Decoding Starlight with Big Survey Data, Machine Learning, and Cosmological Simulations

Stars, and collections of stars, encode rich signatures of stellar physics and galaxy evolution. With properties influenced by both their environment and intrinsic nature, stars retain information about astrophysical phenomena that are not otherwise directly observable. In the time-domain, the observed brightness variability of a star can be used to investigate physical processes occurring at the stellar surface and in the stellar interior. On a galactic scale, comparatively fixed properties of stars, including chemical abundances and stellar ages, serve as a multi-dimensional record of the origin of the galaxy. In the Milky Way, together with orbital properties, this informs the details of the subsequent evolution of our Galaxy since its formation. Extending beyond the Local Group, the attributes of unresolved stellar populations allow us to study the diversity of galaxies in the Universe. By examining the properties of stars, and how they vary across a range of spatial and temporal scales, this Dissertation connects the information residing within stars, to global processes in galactic formation and evolution. We develop new approaches to determine stellar properties, including rotation and surface gravity, from the variability that we directly observe. We offer new insight into the chemical enrichment history of the Milky Way, tracing different stellar explosions, that capture billions of years of evolution. We advance knowledge and understanding of how stars and galaxies are linked, by examining differences in the initial stellar mass distributions comprising galaxies, as they form. In building up this knowledge, we highlight current tensions between data and theory. By synthesizing numerical simulations, large observational data sets, and machine learning techniques, this work makes valuable methodological contributions to maximize insights from diverse ensembles of current and future stellar observations

CANDELS Visual Classifications: Scheme, Data Release, and First Results

We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H \u3c 24.5 involving the dedicated efforts of over 65 individual classifiers. Once completed, we expect to have detailed morphological classifications for over 50,000 galaxies spanning 0 \u3c z \u3c 4 over all the fields, with classifications from 3 to 5 independent classifiers for each galaxy. Here, we present our detailed visual classification scheme, which was designed to cover a wide range of CANDELS science goals. This scheme includes the basic Hubble sequence types, but also includes a detailed look at mergers and interactions, the clumpiness of galaxies, k-corrections, and a variety of other structural properties. In this paper, we focus on the first field to be completed—GOODS-S, which has been classified at various depths. The wide area coverage spanning the full field (wide+deep+ERS) includes 7634 galaxies that have been classified by at least three different people. In the deep area of the field, 2534 galaxies have been classified by at least five different people at three different depths. With this paper, we release to the public all of the visual classifications in GOODS-S along with the Perl/Tk GUI that we developed to classify galaxies. We present our initial results here, including an analysis of our internal consistency and comparisons among multiple classifiers as well as a comparison to the Sérsic index. We find that the level of agreement among classifiers is quite good (\u3e70% across the full magnitude range) and depends on both the galaxy magnitude and the galaxy type, with disks showing the highest level of agreement (\u3e50%) and irregulars the lowest (k-corrections between the V-band and H-band observations and find that a small fraction (84 galaxies in total) are classified as being very different between these two bands. These galaxies typically have very clumpy and extended morphology or are very faint in the V-band

KELT-17B: A HOT-JUPITER TRANSITING AN A-STAR IN A MISALIGNED ORBIT DETECTED WITH DOPPLER TOMOGRAPHY

We present the discovery of a hot-Jupiter transiting the V=9.23 mag main-sequence A-star KELT-17 (BD+14 1881). KELT-17b is a 1.31 -0.29/+0.28 Mj, 1.525 -0.060/+0.065 Rj hot-Jupiter in a 3.08 day period orbit misaligned at -115.9 +/- 4.1 deg to the rotation axis of the star. The planet is confirmed via both the detection of the radial velocity orbit, and the Doppler tomographic detection of the shadow of the planet over two transits. The nature of the spin-orbit misaligned transit geometry allows us to place a constraint on the level of differential rotation in the host star; we find that KELT-17 is consistent with both rigid-body rotation and solar differential rotation rates (alpha < 0.30 at 2 sigma significance). KELT-17 is only the fourth A-star with a confirmed transiting planet, and with a mass of 1.635 -0.061/+0.066 Msun, effective temperature of 7454 +/- 49 K, and projected rotational velocity v sin I_* = 44.2 -1.3/+1.5 km/s; it is amongst the most massive, hottest, and most rapidly rotating of known planet hosts.Comment: 15 pages, 9 figures, accepted for publication in A