arXiv subjects

Yuan-Sen Ting

Publications and source records attributed to Yuan-Sen Ting.

At least 145 records · Page 8Linked to original sources

arXiv2023

Overview of the DESI Milky Way Survey

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory. Over the next 5 yr DESI MWS will observe approximately seven million stars at Galactic latitudes |b|>20 degrees, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines for deriving radial velocities, atmospheric parameters, and chemical abundances. We use ~500,000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to ~1 km/s and [Fe/H] accurate to ~0.2 dex for typical stars in our main sample. We find the stellar parameter distributions from ~100 sq. deg of SV observations with >90% completeness on our main sample are in good agreement with expectations from mock catalogs and previous surveys.

astro-ph.GA↗

arXiv2023

DESI Observations of the Andromeda Galaxy: Revealing the Immigration History of our Nearest Neighbor

We present DESI observations of the inner halo of M31, which reveal the kinematics of a recent merger - a galactic immigration event - in exquisite detail. Of the 11,416 sources studied in 3.75 hour of on-sky exposure time, 7,438 are M31 sources with well measured radial velocities. The observations reveal intricate coherent kinematic structure in the positions and velocities of individual stars: streams, wedges, and chevrons. While hints of coherent structures have been previously detected in M31, this is the first time they have been seen with such detail and clarity in a galaxy beyond the Milky Way. We find clear kinematic evidence for shell structures in the Giant Stellar Stream, the Northeast Shelf and Western Shelf regions. The kinematics are remarkably similar to the predictions of dynamical models constructed to explain the spatial morphology of the inner halo. The results are consistent with the interpretation that much of the substructure in the inner halo of M31 is produced by a single galactic immigration event 1 - 2 Gyr ago. Significant numbers of metal-rich stars ([Fe/H]$>-0.5$) are present in all of the detected substructures, suggesting that the immigrating galaxy had an extended star formation history. We also investigate the ability of the shells and Giant Stellar Stream to constrain the gravitational potential of M31, and estimate the mass within a projected radius of 125 kpc to be ${\rm log_{10}}\, M_{\rm NFW}(<125\,{\rm kpc})/M_\odot = 11.80_{-0.10}^{+0.12}$. The results herald a new era in our ability to study stars on a galactic scale and the immigration histories of galaxies.

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arXiv2022

Evidence for Populations-dependent vertical motions and the Long-lived Non-Steady Lopsided Milky Way Warp

We present the Galactic disk vertical velocity analysis using OB type stars (OB), Red Clump stars (RC), and Main-Sequence-Turn-Off stars (MSTO) with different average age populations crossed matched with LAMOST DR5 and Gaia DR3. We reveal the vertical velocities of the three populations varies clearly with the Galactocentric distance ($R$) and the younger stellar population has stronger increasing trend in general. The bending and breathing modes indicated by the vertical motions are dependent on the populations and they are varying with spatial locations. These vertical motions may be due to the Galactic warp, or minor mergers, or non-equilibrium of the disk. Assuming the warp is the dominant component, we find that the warp amplitude ($γ$, $Z_ω$) for OB (younger population) is larger than that for RC (medium population) and the later one is also larger than that for MSTO (older population), which is in agreement with other independent analyses of stellar density distribution, and supports the warp is long-lived, non-steady structure and has time evolution. This conclusion is robust whether or not the line-of-nodes $ϕ_w$ is fixed or as a free parameter (with $ϕ_w$ is around 3$-$8.5$^{\circ}$ as best fit). Furthermore, we find that warp is lopsided with asymmetries along azimuthal angle ($ϕ$).

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arXiv2022

Panel Discussion: Practical Problem Solving for Machine Learning

Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.

astro-ph.IM↗

arXiv2022

A Ghost in Boötes: The Least Luminous Disrupted Dwarf Galaxy

We report the discovery of Specter, a disrupted ultrafaint dwarf galaxy revealed by the H3 Spectroscopic Survey. We detected this structure via a pair of comoving metal-poor stars at a distance of 12.5 kpc, and further characterized it with Gaia astrometry and follow-up spectroscopy. Specter is a $25^\circ \times 1^\circ$ stream of stars that is entirely invisible until strict kinematic cuts are applied to remove the Galactic foreground. The spectroscopic members suggest a stellar age $τ\gtrsim 12$ Gyr and a mean metallicity $\langle\text{[Fe/H]}\rangle = -1.84_{-0.18}^{+0.16}$, with a significant intrinsic metallicity dispersion $σ_{ \text{[Fe/H]}} = 0.37_{-0.13}^{+0.21}$. We therefore argue that Specter is the disrupted remnant of an ancient dwarf galaxy. With an integrated luminosity $M_{\text{V}} \approx -2.6$, Specter is by far the least-luminous dwarf galaxy stream known. We estimate that dozens of similar streams are lurking below the detection threshold of current search techniques, and conclude that spectroscopic surveys offer a novel means to identify extremely low surface brightness structures.

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arXiv2022

Dwarf galaxy archaeology from chemical abundances and star formation histories

We model the stellar abundances and ages of two disrupted dwarf galaxies in the Milky Way stellar halo: Gaia-Sausage Enceladus (GSE) and Wukong/LMS-1. Using a statistically robust likelihood function, we fit one-zone models of galactic chemical evolution with exponential infall histories to both systems, deriving e-folding timescales of $τ_\text{in} = 1.01 \pm 0.13$ Gyr for GSE and $τ_\text{in} = 3.08^{+3.19}_{-1.16}$ Gyr for Wukong/LMS-1. GSE formed stars for $τ_\text{tot} = 5.40^{+0.32}_{-0.31}$ Gyr, sustaining star formation for $\sim$$1.5 - 2$ Gyr after its first infall into the Milky Way $\sim$10 Gyr ago. Our fit suggests that star formation lasted for $τ_\text{tot} = 3.36^{+0.55}_{-0.47}$ Gyr in Wukong/LMS-1, though our sample does not contain any age measurements. The differences in evolutionary parameters between the two are qualitatively consistent with trends with stellar mass $M_\star$ predicted by simulations and semi-analytic models of galaxy formation. Our fitting method is based only on poisson sampling from an evolutionary track and requires no binning of the data. We demonstrate its accuracy by testing against mock data, showing that it accurately recovers the input model across a broad range of sample sizes ($20 \leq N \leq 2000$) and measurement uncertainties ($0.01 \leq σ_\text{[$α$/Fe]}, σ_\text{[Fe/H]} \leq 0.5$; $0.02 \leq σ_{\log_{10}(\text{age})} \leq 1$). Our inferred values of the outflow mass-loading factor reasonably match $η\propto M_\star^{-1/3}$ as predicted by galactic wind models. Due to the generic nature of our derivation, this likelihood function should be applicable to one-zone models of any parametrization and easily extensible to other astrophysical models which predict tracks in some observed space.

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arXiv2022

Chemical Abundances of the Typhon Stellar Stream

We present the first high-resolution chemical abundances of seven stars in the recently discovered high-energy stream Typhon. Typhon stars have apocenters >100 kpc, making this the first detailed chemical picture of the Milky Way's very distant stellar halo. Though the sample size is limited, we find that Typhon's chemical abundances are more like a dwarf galaxy than a globular cluster, showing a metallicity dispersion and no presence of multiple stellar populations. Typhon stars display enhanced $α$-element abundances and increasing r-process abundances with increasing metallicity. The high-$α$ abundances suggest a short star formation duration for Typhon, but this is at odds with expectations for the distant Milky Way halo and the presence of delayed r-process enrichment. If the progenitor of Typhon is indeed a new dwarf galaxy, possible scenarios explaining this apparent contradiction include a dynamical interaction that increases Typhon's orbital energy, a burst of enhanced late-time star formation that raises [$α$/Fe], and/or group preprocessing by another dwarf galaxy before infall into the Milky Way. Alternatively, Typhon could be the high-energy tail of a more massive disrupted dwarf galaxy that lost energy through dynamical friction. We cannot clearly identify a known low-energy progenitor of Typhon in the Milky Way, but 70% of high-apocenter stars in cosmological simulations are from high-energy tails of large dwarf galaxies. Typhon's surprising combination of kinematics and chemistry thus underscores the need to fully characterize the dynamical history and detailed abundances of known substructures before identifying the origin of new substructures.

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arXiv2022

A New Census of the 0.2< z <3.0 Universe, Part II: The Star-Forming Sequence

We use the panchromatic SED-fitting code Prospector to measure the galaxy logM$^*$-logSFR relationship (the `star-forming sequence') across $0.2 < z < 3.0$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at $\sim 0.5$ dex and $\sim0.2$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM$^*$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization $0.2-0.5$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing $\sim0.2-0.5$ dex offset with observations at $0.5<z<3$. The fully trained normalizing flow including a nonparametric description of $ρ(\log{\rm M}^*,\log{\rm SFR},z)$ is made available online to facilitate straightforward comparisons with future work.

astro-ph.GA↗

arXiv2022

The Stellar Halo of the Galaxy is Tilted & Doubly Broken

Modern Galactic surveys have revealed an ancient merger that dominates the stellar halo of our Galaxy (\textit{Gaia}-Sausage-Enceladus, GSE). Using chemical abundances and kinematics from the H3 Survey, we identify 5559 halo stars from this merger in the radial range $r_{\text{Gal}}=6-60\text{ kpc}$. We forward model the full selection function of H3 to infer the density profile of this accreted component of the stellar halo. We consider a general ellipsoid with principal axes allowed to rotate with respect to the Galactocentric axes, coupled with a multiply-broken power law. The best-fit model is a triaxial ellipsoid (axes ratios 10:8:7) tilted $25^\circ$ above the Galactic plane towards the Sun and a doubly-broken power law with breaking radii at 12 kpc and 28 kpc. This result resolves the long-standing dichotomy in literature values of the halo breaking radius, being at either $\sim15\text{ kpc}$ or $\sim30\text{ kpc}$ assuming a singly-broken power law. N-body simulations suggest that the breaking radii are connected to apocenter pile-ups of stellar orbits, and so the observed double-break provides new insight into the initial conditions and evolution of the GSE merger. Furthermore, the tilt and triaxiality of the stellar halo could imply that a fraction of the underlying dark matter halo is also tilted and triaxial. This has important implications for dynamical mass modeling of the Galaxy as well as direct dark matter detection experiments.

astro-ph.GA↗

arXiv2022

The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm

Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical enrichment in the Milky Way. Here we leverage a large sample of $\sim600,000$ high-resolution stellar spectra from the GALAH survey plus a machine learning algorithm to find 54 candidates with estimated [Fe/H]~$\leq$~-3.0, 6 of which have [Fe/H]~$\leq$~-3.5. Our sample includes $\sim 20 \%$ main sequence EMP candidates, unusually high for \emp surveys. We find the magnitude-limited metallicity distribution function of our sample is consistent with previous work that used more complex selection criteria. The method we present has significant potential for application to the next generation of massive stellar spectroscopic surveys, which will expand the available spectroscopic data well into the millions of stars.

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arXiv2022

Detecting the non-Gaussianity of the 21-cm signal during reionisation with the Wavelet Scattering Transform

Detecting the 21-cm hyperfine transition from neutral hydrogen in the intergalactic medium is our best probe for understanding the astrophysical processes driving the Epoch of Reionisation (EoR). The primary means for a detection of this 21-cm signal is through a statistical measurement of the spatial fluctuations using the 21-cm power spectrum (PS). However, the 21-cm signal is non-Gaussian meaning the PS, which only measures the Gaussian fluctuations, is sub-optimal for characterising all of the available information. The upcoming Square Kilometre Array (SKA) will perform a deep, 1000 hr observation over 100 deg$.^{2}$ specifically designed to recover direct images of the 21-cm signal. In this work, we use the Wavelet Scattering Transform (WST) to extract the non-Gaussian information directly from these two-dimensional images of the 21-cm signal. The key advantage of the WST is its stability with respect to statistical noise for measuring non-Gaussian information, unlike the bispectrum whose statistical noise diverges. We introduce a novel method to isolate this non-Gaussian information from mock 21-cm images and demonstrate its detection at 150 (177)~MHz ($z\sim8.5$ and $\sim7$) for a fiducial model with signal-to-noise of $\sim$5~(8) assuming perfect foreground removal and $\sim2$~(3) assuming foreground wedge avoidance.

astro-ph.CO↗

arXiv2022

Mass and Age determination of the LAMOST data with different Machine Learning methods

We present a catalog of 948,216 stars with mass label and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training dataset is cross matched from the LAMOST (The Large Sky Area Multi-Object Fiber Spectroscopic Telescope) DR5 and high resolution asteroseismology data, mass and age are predicted by random forest method or convex hull algorithm. The stellar parameters with high correlation with mass and age are extracted and the test dataset shows that the median relative error of the prediction model for the mass of large sample is 3\% and meanwhile, the mass and age of red clump stars are 4\% and 7\%. We also compare the predicted age of red clump stars with the recent works and find that the final uncertainty of the RC sample could reach 18\% for age and 9\% for mass, in the meantime, final precision of the mass for large sample with different type of stars could reach 13\% without considering systematics, all these are implying that this method could be widely used in the future. Moreover, we explore the performance of different machine learning methods for our sample, including bayesian linear regression (BYS), gradient boosting decision Tree (GBDT), multilayer perceptron (MLP), multiple linear regression (MLR), random forest (RF) and support vector regression (SVR). Finally we find that the performance of nonlinear model is generally better than that of linear model, and the GBDT and RF methods are relatively better.

astro-ph.GA↗

arXiv2022

Wide twin binaries are extremely eccentric: evidence of twin binary formation in circumbinary disks

The Gaia mission recently revealed an excess population of equal-mass "twin" wide binaries, with mass ratio $q\gtrsim 0.95$, extending to separations of at least 1000 AU. The origin of this population is an enigma: twin binaries are thought to form via correlated accretion in circumbinary disks, but the typical observed protostellar disks have radii of $\sim100$ AU, far smaller than the separations of the widest twins. Here, we infer the eccentricity distribution of wide twins from the distribution of their $v$-$r$ angles, i.e., the angle between the components' separation and relative velocity vectors. We find that wide twins must be on extremely eccentric orbits. For the excess-twin population at 400-1000 AU, we infer a near-delta function excess of high-eccentricity system, with eccentricity $0.95 \lesssim e \leq 1$. These high eccentricities for wide twins imply pericenter distances of order $10$ AU and suggest that their orbits were scattered via dynamical interactions in their birth environments, consistent with a scenario in which twins are born in circumbinary disks and subsequently widened. These results further establish twin wide binaries as a distinct population and imply that wide twins can be used as a probe of the dynamical history of stellar populations.

astro-ph.SR↗

arXiv2022

Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows

Stellar spectra encode detailed information about the stars. However, most machine learning approaches in stellar spectroscopy focus on supervised learning. We introduce Mendis, an unsupervised learning method, which adopts normalizing flows consisting of Neural Spline Flows and GLOW to describe the complex distribution of spectral space. A key advantage of Mendis is that we can describe the conditional distribution of spectra, conditioning on stellar parameters, to unveil the underlying structures of the spectra further. In particular, our study demonstrates that Mendis can robustly capture the pixel correlations in the spectra leading to the possibility of detecting unknown atomic transitions from stellar spectra. The probabilistic nature of Mendis also enables a rigorous determination of outliers in extensive spectroscopic surveys without the need to measure elemental abundances through existing analysis pipelines beforehand.

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arXiv2022

Galaxy Merger Reconstruction with Equivariant Graph Normalizing Flows

A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved under the paradigm of the $Λ$CDM model. A critical limiting factor lies in the lack of robust tools to describe the merger history through a statistical model. In this work, we employ a generative graph network, E(n) Equivariant Graph Normalizing Flows Model. We demonstrate that, by treating the progenitors as a graph, our model robustly recovers their distributions, including their masses, merging redshifts and pairwise distances at redshift z=2 conditioned on their z=0 properties. The generative nature of the model enables other downstream tasks, including likelihood-free inference, detecting anomalies and identifying subtle correlations of progenitor features.

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arXiv2022

Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer

We introduce Astroconformer, a Transformer-based model to analyze stellar light curves from the Kepler mission. We demonstrate that Astrconformer can robustly infer the stellar surface gravity as a supervised task. Importantly, as Transformer captures long-range information in the time series, it outperforms the state-of-the-art data-driven method in the field, and the critical role of self-attention is proved through ablation experiments. Furthermore, the attention map from Astroconformer exemplifies the long-range correlation information learned by the model, leading to a more interpretable deep learning approach for asteroseismology. Besides data from Kepler, we also show that the method can generalize to sparse cadence light curves from the Rubin Observatory, paving the way for the new era of asteroseismology, harnessing information from long-cadence ground-based observations.

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arXiv2022

An Unsupervised Learning Approach for Quasar Continuum Prediction

Modeling quasar spectra is a fundamental task in astrophysics as quasars are the tell-tale sign of cosmic evolution. We introduce a novel unsupervised learning algorithm, Quasar Factor Analysis (QFA), for recovering the intrinsic quasar continua from noisy quasar spectra. QFA assumes that the Ly$α$ forest can be approximated as a Gaussian process, and the continuum can be well described as a latent factor model. We show that QFA can learn, through unsupervised learning and directly from the quasar spectra, the quasar continua and Ly$α$ forest simultaneously. Compared to previous methods, QFA achieves state-of-the-art performance for quasar continuum prediction robustly but without the need for predefined training continua. In addition, the generative and probabilistic nature of QFA paves the way to understanding the evolution of black holes as well as performing out-of-distribution detection and other Bayesian downstream inferences.

astro-ph.CO↗

arXiv2022

Reliable stellar abundances of individual stars with the MUSE integral-field spectrograph

We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the LAMOST and MUSE instruments, we adopt the Data-Driven Payne (DD-Payne) model used on LAMOST observations and apply it to stars observed in MUSE fields. Remarkably, in spite of instrumental differences, according to the cross-validation of 27 LAMOST-MUSE common stars, we are able to determine stellar labels with precision better than 75K in $T_{\rm eff}$, 0.15 dex in $\log g$, and 0.1 dex in abundances of [Fe/H], [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe] and [Cr/Fe] for current MUSE observations over a parameter range of 3800<$T_{\rm eff}$<7000 K, -1.5<[Fe/H]<0.5 dex. To date, MUSE has been used to target 13,000 fields across the southern sky since it was first commissioned six years ago and it is unique in its ability to study dense star fields such as globular clusters or the Milky Way bulge. Our method will enable the automated determination of stellar parameters for all stars in these fields. Additionally, it opens the door for applications to data collected by other spectrographs having resolution similar to LAMOST. With the upcoming BlueMUSE and MAVIS, we will gain access to a whole new range of chemical abundances with higher precision, especially critical s-process elements such as [Y/Fe] and [Ba/Fe] that provide key age diagnostics for stellar targets.

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