). The number of detected excessive bands depends strongly on the  flux density. Only sources in Group II, the dust temperature can be fully determined for the single temperature dust model, while in Group I, by combining FIS data with the upper limits at the WISE 22 m, we can derive an upper limit on the dust temperature. Among 83 debris disk candidates, majority (72) are in the Group II, and 58 in three or more bands. In passing, we note that 18 objects are detected in two or more  bands because they are bright in the 90m, and a significant fraction of these sources do display mid-infrared excesses. Similarly, bright sources are more likely to show 22m excess, but 22m excess is more affected by the dust temperature.

### 3.1 Stellar Properties of Debris Disk Hosts

Fig. 1 shows different sub-samples on H-R diagram. It is evident that the debris stars do not evenly sample its parent Hipparcos stars, but are biased more to early type stars, consistent with previous studies (Rhee et al., 2007). It is puzzling that these stars are not particularly close to the lines of zero age main sequence (ZAMS), while previous studies suggested that incidence of debris disk decreases with the increase of the stellar age. This may be caused by three factors, the contamination of pre-main sequence stars, large errors in the parallax and large interstellar reddening. Note even after rejecting these pre-main sequence stars, there is still no apparent trend towards the ZAMS, suggesting that the latter two possibilities. If we only includes these sources with an accuracy in the parallax measurement to 10%, most sources tend to distribute near the ZAMS (pink plus in Fig. 1). But there are still some stars far from the ZAMS. We check the fitted E(B-V) and found that indeed, they showed large reddening (see Table 3).

### 3.2 Disk properties

By fitting the IR excess flux densities, we will derive the dust temperature and the fraction of the stellar luminosity reprocessed by dust. In combining with additional stellar parameters, we can estimate the dust location and other quantities. The inferred basic disk properties including the dust temperature , dust location and dust mass are listed in columns (6), (7) and (8), respectively, in Table 3. The fractional luminosity is listed in column (9). In the follow subsection, we will describe the method and results in detail.

#### Dust Temperature

We fit the stellar photospheric emission subtracted flux densities in the  and  bands with a single temperature black body model as described before convolved with the response functions of the correspondent filters. In the case of single band excess (Group I, 11 star in total), we derive a maximum temperature by combining the excess  flux with the upper limits at 22 m, and the normalization of blackbody radiation at the maximum temperature. Noting that this normalization is usually higher than the one assuming a temperature that blackbody peaks at the detected infrared band, as has been made by Rhee et al. (2007). In the case of two bands excess, we can find the blackbody solution directly to determine the temperature and normalization. We estimate the uncertainty of a parameter by using as a function of the parameter. We adopt in the error estimate, i.e., at 90% confidence level for one interesting parameter. In the case of more than two band excesses, the best fit parameters are determined by minimizing , and again the uncertainties of parameters are set at . The typical uncertainty in the dust temperature is about 3 K. We do not use  fluxes because these fluxes may suffer from contaminations due to its low spatial resolution, in particular for the objects beyond 100 parsecs, where the contamination of cirrus is sever due to poor spatial resolution of .

In most cases, a single temperature blackbody gives an acceptable fit to the data for sources with multi-band excesses (Group II). Examples of SED fitting are shown in Figure 3. The fitting minimum for three bands and for four bands are considered to be unacceptable (atprobability). When using this criterion, a small fraction of objects (7 in total, marked with ’e’ in the column (11) of Table 3 shown) require a more complicated model. This indicates either multi-temperature dust emission or non-black body nature of dust grain. In our solar system, the dust in the asteroid belt and in the Kuiper belt represents two populations with very different temperatures. The SED fitting of these 7 stars are shown in Figure 4. While in the left 65 objects with single blackbody model, we notice that there are still a substantial fraction of objects show weak mid-infrared excesses extending to 12 m (marked with ’1’ in the column (11) of Table 3 shown). The significance of excess to the stellar photospheric emission and the single blackbody component in 12 µm are same to FIR excess as described in §2.3. They may also possess the warm component as the 7 objects shown in Figure 4. Note weak 12 µm excesses does not significantly affect the fit to the cold component therefore we do not take them into account in our fitting. In the rest of this paper, we will focus only on the cold component.

The best fitted is listed in column (6) in Table 3. The distribution of is shown in Fig. 5 (b). Dust temperature are falling in the range of 28 to 173 K with a median value of 78 K. The dust temperatures of the disk correspond to the black body emission’s peak from 30 to 182 .

#### Fractional Luminosity

Fractional luminosity is defined as the ratio of infrared luminosity from dust to that of the star, frequently used to characterize the effective optical depth of the debris disk,

 fd=Lir/L⋆ (4)

where is the infrared luminosity estimated by the fitted infrared black body model. The stellar luminosity is calculated from the best-fit Kurucz model. The uncertainties of can be estimated by a combination of the uncertainties in the temperatures and normalization. The typical uncertainty of is 14% for our sample.

We plot the distribution of the in Figure 5(c). Our sample spends a large range of , , with a median value of 0.0016. Limited by the sensitivity of the , the distribution itself should not be taken too seriously because disks with low can only be detected by  only for very bright nearby stars, resulting a distribution strongly biased to the higher .

#### Dust location, Dust mass

With the assumption that the debris disk is optically thin in thermal equilibrium with the stellar radiation field. In this case, the temperature of a dust grain with a given chemical composition and grain size depends on the radial distance to the central star only (Kim et al., 2005). Assuming that the dust locates in a narrow ring between and , one can write the radius of dust ring by the following formula (Backman & Paresce, 1993) :

 Rd=(278/Td)2(L⋆/L⊙)0.5 (5)

Because this formula assumes that the dust is blackbody-like, the resulting corresponds to a minimum possible radius (Moór et al., 2011). The uncertainties of is estimated from the error propagation of the uncertainties of temperatures . This gives typical error of 10% in . Fig. 5 (d) shows the distribution of the dust location .

The total mass M of dust can be written as following formula (Rhee et al., 2007):

 Md=(4πa3/3)ρN (6)

where N is the total number of grains in the disk and and are the radius and density of a typical grain. For an optically thin dusty ring/shell of characteristic radius R,

 fd=Nπa2/(4πR2d). (7)

Then,

 fd/Md∝1/(ρaR2d). (8)

If the characteristic grain size and density do not vary much among various optically thin dust disks, then one expects / to vary as the inverse square power of the disk radius, (Rhee et al., 2007). The slope is a constant which can be fitted by the disks whose masses were derived from sub-millimeter data. We use the fitted result of Rhee et al. (see the Figure 5 in Rhee et al. [2007]). So we can change the equation as following form:

 Md=fd(R2d/9.115)M\earth. (9)

where, and are taken from Table 3 in column (9) and column (7) respectively. Then the calculated dust mass was listed at column (8) in Table 3. Note that we have adopted several assumptions in deriving dust mass, that are only valid statistically, thus the dust mass is only a rough estimate for individual object.

## 4 Discussion

An effective way of characterizing the sample is to make comparison with other samples in literature. A similar one is  debris disk sample, which was constructed by cross-correlating  main-sequence star catalog with  Point Source Catalog (PSC) and Faint Source Catalog (FSC) (Rhee et al., 2007). The sample consists of 146 stars within 120 pc of the Earth that show excess emission at 60 . The distance limit is so set to avoid possible heavy contamination arising from interstellar cirrus or star-forming regions. Most of these stars are belong to early types, from late B to early K-type stars, similar to our sample. Despite similar sensitivity of  at 60 µm band and  at 90 µm  only 29 stars in Rhee et al. [2007] are in common with our IR excess sample, while 37 stars in total are of distance within 120 pc in our sample.

To understand what causes the difference, we search the flux density at 60 of Rhee’s sample from  PSC and FSC catalogues with a matching radius of 45″ as described in Rhee et al. [2007]. The undetected sources in the  source catalog are caused by the shallower survey limit of the . Almost all IRAS sources with Jy are undetected by , while at Jy or Jy 6, a similar number of sources were detected by  and . However, even at this flux limit, above 1/3 sources are different in the two samples. The difference may be partly attributed to the inhomogeneous depth of both surveys. In the most sensitive areas, the  can detect a source to a flux 0.2 Jy whereas in other areas, it reaches to only above 0.5 Jy. Another interesting possibility is that infrared excess is variable. Variable infrared excess has been reported in (Meng et al., 2012), which may attributed to coronal mass ejections (Osten et al., 2013).

For stars at distance larger than 120 pc, the flux ratios between IRAS 60 m and that of  90 m are substantially higher than those nearby stars, especially for sources with 1 Jy. This is likely caused by source confusion in the . With a factor of two improvement in the spatial resolution, the contamination is greatly reduced in  flux.

We over-plot the fundamental disk properties(see Table 2 in Rhee et al. [2007]) of Rhee’s sample to Figure 5(b),(c),(d). The dotted line is Rhee’s sample. From the histogram of dust temperature as Figure 5(b) shown, the sample of Rhee has a broader temperature distribution. From Figure 5(c) and Figure 5(d), our sample tends to show larger excesses and to possess more distant disks. A caution should be given here that both dust temperature and normalization in Rhee et al. [2007] is based on solely , which have much poorer sensitivity in the mid-infrared in comparison with . Therefore, the dust temperature for a large fraction of objects in Rhee’s sample cannot be determined, and was artificially assigned to 85 K so the peak emission is at 60 m. Even for those objects with multi-bands  detections, the dust temperatures were less well determined as in this paper.

At last, we compare our sample to previous samples (Oudmaijer et al., 1992; Rhee et al., 2007; Bryden et al., 2006; Chen et al., 2006; Decin et al., 2003; Habing et al., 1999, 2001; Kalas et al., 2002; Meyer et al., 2008; Moór et al., 2006, 2011; Rebull et al., 2008; Rieke et al., 2005; Su et al, 2006; Trilling et al, 2007, 2008; Booth et al., 2013; Koerner et al., 1998), and find 49 stars in common. The common stars information are listed in column (12) in Table 3. Figure 5 (a) shows the histogram of the distance from the Earth of our 83 IR excess sample. Although the sensitivity of  is limited, we are still able to newly identify 34 debris disk candidates. As Kalas et al. [2002] points out: Pleiades-like dust detected around the star is capable of producing the FIR emission rather than the Vega phenomenon. So these 34 new IR excess stars need to further checkout by Coronagraphic optical observation.

## 5 Summary

In this paper we cross-correlate AKARIBSC with  MS star catalog using a matching radius adapted to the local stellar surface density and yield a sample 137 far-infared detected stars (at % reliability) at least in one band. After rejecting 54 stars classified as young stellar objects, pre-main sequence stars and other type stars with known dust disks or with potential contaminations, we obtain a sample of 83 candidate stars with debris disks. The stars in the sample spans from B to K-types, with only 2 G-type and 2 K-type stars.

With the shallow limit of , the survey can only recover the brightest debris disks. This represents a unique sample of luminous debris disks that derived uniformly from all sky survey with a spatial resolution a factor of two better than the previous survey by . This sample is also a good complementary to the deep, small area surveys or deep surveys of nearby stars as already carried out with  and , that find out mostly faint debris systems. Moreover, by collecting the infrared photometric data from other public archives, 85% of them have infrared excesses in more than one bands, allowing the estimate of the dust temperatures. We fit the blackbody model to the broad band spectral energy distribution of these stars to derive the statistical distribution of the disk parameters. Our results suggest that a warm dust component is common among these bright debris disk systems.

This work is based on observations with , a JAXA project with the participation of ESA and make use of data products from  Catalogs ( the primary result of the Hipparcos space astrometry mission, undertaken by the European Space Agency),  (a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center / California Institute of Technology),  (a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology). This research has made use of ATLAS9 model and the SIMBAD database, operated at the CDS, Strasbourg, France.

### Footnotes

1. affiliation: Key Laboratory for Research in Galaxies and Cosmology, The University of Sciences and Technology of China, Chinese Academy of Sciences, Hefei, Anhui 230026, China; jonecy@mail.ustc.edu.cn
2. affiliation: Key Laboratory for Research in Galaxies and Cosmology, The University of Sciences and Technology of China, Chinese Academy of Sciences, Hefei, Anhui 230026, China; jonecy@mail.ustc.edu.cn
3. affiliation: Key Laboratory for Research in Galaxies and Cosmology, The University of Sciences and Technology of China, Chinese Academy of Sciences, Hefei, Anhui 230026, China; jonecy@mail.ustc.edu.cn
4. http://www.circumstellardisks.org
5. http://wwwuser.oat.ts.astro.it/castelli/grids.html
6. We have adopt the average for all sources
7. footnotetext: Note. –Col.(1):  identification. Col.(2):  source name Col.(3): Contamination source. Col.(4): Reason for rejection.
1. O star.
2. Be star.
3. Young stellar objects (YSOs) or Pre-main sequence stars (PMS).
4. Contamination.
5. Rhee et al. 2007 rejected these stars based on  observation.
6. Post AGB star.
7. Quasar.
8.  flux density is not reliable.
9. No FIR excess.
8. footnotetext: Note.– Col.(1):  identification. Col.(2): B magnitude. Col.(3): V magnitude. Col.(4): J magnitude. Col.(5): H magnitude.Col.(6): K magnitude. Col.(7):  W1 magnitude. Col.(8):  W2 magnitude. Col.(9):  W3 magnitude. Col.(10):  W4 magnitude. Col.(11):  90 µm flux density. Col.(12):Flux density quality flag in  90 µm : 3=High quality (the source is confirmed and flux is reliable); 2=The source is confirmed but flux is not reliable (see FLAGS); 1=The source is not confirmed; 0=Not observed (no scan data available). Col.(13):  position offset.
9. footnotetext: Note. – Col.(1):  identification. Col.(2): Distance. Col.(3): Teff. Col.(4): log. Col.(5): E(B-V). Col.(6): Dust temperature. Col.(7): Dust location. Col.(8): Total dust mass(). Col.(9): dust fractional luminosity. Col.(10): Spectral type. Col.(11): Notes: 1–12 µmexcess. e – SED fitting with 2 blackbody model. Col.(15): References – (1)Oudmijer et al. 1992; (2)Rhee et al. 2007; (3)Bryden et al. 2006; (4)Chen et al. 2006; (5)Decin et al. 2003; (6)Habing et al. 1999; (7)Habing et al. 2001; (8)Kalas et al. 2002; (9)Meyer et al. 2008; (10)Mooŕ et al. 2006; (11)Mooŕ et al. 2011; (12)Rebull et al. 2008 (13)Rieke et al. 2005; (14)Su et al. 2006; (15)Trilling et al. 2007; (16)Trilling et al. 2008; (17)Booth et al. 2013; (18)Koerner et al. 1998.

### References

1. Abraham, P., Leinert, C., Burkert, A., Lemke, D., Henning, T. 1999, A&A, 338, 91
2. Armitage, P. J. 2007, ArXiv Astrophysics e-prints, arXiv:astro-ph/0701485
3. Aumann, H. H., et al. 1984, ApJ, 278, L23
4. Backman, D. E., & Paresce, F. 1993, in Protostars and Planets III, ed. V. Mannings, A.P. Boss, & S. S. Russell (Tucson: Univ. Arizona Press), 1253
5. Beichman, C. A., Bryden, G., Stapelfeldt, K. R. et al. 2006, ApJ, 652, 1674
6. Bessell, M. 2000, PASP, 112, 961
7. Booth, M., Kennedy, G. et al. 2013, MNRAS, 428, 1263
8. Bryden, G. et al. 2006, ApJ, 636, 1098
9. Castelli, F. & Kurucz, R. L. 2004, arXiv:astro-ph/0405087
10. Chen, C. H., Patten, B. M., Werner, M. W., Dowell, C. D., Stapelfeldt, K. R., Song, I., Stauffer, J. R., Blaylock, M., Gordon, K. D., Krause, V. 2005, ApJ, 634, 1372
11. Chen, C.H., et al. 2006, ApJS, 166, 351
12. Cox, A. N., ed. 2000, Allen’s Astrophysical Quantities
13. Decin, G., Dominik, C., Waters, L. B. F. M., Waelkens, C. 2003, ApJ, 598, 636
14. Eiroa, C., Marshall, J. P. et al. 2011, A&A, 536, 4
15. Fajardo-Acosta, S. B., Stencel, R. E., Backman, D. E., Thakur, N. 1999, ApJ, 520, 215
16. Fukagawa, M., Murakami, H., Hirao, T., et al. 2009, aspc, 418, 99
17. Fujiwara, H., Yamashita, T., et al. 2009, ApJ, 695, 88
18. Fujiwara, H., Onaka, K., et al. 2010, ApJ, 714, 152
19. Fujiwara, H., Ishihara, D., et al. 2010, cosp, 38, 2470
20. Fujiwara, H., Ishihara, D., et al. 2013, A&A, 550, 45
21. Greaves, J. S., Holland, W. S., Wyatt, M. C., et al. 2005, ApJ, 619, 187
22. Habing, H., Dominik, C., Jourdain de Muizon, M., et al. 1999, Nature, 401, 456
23. Habing, H., Dominik, C., et al. 2001, A&A, 365, 545
24. Kalas, P., Graham, J. R., Beckwith, S. V. W., Jewitt, D. C., Lloyd, J.P. 2002, ApJ, 567, 999
25. Kalas, P., Graham, J. R., Clampin, M. C., Fitzgerald, M. P. 2006, ApJ, 637, 57
26. Kawada, M., Baba, H., et al. 2007, pasj, 59, 389
27. Kessler, M. F., Steinz, J. A., et al. 1996, A&A, 315, 27
28. Kim, J. S., Hings, D. C., Rivinius 2005, ApJ, 632, 659
29. Koerner, D. W., Ressler, M. E., Werner, M. W., Backman, D. E. 1998, ApJ, 503, 83
30. Krivov, A.V. 2010, raa, 10, 383
31. Lagrange, A.M., Milli, J., Boccaletti, A. et al. 2012, A&A, 546, 38
32. Maldonado, J., Eiroa, C. et al. 2012, A&A, 541, 40
33. Meng, H., Rieke, G. H., Su, K. Y. L. et al. 2012, ApJ, 751, L17
34. Mannings, V., Barlow, M. J. 1998, ApJ, 497, 330
35. Meyer, M.R., et al. 2008, ApJ, 673, L181
36. Moór, I., Abraham, P., Derekas, A. et al. 2006, ApJ, 644, 525
37. Moór, I., Pascucci, A. et al. 2011, ApJS, 193, 4
38. Neugebauer, G., Habing, H. J., et al. 1984, ApJ, 278, 1
39. Olofsson, J., Juhaśz, A. et al. 2012, A&A, 542, 90
40. Osten, R., Livio, M. et al. 2013, ApJ, 765, L44
41. Oudmaijer, R. D., van der Veen, W. E. C. J. et al. 1992, A&A, 96, 625
42. Perryman, M.A.C., Lindegren, L., et al. 1997, A&A, 323, L49
43. Plavchan, P., Werner, M. W., Chen, C. H. et al. 2009, ApJ, 698,1068
44. Porter, J., Rivinius, T. 2003, PASP, 115, 1153
45. Raymond, S. N., Armitage, P. J. et al. 2011, A&A, 530, 62
46. Raymond, S. N., Armitage, P. J. et al. 2012, A&A, 541, 11
47. Rebull, L.M., et al. 2008, ApJ, 681, 1484
48. Rhee, J. H., Song, I. R., Zuckerman, B., McElwain, M. 2007, ApJ, 660, 1556
49. Ribas, A.́, Meriń, B. et al. 2012, A&A, 541, 38
50. Rieke, G.H., et al. 2005, ApJ, 620, 1010
51. Schutz, O., Meeus, G., Sterzik, M. F. 2005, A&A, 431, 175
52. Siegler, N., Muzerolle, J., Young, E. T., Rieke, G. H., Mamajek, E. E., Trilling, D. E., Gorlova, N., & Su, K. Y. L. 2007, ApJ, 654, 580
53. Silverstone M. 2000, The Vega phenomenon: evolution and multiplicity. PhD thesis. Univ. Calif., Los Angeles. 194 pp.
54. Skrutskie, M. F., Cutri, R. M., Weinberg, M. D. et al. 2006, AJ, 131, 1163
55. Spangler, C., et al. 2001, ApJ, 555, 932S
56. Su, K. Y. L., Rieke, G. H., Stansberry, J. A., Bryden, G. et al. 2006, ApJ, 653, 675
57. Su, K. Y. L., Rieke, G. H., Stapelfeldt, K. R. et al. 2008, ApJ, 679, 125
58. Trilling, D. E., Stansberry, J. A. et al. 2007 , ApJ, 658, 1289
59. Trilling, D. E., Bryden, G., Beichman, C. A. et al. 2008, ApJ, 674, 1086
60. Urban, L. E., Rieke, G. et al. 2012, ApJ, 750, 98
61. Wright, E. L., et al. 2010, AJ, 140, 1868
62. Wu, H., Wu, C. J., Cao, C. 2012, raa, 12, 513
63. Wyatt, M. C. , Greaves, J. S., Dent, W. R. F., Coulson, I. M. 2005, ApJ, 620, 492
64. Wyatt, M. C. 2008, ARA&A, 46, 339
65. Yamamura, S., et al. 2010, cosp, 38, 2496Y
66. Zuckerman, B., & Song, I. 2004, ApJ, 603, 738
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