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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.
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
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 band
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 metal
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
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