Recent observational evidence for initial mass function (IMF) variations in massive quiescent galaxies at z = 0 challenges the long-established paradigm of a universal IMF. While a few theoretical ...models relate the IMF to birth cloud conditions, the physical driver underlying these putative IMF variations is still largely unclear. Here we use post-processing analysis of the Illustris cosmological hydrodynamical simulation to investigate possible physical origins of IMF variability with galactic properties. We do so by tagging stellar particles in the simulation (each representing a stellar population of ) with individual IMFs that depend on various physical conditions, such as velocity dispersion, metallicity, or star formation rate, at the time and place in which the stars are formed. We then follow the assembly of these populations throughout cosmic time and reconstruct the overall IMF of each z = 0 galaxy from the many distinct IMFs it is composed of. Our main result is that applying the observed relations between IMF and galactic properties to the conditions at the star formation sites does not result in strong enough IMF variations between z = 0 galaxies. Steeper physical IMF relations are required for reproducing the observed IMF trends, and some stellar populations must form with more extreme IMFs than those observed. The origin of this result is the hierarchical nature of massive galaxy assembly, and it has implications for the reliability of the strong observed trends, for the ability of cosmological simulations to capture certain physical conditions in galaxies, and for theories of star formation aiming to explain the physical origin of a variable IMF.
Abstract
It is well established that the chemical structure of the Milky Way exhibits a bimodality with respect to the
α
-enhancement of stars at a given Fe/H. This has been studied largely based on ...a bulk
α
abundance, computed as a summary of several individual
α
-elements. Inspired by the expected subtle differences in their nucleosynthetic origins, here we probe the higher level of granularity encoded in the inter-family Mg/Si abundance ratio. Using a large sample of stars with
APOGEE
abundance measurements, we first demonstrate that there is additional information in this ratio beyond what is already apparent in
α
/Fe and Fe/H alone. We then consider
Gaia
astrometry and stellar age estimates to empirically characterize the relationships between Mg/Si and various stellar properties. We find small but significant trends between this ratio and
α
-enhancement, age, Fe/H, location in the Galaxy, and orbital actions. To connect these observed Mg/Si variations to a physical origin, we attempt to predict the Mg and Si abundances of stars with the galactic chemical evolution model
Chempy
. We find that we are unable to reproduce abundances for the stars that we fit, which highlights tensions between the yield tables, the chemical evolution model, and the data. We conclude that a more data-driven approach to nucleosynthetic yield tables and chemical evolution modeling is necessary to maximize insights from large spectroscopic surveys.
Abstract
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations ...with stellar physics and to understand the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as the rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time-series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of Kepler data, we recover the stellar properties, including the surface gravity for red giant stars (with an uncertainty of ≲0.06 dex) and rotation period for main-sequence stars (with an uncertainty of ≲5.2 days, and unbiased from ≈5 to 40 days). Shortening the Kepler data to a 27 days Transiting Exoplanet Survey Satellite–like baseline, we recover the stellar properties with a small decrease in precision, ∼0.07 for log
g
and ∼5.5 days for
P
rot
, unbiased from ≈5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal or no feature engineering, and can be generalized to other surveys and data sets. This has the potential to provide stellar property estimates for many millions of stars in current and future surveys.
It is well established that the chemical structure of the Milky Way exhibits a bimodality with respect to the -enhancement of stars at a given Fe/H. This has been studied largely based on a bulk ...abundance, computed as a summary of several individual -elements. Inspired by the expected subtle differences in their nucleosynthetic origins, here we probe the higher level of granularity encoded in the inter-family Mg/Si abundance ratio. Using a large sample of stars with APOGEE abundance measurements, we first demonstrate that there is additional information in this ratio beyond what is already apparent in /Fe and Fe/H alone. We then consider Gaia astrometry and stellar age estimates to empirically characterize the relationships between Mg/Si and various stellar properties. We find small but significant trends between this ratio and -enhancement, age, Fe/H, location in the Galaxy, and orbital actions. To connect these observed Mg/Si variations to a physical origin, we attempt to predict the Mg and Si abundances of stars with the galactic chemical evolution model Chempy. We find that we are unable to reproduce abundances for the stars that we fit, which highlights tensions between the yield tables, the chemical evolution model, and the data. We conclude that a more data-driven approach to nucleosynthetic yield tables and chemical evolution modeling is necessary to maximize insights from large spectroscopic surveys.
ABSTRACT We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H < 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 < z < 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 (>70% across the full magnitude range) and depends on both the galaxy magnitude and the galaxy type, with disks showing the highest level of agreement (>50%) and irregulars the lowest (<10%). A comparison of our classifications with the Sérsic index and rest-frame colors shows a clear separation between disk and spheroid populations. Finally, we explore morphological 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.
The rotation periods of planet-hosting stars can be used for modeling and mitigating the impact of magnetic activity in radial velocity measurements and can help constrain the high-energy flux ...environment and space weather of planetary systems. Millions of stars and thousands of planet hosts are observed with the Transiting Exoplanet Survey Satellite (TESS). However, most will only be observed for 27 contiguous days in a year, making it difficult to measure rotation periods with traditional methods. This is especially problematic for field M dwarfs, which are ideal candidates for exoplanet searches, but which tend to have periods in excess of the 27 day observing baseline. We present a new tool, Astraea, for predicting long rotation periods from short-duration light curves combined with stellar parameters from Gaia DR2. Using Astraea, we can predict the rotation periods from Kepler 4 yr light curves with 13% uncertainty overall (and a 9% uncertainty for periods >30 days). By training on 27 day Kepler light-curve segments, Astraea can predict rotation periods up to 150 days with 9% uncertainty (5% for periods >30 days). After training this tool on these 27 day Kepler light-curve segments, we applied Astraea to real TESS data. For the 195 stars that were observed by both Kepler and TESS, we were able to predict the rotation periods with 55% uncertainty despite the wild differences in systematics.
ABSTRACT 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 , hot-Jupiter in a 3.08-day period orbit misaligned at −115 9 4 ...1 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 during 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 ( at significance). KELT-17 is only the fourth A-star with a confirmed transiting planet, and with a mass of , an effective temperature of 7454 49 K, and a projected rotational velocity of it is among the most massive, hottest, and most rapidly rotating of known planet hosts.
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.
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with ...stellar physics, and for understanding the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of \textit{Kepler} data, we recover stellar properties, including surface gravity for red giant stars (with an uncertainty of \(\lesssim\) 0.06 dex), and rotation period for main sequence stars (with an uncertainty of \(\lesssim\) 5.2 days, and unbiased from \(\approx\)5 to 40 days). Shortening the \textit{Kepler} data to a 27-day TESS-like baseline, we recover stellar properties with a small decrease in precision, \(\sim\)0.07 dex for log \(g\) and \(\sim\)5.5 days for \(P_{\rm rot}\), unbiased from \(\approx\)5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal feature engineering, and can be generalized to other surveys and datasets. This has the potential to provide stellar property estimates for many millions of stars in current and future surveys.
It is well established that the chemical structure of the Milky Way exhibits
a bimodality with respect to the $\alpha$-enhancement of stars at a given
Fe/H. This has been studied largely based on a ...bulk $\alpha$ abundance,
computed as a summary of several individual $\alpha$-elements. Inspired by the
expected subtle differences in their nucleosynthetic origins, here we probe the
higher level of granularity encoded in the inter-family Mg/Si abundance
ratio. Using a large sample of stars with APOGEE abundance measurements, we
first demonstrate that there is additional information in this ratio beyond
what is already apparent in $\alpha$/Fe and Fe/H alone. We then consider
Gaia astrometry and stellar age estimates to empirically characterize the
relationships between Mg/Si and various stellar properties. We find small but
significant trends between this ratio and $\alpha$-enhancement, age, Fe/H,
location in the Galaxy, and orbital actions. To connect these observed Mg/Si
variations to a physical origin, we attempt to predict the Mg and Si abundances
of stars with the galactic chemical evolution model Chempy. We find that we are
unable to reproduce abundances for the stars that we fit, which highlights
tensions between the yield tables, the chemical evolution model, and the data.
We conclude that a more data-driven approach to nucleosynthetic yield tables
and chemical evolution modeling is necessary to maximize insights from large
spectroscopic surveys.
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