J/MNRAS/429/1981 NGC 2264 T Tauri star properties (Barentsen+, 2013)
Bayesian inference of T Tauri star properties using multi-wavelength
survey photometry.
Barentsen G., Vink J.S., Drew J.E., Sale S.E.
<Mon. Not. R. Astron. Soc., 429, 1981-2000 (2013)>
=2013MNRAS.429.1981B 2013MNRAS.429.1981B (SIMBAD/NED BibCode)
ADC_Keywords: Clusters, open ; Stars, pre-main sequence ; Photometry, infrared
Keywords: accretion, accretion discs - methods: data analysis - surveys -
stars: pre-main-sequence -
open clusters and associations: individual: NGC 2264
Abstract:
There are many pertinent open issues in the area of star and planet
formation. Large statistical samples of young stars across
star-forming regions are needed to trigger a breakthrough in our
understanding, but most optical studies are based on a wide variety of
spectrographs and analysis methods, which introduces large biases.
Here we show how graphical Bayesian networks can be employed to
construct a hierarchical probabilistic model which allows
pre-main-sequence ages, masses, accretion rates and extinctions to be
estimated using two widely available photometric survey data bases
(Isaac Newton Telescope Photometric Hα Survey r'/Hα/i' and
Two Micron All Sky Survey J-band magnitudes). Because our approach
does not rely on spectroscopy, it can easily be applied to
homogeneously study the large number of clusters for which Gaia will
yield membership lists.
We explain how the analysis is carried out using the Markov chain
Monte Carlo method and provide python source code. We then demonstrate
its use on 587 known low-mass members of the star-forming region NGC
2264 (Cone Nebula), arriving at a median age of 3.0Myr, an accretion
fraction of 20±2 per cent and a median accretion rate of
10-8.4M☉/yr.
The Bayesian analysis formulated in this work delivers results which
are in agreement with spectroscopic studies already in the literature,
but achieves this with great efficiency by depending only on
photometry. It is a significant step forward from previous photometric
studies because the probabilistic approach ensures that nuisance
parameters, such as extinction and distance, are fully included in the
analysis with a clear picture on any degeneracies.
Description:
We showed how the theory of graphical Bayesian networks can be used to
define a probabilistic model which allows extinction, age, mass and
accretion rate to be inferred from IPHAS r'/Hα/i' and 2MASS
J-band photometry without the need for spectroscopy.
We applied the method to 587 low-mass members of the NGC 2264
star-forming region
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table3.dat 92 587 IPHAS and 2MASS photometry for known members of
NGC 2264 which satisfy our selection and
quality criteria
table4.dat 73 587 *Posterior expectation values and standard
deviations for parameters of NGC 2264 members
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Note on table4.dat: obtained from IPHAS and 2MASS photometry using Bayesian
inference.
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See also:
II/321 : IPHAS DR2 Source Catalogue (Barentsen+, 2014)
J/AJ/135/441 : VRIHα photometry in NGC 2264 (Sung+, 2008)
Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 6 A6 --- Sung Object identifier (CNNNNN) as defined by
Sung et al. (2008, Cat. J/AJ/135/441) (G1)
8- 26 A19 --- IPHAS IPHAS name (JHHMMSS.ss+DDMMSS.s)
28- 32 F5.2 mag rmag IPHAS r magnitude
34- 37 F4.2 mag e_rmag rms uncertainty on rmag
39- 43 F5.2 mag Hamag IPHAS Hα magnitude
45- 48 F4.2 mag e_Hamag rms uncertainty on Hamag
50- 54 F5.2 mag imag IPHAS i magnitude
56- 59 F4.2 mag e_imag rms uncertainty on imag
61- 65 F5.2 mag Jmag 2MASS J magnitude
67- 70 F4.2 mag e_Jmag rms uncertainty on Jmag
72- 76 F5.2 mag Hmag 2MASS H magnitude
77 A1 --- u_Hmag Uncertainty flag on Hmag
78- 81 F4.2 mag e_Hmag ? rms uncertainty on Hmag
83- 87 F5.2 mag Kmag 2MASS K magnitude
88 A1 --- u_Kmag Uncertainty flag on Kmag
89- 92 F4.2 mag e_Kmag ? rms uncertainty on Kmag
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Byte-by-byte Description of file: table4.dat
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Bytes Format Units Label Explanations
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1- 6 A6 --- Sung Object identifier (CNNNNN) as defined by
Sung et al. (2008, Cat. J/AJ/135/441) (G1)
8- 12 F5.2 [mag] logA0 Extinction parameter
14- 17 F4.2 [mag] e_logA0 rms uncertainty on logA0
19- 23 F5.2 [Msun] logMass Stellar mass
25- 28 F4.2 [Msun] e_logMass rms uncertainty on logMass
30- 33 F4.2 [yr] logAge Stellar age
35- 38 F4.2 [yr] e_logAge rms uncertainty on logAge
40- 44 F5.2 [0.1nm] logEWHa Hα emission equivalent width
46- 49 F4.2 [0.1nm] e_logEWHa rms uncertainty on logEWHa
51- 54 F4.1 [Lsun] logLHa Excess Hα luminosity
56- 58 F3.1 [Lsun] e_logLHa rms uncertainty on logLHa
60- 64 F5.1 [Msun/yr] logdMacc/dt ? Accretion rate
66- 68 F3.1 [Msun/yr] e_logdMacc/dt ? rms uncertainty on logdMacc/dt
70- 73 A4 --- Com Comments
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Global notes:
Note (G1): Cl* NGC 2264 CID CNNNNN in Simbad
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 14-Jun-2017