星形成ゼミ
2020/05/22
SFN 328 #25-#32

長谷川哲夫（国立天文台）

1

Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, MS 66, Cambridge, MA 02138, USA; 2 Dept ofAstronomy,
University ofofMassachusetts,
Amherst,MA 01003,
USA; 3 II
Dept of Physics and Astronomy, San José State
328 Distribu4on
Water Vapor
in
Molecular
Clouds.
4
University,
Jose, CA
95192,
Astronomy,
TheEdwin
Universityof etMichigan,
500 Church Street, Ann
GarySanJ. Melnick1,
VolkerTolls1,USA;
Ronald L.Dept
Snell2,ofMichaelJ. Kaufman3,
A. Bergin4
al.
#28
Arbor, MI48109, USA
2020ApJ...892...22M
1E-mail contact: vtolls at cfa.harvard.edu
The depth-dependent abundance of both gas-phase and solid-state water within dense, quiescent, molecular clouds isimportant to both the cloud chemistry and gas cooling. Where water is in the gas phase, it’s free to participate in
the network of ion-neutral reactions that lead to a host of oxygen-bearing molecules, and its many ortho and para
energy levels make it an eﬀective coolant for gas temperatures greater than 20 K. Where water is abundant as ice
on grain surfaces, and unavailable to cool the gas, significant amounts of oxygen are removed from the gas phase,suppressing the gas-phase chemical reactions that lead to a number of oxygen-bearing species, including O2 . Modelsof FUV-illuminated clouds predict that the gas-phase water abundance peaks in the range AV ∼ 3 and 8 mag of thecloud surface, depending on the gas density and FUV field strength. Deeper within such clouds, water is predicted
18 models are used to analyze a variety of other regions,to exist mainly as ice on grain surfaces. More broadly, theseincluding outflow cavities associated with young stellar objects and the surface layers of protoplanetary disks. In thispaper, we report the results of observational tests of FUV-illuminated cloud models toward the Orion Molecular Ridge
and Cepheus B using data obtained from the Herschel Space Observatory and the Five College Radio Astronomy
Observatory. Toward Orion, 2220 spatial positions were observed along the face-on Orion Ridge in the H2 O 110 –101
557 GHz and NH3 J, K = 1, 0–0, 0 572 GHz lines. Toward Cepheus B, two strip scans were made in the same lines
across the edge-on ionization front. These new observations demonstrate that gas-phase water exists primarily withina few magnitudes of dense cloud surfaces, strengthening the conclusions of an earlier study based on a much smaller
data set, and indirectly supports the prediction that water ice is quite abundant in dense clouds.
Accepted by ApJ
http://arxiv.org/pdf/2003.04897

A Random Forest Approach to Identifying Young Stellar Object Candidates in the
Lupus Star-Forming Region
Elizabeth Melton1
1

Pennsylvania State University, Davey Laboratory, University Park, PA

E-mail contact: efm5151 at psu.edu

2

Dec. Offset (arcminutes)

30

20

10

0

-10

-20
20

10

0

-10

-20

R.A. Offset (arcminutes)

3

Page 58

Page 53

C2H

1

HCN

N2H+

0

-1

HCN
CN
H2O

0 ≤ A ≤ 30

-1

0

NH3

0 ≤ A ≤ 30

1

Principal Component 1

-1

0

1

Principal Component 2

Fig. 17. Results of the Principal Component Analysis for H2 O, C2 H, CN, HCN, NH3 , andN2 H+ for 820 Orion Molecular Ridge positions possessing cloud depths between AV = 0 and
30. Principal Components 1 and 2 capture a cumulative fraction of 87% of the total variancewhile Principal Components 2 – 3 capture 94% of the total variance. Clearly, gas-phase H2 O isstrongly correlated with HCN, CN, and C2 H while H2 O is largely uncorrelated with NH3 andN2 H+ , species that are expected to have their peak abundance deep within dense clouds.

4

A Random Forest Approach to Identifying Young Stellar Object Candidates in the
Lupus Star-Forming Region

328 A Random 1Forest Approach to Iden4fying Young Stellar Object Candidates in the Lupus
Elizabeth Melton#29
Star-Forming Region
1
Pennsylvania
State University, Davey Laboratory, University Park, PA
Elizabeth Melton1

E-mail 2020AJ....159..200M
contact: efm5151 at psu.edu

The identification and characterization of stellar members within a star-forming region are critical to many aspectsof star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation
history. Previous surveys of the Lupus star-forming region have identified members through infrared excess andaccretion signatures. We use machine learning to identify new candidate members of Lupus based on surveys from two
space-based observatories: ESA’s Gaia and NASA’s Spitzer. Astrometric measurements from Gaia’s Data Release 2
and astrometric and photometric data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope, as
well as from other surveys, are compiled into a catalog for the Random Forest (RF) classifier. The RF classifiers aretested to find the best features, membership list, non-membership identification scheme, imputation method, training
set class weighting and method of dealing with class imbalance within the data. We list 27 candidate members of
the Lupus star-forming region for spectroscopic follow-up. Most of the candidates lie in Clouds V and VI, where only
one confirmed member of Lupus was previously known. These clouds likely represent a slightly older population ofstar-formation.
Accepted by The Astronomical Journal
https://arxiv.org/pdf/2003.10575

Strong surface outflows on accretion discs
C.J. Nixon1 and J.E. Pringle1,2
Department of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, UK; 2 Institute of Astronomy,
Madingley Road, Cambridge, CB3 0HA, UK

1E-mail contact: cjn at leicester.ac.uk
In order to provide an explanation for the unexpected radial brightness distribution of the steady accretion discs seen
in nova-like variables, Nixon & Pringle (2019) proposed that the accretion energy is redistributed outwards by meansof strong, magnetically driven, surface flows. In this paper we note that the “powerful, rotating disc winds” observedin the soft states of black hole X-ray binaries, and also in the disc around a magnetized neutron star in Her X-1,
5 havethe properties of the outflows postulated by Nixon & Pringle to exist in the nova-like variable accretion discs aroundランダムフォレスト (random forest)

top works

2020/05/22 6'09

info

ランダムフォレスト (random forest)2017-07-30 - 2018-05-30 (update)

ランダムフォレスト とは決定木を使った機械学習のアルゴリズムの１つです．決定木では，例えば，ある１つのデータを

根(root)に入力すると，分岐を繰り返して１つの葉(leaf)を決めてくれます．決定木では，進んだ先の葉によって入力したデータを識別します．
決定木を構成するノードは，入力したデータを見てどちら分岐するを決めるパラメータを持っています．
そのパラメータは，学習用のデータをうまく見分けられるように設定することになります．

ランダムフォレストでは決定木を複数作成します．各決定木は，学習用のデータからランダムに選んだサンプルを使って，少しずつ性質を変えて学習させます（図１）．学習の後にデータの識別を行う時は，各決定木の識別結果を統合して１つの解を決定します．

このように複数の識別器を利用した学習方法は，アンサンブル学習(ensemble learning)と呼ばれています．

http://www.sanko-shoko.net/note.php?id=p9mt

1 / 5 ページ6

“Spectra only”, “Background only” (no class imbalance present), “Inconsistent proper motion” and “Mix small”. The
use of class weights in the RF is compared for the training sets with “Mix small” non-member lists. Both class weights
and balanced RF methods are evaluated on “10% mix”, “5% mix” and “2.5% mix”. Some RFs are also trained without
using a compensation for class imbalance for comparison.
4. COMPARING RANDOM FORESTS

Table 6 lists all of the input parameters whose combinations go into 2,240 di↵erent RFs for comparison.
10
All but 42 of the member sources have at least one feature that must be imputed. To ensure that the imputed values
Table
6. member
Parameters
for training
RFs.
for the members best reflect the propertiesof thepopulation,
theimputation method utilized by the classifier
is run first on just the member data, then on the non-member training set and the imputed member data set together.
NumberNames
The classifiers using the “2.5% mix” training set are trained on a random 80% subset of the data.
Member List
2
Full member list, Constrained member list
In order to compare the RFs, the classifiers are each used to predict the class labels for the full “2.5% mix” training
Non-Member Lists
7
Spectra only, Background only, Inconsistent proper motion, Mix small, 10% mix, 5% mix, 2.5% mix
setfeaturesources.
This is the most4 fair Measured
comparisonas this data set best reflects the qualities of the catalog. Due to the
Combinationsµ, Photometry, Combined µ, color indicesbootstrapping
process inherent
RF implementationeach tree
withinthe forest
only fraction
grown choices)on about two-thirds
Imputation Methods
10 in theOn-the-fly-imputation(five iteration
choices),
missForest(fiveisfeature
3
None,
Balancedof12Imbalance
the data Compensation
so there is ano danger
of
anyClass
treeweights,
over-fitting
the data and the ensemble nature of the classifier protects
Total
RFs trained
eachforest from over-fitting2,240
the training
set. Rather than return simple class labels for each source, the RF returns aa“probability”
that the issourcea memberstar-forming
region.
The number returned by the RF is not
Imbalance compensation
appliedisonly
to some ofof
thetheRF Lupus
combinations(see Section
3.3 for details)
an actual probability of membership, but rather a probability analog.
Four di↵erent metrics of performance are used to rank the classifiers: the F1 score, Matthews correlation coefficient(MCC), the area under the receiver operator characteristic (ROC) curve and the area under the precision recall curve.
These
metrics
are calculatedwith theaid of theconfusionMatrix
fromthe CRAN
package“caret”imputation(Kuhn etTang andIshwaran(2017). feature
fractionsof 0.1,
0.25, 0.5, 0.75function
and 1 arecompared
for themissForest
al.
2018)
in
the
R
statisticalsoftwareenvironment
(R
Core
Team
2013).
The
F1
score
and
MCC
changefor a RFmethod.
Gaia Proper Motion
depending on the probability cuto↵ defined before the calculation of the metric. Therefore, the maximum F1 score and[3.6] (dereddened)
MCC from all possible probability cuto↵3.3.
valuesare usedCompensation
to rank the RFs.
The probability cuto↵ values are calculated
Imbalance
Options●●●
Parallax●[3.6]−[4.5](dereddened)
Imbalance between classes needs to be compensated for, so that the machine learning classifier optimizes class●
IRAC
Proper Motion
distinction rather thanaccuracy(which can still be high if you naively
assign the majority class to all cases with a●K−[3.6](dereddened)svery small minority class).
Chen, Liaw & Breiman (2004) demonstrates two methods for handling class imbalance●s (dereddened)with the RF which both Kperform
well, even for a data
set with a minority class percentage of 2.3%, class weights and●(dereddened)H−Ksbalanced RF.
●
IRAC Proper Motion Position Angle
Class weights
are implemented such that a sample of twice the length of the member list passed to the RF is drawn●
Gaia Proper Motion Position Angle
from the overall training set with a probability weight of the percentage of member class in the training set for non●i−Ks (dereddened)
members and a probability weight of one minus● the percentage of member class in the training set for members to
J−Ks (dereddened)
grow each decision tree.
Balanced RFs are set● so that each decision tree is grown using a sample of sources drawn
G−Ks (dereddened)with replacement equal to 0.9 times the length of the number of members in the training set from both the labeledtraining members and the labeled training non-members.
0.0
0.1 four0.2
0.3 without
0.4 any compensation
0.5
0.6 for0.7
Of the seven training sets described in Section
3.1.3
are tested
class imbalance;“Spectra only”, “Background only” (no class imbalance present), “Inconsistent proper motion” and “Mix small”. The
use of class weights in the RF is compared for the training sets with
“Mix Importancesmall” non-member lists. Both class weights
Variableand balanced RF methods are evaluated on “10% mix”, “5% mix” and “2.5% mix”. Some RFs are also trained without
using a compensation for class imbalance for comparison.

Figure 3. Feature importance plot for the highest-ranked RF classifier showing the relative values of importance for eachfeature given to the classifier.
4. COMPARING RANDOM FORESTS

7

5

Figure 2. Positions of the members of the Lupus star-forming region (red dots) and the proposed candidate members (greendots) overlaid on an extinction map of the Lupus star-forming region (Dobashi et al. 2005). The area of the Lupus star-forming
region with good IRAC proper motion is outlined in blue.

8

Key Lab of Optical Astronomy, National Astronomical Observatories, CAS, Beijing 100101, China; Department of
Astronomy, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA; 3 Key Lab of Space Astronomy
328Technology,
Discoveryof twonearby post-T
Tauri stellar
associa4ons
andNational
Astronomical
Observatories,
CAS, Beijing,
100101, China; 4 University of Chinese Academy
Jiaming Liu1, Min Fang2, and Chao Liu3,4
of#25
Sciences, Beijing 100049, China
2020AJ....159..105L

E-mail contact: liuchao at nao.cas.cn
In this work we report the discovery of 2 new stellar associations in close vicinity of the Sun at roughly 180 and
150 pc. These two associations, named as u Tau assoc and e Tau assoc, were detected based on their clustering ina multi-dimensional parameter space including α, δ, µα , µδ and ϖ of Gaia. The fitting of pre-main-sequence model
isochrones in their color-magnitude diagrams suggests that the two associations are of about 50 Myr old and the groupmembers lower than ∼0.8 M⊙ are at the stage of post-T Tauri.
Accepted by AJ
http://arxiv.org/pdf/2003.11725• Gaia DR2データから若いstellar

associa4onを発見
The→IMF
natureof amass
primary
jet within a circumbinary disc outflow in a young stellar system
in low
range、early
Chris
Lynch1 ofandMichaelsystems
D. Smith1
evolu4onplanetary1and for
BDsAstrophysics & Planetary Science, The University of Kent, Canterbury, Kent CT2 7NH, U.K
Centre• Clustering
analysis at kent.ac.uk
E-mailcontact: m.d.smith
Most天球上の位置（α、δ）、固有運動
stars form in binaries, and both stars may grow by accreting material from a circumbinary disc onto their own（μα、μδ）、年周視差（ω）• u Tau associa4on（距離180 pc)、
e Tau associa4on（距離150ｐｃ）
17• 年齢 about 50 Myr、0.8 M_sunより軽い星は post T Tauri stage(no disk)

9

1
1
Chris
and Michael
D. Smith
328 Lynch
The natureof a primary
jet within a circumbinary disc ouglow in a young stellar system

Lynch1 and Michael
D. Smith1 Science, The University of Kent, Canterbury, Kent CT2 7NH, U.K
Centre Chrisfor Astrophysics& Planetary#26
2020MNRAS.494.2299L

1

E-mail contact: m.d.smith at kent.ac.uk

Most stars form in binaries, and both stars may grow by accreting material from a circumbinary disc onto their own
discs. We suspect that in many cases a wide molecular wind will envelope a collimated atomic jet emanating from close
to an orbiting young star. This so-called Circumbinary Scenario is explored here in order to find common identifiable
properties. The dynamical set up is studied with three dimensional
simulations with chemistry and cooling included.
17
We extract the properties on scales of order 100 AU and compare to the Co-Orbital Scenario in which the wind and jet
sources are in orbit. We find that the rapid orbital motion generates a wide ionised sheath around the jet core with alarge opening angle at the base. This is independent of the presence of the surrounding molecular outflow. However,
the atomic jet is recollimated beyond ∼ 55 AU when the molecular outflow restricts the motion of the ambient medium
which, in turn, confines the jet. These physical properties are related to the optical Hα imaging, providing a means ofdistinguishing between models. The high excitation sheath and recollimation region can be explored on these scales
through the next generation of instruments. However, in general, the amount and location of the ionised material,
whether in the knots or the sheath, will depend on several parameters including the orbital period, axis alignment and2
C. Lynch & M.D. Smithpulse
amplitude.
inner radius is small (3 AU) because of the tight binary orbit
Accepted by Monthly Notices of the Royal whose
Astronomical
Society(0.75
AU).

http://arxiv.org/pdf/2003.01806

It should also be remarked that the orbital and disc rotation
axes are taken to be aligned in this work. This probably minimises
the interaction; misalignment between the outflow axes is likely(Hirano & Machida 2019) and merits further study. On the other
hand, the remarkable collapse simulations described above have notquite reached the capability to yield binary protostellar cores, discs
and sets of jets.

Diﬀerent Modes of Star Formation II: Gas Accretion Phase of Initially Subcritical StarForming Clouds
Masahiro N. Machida1,2 and Shantanu Basu2
1

2

METHOD

Department of Earth and Planetary Sciences, Faculty of Sciences, Kyushu University, Fukuoka, Fukuoka 819-0395,
Parameters for the circumbinary simulations were fixed by the folJapan; 2 Department of Physics and Astronomy,
The University
ofmorphologicalWestern modelling
Ontario,lowing considerations.
Kinematic andof London, ON N6A 3K7, Canada
the slow molecular outflow associated with HH 30 by Tambovt-

seva & Grinin (2008) showed that the closest resemblance ocE-mailcontact:machida.masahiro.018
Figure1. An illustrationof the Circumbinary
and Co-orbital Scenarios. Aat m.kyushu-u.ac.jp
simulation was performed within each model series without the molecularflow as a control.

curred when a very short-period binary orbit and a circumbinary

10
disc source wereintaken.
As a result, the jet wiggling is attributed
toThe accretion phase of star formation is investigatedmagnetically-dominatedcloudsthat have an initial subcriticalprecession of the atomic jet source. We adopt a set of parametersForming Clouds
1,2
Masahiro
N. Machida
Shantanu
Basu2 II: Gas Accre4on Phase of Ini4ally Subcri4cal Star328 Diﬀerent
Modesandof StarForma4onDepartmentof Earthand Planetary Sciences, Faculty of Sciences, Kyushu University, Fukuoka, Fukuoka 819-0395,#27
Forming
Clouds2

1

Japan; Masahiro
Departmentof Physics
Astronomy,
The University of Western Ontario, London, ON N6A 3K7, Canada
N. Machida1,2
and and
Shantanu
Basu2
2020MNRAS.494..827M
E-mail contact:
machida.masahiro.018 at m.kyushu-u.ac.jp

The accretion phase of star formation is investigated in magnetically-dominated clouds that have an initial subcriticalmass-to-flux ratio. We employ nonideal magnetohydrodynamic simulations that include ambipolar diﬀusion and ohmic
dissipation. During the early prestellar phase the mass-to-flux ratio rises toward the critical value for collapse, andduring this time the angular momentum of the cloud core is reduced significantly by magnetic braking. Once a protostar
is formed in the core, the accretion phase is characterized by the presence of a small amount of angular momentum
but a large amount of magnetic flux in the near-protostellar environment. The low angular momentum leads to avery small (or even nonexistent) disk and weak outflow, while the large magnetic flux can lead to an interchange
instability that rapidly removes flux from the central region. The eﬀective magnetic braking in the early collapse
phase can even lead to a counter-rotating disk and outflow, in which the rotation direction of the disk and outflow is
opposite to that of the infalling envelope. The solutions with a counter-rotating disk, tiny disk, or nonexistent disk
(direct collapse) are unique outcomes that are realized in collapse from magnetically-dominated clouds with an initialsubcritical mass-to-flux ratio.

34

Machida & Basu

Accepted by MNRAS

https://arxiv.org/pdf/2003.03078

Distribution of Water Vapor in Molecular Clouds. II
Gary J. Melnick1 , Volker Tolls1 , Ronald L. Snell2 , Michael J. Kaufman3 , Edwin A. Bergin4 et al.
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, MS 66, Cambridge, MA 02138, USA; 2 Dept ofAstronomy, University of Massachusetts, Amherst, MA 01003, USA; 3 Dept of Physics and Astronomy, San José State
University, San Jose, CA 95192, USA; 4 Dept of Astronomy, The University of Michigan, 500 Church Street, Ann
Arbor, MI 48109, USA
1E-mail contact: vtolls at cfa.harvard.edu

Figure 10. Schematic views of supercritical and subcritical cases.

The depth-dependent abundance of both gas-phase and solid-state water within dense, quiescent, molecular clouds is
11
important to both the cloud chemistry and gas cooling. Where water is in the gas phase, it’s free to participateinX-ray eclipses
are caused by occultations of the X1,2
C.J. Nixon1 and1972).
J.E.ThePringleray flux by a binary companion, and the main 1.7 day variation of

0134120101
270
8732
0.043
0153950301
230
1764
0.184
1
the opticalandlight Astronomy,
curve is identifiedUniversity
as primarily causedby heating Leicester,
0673510501
1000 UK; 21528
Department of Physics
of Leicester,LE1 7RH,
Institute of0.926
Astronomy,of onesideof the star by the X-ray flux from its compact com0673510601
360
4688
0.109
C.J.
Nixon1
andJ.E.
Pringle1,2
Madingley Road, Cambridge,
0HA,
UK
panion (Bahcall CB3& Bahcall
1972;
Joss et al. 1973; Pringle 1973).
0673510901
600
536
1.583
2020A&A...636A..34N
Thus, Her X-1 is the X-ray counterpart of the variable star
0783770501
550
627
1.241
E-mail contact: cjnsystem
at leicester.ac.uk
HZ Her. The binary system consists of a 2.3 M⊙ pri0783770601
450
816
0.780
star of spectral typeF to unexpected
A depending onradial
whetherbrightness
one Table 1.
Table summarising
relevant
data adaptedfrom Kosec
et al.
In order to providemaryan explanation
for thedistributionof thethesteadyaccretion
discs
seen
is observing the X-ray illuminated side or not. The secondary (2020). The columns are the Observation IDs (column 1), the meain nova-like variables,
Nixon &
Pringleis (2019)that theredistributed
bythemeans
sured radialenergy
velocitiesis(column2), the escapeoutwards
velocity fromes(less massive)
componenta neutronproposedstar with assumed
massaccretion(column
3),
and
the
ratio
timatedmaximum
launchingradius
R
max
1.4
M
.
There
is
Roche
lobe
mass
transferfromtheprimarytoof strong, magnetically⊙ driven, surface flows. In this paper we note that√the “powerful, rotating disc winds” observed
f = !R 2/!esc (column 4).
secondary,accretion disc.
disc
truncatedin the soft states oftheblack
holeforming
X-rayanbinaries,
andThealso
inisthedisc arounda magnetized neutron star in Her X-1, haveby the magnetosphere of the neutron star at a radius of about
811
the properties of the
Nixon&RPringle
R ≈ outflows
2 × 10 cm. postulated
The outer edge byof thedisc is at≈ 2 × 10 tocm,exist in the nova-like variable accretion discs around
11
and
the
binaryseparationisaround
a
≈
6
×
10
cm.
white dwarfs. The relevant properties are that the flows are not winds,
but are, instead, bound flows (traveling at lessIdentification of the cause of the 35 day X-ray light curve 5) and 0783770701 (for which the error bars given in Kosec etthan the escape velocity)and that the mass fluxes in the flows are a substantial fraction of the accretion rate in theproved more problematic. Gerend & Boynton (1976) proposed al., Tables 3 & 5 are too substantial).
disc.
a phenomenological model for this behaviour, which also explained subtle variations in the optical light curve over the 35
Her X-1
Accepted by A&Aday cycle. They proposed that the neutron star was encircled• Nixon & Pringle(2019)GRO J1655-40
by a large, tilted accretion disc, which precessed with the pe4U 1630-47
http://arxiv.org/pdf/2003.01726
riod of 35days. The explanation
as to why
the accretion
discnova-like variableaccre4ondisks aroundwhite
dwarfs* * * H 1743-322
might be tilted and precess was later provided in terms of→magne4callydrivensurface
ﬂows
on
the
disks
radiation-driven disc warping (Iping & Petterson 1990; Pringle
1996; Wijers &rota4ng
Pringle 1999).
Thiswinds”
model is also
capable• This paper: “powerfuldiscobserved
19 of providing an explanation for detailed behaviour of the X-ray fluxin the sop states
throughof
theblack
35 day hole
period X-ray(see, forbinaries
example, Scott et al. 2000;
Leahy 2002).
• Flows are bound--- do not exceed escape velocity
In this system, Kosec et al. (2020) report evidence for blue*
shiftedabsorption features,seen(1
primarily
in highionization• Mass ﬂuxes --substan4al
frac4on– 10 %)of thelines of N VII, O VIII, N X and Fe XXV/XXVI. For these feaaccre4on ﬂow
tures they identify (given in their Table 3) a radial velocity (!R ),relative to the neutron star, as well as an ionization parameter,*
ξ, for the absorbing plasma. From the ionization parameter they
0
5000
1×104
are able to estimate a maximum radial distance, Rmax , that theabsorbing material can have from the neutron star (given in their
Table 5). In line with previous work (see Section 3) Kosec et al.
(2020) interpret this outward moving material as evidence for adisc wind.
As noted by Kosec et al. (2020), the absorption features seenin the Her X-1 X-ray data lie along the line of sight between
the observer and the neutron star. Moreover, because the 35 day
variation in the Her X-1/HZ Her system is caused by a warped
12
and tilted disc moving in and out of the line of sight to the neutron star, it is evident that when the X-ray source is visible, those
0

2000

4000

6000

8000

328 Strong surface ouglows on accre4on discs#30

Deciphering the 3-D Orion Nebula-I: Expanding Shells in the Huygens Region1
328
Deciphering
3-D 2Orion Nebula-I: Expanding Shells in the Huygens Region
C.
R. O’Delland N.theP. Abel1

C. R. O’Dell1 and N.2P. Abel2#31
Vanderbilt
University; Clermont College, University of Cincinnati; 3 University of Kentucky
2020ApJ...891...46O

E-mail contact: N71932 at comcast.net
Based on imaging and spectroscopic data, we develop a 3-D model for the Huygens Region of the Orion Nebula.
θ1 Ori C, the hottest star in the Trapezium, is surrounded by a wind-blown Central Bubble that opens SW into the
Extended Orion Nebula. Outside of this feature lies a layer of ionized gas at about 0.4 pc from θ1 Ori C. Both of thesefeatures are moving rapidly away from θ1 Ori C with an expansion age for the Central Bubble of only 15,000 yrs.
Accepted by Astrophysical Journal
http://arxiv.org/pdf/2003.01840

Census of young stellar population in the Galactic H II region Sh2-242
Alik Panja1 , Soumen Mondal1 , Somnath Dutta2 , Santosh Joshi3 , Sneh Lata3 et al.

S.N. Bose National Centre for Basic Sciences, Kolkata 700106, India; 2 Institute of Astronomy and Astrophysics,
Academia Sinica, Taipei 10617, Taiwan; 3 Aryab13.4761300538.(K)17(f)(R)-1(e)-1(s)-3erarhInst34(i)1(t)3(u)1(t)3ed ofO
1

13

S.N. Bose National Centre for Basic Sciences, Kolkata 700106, India; 2 Institute of Astronomy and Astrophysics,
328 Census of young stellar popula4on in the Galac4c H II region Sh2-242
Academia Sinica, Taipei 10617, Taiwan; 3 Aryabhatta Research Institute of Observational Sciences, Nainital 263002,
Panja1, Soumen Mondal1, Somnath Duva2, Santosh Joshi3, Sneh Lata3 et al.
#32 Alik
India
2020AJ....159..153P
1

E-mail contact: alik.panja at gmail.com
We present here identification and characterization of the young stellar population associated with an active starforming site Sh2-242. We used our own new optical imaging and spectroscopic observational data, as well as several
archival catalogs, e.g., Pan-STARRS 1, Gaia DR2, IPHAS, WIRCam, 2MASS, and Spitzer. Slit spectroscopic results
confirm the classification of the main ionizing source BD+26 980 as an early-type star of spectral type B0.5 V. The
spectrophotometric distance of the star is estimated as 2.08±0.24 kpc, which confirms the source as a member of thecluster. An extinction map covering a large area (diameter ∼50′ ) is generated with H and K photometry toward theregion. From the map, three distinct locations of peak extinction complexes (AV ≈ 7–17 mag) are identified for thevery first time. Using the infrared color excess, a total of 33 Class I and 137 Class II young objects are classified
within the region. The IPHAS photometry reveals classification of 36 Hα emitting sources, which might be class II
objects. Among 36 Hα emitting sources, 5 are already identified using infrared excess emission. In total, 201 young
objects are classified toward S242 from this study. The membership status of the young sources is further windowedwith the inclusion of parallax from the Gaia DR2 catalog. Using the optical and infrared color-magnitude diagrams,
the young stellar objects are characterized with an average age of ∼1 Myr and the masses in the range 0.1–3.0 M⊙ .
The census of the stellar content within the region is discussed using combined photometricand spectroscopic data.15
Young stellar population in S242
Accepted by AJ

Young stellar population in S2423

http://arxiv.org/pdf/2003.09581

A Semi-Automated Computational Approach for Infrared Dark Cloud Localization: A
Catalog of Infrared Dark Clouds
Jyothish Pari1 and Joseph L. Hora1
1

Center for Astrophysics | Harvard & Smithsonian, 60 Garden St, MS-65, Cambridge, MA 02138-1516 USA

E-mailcontact: jhora at cfa.harvard.edu
Figure 1. (a) Optical color composite image of the S242 region, created with B 360 nm (blue), V 550 nm (green), and I 900 nm(red) bands for a sky area of 18◊18 arcmin2 , acquired with the 1.3 m DFOT. Spectroscopically observed sources from the 2 m

Figure 12. Spatial distribution of the PMS sources, overplotted on the WISE W1 3.4 µm mosaic image. The Class I, Class II,
and H– emitting sources are represented by red circles, blue triangles, and green boxes, respectively. The main ionizing source
BD+26 980 is represented by a black star symbol. The FOV for different observed and archival catalogs (see Section 4.4 fordetails) are indicated.

are numbered
and marked with yellow
circles andthe mainionizing source
BD+26 980 is depicted.
(b) Color
composite
The HCT
fieldof computer
visionhas
greatlymaturedin thepast
decade, and many of the methods and techniques canimage of the S242 cluster with optical and infrared counterparts taken from DSS2 R 0.70 µm (blue), 2MASS K 2.2 µm (green),
and WISE Wfor
2 4.6 µm(red) bands.
be usefulastronomical
applications. One example is in searchinglarge imaging surveys for objects of interest,
14
1 Myr. Although, as the subregions ‘A’ and ‘C’ suffer
the highest extinction,
it may cause
undercounting duefor.
especially when itTableis1.diﬃcult
tospecifythecharacteristics
oftheobjects
being
searchedWe
havedevelopeda
Log of Optical Photometric and Spectroscopic Observations.
to the undetectable background stars. Also limitations