Populations of Young Stellar Objects in Nearby Molecular Clouds

3.4 MB·English

by  Tien-Hao Hsieh

PreprinttypesetusingLATEXstyleemulateapjv.5/2/11
POPULATIONS OF YOUNG STELLAR OBJECTS IN NEARBY MOLECULAR CLOUDS
Tien-Hao Hsieh and Shih-Ping Lai
InstituteofAstronomy,NationalTsing-HuaUniversity,Hsinchu30013,Taiwan
[email protected]
[email protected]
ABSTRACT
We develop a new method to identify YSOs from star-forming regions using the photometry data
from Spitzer’s c2d Legacy Project. The aim is to obtain YSO lists as complete as possible for study-
ing the statistical properties, such as Star Formation Rate (SFR) and lifetimes of YSOs in different
3
evolutionary stages. The largest obstacle for identifying YSOs comes from background galaxies with
1
similar SEDs to YSOs. Traditionally, selected color-color and color-magnitude criteria are used to
0
separateYSOsandgalaxies. However,sincethereisnoobviousboundarybetweenYSOsandgalaxies
2
inColor-ColorDiagrams(CCDs)andColor-MagnitudeDiagrams(CMDs),thosecriteriamayexclude
n faintYSOsneartheboundary. Inthispaper,weseparatetheYSOsandgalaxiesinmulti-dimensional
a
(Multi-D)magnitudespace,whichisequivalenttousingallvariationsofCMDssimultaneously. Com-
J
paring sources from molecular clouds to Spitzer’s SWIRE data, which have negligible amount of
3 YSOs, we can naturally identify YSO candidates locating outside of the galaxy populated regions
2
in the Multi-D space. In the five c2d-surveyed clouds, we select 322 new YSO candidates (YSOc),

miss/exclude 33 YSOc compared to Evans et al. (2009) and result in 1313 YSOc in total. As a re-
]
A sult, SFR increases 28% correspondingly, but the lifetimes of YSOs in different evolutionary stages
remain unchanged. Comparing to theories (Krumholz & McKee 2005), our derived SFR suggests
G
that star formation in large scale is dominated by supersonic turbulence rather than magnetic fields.
. Furthermore, we identify 7 new Very Low Luminosity Objects.
h
p Subject headings: ISM: clouds – Stars: luminosity function, mass function – Stars: pre-main sequence
- – Stars: protostars – brown dwarfs – Stars: formation
o
r
t
s 1. INTRODUCTION not been able to determine which is the dominated sup-
a portingforce. Krumholz&McKee(2005)andKrumholz
A full census of the Young Stellar Object (YSO) pop-
[ & Tan (2007) derive analytical relations between SFR
ulation in star-forming regions is essential for accurately
andviralparameter(theratioofkineticenergyandgrav-
1 calculatingstatisticalproperties,suchasStarFormation
itational energy), and show that SFR is linearly propor-
v Rate (SFR) and lifetimes of YSOs indifferent evolution-
6 arystages,whicharefundamentalparametersforassess- tional to viral parameter for magnetic field dominated
2 ingthephysicalmechanismofglobalstarformation. The scenario while SFR decreases exponentially versus viral
4 idea that SFR should be related to gas surface density parameter for turbulence dominated scenario. Here we
5 was first proposed by Schmidt (1958) and the relation is attempt to obtain SFR as accurate as possible by devel-
. oping a new YSO census method. In addition, a better
1 measuredinagalaxysamplebyKennicutt(1998),which
census method will provide better estimates in the life-
0 isknownasKennicut–Schmidtrelation. Toexaminethis
timesofYSOsindifferentevolutionarystages,whichare
3 relationinourGalaxy,Evansetal.(2009)countthenum-
usually estimated by comparing the fractions of YSOs
1 bers of YSOs in Giant Molecular Clouds (GMCs), and
in each stage, and successful theories should be able to
: assume a mean mass and formation timescale for these
v explain the observed YSO lifetime scales.
YSOs. They find that their measured SFRs are higher
i Spitzer Space Telescope (Werner et al. 2004) provides
X than that indicated by the Kennicut–Schmidt relation,
high sensitivity surveys with the Infrared Array Cam-
and claim that is because the Kennicut–Schmidt rela-
r
era (IRAC) at 3.6, 4.5, 5.8, and 8.0 µm (Fazio et al.
a tion applies to average over much larger regions than
2004) and the Multiband Imaging Photometer (MIPS)
individual clouds. Heiderman et al. (2010) extend the
at24,70,and160µm(Riekeetal.2004),whichallowus
sample and obtain a similar result to that from Evans
to search for YSOs in star-forming regions. Identifying
et al. (2009). In addition, Lada et al. (2010) find that
YSOs is usually achieved by removing stars and back-
the SFR is linearly proportional to the mass of cloud
ground galaxies in the data (Young et al. 2005; Harvey
above an extinction threshold and this relation shows
et al. 2007a; Rebull et al. 2007, 2010). Stars can be eas-
excellent agreement between galactic and extragalactic
ily selected by fitting SEDs with reddened stellar atmo-
star-forming activity.
sphere. However, the SED morphologies of galaxies are
However, what physical mechanism determines the
very similar to those of YSOs at infrared wavelengths
SFR is still unclear. It has long been known that the
(Harvey et al. 2006), and Spitzer can detect substan-
observed SFR is too low for a GMC collapsing on its
tial amount of background galaxies because of its high
free-falltimescalewithoutsupportagainstgravity(Zuck-
sensitivity. Therefore, separating background galaxies
erman & Evans 1974). Supporting force such as super-
and YSOs becomes a difficult problem. Several methods
sonicturbulenceand/ormagneticfieldshasbeenusedto
havebeendevelopedtoidentifyYSOsfromthemolecular
explain the low SFR; however, observational data have
2
clouds (Harvey et al. 2007a; Gutermuth et al. 2005; Re- We treat the number distribution of our galaxy sample
bulletal.2010)usingSpitzerdata. Thesemethodsiden- inMulti-Dmagnitudespaceasgalaxyprobabilitydistri-
tify YSOs by eliminating the background galaxies based butionandsourceslocatedintheregionwithoutgalaxies
oncomparingthedistributionsoftheobserveddatawith areclassifiedasYSOc. Weapplythismethodtothefive
galaxydatainColor-ColorDiagrams(CCDs)andColor- c2d-surveyed clouds and the number of YSOc sample is
Magnitude Diagrams (CMDs). However, there are no increasedbyafactorof28%. Therefore,theSFRwecal-
obviousboundariesbetweenYSOsandgalaxiesinCCDs culated in this work are 28% larger than that calculated
andCMDs. Hence,basedondifferentconsiderations,dif- by Evans et al. (2009) due to the increased number of
ferent works use different sets of CCDs and CMDs, and YSOc, but the life times of YSOs in different evolution-
settheirownboundariesinCCDsandCMDstoidentify ary stages remain unchanged due to the similar ratios
YSOs. Therefore,sourceslocatedclosetotheboundaries between different stages of newly found YSOc. We de-
are possibly classified as different objects using different scribethedataweusedin§2andourYSOidentification
methods. method in §3. In §4, we discuss how reliable our new
A large YSO survey toward five nearby molecular YSOc are. The statistical analysis and discussion are
clouds has been done by the Spitzer Legacy Project presented in §5 and the conclusions are summarized in
“From Molecular Cores to Planet Forming Disk” (c2d; §6.
Evans et al. 2003). The c2d project used an unnormal-
izedgalaxyprobability(Harveyetal.2007a)toeliminate 2. DATA
possible galaxies and identify YSO candidates (YSOc)
The data we used in this paper are from two Spitzer
from the five clouds. (In the paper, YSOc is used for
Legacy Projects, c2d and SWIRE. c2d project observed
sourcesselectedbyourmethodorthec2dproject,which
five nearby molecular clouds and SWIRE project ob-
may or may not be confirmed as true YSOs by other
servedextragalacticfieldsthatcontainnegligibleamount
studies.) This galaxy probability is primarily calculated
ofYSOs. OuraimistoidentifyYSOsfromthec2ddata
from the locations of sources in three CMDs by com-
by comparing them to SWIRE data in Multi-D magni-
paring to the Spitzer Wide-Area Infrared Extragalactic
tude space.
Survey(SWIRE)data(Lonsdaleetal.2003),whichcon-
tainnegligibleamountofYSOsbecausetheobservations
2.1. c2d Data
were towards high galactic latitude. Since this galaxy
probability was calculated only for sources detected in Perseus, Serpens, Ophiuchus, Lupus (I, III and IV)
all IRAC and MIPS1 (24 µm), the c2d YSO catalog andChamaeleonIIhavebeenobservedbyc2dprojectin
may miss faint YSOs which are undetected at one or IRAC1–4(3.6,4.5,5.8and8.0µm)andMIPS1–3(24,70
more of the five bands. In this paper, we develop a new and160µm)bands. Hereafter,weuseIRandMPtorep-
YSO identification method and apply it to the five c2d- resentIRACandMIPS,e.g.,IR1asIRAC1. Alldataare
surveyed molecular clouds, providing a more complete processed with the c2d standard pipeline and these pro-
YSO catalog in these clouds. cesses are described in the c2d data delivery document
With a relatively complete YSO sample, we will be indetail(Evansetal.2007). Thepipelineextractspoint
abletoidentifymoreVeryLowLuminosityObjects(VeL- sources from images at all bands and performs photo-
LOs), which are defined as protostars with internal lu- metric measurements. The Spitzer photometry data are
minosity, L , smaller than 0.1 L . The first VeLLO, merged with 2 Micron All-Sky Survey (2MASS) data (J,
int (cid:12)
L1014-IRS, was observed by Young et al. (2004). Dun- H, K bands) into source catalogs using a 2(cid:48)(cid:48) matching
ham et al. (2008) then design a set of color criteria and radius. These source catalogs and images are released
also use the c2d galaxy probability to select faint Class to Spitzer Heritage Archive. Detail studies of individual
0 and early Class I sources as VeLLO candidates. The cloudsusingthesecatalogshavebeenpresentedinseveral
faint nature of VeLLOs suggests that they could be ei- papers (e.g., Perseus: Jørgensen et al. 2006; Serpens:
ther very young protostars or very low mass protostars, Harvey et al. 2007a; Ophiuchus: Padgett et al. 2008;
but recent works have shown that the low luminosity Lupus: Mer´ın et al. 2008; Chamaeleon II: Porras et al.
can be explained by protostars in the quiescent phase of 2007). Two kinds of catalogs, FULL and HREL (High
episodic accretion processes (Dunham et al. 2010). To Reliability), are provided by the c2d project. FULL cat-
identify more VeLLOs, Dunham et al. (2008) use the alogsincludealldetectedsourcesbutsomeofthemcould
galaxy probability from c2d project but adopt a higher befakedetectionmisidentifiedbyautomatedphotometry
cutoff,whichallowstofindmorefaintsources;thegalaxy processes. SourcesinHRELcatalogshavetobedetected
probability are calculated based on the general fact that in one band with S/N > 7 accompanied by a detection
the galaxies are faint (Dunham et al. 2008; Harvey et in another band with S/N > 5. In this paper, we select
al. 2007a). However, the change of cutoff may result in YSOc only from HREL catalogs in order to avoid fake
higher galaxy contamination and more analysis are es- sources.
sential for confirming those VeLLOs of Dunham et al. We adopt the photometry results from the c2d cat-
(2008). Therefore, a method that identifies faint YSOc alogs for our analysis. Source flux in the c2d catalogs
naturally may help to reduce the galaxy contamination. are measured with Point Spread Function (PSF) fitting.
We develop a new method, multi-dimensional (Multi- An important factor, image type (imtype), is used to
D) method to identify YSOs naturally. While a CMD indicate the quality of PSF fitting. Sources can be well
(magA,magB-magC)containsonlyinformationfromthe fitted with PSF are assigned imtype = 1 and sources
three consisting magnitudes, Multi-D magnitude space with imtype (cid:54)= 1 have high uncertainty in photometry
containsalltheinformationoftheconsistingmagnitudes. resulting from low S/N ratio, extended size, or non-
circular shape, etc (Evans et al. 2007). For example,
3
Fig. 2.— The giant star selection criteria. Green points are
theSWIREdataexcludingstarsandredpointsaregiantstarsin
SerpensidentifiedbyOliveiraetal.(2009). Sourceslocatedinthe
regionbelowthedashedlineareclassifiedasgiantstars.
vey et al. (2007b) and Evans et al. (2007), which se-
lect stars with reddened stellar atmosphere SED tem-
Fig. 1.— The YSO identification process. Sources is consistent plates. The extinction law with RV = 5.5 from Wein-
withthecriteriaintheblockwillentertheblockconnectedby“Y” gartner & Draine (2001) is used to obtain the best
arrowand“N”forinconsistent. fit extinction value, A . Weingartner and Draine R
V V
= 5.5 model is suggested to be a good description
imtype = 7 indicates that the S/N ratio is too low
for dusts in dense molecular clouds (Chapman et al.
to be tested for shape and we found that it is mostly
2009).
due to the confusion from surrounding cloud structure.
Therefore, we will check the images of sources with 2. Since our galaxy sample (SWIRE) is observed toward
imtype (cid:54)=1 to determine whether they are real sources regionswithnegligibleextinctions,wede-reddenedthe
(see §3). whole c2d catalog in order to compare it with our
galaxy sample. We use the same computer program
developed by the c2d project (Evans et al. 2007) to
2.2. SWIRE Data
construct the extinction maps from the extinction of
The SWIRE data used in this work are the same data background stars. The average errors of all pixels in
used by Harvey et al. (2007a), which was processed with eachextinctionmapare0.03–0.05magnitudeforeach
c2d pipeline to eliminate the systematic uncertainty in- cloud. Sourcesinthec2dcatalogarethende-reddened
troducedfromdifferentdatareductionprocesses. There- according to the extinction value of its position in the
fore, it can be used to compare with the c2d data fairly. map. We use the de-reddened fluxes in this paper ex-
The SWIRE data used here are the observation toward cept for the analysis with α (section 5.3.2).
ELAIS N1 extragalactic field which is at high latitudes
(b∼44◦)andtheobservedfieldhascoverageof5.3deg2 3. Giant stars were removed using a CCD, IR2 – IR3
versus IR3 – MP1 (Fig. 2), as described in §3.3.
usingbothIRACandMIPS.SincetheYSOsareconcen-
trated in the galactic plane, there should be almost no
4. We found that some sources labeled as “U” (unde-
YSOsintheSWIREregion. Inordertoacquireagalaxy
tected) for MP1 in the c2d catalog are in fact satu-
sample as pure as possible, we removed the stars in the
rated. Since bright MP1 flux will make the source far
SWIREdatausingthesamemethodtothatforc2ddata
awayfromthegalaxypopulatedregionintheMulti-D
(§3.2). Thus, it can be used to represent a full collection
array, we believe these sources are very young YSOs
of the SED features of background galaxies.
rather than galaxies. Therefore, we classify these
MP1 saturated sources as YSOc. To find the satu-
3. YSO IDENTIFICATION
rated sources, we search all sources labeled as “U” at
3.1. Selection Process MP1withpixelvaluelargerthan800MJysr−1 within
Our YSO identification procedure is shown in Fig. 1 7.66(cid:48)(cid:48)radius. The cutoff 800 MJy sr−1 is determined
and described below. Basically, this process identifies from experience by checking the pixel values around
YSOc by eliminating the non-YSO sources. many saturated sources, and we found that it is small
enoughforustopickupallsaturatedsourcesandalso
not too small for us to obtain too many fake sources.
1. The main sequence stars were first removed by SED The 7.66(cid:48)(cid:48) radius is the 3σ radius of MP1 PSF. Then,
fitting. ThefittingprocessisthesameasthatinHar- we examine the images of selected sources by eyes to
4
confirmwhethertheyareindeedsaturatedsourcesand allIR1–4andMP1bands(correspondingtoGalprob2).
theexcessbrightfluxisindeedfromasaturatedsource MP2andMP3bandsareexcludedduetotheirlowdetec-
but not from a bad pixel. The typical images of sat- tionnumbersandtheirpoorangularresolutions(18(cid:48)(cid:48)and
urated sources are PSF-like with saturated holes (no 40(cid:48)(cid:48), respectively). The J–MP1 array covers the widest
valuesorlowvalues)incentersandbrightringsaround rangeofwavelengthsandtheIR1–MP1arrayisdesigned
the centers. forselectingYSOcthathaveno2MASSdetections. The
IR1–MP1arrayisnecessaryforidentifyingyoungembed-
5. To eliminate background galaxies, many Multi-D ar-
ded YSOc since they are very faint at short wavelengths
rays are constructed for the SWIRE data to calculate
and are often undetected in 2MASS bands.
the smoothed galaxy density. According to the loca-
BecausethegalaxylocationsarediscreteintheMulti-
tion of the c2d sources in the Multi-D arrays, we cal-
D arrays, we need to carefully choose the cell size and
culate four galaxy probabilities: Gal prob 1, Gal prob
also employ a smoothing process to produce a smoothed
2,Galprob1pandGalprob2p,whereGalprob1and
galaxy distribution, so that the galaxies fall in between
Gal prob 2 are the galaxy density in (J, Ks, IR2, IR4
SWIRE sources will not be classified as YSOc. We find
and MP1) and (IR1, IR2, IR3, IR4 and MP1) arrays
thatusingcellsizeof0.2magforallbandsisadequatefor
or their subarrays (see §3.2 for detail), and Gal prob
our data. We smooth the data using Multi-D Gaussian
1pandGalprob2parethegalaxydensityfromthear-
beams with a peak value of 1, a standard deviation σ =
rays discounting the bands with imtype (cid:54)= 1. Sources
2 cells (0.4 magnitude) in every dimension and an outer
withbothGalprob1and2≥1areclassifiedasgalaxy
cutoff at radius of 7 cells. The galaxy probability for a
candidates and removed.
sourcelocatedat(x ,x ,x ,x ,x )isdefinedasthetotal
1 2 3 4 5
numberofgalaxies,A(x ,x ,x ,x ,x ),aftersmoothed
6. The remaining sources are with Gal prob 1 < 1 or 1 2 3 4 5
with Multi-D Gaussian beams,
Gal prob 2 < 1, thus are potential YSOc. Images of
sources with Gal prob 1 (2) < 1 but Gal prob 1p (2p)
≥ 1 (meaning an unreliable detection would affect its Asmooth(x1,x2,x3,x4,x5)=
gwailtahyupnrroebliaabbilleitpyh)owtoilml beteryexsaumchinaesdjebtykenyoetss. oSroculrocueds x(cid:48)1=(cid:88)x1+7 x(cid:48)2=(cid:88)x2−7 x(cid:48)3=(cid:88)x3+7 x(cid:48)4=(cid:88)x4+7 x(cid:48)5=(cid:88)x5+7
structures are removed (see §3.2). x(cid:48)1=x1−7 x(cid:48)2=x2−7 x(cid:48)3=x3−7 x(cid:48)4=x4−7 x(cid:48)5=x5−7
7. For all the remaining sources, we also check the IR1 A(x(cid:48)1,x(cid:48)2,x(cid:48)3,x(cid:48)4,x(cid:48)5)e((x12−σx(cid:48)1)2+(x22−σx(cid:48)2)2+(x32−σx(cid:48)3)2+(x42−σx(cid:48)4)2+(x52−σx(cid:48)5)2),
icmanagbeestboreiglihmtinanatdetthhuesnewairtbhylroewsoglvaeldaxgyalpaxroiebsawbihliitcyh. (x1−x(cid:48)1)2+(x2−x(cid:48)2)2+(x3−x(cid:48)3)2+(x4−x(cid:48)4)2+(x5−x(cid:48)5)2≤49.
(1)
Eleven nearby galaxies are removed through this pro-
cess. Therefore, Gal prob 1 = Asmooth (J, Ks, IR2, IR4 and
MP1) and Gal prob 2 = A (IR1, IR2, IR3, IR4
smooth
8. The remaining sources are classified as YSOc. and MP1). If a galaxy is surrounded by other galaxies,
the galaxy’ galaxy probability should be larger than the
3.2. Multi-D Array Construction and Galaxy
peak value of smoothing beam, which is 1, because the
Probability Calculation
smoothing process will accumulate the number of galax-
The heart and soul of our new method is to use the ies from nearby cells. Therefore, the surface with galaxy
galaxy density in the Multi-D space as an indicator of probability = 1 is expected to enclose the galaxy popu-
the “galaxy probability”; anything outside of the galaxy lated region in Multi-D magnitude space except isolated
populatedregionwouldhavegalaxydensitysmallerthan galaxies. For an isolated galaxy, its galaxy probability
1 and thus will be selected as YSOc. We assume that will be one. Out of 135400 SWIRE galaxies, there are
SWIREdataislargeenoughtocontainacompletesetof only three galaxies with Gal prob 1 equal to one and no
galaxy sample. Note that our “galaxy probability” here galaxies with Gal prob 2 equal to one, which indicates
is not a real probability for a source to be a galaxy, it that the galaxy sample is well smoothed in the Multi-D
is in fact an unnormalized number which indicates how arrays. Therefore, sources located out of the smoothed
many galaxies near a specific position in Multi-D space. galaxy distribution will have galaxy probability < 1 and
In this section, we describe the details for constructing can be classified as YSOc.
Multi-D space and defining the galaxy probabilities. Since a large number of sources are not detected in
The data we used contain ten bands (J, H, Ks, IR1, all five bands of these two 5-D arrays, for these sources,
IR2, IR3, IR4, MP1, MP2 and MP3). Although a high- Gal prob 1 and Gal prob 2 are calculated from 3- and
dimensional array includes more information than that 4-D subarrays instead. Thus, sources with detections in
of a low-dimensional array, the maximum dimension of three to five bands of the five bands for Gal prob 1 are
thedataarraywecanhandleislimitedbyourcomputing assignedGalprob1calculatedfrom3-to5-Dmagnitude
resource. If we want to calculate the galaxy probability arrays, respectively, and same as Gal prob 2 (the detec-
from all 10-band data using a 10-D array, our computer tionthresholdissettoS/N=2). Asaresult,15subarrays
will need random access memory of ∼108 G byte (with (five 4-D and ten 3-D subarrays) for each 5-D array are
thecell sizeswechoose, seebelow), which largelyexceed constructed. The array cell size and/or the smoothing
the limitation of our computer. Therefore, we choose to lengths of the subarrays need to be modified, because
analyze our data with two 5-D main arrays construct- the galaxy distribution is condensed from 5-D to 3- or
ing from the bands that can represent the main SED 4-D and thus the number of galaxies in 3-D or 4-D array
features; one contains J, Ks, IR2, IR4 and MP1 bands cell are much larger than that in 5-D array cell. How-
(corresponding to Gal prob 1) and the other contains ever, it is very difficult to make the galaxy probability
5
1 make the sources deviated from the galaxy populated
region. For these sources, images of IR1–IR4 and MP1
bands are examined with eyes. If a reliable point source
is found in the images at bands with imtype (cid:54)= 1, the
Gal prob 1 (or 2) calculated containing such bands is re-
liable and the sources are classified as a YSOc. A large
amount of examined sources were found to have unre-
liable photometry measurement (70%–90% for different
clouds) due to jet knots, nearby bright sources and ex-
tended cloud structure, and are removed from our YSOc
list.
3.3. Giant Star Contamination
Giant stars could be bright and have similar SEDs to
Class II/III YSO in infrared (Oliveira et al. 2009), thus
are very difficult to be removed only using IRAC and
MIPS data. We are not able to exclude the giant stars
by finding the natural boundary of them in the Multi-D
array,becausethegiantstarsampleistoosmalltorepre-
sent a full collection of the SED features. From SWIRE
Fig. 3.— Reducingbeamsizefrom2-Dto1-D.Thetwocircles
indicatethebeamsizein2-Dandthetwohalf-ellipsesindicatethe data, we found that there are 30 bright sources that are
projected beam size in 1-D. The “galaxy probability” along the not group with the majority of the SWIRE sources in
dashed line, diagonal direction, in 2-D will be the same to that the(IR2–IR3,IR3–MP1)CCD.Becausewethinkthat
(cid:112)
alongx-axisin1-Difareducingbeamlengthof 1/2σisapplied. SWIREdataonlycontaingalaxies, main-sequencestars,
and giant stars, we suspect that these 30 bright sources
of each source be the same in different dimensions and aregiantstars. Therefore,wedefine3criteriatoexclude
fairly compare them in different dimensions. Therefore, the giant stars from the c2d data: IR3 – MP1 < 2, IR2
we modified the smoothing beam to make the threshold – IR3 > IR3 – MP1 and IR2 – IR3 > 0 (Fig. 2). The
of galaxy probability, 1, a reasonable number for sep- numbers of giant stars selected from these criteria are 9,
arating YSOc and galaxies in arrays with different di- 8, 5, 2 and 1 in Serpens, Ophiuchus, Lupus, Perseus and
mensions. Here we demonstrate the principle of beam Chamaeleon II, respectively. However, these criteria do
size modification in the case of 2-D to 1-D. We assume not remove 17 sources in Serpens which have been iden-
that there are very few galaxies close to the surface of tified to be giant stars by Oliveira et al. (2009) based on
galaxy populated region where the galaxy probability is the optical spectrum. Because the locations of these 17
∼1. Considering two galaxies locating near the surface sources in Fig. 2 are closer to the SWIRE sources than
andalongthediagonaldirection,wefindthatthegalaxy our selected giant stars, we suspect that these spectro-
probability profile in 1-D is exactly the same as that in scopically identified giant stars may have small disk or
2-D along the diagonal direction if we reduce the stan- dustyenvelopesthatincreasetheirIR3–MP1color. We
dard deviation of Gaussian beam in 2-D by a factor of laterremovedthese17sourcesfromourYSOclist. Since
(cid:112) we did not find any spectroscopic data for giant stars in
1/2 (Fig. 3).
(cid:112) Perseus, Ophiuchus, Lupus and Chamaeleon II, we are
Therefore, weusethestandarddeviationσ of 3/5×
not able to remove spectroscopic identified giant stars in
2cells(theσin5-Darrayis2cells)forthe3-Darraysand
these clouds. However, because Serpens molecular cloud
(cid:112)
4/5 × 2 cells for the 4-D arrays, and we keep the cell is located in the direction closed to the Galactic plane
size unchanged. Such modification will produce similar while other clouds are not, Serpens has the most serious
galaxyprobabilitydistributionintheregionsclosetothe giant star contamination among these clouds. Thus, the
boundary of galaxy populated regions for arrays with giant star contamination should be much smaller in the
different dimensions, thus the same threshold of galaxy other four clouds.
probability (i.e., 1) can be used to identify YSOc.
We found that a lot of sources with PSF fitting flag 3.4. Advantages of Multi-D Method
“imtype (cid:54)= 1” (§2.1) have contaminated flux measure-
Using the Multi-D space as a whole instead of using
ments due to jet knots, nearby bright sources and ex-
limited numbers of selected CMDs to identify YSOs has
tended cloud structure, which may change the locations
the following advantages:
ofthesourcesintheMulti-Darrays. Thus,wedefineGal
prob 1p and Gal prob 2p which are calculated from sub- 1. Avoidance of selecting specific CMDs. In order to
arrays only using the detections with imtype = 1 (well- identify YSOs in molecular clouds, previous works
fitted as a point source), and use them along with Gal adopteddifferentCMDsastheirselectioncriteria. Al-
prob 1 and Gal prob 2 for separating YSOs and galax- though these CMDs are all selected with justifiable
ies. Sinceimtype=1indicatesthatthesourcehasamore reasons, it is difficult to argue that which CMD sepa-
precise flux measurement, source with Gal prob 1p (or ratesYSOsandgalaxiesbestandhowmanyCMDsare
2p)<1impliesthatthissourcelocatesoutofthegalaxy sufficient for the identification. The various selections
populated region in Multi-D space with high confidence. of CMDs in use make the YSO identification results
If a source has Gal prob 1 (or 2) < 1 but Gal prob 1p differentbydifferentworks. SinceMulti-Dmagnitude
(or 2p) > 1, it is obvious that the bands with imtype (cid:54)= space includes the information from all the possible
6
Fig. 4.— All variations of MMDs consisting any two bands of IR1, IR2, IR3, IR4, and MP1 for Perseus. Red, blue and green points
indicatethe“Same”YSOc,newlyidentifiedYSOcandSWIREgalaxies,respectively. Thearrowsrepresentthediagonaldirectionwhichis
relatedtosourcebrightness.
Fig. 5.—ThesametoFig. 4forSerpens.
7
Fig. 6.—ThesametoFig. 4forOphiuchus.
Fig. 7.—ThesametoFig. 4forLupus.
8
Fig. 8.—ThesametoFig. 4forChamaeleonII.
TABLE 1
Numbers of selected YSOc and the comparison with the c2d lists
Cloud YSOcselectedwith YSOc in the c2d YSOc in both YSOc in Group A, YSOc in Group B,
Multi-D Method lists(GroupB) Group A and B but not in Group butnotinGroupA
(GroupA) (“SameYSOc”)a Ba
Perseus 469 385 369(78%) 100(22%) 16
Serpens 296 227 218(74%) 78(26%) 9
Ophiuchus 367 392 287(78%) 80(22%) 5
Lupus 143 94 91(64%) 52(36%) 3
ChamaeleonII 38 26 26(68%) 12(32%) 0
Total 1313 1024 991(75%) 322(25%) 33
Note. —TheYSOcnumberincludesthethreewell-knownsourcesthatarenotselectedfromMulti-DMethod(Table3).
a PercentageiscalculatedwithrespecttotheYSOcnumberinGroupA.
variations ofthe CMDs, we have noneed toselect the inanyprojectionsfromMulti-Dmagnitudespace(see
specific CMDs. Therefore, Multi-D method is rela- Fig. 4–9).
tively complete and unbias compared to CMD meth-
ods. Therefore, Multi-D method provides the best oppor-
tunity to identify YSOs as complete as a photometric
2. A natural boundary of galaxy populated region in dataset can offer.
Multi-D space. We use the natural boundary con-
structed from number distribution of our galaxy sam-
4. RESULT: NEW YSOC LIST
ple. This boundary encloses a relatively accurate
We present our YSOc lists for the five c2d-surveyed
galaxy SED ensemble, which reduces the bias in iden-
clouds selected with the Multi-D method. The total
tifying YSOc near the boundary.
number of our YSOc and the difference between our
3. Uncovering YSOs that cannot be found with all pos- YSOc numbers and the c2d numbers are listed in Table
sible CMDs. A CMD is equal or equivalent to a pro- 1, which shows that we increase the total YSOc num-
jection in Multi-D magnitude space. However, even ber by 28% (from 1024 to 1313). The YSOc we selected
all the possible variations of the CMDs are used, it is inPerseus,Serpens,Ophiuchus,Lupus,andChamaeleon
possiblethatsomeYSOsmaynotbefoundiftheyare II, along with their galaxy probabilities are listed in Ta-
locatedinaregionwithoutgalaxiesinMulti-Dmagni- ble 2. Note that the smaller a galaxy probability is, the
tude space but immerses in galaxy populated regions moredistantthesourcesislocatedawayfromthegalaxy
in all CMDs; that is, some YSOs cannot be revealed populated region.
9
TABLE 2
YSO candidates
R.A. Dec Galprob1a Galprob2a Galprob1pa Galprob2pa Steden YSOprob c2dclassificationb
degree degree log(num) log(num) log(num) log(num) log(pc−2) log(num)
Perseus
51.1968071 30.4656086 -1.99 2.44 -1.99 2.44 -0.06 1.03 –
51.3304656 30.7577636 – -0.06 – -0.36 0.47 0.46 –
51.3313375 30.5733784 -1.36 -∞ – – 0.43 0.95 YSOc star+dust(IR1)
51.3430184 30.7538668 -∞ -∞ – – 0.49 0.00 YSOc red
51.4009155 30.7543631 -∞ -∞ – – 0.44 0.23 red
51.4020445 30.7561528 -∞ -∞ -∞ -∞ 0.43 0.00 YSOc red
51.4092048 30.4527241 -0.46 1.14 0.38 1.86 0.04 0.01 –
51.4117940 30.7350532 -∞ -∞ – – 0.51 0.03 YSOc red
51.4130155 30.7328223 -∞ -∞ – -∞ 0.52 0.01 YSOc red
51.6561277 30.2578016 -∞ -∞ – – 0.17 0.06 YSOc red
51.8292253 30.2883997 -0.32 -0.44 – – 0.63 0.14 –
51.8393548 30.8312253 -2.22 -∞ – – -0.16 0.00 –
51.9093741 30.2329495 -∞ -∞ – – 0.87 0.87 YSOc star+dust(IR4)
51.9109468 30.2267923 -1.37 -0.31 -1.19 0.47 0.91 1.50 –
51.9117155 30.2235869 -1.24 -0.24 -0.34 0.81 0.88 1.09 –
51.9128437 30.2175414 -∞ -∞ -∞ -∞ 0.89 0.00 YSOc red
51.9228069 30.3379885 -∞ -∞ – – 0.43 1.64 YSOc star+dust(IR2)
51.9301209 30.2080268 -∞ -∞ -∞ -∞ 1.03 0.00 YSOc red
51.9486420 30.2012577 -∞ -∞ -∞ – 1.18 0.62 YSOc star+dust(IR1)
52.0003793 30.1463963 -∞ -∞ – – 0.87 1.36 YSOc star+dust(IR2)
a Wecalculatefourgalaxyprobabilities: Galprob1,Galprob2,Galprob1pandGalprob2p,whereGalprob1andGalprob2arethegalaxy
densityin(J,Ks,IR2,IR4andMP1)and(IR1,IR2,IR3,IR4andMP1)arraysortheirsubarrays(see§3.2fordetail),andGalprob1pandGal
prob2parethegalaxydensityfromthearraysdiscountingthebandswithimtype(cid:54)=1.
b If the c2d source type does not contains “YSO”, the source is added to the YSOc list by Evans et al. (2009) based on ancillary data from
literature. Sourceswithoutc2dclassification,“–”,arenewlyidentifiedYSOcfromthiswork.
We discuss the properties of our newly selected YSOc difference between sources is the brightness (distance).
in §4.1 and argue that they are likely to be real YSOs. Therefore, if the newly identified YSOc are real YSOs,
In §4.2, wepresentthereasons forexcludingsome YSOc they should align with the previously identified YSOc in
from the c2d list and retrieve three missed established the diagonal direction. Fig. 4–8 show the populations
YSOs. In §4.3, we further discuss the YSOc with “PAH- of newly identified YSOc (blue), galaxy sample (green)
emission” feature and label them as less reliable YSOc, and YSOc identified by both c2d and our method (red)
which composes less than 3% of all YSOc. Finally in (hereafter we call these YSOc as “Same” YSOc) in all
§4.4, we discuss the uncertainty resulting from our band variations of MMDs consisting any two bands of IR1,
selections for constructing two 5-D arrays. IR2, IR3, IR4, and MP1. As we expected, the newly
identified YSOc are well aligned with the “Same” YSOc
4.1. Analysis of The Newly Identified YSOc
inthediagonaldirection, butatfainterendsclosetothe
InordertoexaminewhetherournewlyidentifiedYSOc galaxy populated regions. Although in Fig. 4–8, some
are likely to be real YSOs, we compare our list and the faintYSOcareappearedtobemixedwithgalaxies,they
c2dlistinthefollowingthreeaspects. First,weshowthe are in fact separated in Multi-D space. Fig. 9 shows
distributionsofYSOcfromthetwolistsareconsistentin an example of a 3-D space consisting of IR2, IR4 and
all MMDs and the two lists of YSOc have similar lumi- MP1magnitudes,inwhichYSOcandgalaxiesarebetter
nosity function. Second, we show that our YSOc stellar separated than in any 2-D space.
surface density are more consistent with the c2d’s YSOc We also compare the infrared luminosity distribution
stellar density than galaxy surface density. Finally, we of our YSOc list and the “Same” YSOc list (infrared
showthatthedistributionsofthetwoYSOclistsarealso luminosity is obtained from J to MP2), and find that
consistent in the multi-dimentional color space. Note most of the newly identified YSOc are at the faint end
that these analyses can only give us the hints of that of the distribution (Fig. 10a). The distribution of the
whether the newly identified YSOc are likely to be real newlyidentifiedYSOcismorelikeanextensionofYSOc
YSOs or not. Without other data such as spectroscopy, sample at low luminosity end rather than part of the
we are not able to confirm whether they are real YSOs. galaxy distribution. Therefore, both the YSOc locations
in MMDs and the luminosity distribution support that
4.1.1. NewlyIdentifiedYSOcinTheMagnitude-Magnitude the Multi-D method are able to identify fainter YSOc
Diagrams and Their Infrared Luminosity Function
which are not found by previous works.
Because we are not able to plot a Multi-D figure with
4.1.2. Stellar Surface Density
more than 3 dimensions, we display the distribution of
ournewlyidentifiedYSOcinallpossibleMMDs,inorder SinceYSOstendtoforminclustersinmolecularclouds
to examine whether their distributions in the MMDs are and background galaxies have relatively random distri-
similar to the previously identified YSOc. For the same butioninthesky,thenewlyidentifiedYSOcareexpected
type of sources with similar SEDs, their locations in the to have higher probability to locate in the regions with
CCDs will be close to each other and their locations in otherYSOciftheyarerealYSOs. Weusestellarsurface
MMDswillbealongthediagonaldirectionsincetheonly density as the parameter to indicate whether a newly
10
2009), as a surface density reference. The stellar surface
densityforeachsourceiscalculatedfromwheretheyare
with respect to the “Same” YSOc population, and the
results are listed in column 7 of Table 2. In Fig. 11,
we compare the stellar surface density for the “Same”
YSOc sample, newly identified YSOc, and background
galaxies for each cloud. We select background galaxies
as sources with both Gal prob 1 and Gal prob 2 larger
than 10. We use Kolmogorov-Smirnov test to determine
whether the stellar surface density distributions of our
newlyidentifiedYSOcissimilartothatof“Same”YSOc
sample or galaxies.
The results (P-value) of Kolmogorov-Smirnov test are
shown in Table 3. If the P-value is significantly larger
than 0.05, we cannot reject the hypothesis that the dis-
tributionsofthetwosamplesarethesame(SciPyRefer-
enceGuide,2012). ForPerseus,SerpensandOphiuchus,
the P-values calculated from newly identified YSOc and
“Same” YSOc are larger than that from newly identi-
Fig. 9.— The 3-D magnitude space with axes of IR2, IR4 and fied YSOc and galaxies. These results suggest that the
MP1. Color points indicate the sources as same as that in Fig. 4 stellar surface density distributions of our newly identi-
andallYSOcinfivecloudsareplottedtogether.
fied YSOc are like to that of “Same” YSOc rather than
backgroundgalaxiesinPerseus, SerpensandOphiuchus.
70 (a) YSOc In Chamaeleon II and Lupus, the distributions of both
"Same" YSOc newly identified and the “Same” YSOc sample show low
60 PAH
0.1 times gal stellar surface density; therefore, the distributions have
50 lowstatisticalsignificanceindeterminingwhethernewly
ber40 identified YSOc are similar to real YSOs or background
m
u galaxies.
N
30
20 4.1.3. YSO Probability
10
IfYSOshavesimilarSEDs,theyshoulddistributecon-
0 3 2 1 0 1 tinuouslyinaratherconcentratedregioninMulti-Dcolor
(cid:0) (cid:0) log(LIR(1.25(cid:1)70(cid:0)) (L(cid:2))) space (Fig. 12). We examine this property by calcu-
lating a new parameter, YSO probability, in Multi-D
10 (b) Y"SSaOmce" YSOc color space, which is calculated in the way similar to
VeLLOs from Dunham et al. (2008) galaxy probability in Multi-D magnitude space. Here
8
we calculate the YSO probability from the number dis-
tribution of our identified YSOc in Multi-D color space
mber 6 constructed with four axes (IR1-IR2, IR1-IR3, IR1-IR4
u
N and IR1-MP1). Similar to the galaxy probability calcu-
4
lation, the subarrays (2- and 3-D arrays) are also used
for YSOc with detections only in three and four of the
2
all five bands. The cell size is 0.2 magnitude as same
as that used in galaxy probability calculations, and the
0
(cid:0)2.0 (cid:0)1.5 (cid:0)1lo.0g(LIR(1.2(cid:0)5(cid:1)070.)5 (L(cid:2))) 0.0 0.5 1.0 σ of smoothing beam are 2, (cid:112)3/4 × 2 and (cid:112)2/4 × 2
cellsfor4-D,3-Dand2-Darrays,respectively. TheYSO
Fig. 10.—(a)Thedistributionsofinfrared-luminosityforYSOc
probability for each source is listed in column 8 of Ta-
selectedwithMulti-Dmethods(blue),“Same”YSOc(red),YSOc
withPAHfeature(green),andbackgroundgalaxies(blackdashed ble 2. YSOc located closer to the center of the YSOc
line). The background galaxies are sources with both Gal prob 1 populated region in Multi-D color space will have higher
andGalprob2≤10andtheirdistributionisscaledbyafactorof YSO probability, and only isolated YSOc will have YSO
0.1. (b)Sameas(a),butonlyforClass0andClassIsourceswith
probability equal to 1, which is the minimum value for
MP2detectionsandtheblacklineareVeLLOcandidatesselected
byDunhametal.(2008). YSO probability. Out of 1313 identified YSOc, only 49
YSOc have YSO probability = 1 and 18 of them are
newly identified YSOc. The chances for these “isolated”
identifiedYSOcarelocatedwithinYSOclusters. Stellar
YSOc in Multi-D color space not being real YSO are
surface density is defined as
higher; however, it is still possible that small numbers of
n−1 YSOc contain unusual SED features. Since the SEDs of
σ{ı,}= (2) YSOs are not exactly identical, lower YSO probability
πr2{ı,}
n suggests that the type of SEDs are less common if they
where n is the n-th closed star and r is the distance to are indeed YSOs. Note that the YSO probability can-
n
the n-th closed star (Gutermuth et al. 2005, 2009). Here not be used to identify YSOc, because galaxies occupy
we adopt n=6, same as that used in (Gutermuth et al. similar color space.