arXiv subjects

Melissa Ness

Publications and source records attributed to Melissa Ness.

At least 37 records · Page 2Linked to original sources

arXiv2022

Elemental Abundances of Kepler Objects of Interest in APOGEE DR17

The elemental abundances of planet host stars can shed light on the conditions of planet forming environments. We test if individual abundances of 130 known/candidate planet hosts in APOGEE are statistically different from those of a reference doppelganger sample. The reference set comprises objects selected with the same Teff, logg, [Fe/H], and [Mg/H] as each Kepler Object of Interest (KOI). We predict twelve individual abundances (X = C, N, O, Na, Al, Si, Ca, Ti, V, Cr, Mn, Ni) for the KOIs and their doppelgangers using a local linear model of these four parameters, training on ASPCAP abundance measurements for a sample of field stars with high fidelity (SNR > 200) APOGEE observations. We compare element prediction residuals (model-measurement) for the two samples and find them to be indistinguishable, given a high quality sample selection. We report median intrinsic dispersions of ~0.038 dex and ~0.041 dex, for the KOI and doppelganger samples, respectively, for these elements. We conclude that the individual abundances at fixed Teff, logg, [Fe/H], and [Mg/H] are unremarkable for known planet hosts. Our results establish an upper limit on the abundance precision required to uncover any chemical signatures of planet formation in planet host stars.

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arXiv2022

Chemodynamical signatures of bar resonances in the Galactic disk: current data and future prospects

The Galactic disk exhibits complex chemical and dynamical substructure thought to be induced by the bar, spiral arms, and satellites. Here, we explore the chemical signatures of bar resonances in action and velocity space and characterize the differences between the signatures of corotation and higher-order resonances using test particle simulations. Thanks to recent surveys, we now have large datasets containing metallicities and kinematics of stars outside the solar neighborhood. We compare the simulations to the observational data from Gaia EDR3 and LAMOST DR5 and find weak evidence for a slow bar with the "hat" moving group ($250~\text{km/s} \lesssim v_ϕ\lesssim 270~\text{km/s}$) associated with its outer Lindblad resonance and "Hercules" ($170~\textrm{km/s} \lesssim v_ϕ\lesssim 195~\text{km/s}$) with corotation. While constraints from current data are limited by their spatial footprint, stars closer in azimuth than the Sun to the bar's minor axis show much stronger signatures of the bar's outer Lindblad and corotation resonances in test particle simulations. Future datasets with greater azimuthal coverage, including the final Gaia data release, will allow reliable chemodynamical identification of bar resonances.

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arXiv2022

The chemical enrichment of the Milky Way disk evaluated using conditional abundances

Chemical abundances of stars in the Milky Way disk are empirical tracers of its enrichment history. However, they capture joint-information that is valuable to disentangle. In this work, we seek to quantify how individual abundances evolve across the present-day radius of the disk, at fixed supernovae contribution ([Fe/H], [Mg/Fe]). We use 18,135 APOGEE DR17 red clump stars and 7,943 GALAH DR3 main sequence stars to compare the abundance distributions conditioned on ([Fe/H], [Mg/Fe]) across $3-13$ kpc and $6.5-9.5$ kpc, respectively. In total we examine 15 elements: C, N, Al, K (light), O, Si, S, Ca, ($α$), Mn, Ni, Cr, Cu, (iron-peak) Ce, Ba (s-process) and Eu (r-process). We find that the conditional neutron capture and light elements most significantly trace variations in the disk's enrichment history, with absolute conditional radial gradients $\leq 0.03$ dex/kpc. The other elements studied have absolute conditional gradients $\lesssim 0.01$ dex/kpc. We uncover structured conditional abundance variations as a function of [Fe/H] for the low-$α$, but not the high-$α$ sequence. The average scatter between the mean conditional abundances at different radii is $σ_\text{intrinsic} \approx$ 0.02 dex (with Ce, Eu, Ba $σ_\text{intrinsic} >$ 0.05 dex). These results serve as a measure of the magnitude via which different elements trace Galactic radial enrichment history once fiducial supernovae correlations are accounted for. Furthermore, we uncover subtle systematic variations in all moments of the conditional abundance distributions that will presumably constrain chemical evolution models of the Galaxy.

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arXiv2021

The COMBS Survey -- III. The Chemodynamical Origins of Metal-Poor Bulge Stars

The characteristics of the stellar populations in the Galactic Bulge inform and constrain the Milky Way's formation and evolution. The metal-poor population is particularly important in light of cosmological simulations, which predict that some of the oldest stars in the Galaxy now reside in its center. The metal-poor bulge appears to consist of multiple stellar populations that require dynamical analyses to disentangle. In this work, we undertake a detailed chemodynamical study of the metal-poor stars in the inner Galaxy. Using R$\sim$ 20,000 VLT/GIRAFFE spectra of 319 metal-poor (-2.55 dex$\leq$[Fe/H]$\leq$0.83 dex, with $\overline{\rm{[Fe/H]}}$=-0.84 dex) stars, we perform stellar parameter analysis and report 12 elemental abundances (C, Na, Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Zn, Ba, and Ce) with precisions of $\approx$0.10 dex. Based on kinematic and spatial properties, we categorise the stars into four groups, associated with the following Galactic structures: the inner bulge, the outer bulge, the halo, and the disk. We find evidence that the inner and outer bulge population is more chemically complex (i.e., higher chemical dimensionality and less correlated abundances) than the halo population. This result suggests that the older bulge population was enriched by a larger diversity of nucleosynthetic events. We also find one inner bulge star with a [Ca/Mg] ratio consistent with theoretical pair-instability supernova yields and two stars that have chemistry consistent with globular cluster stars.

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arXiv2021

The Astrophysical Variance in Gaia-RVS Spectra

Large surveys are providing a diversity of spectroscopic observations with Gaia alone set to deliver millions of Ca-triplet-region spectra across the Galaxy. We aim to understand the dimensionality of the chemical abundance information in the Gaia-RVS data to inform galactic archaeology pursuits. We fit a quadratic model of four primary sources of variability, described by labels of $T_{\rm eff}$, $\log g$, [Fe/H], and [$α$/Fe], to the normalized flux of 10,802 red-clump stars from the Gaia-RVS-like ARGOS survey. We examine the residuals between ARGOS spectra and the models and find that the models capture the flux variability across $85\%$ of the wavelength region. The remaining residual variance is concentrated to the Ca-triplet features, at an amplitude up to $12\%$ of the normalized flux. We use principal component analysis on the residuals and find orthogonal correlations in the Ca-triplet core and wings. This variability, not captured by our model, presumably marks departures from the completeness of the 1D-LTE label description. To test the indication of low-dimensionality, we turn to abundance-space to infer how well we can predict measured [Si/H], [O/H], [Ca/H], [Ni/H], and [Al/H] abundances from the Gaia-RVS-like RAVE survey with models of $T_{\rm eff}$, $\log g$, [Fe/H], and [Mg/Fe]. We find that we can near-entirely predict these abundances. Using high-precision APOGEE abundances, we determine that a measurement uncertainty of $<$ 0.03 dex is required to capture additional information from these elements. This indicates that a four-label model sufficiently describes chemical abundance variance for $\approx$ S/N $<$ 200 per pixel, in Gaia-RVS spectra.

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arXiv2021

Disentangled Representation Learning for Astronomical Chemical Tagging

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra, and making precise and accurate chemical abundance measurements are challenging. Here, we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e. \teff, \logg, \feh). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves, and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like dataset. We show that this recovery declines as a function of the signal to noise ratio, but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.

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arXiv2021

An unsupervised method for identifying $X$-enriched stars directly from spectra: Li in LAMOST

Stars with peculiar element abundances are important markers of chemical enrichment mechanisms. We present a simple method, tangent space projection (TSP), for the detection of $X$-enriched stars, for arbitrary elements $X$, even from blended lines. Our method does not require stellar labels, but instead directly estimates the counterfactual unrenriched spectrum from other unlabelled spectra. As a case study, we apply this method to the $6708~$Å Li doublet in LAMOST DR5, identifying 8,428 Li-enriched stars seamlessly across evolutionary state. We comment on the explanation for Li-enrichement for different subpopulations, including planet accretion, nonstandard mixing, and youth.

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arXiv2021

The challenge of simultaneously matching the observed diversity of chemical abundance patterns in cosmological hydrodynamical simulations

With the advent of large spectroscopic surveys the amount of high quality chemo-dynamical data in the Milky Way (MW) increased tremendously. Accurately and correctly capturing and explaining the detailed features in the high-quality observational data is notoriously difficult for state-of-the-art numerical models. In order to keep up with the quantity and quality of observational datasets, improved prescriptions for galactic chemical evolution need to be incorporated into the simulations. Here we present a new, flexible, time resolved chemical enrichment model for cosmological simulations. Our model allows to easily change a number of stellar physics parameters such as the shape of the initial mass function (IMF), stellar lifetimes, chemical yields or SN Ia delay times. We implement our model into the Gasoline2 code and perform a series of cosmological simulations varying a number of key parameters, foremost evaluating different stellar yield sets for massive stars from the literature. We find that total metallicity, total iron abundance and gas phase oxygen abundance are robust predictions from different yield sets and in agreement with observational relations. On the other hand, individual element abundances, especially $α$-elements show significant differences across different yield sets and none of our models can simultaneously match constraints on the dwarf and MW mass scale. This offers a unique way of observationally constraining model parameters. For MW mass galaxies we find for most yield tables tested in this work a bimodality in the $[α$/Fe] vs. [Fe/H] plane of rather low intrinsic scatter potentially in tension with the observed abundance scatter.

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arXiv2021

Universal properties of the high- and low-α disk: small intrinsic abundance scatter and migrating stars

The detailed age-chemical abundance relations of stars measures time-dependent chemical evolution.These trends offer strong empirical constraints on nucleosynthetic processes, as well as the homogeneityof star-forming gas. Characterizing chemical abundances of stars across the Milky Way over time has been made possible very recently, thanks to surveys like Gaia, APOGEE and Kepler. Studies of the low-$α$ disk have shown that individual elements have unique age-abundance trends and the intrinsic dispersion around these relations is small. In this study, we examine and compare the age distribution of stars across both the high and low-$α$ disk and quantify the intrinsic dispersion of 16 elements around their age-abundance relations at [Fe/H] = 0 using APOGEE DR16. We find the high-$α$ disk has shallower age-abundance relations compared to the low-$α$ disk, but similar median intrinsic dispersions of ~ 0.04 dex, suggesting universal element production mechanisms for the high and low-$α$ disks, despite differences in formation history. We visualize the temporal and spatial distribution of disk stars in small chemical cells, revealing signatures of upside-down and inside-out formation. Further,the metallicity skew and the [Fe/H]-age relations - across radius indicates different initial metallicity gradients and evidence for radial migration. Our study is accompanied by an age catalogue for 64,317 stars in APOGEE derived usingThe Cannon with ~ 1.9 Gyr uncertainty across all ages (APO-CAN stars) as well as a red clump catalogue of 22,031 stars with a contamination rate of 2.7%.

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arXiv2021

The Swan: Data-Driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves

Stellar light curves are well known to encode physical stellar properties. Precise, automated and computationally inexpensive methods to derive physical parameters from light curves are needed to cope with the large influx of these data from space-based missions such as Kepler and TESS. Here we present a new methodology which we call The Swan, a fast, generalizable and effective approach for deriving stellar surface gravity ($\log g$) for main sequence, subgiant and red giant stars from Kepler light curves using local linear regression on the full frequency content of Kepler long cadence power spectra. With this inexpensive data-driven approach, we recover $\log g$ to a precision of $\sim$0.02 dex for 13,822 stars with seismic $\log g$ values between 0.2-4.4 dex, and $\sim$0.11 dex for 4,646 stars with Gaia derived $\log g$ values between 2.3-4.6 dex. We further develop a signal-to-noise metric and find that granulation is difficult to detect in many cool main sequence stars ($T_{\text{eff}}$ $\lesssim$ 5500 K), in particular K dwarfs. By combining our $\log g$ measurements with Gaia radii, we derive empirical masses for 4,646 subgiant and main sequence stars with a median precision of $\sim$7%. Finally, we demonstrate that our method can be used to recover $\log g$ to a similar mean absolute deviation precision for TESS-baseline of 27 days. Our methodology can be readily applied to photometric time-series observations to infer stellar surface gravities to high precision across evolutionary states.

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arXiv2021

The COMBS Survey -- II. Distinguishing the Metal-Poor Bulge from the Halo Interlopers

The metal-poor stars in the bulge are important relics of the Milky Way's formation history, as simulations predict that they are some of the oldest stars in the Galaxy. In order to determine if they are truly ancient stars, we must understand the origins of this population. Currently, it is unclear if the metal-poor stars in the bulge ([Fe/H] < -1 dex) are merely halo interlopers, a unique accreted population, part of the boxy/peanut-shaped (B/P) bulge or a classical bulge population. In this work, we use spectra from the VLT/FLAMES spectrograph to obtain metallicity estimates using the Ca-II triplet (CaT) of 473 bulge stars (187 of which have [Fe/H]<-1 dex), targeted using SkyMapper photometry. We also use Gaia DR2 parallaxes and proper motions to infer the Galactic positions and velocities along with orbital properties for 523 bulge stars. We employ a probabilistic orbit analysis and find that about half of our sample has a > 50\% probability of being bound to the bulge, and half are halo interlopers. We also see that the occurrence rate of halo interlopers increases steadily with decreasing metallicity across the full range of our sample (-3 < [Fe/H] < 0.5). Our examination of the kinematics of the confined compared to the unbound stars indicates the metal-poor bulge ([Fe/H] < -1 dex) comprises at least two populations; those confined to the boxy/peanut bulge and halo stars passing through the inner galaxy. We conclude that an orbital analysis approach, as we have employed, is important to uncover and understand the composite nature of the metal-poor stars in the inner region.

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arXiv2020

Mapping the tilt of the Milky Way bulge velocity ellipsoids with ARGOS and $Gaia$ DR2

Until the recent advent of $Gaia$ Data Release 2 (DR2) and deep multi-object spectroscopy, it has been difficult to obtain 6-D phase space information for large numbers of stars beyond 4 kpc, in particular towards the Galactic centre, where dust and crowding effects are significant. In this study we combine line-of-sight velocities from the Abundances and Radial velocity Galactic Origins Survey (ARGOS) spectroscopic survey with proper motions from $Gaia$ DR2, to obtain a sample of $\sim$ 7,000 red clump stars with 3-D velocities. We perform a large scale stellar kinematics study of the Milky Way (MW) bulge to characterize the bulge velocity ellipsoids. We measure the tilt $l_{v}$ of the major-axis of the velocity ellipsoid in the radial-longitudinal velocity plane in 20 fields across the bulge. The tilt or vertex deviation, is characteristic of non-axisymmetric systems and a significant tilt is a robust indicator of non-axisymmetry or bar presence. We compare the observations to the predicted kinematics of an N-body boxy-bulge model formed from dynamical instabilities. In the model, the $l_{v}$ values are strongly correlated with the angle ($α$) between the bulge major-axis and the Sun-Galactic centre line-of-sight. We use a maximum likelihood method to obtain an independent measurement of $α$, from bulge stellar kinematics alone. The most likely value of $α$ given our model is $α= (29 \pm 3)^{\circ}$. In the Baade's window, the metal-rich stars display a larger vertex deviation ($l_{v} = -40^{\circ}$) than the metal-poor stars ($l_{v} = 10^{\circ}$) but we do not detect significant $l_{v}-$metallicity trends in the other fields.

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arXiv2020

The age distribution of stars in the Milky Way bulge

The age and chemical characteristics of the Galactic bulge link to the formation and evolutionary history of the Galaxy. Data-driven methods and large surveys enable stellar ages and precision chemical abundances to be determined for vast regions of the Milky Way, including the bulge. Here, we use the data-driven approach of The Cannon, to infer the ages and abundances for 125,367 stars in the Milky Way, using spectra from Apache Point Observatory Galaxy Evolution Experiment (APOGEE) DR14. We examine the ages and metallicities of 1654 bulge stars within $R_{\text{GAL}}<3.5$ kpc. We focus on fields with $b<12^\circ$, and out to longitudes of $l<15^\circ$. We see that stars in the bulge are about twice as old ($τ=8$ Gyrs), on average, compared to those in the solar neighborhood ($τ=4$ Gyrs), with a larger dispersion in [Fe/H] ($\approx0.38$ compared to 0.23 dex). This age gradient comes primarily from the low-$α$ stars. Looking along the Galactic plane, the very central field in the bulge shows by far the largest dispersion in [Fe/H] ($σ_{[Fe/H]}\approx0.4$ dex) and line of sight velocity ($σ_{vr}\approx90$ km/s), and simultaneously the smallest dispersion in age. Moving out in longitude, the stars become kinematically colder and less dispersed in [Fe/H], but show a much broader range of ages. We see a signature of the X-shape within the bulge at a latitude of $b=8^\circ$, but not at $b=12^\circ$. Future APOGEE and other survey data, with larger sampling, affords the opportunity to extend our approach and study in more detail, to place stronger constraints on models of the Milky Way.

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arXiv2020

Astraea: Predicting Long Rotation Periods with 27-Day Light Curves

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-year 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 \texttt{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.

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arXiv2020

Abundances in the Milky Way across five nucleosynthetic channels from 4 million LAMOST stars

Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date are yet to be fully exploited. We employ The Cannon, a data-driven approach to estimating abundances, to obtain detailed abundances from low-resolution (R = 1800) LAMOST spectra, using the GALAH survey as our reference. We deliver five (for dwarfs) or six (for giants) estimated abundances representing five different nucleosynthetic channels, for 3.9 million stars, to a precision of 0.05 - 0.23 dex. Using wide binary pairs, we demonstrate that our abundance estimates provide chemical discriminating power beyond metallicity alone. We show the coverage of our catalogue with radial, azimuthal and dynamical abundance maps, and examine the neutron capture abundances across the disk and halo, which indicate different origins for the in-situ and accreted halo populations. LAMOST has near-complete Gaia coverage and provides an unprecedented perspective on chemistry across the Milky Way.

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arXiv2020

Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks

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.

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arXiv2019

Discovery of s-process enhanced stars in the LAMOST survey

Here we present the discovery of 895 s-process-rich candidates from 454,180 giant stars observed by the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) using a data-driven approach. This sample constitutes the largest number of s-process enhanced stars ever discovered. Our sample includes 187 s-process-rich candidates that are enhanced in both barium and strontium, 49 stars with significant barium enhancement only and 659 stars that show only a strontium enhancement. Most of the stars in our sample are in the range of effective temperature and log g typical of red giant branch (RGB) populations, which is consistent with our observational selection bias towards finding RGB stars. We estimate that only a small fraction (0.5 per cent) of binary configurations are favourable for s-process enriched stars. The majority of our s-process-rich candidates (95 per cent) show strong carbon enhancements, whereas only five candidates (less than 3 percent) show evidence of sodium enhancement. Our kinematic analysis reveals that 97 percent of our sample are disc stars, with the other 3 percent showing velocities consistent with the Galactic halo. The scaleheight of the disc is estimated to be zh=0.634kpc, comparable with values in the literature. A comparison with the yields from asymptotic giant branch (AGB) models suggests that the main neutron source responsible for the Ba and Sr enhancements is the 13C(alpha,n)16O reaction. We conclude that the s-process-rich candidates may have received their overabundances via mass transfer from a previous ABG companion with an initial mass in the range 1-3Msun.

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arXiv2019

Tidal interactions between binary stars drives lithium production in low-mass red giants

Theoretical models of stellar evolution predict that most of the lithium inside a star is destroyed as the star becomes a red giant. However, observations reveal that about 1% of red giants are peculiarly rich in lithium, often exceeding the amount in the interstellar medium or predicted from the Big Bang. With only about 150 lithium-rich giants discovered in the past four decades, and no distinguishing properties other than lithium enhancement, the origin of lithium-rich giant stars is one of the oldest problems in stellar astrophysics. Here we report the discovery of 2,330 low-mass (1 to 3$\,M_\odot$) lithium-rich giant stars, which we argue are consistent with internal lithium production that is driven by tidal spin-up by a binary companion. Our sample reveals that most lithium-rich giants have helium-burning cores ($80^{+7}_{-6}\%$), and that the frequency of lithium-rich giants rises with increasing stellar metallicity. We find that while planet accretion may explain some lithium-rich giants, it cannot account for the majority that have helium-burning cores. We rule out most other proposed explanations as the primary mechanism for lithium-rich giants, including all stages related to single star evolution. Our analysis shows that giants remain lithium-rich for only about two million years. A prediction from this lithium depletion timescale is that most lithium-rich giants with a helium-burning core have a binary companion.

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