What Sets the Radial Locations of Warm Debris Disks?
Nicholas P. Ballering, George H. Rieke, Kate Y. L. Su, András Gáspár
DDraft version November 8, 2018
Preprint typeset using L A TEX style AASTeX6 v. 1.0
WHAT SETS THE RADIAL LOCATIONS OF WARM DEBRIS DISKS?
Nicholas P. Ballering, George H. Rieke, Kate Y. L. Su, Andr´as G´asp´ar
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA
ABSTRACTThe architectures of debris disks encode the history of planet formation in these systems. Studiesof debris disks via their spectral energy distributions (SEDs) have found infrared excesses arisingfrom cold dust, warm dust, or a combination of the two. The cold outer belts of many systems havebeen imaged, facilitating their study in great detail. Far less is known about the warm components,including the origin of the dust. The regularity of the disk temperatures indicates an underlyingstructure that may be linked to the water snow line. If the dust is generated from collisions in anexo-asteroid belt, the dust will likely trace the location of the water snow line in the primordialprotoplanetary disk where planetesimal growth was enhanced. If instead the warm dust arises fromthe inward transport from a reservoir of icy material farther out in the system, the dust location isexpected to be set by the current snow line. We analyze the SEDs of a large sample of debris diskswith warm components. We find that warm components in single-component systems (those withoutdetectable cold components) follow the primordial snow line rather than the current snow line, so theylikely arise from exo-asteroid belts. While the locations of many warm components in two-componentsystems are also consistent with the primordial snow line, there is more diversity among these systems,suggesting additional effects play a role.
Keywords: circumstellar matter – planetary systems INTRODUCTIONA debris disk comprises a remnant population of plan-etesimals on circumstellar orbits and the dust generatedby their collisional destruction. While observations ofprotoplanetary disks show planetary systems in the earlystages of formation, debris disks reveal the properties ofmore mature systems. The spatial structure of a debrisdisk traces the architecture of the planetary system be-cause planets remove planetesimals from their vicinity.The interpretation of debris disk observations involvesconnecting the properties of the dust to those of theunseen planetesimals and planets. For recent reviewsof debris disk science, see Wyatt (2008) and Matthewset al. (2014b).Hundreds of spatially unresolved debris disks havebeen characterized by the infrared excess observed inthe SEDs of their systems—the thermal emission of thedebris disk dust. This excess can often be modeled sim-ply with one or two blackbody functions, and it typicallytakes the form of a cold component ( <
130 K), a warmcomponent ( ∼
190 K), or both (Morales et al. 2011; Bal- [email protected] lering et al. 2013; Chen et al. 2014). Kennedy & Wyatt(2014) concluded that for most systems the warm andcold components arise from radially distinct distribu-tions of dust (as opposed to being co-located and havingdifferent temperatures due to different grain properties).The cold components are the best-studied parts of de-bris disk systems. They reside far enough (i.e. > tens ofau) from their host stars that some have been resolved,revealing a belt analogous to the Kuiper belt in the solarsystem. The nature of the warm components is less cer-tain, as they reside closer to the star and cannot easilybe spatially resolved. For example, the nearby (7.7 pc)star Fomalhaut hosts a well-studied cold belt that hasbeen resolved at several wavelengths (Kalas et al. 2005;Acke et al. 2012; Boley et al. 2012; MacGregor et al.2017). From analyses of its SED and infrared images,Fomalhaut also hosts a warm component (Stapelfeldt Su & Rieke (2014) identified five dust components that a de-bris disk can possess, which, in addition to the warm and coldcomponents described here, also include: a blowout halo of smallgrains outside of the cold belt (Augereau et al. 2001; Su et al.2005); exozodiacal dust that is hotter and nearer to the star thanthe warm dust and emits at ∼ µ m (Kennedy & Wyatt 2013;Ballering et al. 2014); and very hot dust emitting in the near-IR(Absil et al. 2013; Ertel et al. 2014) likely composed of nanograinstrapped in the stellar magnetic field (Rieke et al. 2016). a r X i v : . [ a s t r o - ph . E P ] A ug Ballering et al. et al. 2004; Su et al. 2013), but obtaining resolved im-ages of this warm component to confirm its propertiesremains difficult (Su et al. 2016). In this study, we drawconclusions about warm components by analyzing theSEDs of a large sample of sources and examining howtheir properties vary with the properties of their hoststars.The origin of the warm dust is heavily debated in theliterature. Given that these components originate fromzones likely well-populated by planets, one might expectthe belts to be so strongly disturbed that all traces oftheir origins are erased. However, Morales et al. (2011)found a similarity in the warm belt temperatures amongstars of different masses , suggesting a common under-lying structure, possibly related to the water snow line.There are two general hypotheses regarding the sourceof such structures: (1) the dust is produced in-situ bythe collisional processing of a belt of parent bodies anal-ogous to the asteroid belt in the solar system, or (2)the dust is transported inward from an outer reservoirof cold planetesimals. As we will describe later, bothof these possibilities predict that the locations of warmcomponents will be set by the snow line (i.e. wherewater ice condensation/sublimation occurs). However,these hypotheses differ as to whether it is the primordial snow line or the current snow line that sets the warmdust location. These two snow lines predict different re-lations between the location of the warm dust and themass of the host star. By examining the observed trendbetween warm dust location and stellar mass ( M (cid:63) ), wecan determine which snow line (primordial or current)was responsible for setting the dust location, and thuswhich hypothesis for the origin of the dust is favored.1.1. Hypothesis 1: In-situ Production and thePrimordial Snow Line
If the warm dust is produced in-situ from an exo-asteroid belt, it is expected to occur near the primor-dial snow line. Several mechanisms predict an enhance-ment of solid material and planetesimal formation at (ornear) the snow line in a protoplanetary disk. Water va-por diffusing outward through the disk will condense atthe snow line and increase the density of solid materialthere (Stevenson & Lunine 1988). Icy, roughly meter-size bodies from the outer disk will migrate inward dueto gas drag and will sublimate at the snow line, creatingan enhancement of vapor and solids (Cuzzi & Zahnle Kennedy & Wyatt (2014), on the other hand, found thatfor stars with effective temperatures greater than ∼ r SL ∝ M (cid:63) / ˙ M / κ R / f − / α − / T ice − / , (1)where ˙ M is the mass accretion rate, κ R is the Rosselandmean opacity, f is the gas-to-dust ratio, α is the tur-bulent mixing strength, and T ice is the ice sublimationtemperature. Of these parameters, only ˙ M is believedto vary significantly with stellar mass and thus is rel-evant for estimating the form of the r SL – M (cid:63) relation.The mass accretion rate has been found to vary withstellar mass as ˙ M ∝ M (cid:63) over a large range of stellarmasses, including the masses of the stars in our sample(Calvet et al. 2004; Muzerolle et al. 2005; Natta et al.2006). This implies that r SL ∝ M (cid:63) . . As we will em-phasize later, this relation is shallower than that for thecurrent snow line. Other investigations into the locationof the primordial snow line also predict the r SL – M (cid:63) rela-tion to be significantly shallower than the current snowline relation (Kennedy & Kenyon 2008; Martin & Livio2013a).1.2. Hypothesis 2: Inward Transport and the CurrentSnow Line
If, instead, the warm dust is transported inward froman outer reservoir during the present debris disk phase,it is expected to reside at the current snow line. Thereare two plausible mechanisms for the inward transport.The first mechanism is analogous to that described byNesvorn´y et al. (2010), who found that most of thewarm dust in the inner region of the solar system orig-inates from the disruption of Jupiter family comets(JFCs). JFCs are dynamically controlled by Jupiterand have orbits with significantly lower eccentricitiesand smaller aphelia than Halley-type comets or long-period comets. Simulations show that JFCs likely orig- arm Debris Disks β ). This haltsthe grain’s inward motion and eventually causes it to beexpelled outward. The net result is a pile-up of grains atthe location of the current snow line (Kobayashi et al.2008). However, numerical simulations (van Lieshoutet al. 2014) indicate that the inward flow via this mech-anism may be inadequate to maintain the amount ofwarm dust required for detectable infrared excesses with Spitzer , even with the snow line pile-up.The location of the current snow line is determinedby the incident stellar flux, so it scales as r SL ∝ L / (cid:63) ,where L (cid:63) is the stellar luminosity. Combining this with L (cid:63) ∝ M (cid:63) , the typical relation between stellar luminosityand mass, yields r SL ∝ M (cid:63) . Importantly, the currentsnow line relation is steeper than the primordial snowline relation (index of 2.0 versus 1.2).1.3. Overview
In this paper, we analyze the SEDs of a sample ofdebris disks with warm components and infer the stel-locentric locations of the warm dust. The dust loca-tion derived solely from an SED is subject to manyuncertainties and cannot be determined absolutely forany given system. Therefore we focus our attention onthe relative behavior of dust location with stellar mass,while holding all other parameters (e.g., grain materi-als) constant—except for the minimum grain size, whichis known to vary systematically with stellar properties. We examine the r dust – M (cid:63) trend and compare the slopewith those predicted by the primordial and current snowlines, providing insight into which snow line sets the dustlocation. From this we can deduce the the origin of thewarm dust components. METHODS2.1.
Target Selection
For our sample, we used the systems with a warm com-ponent found by Ballering et al. (2013), where “warm”was defined as warmer than 130 K. All of these systemshave data available from the Multiband Imaging Pho-tometer for
Spitzer (MIPS; Rieke et al. 2004) at 24 and70 µ m and from the Spitzer
Infrared Spectrograph (IRS;Houck et al. 2004).Throughout the analysis, we separated the systemswith only a warm component from those that also pos-sess a detected cold component. The systems without adetected cold component should provide less ambiguousresults, since these warm components could not arisefrom particles moving inward from cold belts via P-Rdrag, although they could still arise from comets orig-inating in cold components that are below the currentdetection limit (Wyatt et al. 2007). In addition, mod-eling systems with a single dust component is less com-plicated and the results have less uncertainty.Ballering et al. (2014) discovered silicate emission fea-tures in the IRS spectra of 22 of these systems. Thesefeatures revealed the presence of exozodiacal dust, whichis believed to reside at a different location than the typi-cal warm component. Ballering et al. (2014) found that,besides the exozodiacal dust, an additional colder com-ponent was also required to fit the full IRS spectra ofthese sources. Whether this remaining excess consistsof one or multiple components is difficult to determine.Thus, to ensure a pure sample of warm components, weexcluded these 22 targets. If, however, the dust com-ponents giving rise to these features are a natural ex-tension of standard warm components, then excludingthese targets may introduce a bias to our sample.We also excluded HIP 32435 (HD 53842) because theIRS data may have been contaminated by backgroundsources (Donaldson et al. 2012). We removed additionaltargets in the course of our fitting procedure, as de-scribed in Section 2.6. The remaining 83 targets usedfor our analysis are listed in Table 1.2.2.
Stellar Properties
The stellar temperature ( T (cid:63) ), luminosity, and distancefrom Earth ( d ) of most of our targets were taken fromMcDonald et al. (2012), who derived T (cid:63) and L (cid:63) by fit-ting the visible and near-IR photometry of these systemswith stellar SED models. We then obtained the stellar Ballering et al. mass ( M (cid:63) ) from L (cid:63) using the (broken) power-law rela-tion by Eker et al. (2015). McDonald et al. (2012) as-sumed a 10% uncertainty on the photometry they usedto derive L (cid:63) , so we also assumed a 10% uncertainty on L (cid:63) . Combining this with the intrinsic 25–38% scatterin the L (cid:63) values Eker et al. (2015) used to derive theirrelations yields a ∼ M (cid:63) values.For the targets not listed in McDonald et al. (2012, de-noted with an “a” after the target name in Table 1), weinferred their stellar properties from their V − K colorusing the tabulated values maintained online by E. Ma-majek as an expanded and updated version of Table 5in Pecaut & Mamajek (2013).We required a model spectrum of the stellar photo-sphere for each of our targets, both for modeling thephotospheric contribution to the observed SED and forcalculating the temperature of dust grains when gener-ating model spectra of the dust emission. We used anATLAS9 (Castelli & Kurucz 2004) photosphere modelwith log g = 4.0, solar metallicity, and T (cid:63) closest to thatfor each target (at most a difference of 125 K). Thesephotosphere model spectra were modeled only out to 160 µ m, so for completeness we extended them to 10,000 µ mby extrapolating with a Rayleigh-Jeans power-law. Wenormalized the integrated spectra to L (cid:63) for each target.(Although during the fitting process we allowed the am-plitude of the model photosphere to vary by a smallamount to improve agreement with the photometry; seeSection 2.6.) 2.3. IRS Data
While many infrared excesses have been identifiedfrom photometric measurements alone (Rieke et al.2005; Su et al. 2006; Wyatt 2008; Matthews et al. 2014b;Sierchio et al. 2014), accurately measuring the temper-ature/location of the emitting dust requires the widespectral coverage offered by IRS.We obtained low-resolution IRS spectra for our targetsfrom the LR7 release of The Combined Atlas of Sourceswith
Spitzer
IRS Spectra (CASSIS; Lebouteiller et al.2011). Both long-low orders (LL1: 19.5–38.0 µ m; LL2:14.0–21.3 µ m) were available for all targets, and one orboth of the short-low orders (SL1: 7.4–14.5 µ m; SL2:5.2–7.7 µ m) were also available for most of the targets.The IRS Astronomical Observation Requests numbers(AORs) for our targets are given in Table 1.We removed outlying points more than 3 σ away froma third-degree polynomial fit to the measurements in The Combined Atlas of Sources with
Spitzer
IRS Spectra(CASSIS) is a product of the IRS instrument team, supported byNASA and JPL. http://cassis.sirtf.com/ each spectral order. To remove offsets between orders,we multiplied the LL1, SL1, and SL2 flux density valuesby correction factors (determined by eye), to align themwith the LL2 order and to each other. The choice topin the other orders to LL2 was arbitrary but had noeffect on the results because, as described in Section2.6, the amplitude of the whole IRS spectrum was variedas part of the fitting process. These correction factors(designated x LL1 , x SL1 , and x SL2 ) are listed in Table 1.During this process we opted to remove HIP 79631 fromour sample because the offsets between the orders weremuch greater than for any other target, suggesting thedata may be unreliable.2.4.
IR and Sub-mm Photometry
In addition to the IRS data, we included in our SEDsphotometry from MIPS at 24 and 70 µ m plus additionalphotometry at wavelengths ≥ µ m from the literature.These data are listed in Table 2. Upper limits are at the3 σ level. 2.5. Modeling Dust Emission
Careful restriction of the model characteristics wasnecessary to avoid degeneracies that would undermineour ability to determine the trend of warm dust loca-tion with stellar mass. We assumed that the dust lies ina circular ring. To test the sensitivity of our conclusionsto the particular ring geometry we used two differentmodels: one with a constant ring width (independent ofradius) and a second with a ring width that was a con-stant fraction of the ring radius. We discuss the first setof models here and the second set in Section 4.1 wherewe explore the robustness of our results. For these mod-els, we assumed that the ring has r out = r in + 2 au( r is the stellocentric distance). We modeled the sur-face number density of grains as Σ( r ) ∝ r − p with p =1, but varying this parameter within reasonable boundshad virtually no effect on our results. We assumed apower-law grain size distribution ( n ( a ) ∝ a − q where a is the grain radius) with size index q = 3 .
65 (G´asp´aret al. 2012), minimum grain size a min = a BOS ( a BOS isthe blowout size), and maximum grain size a max = 1000 µ m. Grains larger than a max contribute negligibly tothe overall emission. We assumed a grain compositionof 60% astronomical silicates and 40% organic refractorymaterial by volume (Ballering et al. 2016). To test howrobust our conclusions are to grain composition, we re-port on similar models with grains composed entirely ofastronomical silicates in Section 4.1. The only free pa-rameters in our dust belt models were r in and the totalmass of dust.Fixing the minimum grain size to the predictedblowout size was an important step in the modeling be-cause a BOS varies systematically with stellar type and arm Debris Disks a min can have a substantial effect on the derived dust lo-cation (a smaller value of a min leads to a larger inferred r in ). That is, we do not simply link the dust tempera-ture and location using the blackbody temperature. Thechoice to use a min = a BOS is justified theoretically andalso empirically. Detailed fits to debris disk spectra ex-hibiting silicate emission features were able to constrainboth the dust location and minimum grain size; thesefits showed the minimum spherical grain size to be con-sistent with the blowout size (Ballering et al. 2014). Fur-thermore, Booth et al. (2013) compared the sizes of colddebris disks, as measured from their resolved
Herschel images and as derived from fitting blackbody functionsto their SEDs, and found that the differences could belargely explained by models assuming a min = a BOS , al-though in a similar analysis Pawellek et al. (2014) found a min values somewhat larger than a BOS .The blowout size is the largest grain size for which β > .
5, where β is the ratio of the radiation force tothe gravitational force on a grain. For spherical grains, β is given by β = 3 L (cid:63) πGM (cid:63) acρ (cid:82) ∞ Q pr ( λ, a ) F λ(cid:63) ( λ ) d λ (cid:82) ∞ F λ(cid:63) ( λ ) d λ , (2)where F λ(cid:63) ( λ ) is the stellar flux density, Q pr ( λ, a ) is theradiation pressure efficiency on the grain, G is the grav-itational constant, c is the speed of light, and ρ = 2.34 gcm − is the grain density (2.7 g cm − for the astronom-ical silicates and 1.8 g cm − for the organic refractorymaterial). We computed Q pr ( λ, a ) and the absorptionefficiency, Q abs ( λ, a ) (needed to model the dust emis-sion as discussed later) from the optical constants usingthe Mie theory code miex (Wolf & Voshchinnikov 2004).The optical constants of this grain composition mixtureare given in Table 3 of Ballering et al. (2016). a BOS foreach target is given in Table 1.To compute the model dust emission, we needed thetemperature of the grains ( T dust ) as a function of theirradial location and size. We calculated this by comput-ing r ( T dust , a ) = 14 π (cid:115) (cid:82) Q abs ( λ, a ) L λ(cid:63) ( λ ) d λ (cid:82) Q abs ( λ, a ) B λ ( λ, T dust ) d λ (3)then inverting it to solve for T dust ( r, a ). L λ(cid:63) ( λ ) isthe stellar spectral luminosity, and B λ ( λ, T dust ) is thePlanck function. Equation 3 is derived from balancingthe heating and cooling power on the grain. Finally, wecalculated the emission spectrum from each grain, F ν ( λ, r, a ) = (cid:16) ad (cid:17) Q abs ( λ, a ) πB ν ( λ, T dust ) , (4)and combined these spectra into a single spectrumweighted by the spatial distribution and grain size dis-tribution of the model. 2.6. Fitting Models to the Observed SEDs
We fit the observed SED of each target, including pho-tometric points at V , J , H , and K , the IRS spectrum,and the additional photometry listed in Table 2. Thestellar photosphere accounts for virtually all of the ob-served flux density in the visible and near-IR, but italso contributes significantly at longer wavelengths. Wemodeled the photospheric contribution to the SED asdescribed in Section 2.1, and we allowed small adjust-ments in the photospheric level to optimize the fits. Tofit the excess emission from the debris disk, we usedthree different models: (1) a single modified blackbody(described later), which we used to double-check for sys-tems that could be fit best by a single cold component;(2) a single warm dust belt, as described in Section 2.5,with the location of the dust ( r warm = r in ) and themass of dust in the belt ( M warm ) as free parameters;and (3) a warm dust belt plus a modified blackbody,with the blackbody accounting for a possible cold com-ponent. We did not fit the cold components with ourdust belt model because their locations were not of in-terest for our analysis.For the modified blackbody, we followed the formula-tion used by Kennedy & Wyatt (2014): F ν ( λ ) = c BB B ν ( λ, T cold ) X ( λ ) − , (5)where c BB is a constant (amplitude), T cold is the tem-perature of the cold component, and X ( λ ) = λ < λ ( λ/λ ) ˜ β λ > λ . (6)The modification to the blackbody, X ( λ ), models thesteeper than Rayleigh-Jeans fall off at long wavelengthsdue to grains not emitting efficiently at wavelengthslonger than their size. The free parameters for the modi-fied blackbody were c BB , T cold , λ , and ˜ β . In the fittingwe required 50 µ m < λ < µ m, 0 < ˜ β <
2, and T cold <
130 K when part of a two-component fit.In the fitting process, we also allowed for a small am-plitude adjustment to the IRS data ( c IRS ), which weallowed to take values between 0.8 and 1.2. This effec-tively corrected any systematic calibration error. Thisprocedure yielded good results with c
IRS constrained attwo points: first, the short end of the IRS data neededto match the photosphere model, which in turn had tomatch the visible/near-IR photometry; second, the IRSdata needed to match the MIPS photometry point at 24 µ m in order for the best fit model to pass through boththe MIPS and IRS data at this wavelength.In practice, we performed a grid search over r in (insteps of 0.1 au) and c IRS (in steps of 0.01). At eachpoint in the grid we then found best values for the restof the free parameters with a Levenberg-Marquardt al-
Ballering et al. gorithm (the Matlab function lsqcurvefit ). The bestfit was the model that minimized the standard χ met-ric. When calculating χ , we enhanced the weights ofthe photometry points at ≥ µ m by a factor of 25 tobalance their influence on the fit against the large num-ber of points in the IRS spectra. Without this extraweighting, the behavior of the model in the far-IR/sub-mm often reflected an extrapolation from the longestwavelength IRS points, rather than fitting to the datain this wavelength regime. Upper limit photometry mea-surements were not included in the χ calculation, butwe inspected the best-fitting models to ensure they wereconsistent with these measurements. Parameters λ and˜ β were often not well-constrained by the fitting, exceptfor the targets with accurately measured far-IR/sub-mmphotometry.We inspected the results of our single blackbody fitsto ensure that we only included genuine warm compo-nents in our sample. Targets that were fit well by asingle blackbody with temperature <
130 K were dis-carded from our sample. These included HIPs 544, 2072,9141, 16852, 17764, 24947, 46843, 51194, 59072, 59960,61960, 65728, 66065, 90936, and 107649. Some of thesetargets had single warm components with temperaturesjust above the 130 K cutoff according to Ballering et al.(2013), but with the additional far-IR photometry andthe new fitting procedure used here, they now fell be-low this cutoff. For others, Ballering et al. (2013) hadfound two components with the warm component be-ing relatively weak, but here we found that the warmcomponent was no longer necessary to fit the IRS data.We also excluded HIP 6276 because it has an ex-tremely weak warm excess (and no evidence for a coldcomponent) from which we could not place any mean-ingful constraints on the dust location. Finally, we ex-cluded HIP 53954 and HIP 65109, because any modelfit to the IRS data significantly over-predicted the far-IR photometry. A similar steep decline of the disk fluxin the far-IR has been noted in a few other disks (Er-tel et al. 2012), requiring a very unusual distribution ofgrain sizes to model.We examined the fits of the remaining targets to de-termine which were best fit with a single warm belt andwhich required a cold component as well. In many cases,the requirement for a cold component came from thefar-IR photometry, with the IRS data fitting well with asingle warm belt. We included a cold component whenthe warm-only model under-predicted the far-IR databy more than 2 σ (with the offset from multiple far-IRpoints combined in quadrature) and the addition of acold component improved the fit. For most systems,the designations agreed with those of Ballering et al.(2013). Five systems that previously had been fit witha single warm component now were fit with two com- ponents (HIPs 1473, 1481, 77432, 78045, and 85922),and two systems that previously had been fit with twocomponents were now fit best with a single warm belt(HIP 63836 and HIP 112542). The best fit cold compo-nent of HIP 117452 had T cold = 130 K (the upper boundallowed by the fit), suggesting this system has an unusu-ally warm cold component. However, the model fit thedata well, so a significantly higher value of T cold is likelynot required. An unusually warm cold component doesnot, however, impact the need for a separate warm belt,as we found that a single component could not fit all thedata.From our χ metric, we found that the median sta-tistical uncertainties on r in were 5% and 13% for thesingle-component and two-component systems, respec-tively. We calculated the luminosities of the best fitmodel components by integrating under the model spec-tra. We then derived the fractional luminosity of eachcomponent: f warm = L warm /L (cid:63) and f cold = L cold /L (cid:63) .The results of the fitting are given in Tables 3 and 4.The best fit model SED for each target is shown in Fig-ures 7 and 8. Our final sample had 29 systems withsingle warm components and 54 systems with two com-ponents. ANALYSIS AND RESULTSWith the locations of the warm dust components forour targets found, we next turned our attention to therelationship between the dust location and the stellarmass. We expected the relation to follow a power-law( r warm ∝ M b(cid:63) ), considering the predicted relations forboth the primordial and current snow lines take thisform. Our goal was to measure the value of the expo-nent b and see if it aligned with the predicted value forthe current or primordial snow line. We did not attemptto compare the absolute values of the measured dust lo-cations to those predicted for the snow lines. The actuallocation of the primordial snow line is less certain thanits predicted relation with stellar mass, considering theuncertainty on the values of all the factors in Equation 1and the fact that the primordial snow line location likelyevolves with time. Nevertheless, Martin & Livio (2013b)did find that the absolute stellocentric distances of warmdebris disks reported in the literature were roughly con-sistent with the location of the primordial snow line.3.1. Single-component Systems
We first considered the systems with a single warmcomponent. Figure 1 plots r warm versus M (cid:63) andshows clear evidence for a positive trend. To quan-tify this trend, we fit the function log( r warm / au) = a + b log( M (cid:63) /M (cid:12) ) to these points. The best fit values of arm Debris Disks b and a were computed as b = log( M (cid:63) ) · log( r warm ) − log( M (cid:63) ) · log( r warm )log( M (cid:63) ) − log( M (cid:63) ) (7)and a = log( r warm ) − b · log( M (cid:63) ). We found b = 1.08, a = 0.566 (i.e. r warm / au = 3 . M (cid:63) /M (cid:12) ) . ). This bestfit trend line is plotted with the data points in Figure 1.Considering the substantial amount of scatter evidentin the data around the trend line, we used a bootstrapprocedure to quantify the significance of our derivedvalue of b . We used 10,000 trials for the bootstrap pro-cedure. For each trial we randomly selected 29 points(with replacement) from our sample and recomputed thebest fit b using Equation 7. Figure 2 shows the distribu-tion of b values found by the bootstrap procedure. Thedistribution was fit by a normal distribution with mean= 1.08 and σ = 0.21 using the Matlab function fitdist .We found that b for the targets with a single warmcomponent was consistent (within 0.6 σ ) with the valuepredicted by the primordial snow line (1.2) but was notconsistent ( > . σ ) with the relation predicted by thecurrent snow line (2.0). Thus we conclude that thesewarm components are more likely to arise from dustproduced by exo-asteroid belts than from disintegratingcomets or dust dragged inward from an outer belt thatis below our current detection limit.3.2. Two-component Systems
Next we examined the r warm – M (cid:63) trend for the two-component systems, shown in Figure 3. There wasmore scatter than for the single-component systems, andno simple relation was evident that represented all thepoints. For comparison, we added to this plot the bestfit trend found for the single-component systems (greendashed line from Figure 1). We found that many of thewarm components in the two-component systems wereconsistent with this same trend, but there were also sev-eral systems both above and below the trend.3.3. Scatter around the Single-component Trend
We subtracted the values of r warm predicted by thetrend line for the single-component systems from themeasured r warm values in both samples. Fitting a nor-mal distribution to residuals of the single-componentsystems (using fitdist ) gave a mean = 0.0 and σ =0.16dex in log( r warm / au). This provided a measure of theinherent scatter around this trend for systems that likelyfollow the primordial snow line relation. The 1 σ regionaround the trend due to this scatter is depicted in Fig-ures 1 and 3 with thin dotted green lines.Figure 4 shows the distribution of the residual warmdust locations from the trend (green for the single-component systems, magenta for the two-component Figure 1 . Location of the warm dust vs, stellar massfor the 29 targets with a single dust component. Thedashed line shows the best fit trend log( r warm / au) =0 .
566 + 1 .
08 log( M (cid:63) /M (cid:12) ), equivalent to r warm / au =3 . M (cid:63) /M (cid:12) ) . . The thin dotted green lines show the mea-sured 1 σ scatter around the trend ( ± M (cid:63) and 5% uncertainty on r warm . systems). The distribution for the two-component sys-tems peaks around the trend (no residual). We note that32/54 = 59% of these systems fall within the 2 σ toler-ance of the trend for the systems with only warm com-ponents. The histogram shows for the two-componentsystems a decreasing tail of warm dust locations belowthe trend (negative residual), whereas the warm dustlocations above the trend show signs of a separate pop-ulation of systems. It may be that some warm com-ponents in two-component systems have locations setby the primordial snow line while others end up off ofthis trend for various reasons. Alternatively, these warmcomponents may have locations scattered more or lessrandomly, with some inevitably falling along the trendset by the single-component systems. In Section 4.2 wediscuss various possibilities for the nature of the warmcomponents in two-component systems. DISCUSSION4.1.
Effect of Model Assumptions
An essential feature of our analysis is that we comparethe trend of warm dust location with stellar mass tothe predicted trend for the two possible snow lines. Bycomparing trends rather than absolute values, we expectthat systematic errors due to our choice of model diskgeometry or grain composition (optical constants) will
Ballering et al. P r i m o r d i a l s no w li ne p r ed i c t i on C u rr en t s no w li ne p r ed i c t i on Figure 2 . Distribution of the results from the bootstrap pro-cedure to estimate the uncertainty on the power-law index( b ) of the observed r warm – M (cid:63) relation for systems with a sin-gle component. The distribution has mean = 1.08 and σ =0.21. The warm components are thus consistent with beingset by the primordial snow line and inconsistent with beingset by the current snow line. not influence our results. To test what role our modelassumptions may have had on our results, we repeatedour analysis twice with different assumptions. First, were-fit the SEDs with dust belts with fractional widths of0.4 r in (in contrast to the fixed belt width of 2 au we usedin the main analysis). The results agreed almost exactlywith those of our main analysis: the single-componentsystems showed a clear trend in belt location with stel-lar mass while the two-component systems showed con-siderable scatter. A bootstrap analysis of the single-component power-law index yielded b = 0 . ± .
19. Sec-ond, instead of using a mixture of astronomical silicatesand refractory organic material for the dust composi-tion, we used 100% astronomical silicates (a commonfirst-order assumption in debris disk modeling). Again,we found very similar results to our main analysis with b = 1 . ± .
21. The r warm vs, M (cid:63) trends for all threesets of model assumptions are plotted together in Fig-ure 5. Thus neither of these modifications to our modelassumptions affected our main conclusion that the lo-cations of single-component systems are consistent withthe primordial snow line and inconsistent with the cur-rent snow line.4.2. Origin of Warm Dust in Two-component Systems
We have shown that warm dust in the single-component systems resides at locations consistent withbeing set by the primordial snow line and not by thecurrent snow line, and thus is likely to originate from
Figure 3 . Location of the warm dust vs, stellar mass for the54 targets with two dust components. The dashed green lineis the best fit trend line derived for the single-componentsystems (as in Figure 1), which we found likely arise fromasteroid belts with locations set by the primordial snow line.The thin dotted green lines show the measured 1 σ scatteraround the trend ( ± M (cid:63) and 13% uncertainty on r warm . exo-asteroid belts. The warm dust locations in two-component systems are much more scattered, althoughmany reside at similar locations to those in single-component systems. Here we turn our attention to thenature of the two-component systems, especially thosewith warm components that clearly do not trace the pri-mordial snow line location.4.2.1. Planets?
Planets are known to sculpt debris disks, and the gapbetween warm and cold components is often ascribed tothe presence of planets. Thus the scatter in warm dustlocations may simply reflect a diversity in the locationsof planet formation or in their dynamical histories. Thewarm dust may arise from the in-situ collisional process-ing of a parent body belt, but its location may not beset by the primordial snow line. However, this scenariomakes no clear prediction as to why more scatter wouldarise in systems with two components than in systemswith only a warm component, unless systems withoutcold components also have fewer planets.4.2.2.
Inward Transport? arm Debris Disks -1 -0.5 0 0.5 100.10.20.30.40.50.60.70.80.91 Figure 4 . Distributions of the warm dust residual locationsfor the single-component (green) and two-component (ma-genta) systems around the trend line found for the single-component systems. The single-component systems show asymmetric distribution of residuals with mean = 0.0 and σ = 0.16 dex. A normal distribution with this mean and σ isshown in the dotted green line. All three curves are unitynormalized in order to better compare their shapes. Thetwo-component systems show a peak centered on the trend,a tail of systems below the trend, and a separate populationof systems above the trend. As discussed earlier, the inward transport of materialfrom the cold outer belt is expected to result in warmdust at a preferential location—the current snow line.However, these warm components span a large range oflocations for a given stellar mass, so it is unlikely thatinward transport could explain all of these systems.The specific scenario of inward transport by dragforces predicts that the warm component of draggedin material should be much fainter than the cold reser-voir from which it originates (Kennedy & Piette 2015).We looked for this in our sample by plotting the frac-tional luminosities of the warm and cold components—and their ratio—against the residual warm dust loca-tion from the trend (Figure 6). We found that the f warm / f cold ratio (bottom panel) is roughly the sameacross our entire sample (or perhaps somewhat larger insystems below the trend). Thus this provides no addi-tional support for the drag scenario. In fact, Geiler &Krivov (2017) found that the f warm / f cold ratios for two-component debris disks are consistent with the steady- Note from the top panel of Figure 6 that the two-componentsystems (magenta points) near the trend have a similar bright-ness distribution as the single-component systems (green points),furthering the notion that these warm components arise from acommon mechanism.
Figure 5 . Warm dust location vs, stellar mass for the 29single-component targets, with the warm dust location de-termined by three different models. The green circles assumethe dust is located in a 2 au wide ring and is composed of amixture of astronomical silicates and organic refractory ma-terial. These results were presented in Section 3 and are thesame points shown in Figure 1. The blue squares assumethe same dust composition, but use a ring width that scalesas 0.4 times the ring’s location. The red diamonds againassume 2 au wide belts, but change the composition of thedust to pure astronomical silicates. The results are nearlyidentical for the three different sets of model assumptions.Therefore our conclusion that these warm belts are likely setby the primordial snow line and not by the current snow lineis robust against our particular choice of model. state collisional evolution of inner and outer parent bodybelts originating from protoplanetary disks with reason-able radial density profiles.Later-type stars may be able to drag in a substantialamount of dust to generate a bright warm component iftheir luminosity is low enough such that no grains areblown out of the system by radiation pressure (thereis no a BOS ). Drag is also enhanced in later-type starsby stronger stellar winds. With no significant radiationforce on the grains, there would also be no pile-up ofgrains when the icy constituents sublimate, so the warmcomponents would not trace any snow line location. Thesystems in our sample, however, have sufficiently highluminosities that they should be able to blow grains out,so we deem this situation unlikely.4.2.3.
Contamination by Exozodiacal Dust?
The warm components that fall below the single-component trend line may result from exozodiacal dustcomponents, which are considered a separate compo-nent from the traditional warm components (Su & Rieke0
Ballering et al. σ below the trend (HIP 1481 and HIP 77432) show clearsilicate features in their best fit model spectra, and sil-icate features are a signature of exozodiacal dust (Bal-lering et al. 2014). Ballering et al. (2014) found that allsystems with exozodiacal dust also had outer compo-nents, consistent with finding such systems only in ourtwo-component sample.4.2.4. Contamination by Outer Dust?
For the systems with warm component locations abovethe trend, it is possible that we are not seeing dust in-terior to the cold component; rather, there is dust witha range of temperatures located within the outer belt.While Kennedy & Wyatt (2014) argued that this is notpossible for most two-component systems, HIP 95270(HD 181327), which we found to have r warm above thetrend, was an exception. In fact, Lebreton et al. (2012)fit the entire SED of this system with dust from a singleouter belt.It is also possible that we are seeing emission fromthe spatially unresolved blowout halo of small grains be-yond the cold parent body belt. These grains must besmall—potentially below the blowout size—so are warmdespite their large stellocentric distance. From the mid-dle panel of Figure 6 we see that the systems above thetrend tend to have slightly higher than average cold com-ponent fractional luminosities, consistent with systemscapable of generating large halos. Detailed studies ofdisks with such halos seen in resolved images have foundthat the halo component’s contribution to the SED of-ten peaks at wavelengths shorter than the cold compo-nent but longer than a typical warm component (as isseen for the systems above the trend in our sample), al-though this varies among specific systems. For example,the halo of γ Ophiuchi peaks at nearly the same wave-length as the cold belt (Figure 3 of Su et al. 2008), thehalo of HR 8799 peaks at a shorter wavelength (Figure9 of Su et al. 2009), and the halo of β Pictoris peaks atan even shorter wavelength (Figure 14 of Ballering et al.2016). In many systems with halos, the halo signal cansimply blend with the cold belt in the SED and be fit asa single component. This may be especially true if thereis also an inner warm component in the system. In fact,HR 8799 (HIP 114189) and γ Ophiuchi (HIP 87108)—which are known to have halos—are in our sample, buttheir halo components are not detected in our fittingseparately from their cold components.HIP 11847 (HD 15745), a system with a large r warm value, has a fan-shaped outer component detected inscattered light images out to 450 au (Kalas et al. 2007; Schneider et al. 2014), which may be a halo component,but no detailed models have shown that this halo couldgive rise to the warm part of the SED. Another such sys-tem, HIP 36948 (HD 61005 a.k.a. “The Moth”) is seenin scattered light to have an outer belt with wings of dustswept back due to interactions with the ISM (Hines et al.2007; Buenzli et al. 2010; Schneider et al. 2014). Studiesof this system’s SED that modeled the outer componentwith a modified blackbody (Kennedy & Wyatt 2010) orwith a narrow belt (Ricarte et al. 2013) found the warmcomponent to be fairly cold ( ∼ r warm found here (36.1 au). Incontrast, Olofsson et al. (2016) modeled this SED witha wide outer belt plus a fainter and hotter ( ∼
220 K)warm component. This suggests the outer belt com-prises grains with a range of temperatures, which ourmodel accounts for with a warm component placed farfrom the star. This system demonstrates how cold com-ponents can add uncertainty to the derived locations ofwarm components. 4.2.5.
Conclusion
We can identify no definitive source for the scatter inthe warm dust locations of these two-component sys-tems. Planets in a diversity of arrangements offer oneexplanation for this scatter. It may also be that thescatter arises from multiple mechanisms operating to-gether, potentially including exo-asteroid belts (as is fa-vored for the single-component systems), the presenceof exozodiacal dust, contamination from warm dust in ahalo or outer belt, and the inward transport of materialby comets (although we deem inward transport by dragforces to be unlikely). SUMMARY1. Warm components of debris disks have been ob-served in the spatially unresolved SEDs of manystars, but the nature and origin of the dust is notknown. There are two plausible hypotheses for itsorigin: the in-situ production of dust via collisionsin an asteroid belt-like population of parent bodyplanetesimals, or the inward transport of materialfrom an outer reservoir.2. The first hypothesis predicts the dust to be lo-cated at the primordial snow line, while the secondhypothesis predicts the dust to be located at thecurrent snow line. The location of the primordialsnow line follows a shallower power-law relationwith stellar mass than does the current snow line,providing a means to distinguish between the two.3. We located the warm dust in 83 debris disk sys-tems observed with
Spitzer /IRS (29 with a single arm Debris Disks -1 -0.5 0 0.5 110 -6 -4 -2 -1 -0.5 0 0.5 110 -6 -4 -2 -1 -0.5 0 0.5 110 -2 -1 Figure 6 . Fractional luminosity (brightness) of the warmcomponents (top panel) and the cold components (middlepanel), and the ratio of the two (bottom panel) vs, theresidual warm dust locations relative to the best fit trend.The single-component systems are in green and the two-component systems are in magenta. The systems above thetrend (on the right side of these plots) tend to have brighterthan average warm and cold components, but the ratio oftheir brightnesses are in line with the sample as a whole. warm component, 54 that also possess a cold com-ponent) by fitting model dust belt emission spectrato their SEDs.4. We found that the r warm – M (cid:63) trend for the single-component systems is consistent with the primor-dial snow line and not consistent with the currentsnow line. We thus favor the in-situ dust produc-tion scenario for these systems. Many of the two-component systems are also consistent with thisrelation. Hence we conclude that the collisionalprocessing of exo-asteroid belts is a common mech-anism to produce warm debris disk components.5. We are not able to definitively explain the scat-ter of warm component locations in the two-component systems. Warm planetesimal beltswith locations set by planets in a diversity of ar-chitectures offer a single mechanism to explain thescatter. Or the scatter could result from a mixtureof systems with warm dust locations set by the pri-mordial snow line and those set by other mecha-nisms. The inward transport of material by cometsremains possible in these systems, and could con-tribute to the scatter. Warm belts nearer the starthan the snow line may be exozodiacal dust, whilethose farther from the star may be warm dust co-located with the cold dust or even beyond the colddust in a halo component.We thank Glenn Schneider and the anonymous refereefor many helpful comments on this paper. This work isbased on observations made with the Spitzer Space Tele-scope , which is operated by the Jet Propulsion Labora-tory, California Institute of Technology, under a contractwith NASA.
Facility:
Spitzer (IRS, MIPS).
Software:
MATLAB, miex.REFERENCES
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Figure 7 . Best fits to the 29 single-component systems. Small black points are the IRS data (with gray error bars), large blackcircles are photometry data, and the black triangles are photometric upper limits. The stellar photosphere is cyan, the warmdust belt model is green, and the total model is red. arm Debris Disks Figure 7 . (continued). Ballering et al. HIP 89770 HIP 95560 HIP 106783 HIP 112542 HIP 115738
Figure 7 . (continued). arm Debris Disks Figure 8 . Best fits to the 54 two-component systems. Small black points are the IRS data (with gray error bars), large blackcircles are photometry data, and the black triangles are photometric upper limits. The stellar photosphere is cyan, the warmdust belt model is green, the cold component is blue, and the total model is red. Ballering et al.
Figure 8 . (continued). arm Debris Disks Figure 8 . (continued). Ballering et al.
Figure 8 . (continued). arm Debris Disks Figure 8 . (continued). Ballering et al.
Table 1 . Target Properties
HIP Spectral
V J H K T (cid:63) L (cid:63) M (cid:63) a BOS d IRS x LL1 x SL1 x SL2
Identifier Type (mag) (mag) (mag) (mag) (K) ( L (cid:12) ) ( M (cid:12) ) ( µ m) (pc) AORSingle-component SystemsHIP 9902 F8V 7.5 6.53 6.3 6.2 6294 1.62 1.12 1 44.17 13621504 1.00 1.07 1.05HIP 14684 G0V 8.49 7.16 6.79 6.7 5528 0.52 0.88 0.5 37.41 5340672 1.00 1.05 · · · HIP 17549 A0 6.88 6.88 6.93 6.92 9820 34.15 2.26 7.5 140.84 14148096 0.94 1.19 1.08HIP 18217 A5m 5.79 5.45 5.41 5.37 7667 9.73 1.69 3.1 50.51 14160640 1.00 1.02 1.02HIP 18481 A2Vn 6.08 5.97 5.97 5.92 8801 15.12 1.88 4.2 70.22 14139648 0.99 1.00 0.99HIP 23871 A5V 5.28 5.11 5.08 5.05 8308 20.32 2.01 5.2 58.11 14139904 1.00 1.10 1.06HIP 28103 a F2V 3.72 3.06 2.98 2.99 7207 7.88 1.60 2.7 14.88 15998720 1.00 1.04 1.04HIP 41967 G5V 8.26 7.05 6.77 6.7 5875 0.89 0.99 0.7 45.07 6596864 0.94 1.00 · · ·
HIP 49593 A7V 4.49 4.27 4.05 4 8000 9.98 1.70 3.2 28.24 14141184 1.00 1.06 1.00HIP 54879 A2V 3.32 3.12 3.19 3.08 8080 92.43 2.92 15.5 50.61 16177152 1.00 1.00 1.00HIP 57524 G4Vp 9.07 7.89 7.57 7.51 5887 1.75 1.14 1 91.66 21799936 1.00 1.06 1.04HIP 57950 F2IV/V 8.25 7.47 7.31 7.28 6730 3.89 1.37 1.7 98.14 21800448 0.95 1.12 1.10HIP 60348 F5V 8.78 7.95 7.76 7.67 6515 2.15 1.19 1.2 93.72 21801728 0.97 1.12 1.11HIP 62134 F2V 8.63 7.88 7.73 7.71 6658 3.79 1.36 1.7 115.61 21802240 1.00 1.12 1.11HIP 63836 F7 9 8.09 7.89 7.87 6501 2.41 1.23 1.3 107.41 21803008 1.00 1.13 1.11HIP 64053 B8V 5.7 5.82 5.75 5.73 9800 51.38 2.52 10.1 100.1 22803712 1.00 1.03 1.02HIP 64877 F5V 8.47 7.62 7.41 7.41 6448 5.21 1.47 2.1 125 22806784 1.00 1.07 1.07HIP 67068 F3V 8.46 7.69 7.52 7.47 6787 2.82 1.27 1.4 91.58 26361856 1.00 0.99 0.96HIP 67230 F5V 8.03 7.14 6.93 6.89 6476 8.73 1.65 2.9 131.75 22802688 1.00 1.08 1.08HIP 70455 B8V 6.96 7.03 7.07 7.08 10460 48.73 2.49 9.7 165.02 14148608 1.00 1.02 1.00HIP 74824 A3Va 4.07 3.93 3.81 3.88 7694 17.73 1.95 4.8 30.55 14141952 1.00 1.00 1.00HIP 77315 A0V 6.92 6.69 6.72 6.67 8880 34.38 2.27 7.6 147.28 22804992 1.00 1.05 1.05HIP 79710 F0V 8.4 7.77 7.65 7.61 7000 5.45 1.48 2.1 127.39 26314752 1.00 0.98 0.98HIP 79881 A0V 4.79 4.86 4.94 4.74 9210 17.93 1.95 4.7 41.29 21809408 1.00 1.09 1.15HIP 89770 F5 6.68 5.85 5.7 5.62 6599 4.87 1.44 2 53.22 15016960 1.00 1.00 0.99HIP 95560 A0V 5.59 5.58 5.63 5.61 8673 25.02 2.11 6.1 72.89 14143488 0.98 1.04 1.04HIP 106783 A2V 6.18 6.13 6.13 6.11 9158 22.37 2.05 5.6 87.64 21812992 0.99 1.10 1.11HIP 112542 B9V 5.68 5.75 5.79 5.73 10016 50.69 2.51 10 97.37 14144000 1.00 1.05 1.05HIP 115738 a A0p... 4.93 5.32 4.98 4.9 9836 37.72 2.34 8.1 47.06 14144256 0.99 1.11 1.05Two-component SystemsHIP 345 A0V 6.39 6.28 6.25 6.26 8936 37.34 2.31 8.1 124.84 12720128 1.00 1.01 1.01HIP 682 G2V 7.59 6.42 6.15 6.12 5962 1.23 1.05 0.8 39.08 5268736 1.00 1.07 · · ·
HIP 1473 A2V 4.52 4.34 4.42 4.46 9005 22.94 2.06 5.7 41.32 14160384 1.00 1.03 1.03HIP 1481 F9V 7.46 6.46 6.25 6.15 6273 1.50 1.10 0.9 41.55 21788160 0.97 1.07 1.06HIP 2472 a A0V 4.77 4.67 4.77 4.7 9459 29.59 2.22 6.7 52.97 21788672 1.00 1.09 1.11HIP 2710 F2 6.91 6.04 5.85 5.75 6400 2.28 1.21 1.2 40.92 25673472 1.02 · · · · · ·
HIP 7805 F2IV/V 7.61 6.84 6.69 6.63 6796 3.27 1.32 1.5 67.25 21789952 1.00 1.13 1.11HIP 7978 F9V 5.52 4.79 4.4 4.34 6000 1.58 1.11 1 17.43 16029952 0.99 1.05 1.06HIP 8241 A1V 5.04 4.99 5.03 4.96 9478 32.98 2.25 7.4 62.03 14139392 1.00 1.06 1.04HIP 11360 F2 6.8 6.03 5.86 5.82 6500 3.18 1.31 1.5 45.23 10885632 1.00 1.18 1.08HIP 11477 A2V 5.13 5.12 5.03 4.94 8900 16.00 1.90 4.4 46.6 21790976 1.00 1.11 1.13HIP 11847 F2V 7.49 6.7 6.61 6.55 6922 3.38 1.33 1.5 63.49 10886144 0.99 1.07 1.08HIP 13141 a A2V 5.26 5.14 5.16 4.97 8390 17.48 1.95 4.7 50.45 21791744 1.00 1.11 1.11
Table 1 continued arm Debris Disks Table 1 (continued)
HIP Spectral
V J H K T (cid:63) L (cid:63) M (cid:63) a BOS d IRS x LL1 x SL1 x SL2
Identifier Type (mag) (mag) (mag) (mag) (K) ( L (cid:12) ) ( M (cid:12) ) ( µ m) (pc) AORHIP 18859 a F5V 5.38 4.71 4.34 4.18 6315 2.59 1.24 1.3 18.83 5308416 1.00 1.01 · · ·
HIP 20901 A7V 5.01 4.79 4.66 4.53 7592 17.44 1.94 4.7 48.85 14146816 1.00 1.03 1.03HIP 22192 A3IV 6.18 5.8 5.73 5.71 7901 8.41 1.64 2.8 56.18 14147584 0.99 1.12 1.06HIP 22226 F3V 7.86 7.1 6.95 6.89 6828 3.75 1.36 1.7 80.26 10887424 1.02 1.16 1.12HIP 23451 A0 8.14 7.69 7.62 7.59 7957 6.19 1.53 2.3 112.36 15903744 1.00 1.05 1.12HIP 26453 F3V 7.25 6.47 6.29 6.28 6758 3.28 1.32 1.5 56.79 5306880 1.00 1.08 · · ·
HIP 26796 A0V 6.34 6.41 6.45 6.43 10130 69.94 2.72 12.6 153.61 12713728 0.99 1.10 1.07HIP 34276 A0V 6.52 6.49 6.48 6.48 9400 23.76 2.08 5.8 102.35 21795584 1.00 1.07 1.07HIP 36948 G8Vk+? 8.22 6.91 6.58 6.46 5598 0.61 0.91 0.5 35.35 5267200 1.00 1.02 · · ·
HIP 41152 A3V 5.54 5.25 5.29 5.25 8077 12.05 1.78 3.6 50.43 14140416 1.00 1.10 1.06HIP 41373 A0V 6.05 5.94 5.91 5.89 8839 15.39 1.88 4.3 69.44 14140672 1.00 1.12 1.08HIP 45167 A0V 6.14 6.14 6.16 6.12 9152 29.56 2.19 6.8 99.3 12710400 1.00 1.04 1.03HIP 47135 G2V 8.59 7.47 7.24 7.16 6050 1.46 1.09 0.9 67.98 25677056 1.00 · · · · · ·
HIP 48423 G5 7.73 6.47 6.14 6.09 5199 0.80 0.97 0.6 32.8 5399808 1.00 1.04 · · ·
HIP 55485 A7Vn 6.44 6.08 6.02 5.99 8000 13.83 1.84 4 80.84 14141440 1.00 1.08 1.03HIP 58720 B9V 5.88 6.01 6.08 6.08 10000 55.31 2.57 10.6 105.71 22799360 0.99 1.08 1.07HIP 60074 a G2V 7.07 5.87 5.61 5.54 5809 1.11 1.04 0.8 27.46 5312256 1.00 0.97 · · ·
HIP 61684 A9V 8.09 7.41 7.27 7.2 7000 5.84 1.51 2.2 111.86 22801152 1.00 1.06 1.04HIP 62657 F5/F6V 8.87 8 7.83 7.72 6417 2.65 1.25 1.3 108.58 13621248 1.01 1.06 1.05HIP 73145 A2IV 7.86 7.6 7.56 7.52 8281 8.75 1.65 2.9 122.7 22803968 0.98 1.14 1.15HIP 74499 F4V 8.74 7.88 7.73 7.65 6545 2.07 1.18 1.1 89.93 21806336 0.98 1.08 1.06HIP 75077 A1V 7.16 6.98 7.04 6.96 8599 18.99 1.98 5 131.58 14151168 1.00 0.97 0.97HIP 75210 B8/B9V 6.64 6.75 6.83 6.76 10642 45.95 2.45 9.3 136.24 14147840 1.00 1.05 1.04HIP 76736 A1V 6.42 6.3 6.34 6.27 8769 13.56 1.83 3.9 78.49 14142208 1.00 1.10 1.12HIP 77432 F5V 8.97 8.11 7.94 7.87 6594 1.98 1.17 1.1 96.34 21808128 1.00 1.12 1.10HIP 77464 A5IV 5.53 5.33 5.27 5.26 8248 14.07 1.84 4 54.02 14142720 1.00 1.03 1.03HIP 78043 F3V 8.95 8.15 7.97 7.94 6639 4.31 1.40 1.8 144.3 26313728 1.00 0.99 0.99HIP 78045 A3V 5.75 5.65 5.66 5.57 8777 17.84 1.95 4.7 66.01 14142464 1.00 1.05 1.05HIP 79516 F5V 8.9 8.02 7.85 7.79 6495 4.04 1.38 1.8 133.69 15554560 1.00 1.05 1.04HIP 79742 F5 9.16 8.28 8.06 8.06 6516 3.80 1.36 1.7 146.2 15555328 1.00 1.00 0.98HIP 83187 A5IV-V 5.65 5.32 5.26 5.19 7800 11.42 1.76 3.5 51.81 14160896 1.00 1.03 1.06HIP 85537 A8V 5.42 4.81 4.88 4.8 7201 18.72 1.97 5 59.63 27224064 1.00 0.98 1.00HIP 85922 A5V 5.62 5.25 5.25 5.14 7800 10.23 1.71 3.2 48.1 14142976 1.00 1.06 1.03HIP 87108 A0V 3.75 3.59 3.66 3.62 8517 25.39 2.11 6.1 31.52 4931328 1.00 1.01 1.02HIP 94114 A2Va 4.1 4.09 3.92 4.05 8400 26.47 2.13 6.3 38.43 14145536 1.00 1.05 1.01HIP 95270 F5/F6V 7.04 6.2 5.98 5.91 6502 3.34 1.32 1.5 51.81 3564032 1.00 1.05 1.07HIP 101612 a F0V 4.76 4.28 4.02 4.04 7233 8.10 1.61 2.8 27.79 21812224 1.00 1.10 1.11HIP 101800 A2V 5.43 5.41 5.37 5.3 9131 19.67 1.99 5.1 57.94 14161408 0.98 1.08 1.08HIP 106741 F4IV 7.17 6.38 6.25 6.18 6786 2.92 1.28 1.4 51.81 15022592 1.00 1.10 · · ·
HIP 114189 A5V 5.95 5.38 5.28 5.24 7033 4.82 1.44 2 39.4 28889856 1.00 1.00 1.00HIP 117452 a A0V 4.58 4.8 4.64 4.53 9673 33.98 2.29 7.4 42.14 14161664 1.00 1.02 1.02 a These targets not listed in McDonald et al. (2012); we inferred their stellar properties from their V − K color. Ballering et al.
Table 2 . Target Photometry
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)Single-component SystemsHIP 9902 24 46.67 ± Spitzer /MIPS Ballering et al. (2013)HIP 9902 70 < Spitzer /MIPS Ballering et al. (2013)HIP 9902 160 < Spitzer /MIPS Mo´or et al. (2009)HIP 14684 24 18.68 ± Spitzer /MIPS Ballering et al. (2013)HIP 14684 70 < Spitzer /MIPS Ballering et al. (2013)HIP 14684 1200 < < ± Spitzer /MIPS Ballering et al. (2013)HIP 17549 70 13.95 ± Spitzer /MIPS Ballering et al. (2013)HIP 18217 24 66.06 ± Spitzer /MIPS Ballering et al. (2013)HIP 18217 70 < Spitzer /MIPS Ballering et al. (2013)HIP 18481 24 45.72 ± Spitzer /MIPS Ballering et al. (2013)HIP 18481 70 < Spitzer /MIPS Ballering et al. (2013)HIP 23871 24 94.01 ± Spitzer /MIPS Ballering et al. (2013)HIP 23871 70 < Spitzer /MIPS Ballering et al. (2013)HIP 28103 24 558.50 ± Spitzer /MIPS Ballering et al. (2013)HIP 28103 70 95.96 ± Spitzer /MIPS Ballering et al. (2013)HIP 28103 100 45.46 ± Herschel /PACS Eiroa et al. (2013)HIP 28103 160 9.37 ± Herschel /PACS Eiroa et al. (2013)HIP 41967 24 18.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 41967 70 < Spitzer /MIPS Ballering et al. (2013)HIP 41967 350 < ± Spitzer /MIPS Ballering et al. (2013)HIP 49593 70 37.46 ± Spitzer /MIPS Ballering et al. (2013)HIP 49593 100 22.07 ± Herschel /PACS Thureau et al. (2014)HIP 49593 160 9.65 ± Herschel /PACS Thureau et al. (2014)HIP 54879 24 401.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 54879 70 66.17 ± Spitzer /MIPS Ballering et al. (2013)HIP 57524 24 11.11 ± Spitzer /MIPS Ballering et al. (2013)HIP 57524 70 < Spitzer /MIPS Ballering et al. (2013)HIP 57950 24 18.41 ± Spitzer /MIPS Ballering et al. (2013)HIP 57950 70 < Spitzer /MIPS Ballering et al. (2013)HIP 60348 24 12.18 ± Spitzer /MIPS Ballering et al. (2013)HIP 60348 70 < Spitzer /MIPS Ballering et al. (2013)HIP 62134 24 8.59 ± Spitzer /MIPS Ballering et al. (2013)HIP 62134 70 < Spitzer /MIPS Ballering et al. (2013)HIP 63836 24 8.40 ± Spitzer /MIPS Ballering et al. (2013)HIP 63836 70 < Spitzer /MIPS Ballering et al. (2013)HIP 64053 24 67.32 ± Spitzer /MIPS Ballering et al. (2013)HIP 64053 70 < Spitzer /MIPS Ballering et al. (2013)HIP 64877 24 22.88 ± Spitzer /MIPS Ballering et al. (2013)HIP 64877 70 < Spitzer /MIPS Ballering et al. (2013)HIP 67068 24 10.25 ± Spitzer /MIPS Ballering et al. (2013)HIP 67068 70 < Spitzer /MIPS Ballering et al. (2013)HIP 67230 24 44.09 ± Spitzer /MIPS Ballering et al. (2013)
Table 2 continued arm Debris Disks Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 67230 70 < Spitzer /MIPS Ballering et al. (2013)HIP 70455 24 24.25 ± Spitzer /MIPS Ballering et al. (2013)HIP 70455 70 < Spitzer /MIPS Ballering et al. (2013)HIP 74824 24 341.80 ± Spitzer /MIPS Ballering et al. (2013)HIP 74824 70 < Spitzer /MIPS Ballering et al. (2013)HIP 77315 24 59.61 ± Spitzer /MIPS Ballering et al. (2013)HIP 77315 70 22.92 ± Spitzer /MIPS Ballering et al. (2013)HIP 79710 24 18.61 ± Spitzer /MIPS Ballering et al. (2013)HIP 79710 70 < Spitzer /MIPS Ballering et al. (2013)HIP 79881 24 106.60 ± Spitzer /MIPS Ballering et al. (2013)HIP 79881 70 < Spitzer /MIPS Ballering et al. (2013)HIP 79881 70 13.00 ± Herschel /PACS Riviere-Marichalar et al. (2014)HIP 79881 100 < Herschel /PACS Riviere-Marichalar et al. (2014)HIP 79881 160 < Herschel /PACS Riviere-Marichalar et al. (2014)HIP 89770 24 90.66 ± Spitzer /MIPS Ballering et al. (2013)HIP 89770 70 16.99 ± Spitzer /MIPS Ballering et al. (2013)HIP 95560 24 61.84 ± Spitzer /MIPS Ballering et al. (2013)HIP 95560 70 < Spitzer /MIPS Ballering et al. (2013)HIP 106783 24 34.38 ± Spitzer /MIPS Ballering et al. (2013)HIP 106783 70 < Spitzer /MIPS Ballering et al. (2013)HIP 112542 24 63.92 ± Spitzer /MIPS Ballering et al. (2013)HIP 112542 70 39.17 ± Spitzer /MIPS Ballering et al. (2013)HIP 115738 24 111.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 115738 70 30.27 ± Spitzer /MIPS Ballering et al. (2013)Two-component SystemsHIP 345 24 35.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 345 70 97.24 ± Spitzer /MIPS Ballering et al. (2013)HIP 682 24 36.49 ± Spitzer /MIPS Ballering et al. (2013)HIP 682 70 170.60 ± Spitzer /MIPS Ballering et al. (2013)HIP 682 160 187.50 ± Spitzer /MIPS Hillenbrand et al. (2008)HIP 682 450 < ± ± ± < < < < ± Spitzer /MIPS Ballering et al. (2013)HIP 1473 70 43.82 ± Spitzer /MIPS Ballering et al. (2013)HIP 1473 100 25.48 ± Herschel /PACS Thureau et al. (2014)HIP 1473 160 12.94 ± Herschel /PACS Thureau et al. (2014)HIP 1481 24 34.76 ± Spitzer /MIPS Ballering et al. (2013)HIP 1481 70 < Spitzer /MIPS Ballering et al. (2013)HIP 1481 70 13.00 ± Herschel /PACS Donaldson et al. (2012)HIP 1481 160 < Herschel /PACS Donaldson et al. (2012)HIP 2472 24 112.50 ± Spitzer /MIPS Ballering et al. (2013)HIP 2472 70 76.98 ± Spitzer /MIPS Ballering et al. (2013)
Table 2 continued Ballering et al.
Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 2710 24 40.62 ± Spitzer /MIPS Ballering et al. (2013)HIP 2710 70 104.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 7805 24 28.74 ± Spitzer /MIPS Ballering et al. (2013)HIP 7805 70 136.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 7978 24 196.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 7978 70 1035.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 7978 70 896.20 ± Herschel /PACS Eiroa et al. (2013)HIP 7978 100 897.10 ± Herschel /PACS Eiroa et al. (2013)HIP 7978 160 635.90 ± Herschel /PACS Eiroa et al. (2013)HIP 7978 160 462.00 ± Spitzer /MIPS Tanner et al. (2009)HIP 7978 250 312.30 ± Herschel /SPIRE Eiroa et al. (2013)HIP 7978 350 179.90 ± Herschel /SPIRE Eiroa et al. (2013)HIP 7978 500 78.40 ± Herschel /SPIRE Eiroa et al. (2013)HIP 7978 870 39.40 ± < ± ± Spitzer /MIPS Ballering et al. (2013)HIP 8241 70 413.80 ± ± Herschel /PACS Mo´or et al. (2015b)HIP 8241 100 403.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 8241 160 277.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 8241 250 94.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 8241 350 43.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 8241 500 3.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 11360 24 60.87 ± Spitzer /MIPS Ballering et al. (2013)HIP 11360 70 454.30 ± Spitzer /MIPS Ballering et al. (2013)HIP 11360 90 427.00 ± ISO
Mo´or et al. (2006)HIP 11360 160 217.30 ± Spitzer /MIPS Mo´or et al. (2011)HIP 11360 450 < ± ± < ± ± ± Spitzer /MIPS Ballering et al. (2013)HIP 11477 70 114.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 11847 24 170.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 11847 70 722.90 ± Spitzer /MIPS Ballering et al. (2013)HIP 11847 90 515.00 ± ISO
Mo´or et al. (2006)HIP 11847 160 230.80 ± Spitzer /MIPS Mo´or et al. (2011)HIP 13141 24 86.22 ± Spitzer /MIPS Ballering et al. (2013)HIP 13141 70 197.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 13141 70 213.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 13141 100 210.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 13141 160 138.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 13141 250 50.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 13141 350 28.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 13141 500 < Herschel /SPIRE Mo´or et al. (2015b)HIP 18859 24 207.90 ± Spitzer /MIPS Ballering et al. (2013)HIP 18859 70 321.70 ± Spitzer /MIPS Ballering et al. (2013)
Table 2 continued arm Debris Disks Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 18859 90 242.00 ± ISO
Mo´or et al. (2006)HIP 18859 160 229.40 ± Spitzer /MIPS Hillenbrand et al. (2008)HIP 18859 870 < < < < < ± Spitzer /MIPS Ballering et al. (2013)HIP 20901 70 182.50 ± Spitzer /MIPS Ballering et al. (2013)HIP 22192 24 46.17 ± Spitzer /MIPS Ballering et al. (2013)HIP 22192 70 65.54 ± Spitzer /MIPS Ballering et al. (2013)HIP 22192 100 40.20 ± Herschel /PACS Draper et al. (2016a)HIP 22192 160 16.40 ± Herschel /PACS Draper et al. (2016a)HIP 22226 24 30.61 ± Spitzer /MIPS Ballering et al. (2013)HIP 22226 70 283.30 ± Spitzer /MIPS Ballering et al. (2013)HIP 22226 90 277.00 ± ISO
Mo´or et al. (2006)HIP 22226 160 120.30 ± Spitzer /MIPS Mo´or et al. (2011)HIP 22226 870 < ± Spitzer /MIPS Ballering et al. (2013)HIP 23451 70 1003.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 23451 70 1038.00 ± Herschel /PACS Donaldson et al. (2013)HIP 23451 100 770.00 ± Herschel /PACS Donaldson et al. (2013)HIP 23451 160 403.00 ± Herschel /PACS Donaldson et al. (2013)HIP 23451 160 < Spitzer /MIPS Maness et al. (2008)HIP 23451 250 153.00 ± Herschel /SPIRE Donaldson et al. (2013)HIP 23451 350 71.00 ± Herschel /SPIRE Donaldson et al. (2013)HIP 23451 500 45.00 ± Herschel /SPIRE Donaldson et al. (2013)HIP 23451 870 < ± ± ± ± Spitzer /MIPS Ballering et al. (2013)HIP 26453 70 123.30 ± Spitzer /MIPS Ballering et al. (2013)HIP 26453 160 < Spitzer /MIPS Hillenbrand et al. (2008)HIP 26453 1200 < ± Spitzer /MIPS Ballering et al. (2013)HIP 26796 70 47.92 ± Spitzer /MIPS Ballering et al. (2013)HIP 34276 24 34.76 ± Spitzer /MIPS Ballering et al. (2013)HIP 34276 70 348.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 34276 100 297.00 ± Herschel /PACS Vican et al. (2016)HIP 34276 160 200.00 ± Herschel /PACS Vican et al. (2016)HIP 36948 24 45.24 ± Spitzer /MIPS Ballering et al. (2013)HIP 36948 70 636.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 36948 160 502.60 ± Spitzer /MIPS Hillenbrand et al. (2008)HIP 36948 350 95.00 ± < < ± ± ± Table 2 continued Ballering et al.
Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 41152 24 83.30 ± Spitzer /MIPS Ballering et al. (2013)HIP 41152 70 209.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 41152 100 181.30 ± Herschel /PACS Morales et al. (2013)HIP 41152 160 106.70 ± Herschel /PACS Morales et al. (2013)HIP 41373 24 57.73 ± Spitzer /MIPS Ballering et al. (2013)HIP 41373 70 136.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 41373 100 120.50 ± Herschel /PACS Morales et al. (2013)HIP 41373 160 46.90 ± Herschel /PACS Morales et al. (2013)HIP 45167 24 46.51 ± Spitzer /MIPS Ballering et al. (2013)HIP 45167 70 82.85 ± Spitzer /MIPS Ballering et al. (2013)HIP 47135 24 12.93 ± Spitzer /MIPS Ballering et al. (2013)HIP 47135 70 36.90 ± Spitzer /MIPS Ballering et al. (2013)HIP 48423 24 36.88 ± Spitzer /MIPS Ballering et al. (2013)HIP 48423 70 33.09 ± Spitzer /MIPS Ballering et al. (2013)HIP 48423 160 < Spitzer /MIPS Hillenbrand et al. (2008)HIP 48423 350 < < ± Spitzer /MIPS Ballering et al. (2013)HIP 55485 70 34.45 ± Spitzer /MIPS Ballering et al. (2013)HIP 58720 24 111.70 ± Spitzer /MIPS Ballering et al. (2013)HIP 58720 70 98.53 ± Spitzer /MIPS Ballering et al. (2013)HIP 60074 24 62.86 ± Spitzer /MIPS Ballering et al. (2013)HIP 60074 70 782.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 60074 350 319.00 ± ± ± ± ± ± ± ± ± Spitzer /MIPS Ballering et al. (2013)HIP 61684 70 69.02 ± Spitzer /MIPS Ballering et al. (2013)HIP 62657 24 42.34 ± Spitzer /MIPS Ballering et al. (2013)HIP 62657 70 214.80 ± Spitzer /MIPS Ballering et al. (2013)HIP 62657 70 205.00 ± Herschel /PACS Draper et al. (2016b)HIP 62657 160 145.00 ± Herschel /PACS Draper et al. (2016b)HIP 62657 1240 1.29 ± ± Spitzer /MIPS Ballering et al. (2013)HIP 73145 70 659.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 73145 70 738.70 ± Herschel /PACS Mo´or et al. (2015a)HIP 73145 100 637.00 ± Herschel /PACS Mo´or et al. (2015a)HIP 73145 160 382.30 ± Herschel /PACS Mo´or et al. (2015a)HIP 73145 250 156.40 ± Herschel /SPIRE Mo´or et al. (2015a)HIP 73145 350 84.30 ± Herschel /SPIRE Mo´or et al. (2015a)HIP 73145 500 35.40 ± Herschel /SPIRE Mo´or et al. (2015a)HIP 73145 870 < ± ± Spitzer /MIPS Ballering et al. (2013)HIP 74499 70 118.80 ± Spitzer /MIPS Ballering et al. (2013)
Table 2 continued arm Debris Disks Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 75077 24 15.64 ± Spitzer /MIPS Ballering et al. (2013)HIP 75077 70 36.80 ± Spitzer /MIPS Ballering et al. (2013)HIP 75210 24 42.33 ± Spitzer /MIPS Ballering et al. (2013)HIP 75210 70 24.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 76736 24 80.67 ± Spitzer /MIPS Ballering et al. (2013)HIP 76736 70 560.60 ± Spitzer /MIPS Ballering et al. (2013)HIP 77432 24 10.28 ± Spitzer /MIPS Ballering et al. (2013)HIP 77432 70 < Spitzer /MIPS Ballering et al. (2013)HIP 77464 24 78.32 ± Spitzer /MIPS Ballering et al. (2013)HIP 77464 70 224.90 ± Spitzer /MIPS Ballering et al. (2013)HIP 78043 24 13.14 ± Spitzer /MIPS Ballering et al. (2013)HIP 78043 70 75.36 ± Spitzer /MIPS Ballering et al. (2013)HIP 78043 1240 0.34 ± ± Spitzer /MIPS Ballering et al. (2013)HIP 78045 70 42.95 ± Spitzer /MIPS Ballering et al. (2013)HIP 79516 24 52.88 ± Spitzer /MIPS Ballering et al. (2013)HIP 79516 70 317.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 79516 1240 1.85 ± ± Spitzer /MIPS Ballering et al. (2013)HIP 79742 70 173.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 79742 1240 0.88 ± ± Spitzer /MIPS Ballering et al. (2013)HIP 83187 70 156.60 ± Spitzer /MIPS Ballering et al. (2013)HIP 85537 24 103.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 85537 70 229.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 85537 70 230.00 ± Herschel /PACS Pascual et al. (2016)HIP 85537 160 150.00 ± Herschel /PACS Pascual et al. (2016)HIP 85537 1300 < ± Spitzer /MIPS Ballering et al. (2013)HIP 85922 70 35.21 ± Spitzer /MIPS Ballering et al. (2013)HIP 87108 24 434.10 ± Spitzer /MIPS Ballering et al. (2013)HIP 87108 70 1166.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 87108 70 1222.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 87108 100 1051.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 87108 160 587.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 87108 250 177.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 87108 350 98.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 87108 450 < ± Herschel /SPIRE Mo´or et al. (2015b)HIP 87108 850 6.40 ± < ± Spitzer /MIPS Ballering et al. (2013)HIP 94114 70 70.92 ± Spitzer /MIPS Ballering et al. (2013)HIP 95270 24 230.30 ± Spitzer /MIPS Ballering et al. (2013)HIP 95270 70 1776.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 95270 70 1827.00 ± Herschel /PACS Lebreton et al. (2012)HIP 95270 90 1410.00 ± ISO
Mo´or et al. (2006)HIP 95270 100 1337.00 ± Herschel /PACS Lebreton et al. (2012)HIP 95270 160 767.00 ± Herschel /PACS Lebreton et al. (2012)HIP 95270 160 770.00 ± Spitzer /MIPS Schneider et al. (2006)
Table 2 continued Ballering et al.
Table 2 (continued)
HIP λ F ν Instrument ReferencesIdentifier ( µ m) (mJy)HIP 95270 170 736.00 ± ISO
Mo´or et al. (2006)HIP 95270 870 51.70 ± ± ± ± ± Spitzer /MIPS Ballering et al. (2013)HIP 101612 70 654.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 101612 70 629.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 101612 100 607.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 101612 160 405.00 ± Herschel /PACS Mo´or et al. (2015b)HIP 101612 250 145.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 101612 350 70.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 101612 500 34.00 ± Herschel /SPIRE Mo´or et al. (2015b)HIP 101612 870 < ± Spitzer /MIPS Ballering et al. (2013)HIP 101800 70 73.32 ± Spitzer /MIPS Ballering et al. (2013)HIP 106741 24 31.58 ± Spitzer /MIPS Ballering et al. (2013)HIP 106741 70 217.20 ± Spitzer /MIPS Ballering et al. (2013)HIP 106741 160 185.60 ± Spitzer /MIPS Mo´or et al. (2011)HIP 106741 450 < ± ± Spitzer /MIPS Ballering et al. (2013)HIP 114189 70 610.00 ± Spitzer /MIPS Ballering et al. (2013)HIP 114189 70 537.00 ± Herschel /PACS Matthews et al. (2014a)HIP 114189 90 585.00 ± ISO
Mo´or et al. (2006)HIP 114189 100 687.00 ± Herschel /PACS Matthews et al. (2014a)HIP 114189 160 555.00 ± Spitzer /MIPS Su et al. (2009)HIP 114189 160 570.00 ± Herschel /PACS Matthews et al. (2014a)HIP 114189 250 309.00 ± Herschel /SPIRE Matthews et al. (2014a)HIP 114189 350 163.00 ± Herschel /SPIRE Matthews et al. (2014a)HIP 114189 500 < Herschel /SPIRE Matthews et al. (2014a)HIP 114189 850 10.30 ± ± ± Spitzer /MIPS Ballering et al. (2013)HIP 117452 70 54.80 ± Spitzer /MIPS Ballering et al. (2013)HIP 117452 100 28.89 ± Herschel /PACS Thureau et al. (2014)HIP 117452 160 < Herschel /PACS Thureau et al. (2014) arm Debris Disks Table 3 . Single-component Fit Results
HIP r warm M warm f warm c IRS
Identifier (au) ( × − M ⊕ ) ( × − )HIP 9902 2.80 0.64 17.00 0.90HIP 14684 4.40 0.25 5.21 0.92HIP 17549 10.00 11.02 8.33 0.92HIP 18217 4.50 0.48 2.65 0.95HIP 18481 3.30 0.51 3.85 0.99HIP 23871 7.00 1.55 2.84 0.91HIP 28103 12.20 0.79 0.82 0.92HIP 41967 3.30 0.20 5.21 0.95HIP 49593 5.10 0.50 2.18 0.94HIP 54879 16.20 4.94 0.98 0.98HIP 57524 4.50 0.78 9.64 0.92HIP 57950 6.20 2.10 10.16 0.98HIP 60348 7.10 2.51 12.24 0.86HIP 62134 5.60 0.72 4.15 0.92HIP 63836 3.70 0.60 8.53 0.87HIP 64053 5.30 3.09 6.03 0.93HIP 64877 8.10 8.03 20.97 0.93HIP 67068 2.60 0.23 5.46 0.99HIP 67230 9.90 19.87 28.98 0.92HIP 70455 8.10 5.87 5.55 0.97HIP 74824 7.40 2.35 4.13 0.93HIP 77315 10.20 22.31 16.16 0.89HIP 79710 3.80 2.04 19.73 1.00HIP 79881 8.40 0.75 1.05 0.90HIP 89770 6.00 3.22 14.85 0.93HIP 95560 7.20 1.93 3.02 0.93HIP 106783 10.70 2.19 1.76 0.92HIP 112542 16.40 12.76 3.21 0.92HIP 115738 9.10 1.72 1.47 0.91 Ballering et al.
Table 4 . Two-component Fit Results
HIP r warm M warm f warm T cold f cold λ ˜ β c IRS
Identifier (au) ( × − M ⊕ ) ( × − ) (K) ( × − )HIP 345 8.00 3.07 3.33 56.17 6.09 100.00 1.00 0.96HIP 682 5.40 0.59 6.18 51.03 35.47 160.00 0.55 0.92HIP 1473 6.00 0.44 0.99 101.53 0.56 237.43 0.11 0.91HIP 1481 1.10 0.04 4.91 109.63 4.14 100.00 1.00 0.93HIP 2472 16.20 3.09 1.02 60.06 0.84 100.00 1.00 0.91HIP 2710 4.30 0.16 1.84 51.24 11.48 100.00 1.00 0.93HIP 7805 5.40 0.95 6.42 57.67 29.95 100.00 1.00 0.92HIP 7978 36.00 16.09 4.23 46.37 24.06 70.00 0.56 0.98HIP 8241 6.80 1.37 2.10 57.79 7.85 154.25 1.20 0.94HIP 11360 7.10 1.28 5.34 53.79 45.39 61.15 0.61 0.99HIP 11477 7.30 0.76 1.44 80.76 3.15 100.00 1.00 0.89HIP 11847 22.60 160.33 77.27 67.11 120.06 71.42 0.80 0.91HIP 13141 8.50 0.66 0.90 54.75 4.68 136.05 0.93 0.91HIP 18859 1.70 0.06 2.76 71.68 7.92 196.02 0.76 0.96HIP 20901 11.30 2.53 2.08 62.27 3.71 100.00 1.00 0.91HIP 22192 6.30 0.40 1.32 64.70 3.63 71.42 1.12 0.94HIP 22226 26.50 37.44 12.21 58.26 74.10 83.52 0.82 0.88HIP 23451 3.20 5.29 62.92 80.41 482.70 87.34 0.47 0.96HIP 26453 5.80 1.40 8.31 77.43 24.96 100.00 1.00 0.91HIP 26796 6.70 3.00 3.39 85.61 3.55 100.00 1.00 0.92HIP 34276 12.00 1.78 1.13 61.12 25.38 71.42 0.16 1.01HIP 36948 36.10 67.92 28.64 49.64 203.69 50.00 0.41 0.93HIP 41152 9.30 1.89 2.63 57.42 6.61 71.42 0.41 0.91HIP 41373 3.70 0.50 3.05 73.92 8.80 100.77 0.97 0.89HIP 45167 4.00 1.16 4.69 70.44 4.96 100.00 1.00 0.94HIP 47135 17.50 3.81 4.32 45.97 16.51 100.00 1.00 0.93HIP 48423 3.20 0.18 5.68 73.49 7.84 100.00 1.00 0.96HIP 55485 3.70 0.65 4.23 75.57 2.82 100.00 1.00 0.94HIP 58720 4.50 2.87 7.19 112.95 7.08 100.00 1.00 0.91HIP 60074 8.70 1.05 4.74 48.28 93.20 349.97 0.71 1.02HIP 61684 5.90 4.16 18.42 86.22 34.30 100.00 1.00 0.91HIP 62657 20.70 125.70 79.60 56.17 135.68 166.66 0.69 0.86HIP 73145 6.10 22.66 77.36 71.83 240.75 194.79 0.73 1.00HIP 74499 5.80 2.27 16.80 66.50 83.11 100.00 1.00 0.92HIP 75077 6.70 1.00 2.01 50.94 4.88 100.00 1.00 1.01HIP 75210 3.20 1.60 7.62 120.77 3.52 100.00 1.00 0.91HIP 76736 7.30 4.41 8.96 59.72 41.58 100.00 1.00 0.98HIP 77432 0.50 0.03 8.68 101.45 13.05 100.00 1.00 0.91HIP 77464 6.70 0.71 1.67 60.46 7.54 100.00 1.00 0.97HIP 78043 6.00 2.78 13.70 60.41 62.21 114.20 0.70 1.02HIP 78045 2.90 0.26 2.25 91.42 2.03 100.00 1.00 0.96HIP 79516 24.90 295.37 104.69 57.92 205.73 50.53 0.48 0.91HIP 79742 14.30 37.63 39.87 69.64 182.99 62.45 0.42 0.96HIP 83187 17.30 5.02 2.25 53.10 5.01 100.00 1.00 0.95HIP 85537 22.80 10.67 2.25 44.59 5.73 93.26 0.98 0.95HIP 85922 1.50 0.14 4.11 99.92 1.77 100.00 1.00 0.93HIP 87108 12.90 5.04 2.72 64.53 7.75 127.18 1.01 0.98HIP 94114 5.70 0.91 2.12 77.84 0.41 100.00 1.00 0.96 Table 4 continued arm Debris Disks Table 4 (continued)
HIP r warm M warm f warm T cold f cold λ ˜ β c IRS
Identifier (au) ( × − M ⊕ ) ( × − ) (K) ( × −5