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Register a new userMeasuring Young Stars in Space and Time -- I. The Photometric Catalog and Extinction Properties of N44
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Victor Francisco Ksoll
Publication date
2020
fields
Physics
and research's language is
English
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In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to $m_mathrm{F555W} simeq 29$ mag and $m_mathrm{F814W} simeq 28$ mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of $approx0.09$ $M_odot$ at the LMC distance. In this first paper we describe the observing strategy of MYSST, the data reduction procedure, and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44s super bubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump feature by applying the machine learning algorithm RANSAC, and we select a set of Upper Main Sequence (UMS) stars as primary probes to build an extinction map, deriving a relatively modest median extinction $A_{mathrm{F555W}}simeq0.77$ mag. The same procedure applied to the red clump provides $A_{mathrm{F555W}}simeq 0.68$ mag.
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The Hubble Space Telescope (HST) survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find $sim26,700$ candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 18 prominent PMS structures at $1$ $sigma$ significance above the mean density with sub-clusters persisting up to and beyond $3$ $sigma$ significance. The most active star-forming center, located at the western edge of N44s bubble, is a subcluster with an effective radius of $sim 5.6$ pc entailing more than 1,100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H II regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.
We present near-IR spectra of a sample of T Tauri, Herbig Ae/Be, and FU Ori objects. Using the FSPEC instrument on the Bok 90-inch telescope, we obtained K-band spectra with a resolution of ~3500. Here we present spectra of the v=2->0 and v=3->1 bandheads of ro-vibrational transitions of carbon monoxide. We observed these spectra over multiple epochs spaced by a few days and approximately one month. Several of our targets show CO emission or absorption features. However we see little evidence of variability in these features across multiple epochs. We compare our results with previous observations, and discuss the physical implications of non-variable CO emission across the sampled timescales.
We collect a sample of stars observed both in LAMOST and Gaia which have colors implying a temperature hotter than 7000 K. We train a machine learning algorithm on LAMOST spectroscopic data which has been tagged with stellar classifications and metallicities, and use this machine to construct a catalog of Blue Horizontal Branch stars (BHBs) with metallicity information. Another machine is trained using Gaia parallaxes to predict absolute magnitudes for these stars. The final catalog of 13,693 BHBs is thought to be about 86% pure, with $sigma_{[Fe/H]}sim$0.35 dex and $sigma_{G}sim$0.31 mag. These values are confirmed via comparison to globular clusters, although a covariance error seems to affect our magnitude and abundance estimates. We analyze a subset of this catalog in the Galactic Halo. We find that BHB populations in the outer halo appear redder, which could imply a younger population, and that the metallicity gradient is relatively flat around [Fe/H] = -1.9 dex over our sample footprint. We find that our metal rich BHB stars are on more radial velocity dispersion dominated orbits ($beta sim 0.70$) at all radii than our metal poor BHB stars ($beta sim 0.62$).
Determining star cluster distances is essential to analyse their properties and distribution in the Galaxy. In particular it is desirable to have a reliable, purely photometric distance estimation method for large samples of newly discovered cluster candidates e.g. from 2MASS, UKIDSS-GPS and VISTA-VVV. Here, we establish an automatic method to estimate distances and reddening from NIR photometry alone, without the use of isochrone fitting. We employ a decontamination procedure of JHK photometry to determine the density of stars foreground to clusters and a galactic model to estimate distances. We then calibrate the method using clusters with known properties. This allows us to establish distance estimates with better than 40% accuracy. We apply our method to determine the extinction and distance values to 378 known open clusters and 397 cluster candidates from the list of Froebrich, Scholz and Raftery (2003). We find that the sample is biased towards clusters of a distance of approximately 3kpc, with typical distances between 2 and 6kpc. Using the cluster distances and extinction values, we investigate how the average extinction per kiloparsec distance changes as a function of Galactic longitude. We find a systematic dependence that can be approximated by A_H(l)[mag/kpc]=0.10+0.001*|l-180deg|/deg for regions more than 60deg from the Galactic Centre.
Using a Bayesian technology we derived distances and extinctions for over 100,000 red giant stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey by taking into account spectroscopic constraints from the APOGEE stellar parameters and photometric constraints from 2MASS, as well as a prior knowledge on the Milky Way. Derived distances are compared with those from four other independent methods, the Hipparcos parallaxes, star clusters, APOGEE red clump stars, and asteroseismic distances from APOKASC (Rodrigues et al. 2014) and SAGA Catalogues (Casagrande et al. 2014). These comparisons covers four orders of magnitude in the distance scale from 0.02 kpc to 20 kpc. The results show that our distances agree very well with those from other methods: the mean relative difference between our Bayesian distances and those derived from other methods ranges from -4.2% to +3.6%, and the dispersion ranges from 15% to 25%. The extinctions toward all stars are also derived and compared with those from several other independent methods: the Rayleigh-Jeans Color Excess (RJCE) method, Gonzalezs two-dimensional extinction map, as well as three-dimensional extinction maps and models. The comparisons reveal that, overall, estimated extinctions agree very well, but RJCE tends to overestimate extinctions for cool stars and objects with low logg.
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