arXiv subjects

Yuan-Sen Ting

Publications and source records attributed to Yuan-Sen Ting.

At least 127 records · Page 7Linked to original sources

arXiv2023

C3PO: Towards a complete census of co-moving pairs of stars. I. High precision stellar parameters for 250 stars

We conduct a line-by-line differential analysis of a sample of 125 co-moving pairs of stars (dwarfs and subgiants near solar metallicity). We obtain high precision stellar parameters with average uncertainties in effective temperature, surface gravity and metallicity of 16.5 K, 0.033 dex and 0.014 dex, respectively. We classify the co-moving pairs of stars into two groups, chemically homogeneous (conatal; |Delta[Fe/H]| $\le$ 0.04 dex) and inhomogeneous (non-conatal), and examine the fraction of chemically homogeneous pairs as a function of separation and effective temperature. The four main conclusions from this study are: (1) A spatial separation of \ds = 10$^6$ AU is an approximate boundary between homogeneous and inhomogeneous pairs of stars, and we restrict our conclusions to only consider the 91 pairs with \ds $\le$ 10$^6$ AU; (2) There is no trend between velocity separation and the fraction of chemically homogeneous pairs in the range \dv $\le$ 4 \kms; (3) We confirm that the fraction of chemically inhomogeneous pairs increases with increasing \teff\ and the trend matches a toy model of that expected from planet ingestion; (4) Atomic diffusion is not the main cause of the chemical inhomogeneity. A major outcome from this study is a sample of 56 bright co-moving pairs of stars with chemical abundance differences $\leq$ 0.02 dex (5\%) which is a level of chemical homogeneity comparable to that of the Hyades open cluster. These important objects can be used, in conjunction with star clusters and the \gaia\ ``benchmark'' stars, to calibrate stellar abundances from large-scale spectroscopic surveys.

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arXiv2023

The dawn is quiet here: Rise in [$α$/Fe] is a signature of massive gas accretion that fueled proto-Milky Way

The proto-Milky Way epoch forms the earliest stars in our Galaxy and sets the initial conditions for subsequent disk formation. Recent observations from APOGEE and H3 surveys showed that the [$α$/Fe] ratio slowly declined between [Fe/H] $=-3$ and $-1.3$ until it reached the lowest value ($\sim 0.25$) among the selected in situ metal-poor stars that most likely formed during the proto-Galaxy epoch. [$α$/Fe] rose to meet the traditional high value commonly associated with the thick disk population at [Fe/H] $=-1$. It was suggested that the rise in [$α$/Fe] could be caused by an increase in the star formation efficiency (SFE), known as the "simmering" phase scenario. However, gas inflow also plays a vital role in shaping the star formation history and chemical evolution of galaxies. We investigate this unexpected [$α$/Fe]-rise with a statistical experiment involving a galactic chemical evolution (GCE). Our model has five free parameters: the mass of the initial reservoir of the cold interstellar medium (ISM) at birth, the frequency of Type Ia supernovae (SNe Ia), the cooling timescale of the warm ISM, the SFE, and the inflow rate of fresh gas. The last two free parameters were allowed to change after [$α$/Fe] reached its lowest value, dividing the proto-Galaxy epoch into two phases. We find that the rise in [$α$/Fe] is caused by a large inflow of fresh gas and conclude that the [$α$/Fe]-rise is a signature of the cold mode accretion whose materials formed the prototype Milky Way preceding disk formation. Although the SFE is essential in regulating the chemical evolution, it does not necessarily increase to facilitate the [$α$/Fe]-rise.

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arXiv2023

Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content

In this paper, we tackle the emerging challenge of unintended harmful content generation in Large Language Models (LLMs) with a novel dual-stage optimisation technique using adversarial fine-tuning. Our two-pronged approach employs an adversarial model, fine-tuned to generate potentially harmful prompts, and a judge model, iteratively optimised to discern these prompts. In this adversarial cycle, the two models seek to outperform each other in the prompting phase, generating a dataset of rich examples which are then used for fine-tuning. This iterative application of prompting and fine-tuning allows continuous refinement and improved performance. The performance of our approach is evaluated through classification accuracy on a dataset consisting of problematic prompts not detected by GPT-4, as well as a selection of contentious but unproblematic prompts. We show considerable increase in classification accuracy of the judge model on this challenging dataset as it undergoes the optimisation process. Furthermore, we show that a rudimentary model \texttt{ada} can achieve 13\% higher accuracy on the hold-out test set than GPT-4 after only a few rounds of this process, and that this fine-tuning improves performance in parallel tasks such as toxic comment identification.

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arXiv2023

Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation

Large Language Models (LLMs) hold immense potential to generate synthetic data of high quality and utility, which has numerous applications from downstream model training to practical data utilisation. However, contemporary models, despite their impressive capacities, consistently struggle to produce both coherent and diverse data. To address the coherency issue, we introduce contrastive expert guidance, where the difference between the logit distributions of fine-tuned and base language models is emphasised to ensure domain adherence. In order to ensure diversity, we utilise existing real and synthetic examples as negative prompts to the model. We deem this dual-pronged approach to logit reshaping as STEER: Semantic Text Enhancement via Embedding Repositioning. STEER operates at inference-time and systematically guides the LLMs to strike a balance between adherence to the data distribution (ensuring semantic fidelity) and deviation from prior synthetic examples or existing real datasets (ensuring diversity and authenticity). This delicate balancing act is achieved by dynamically moving towards or away from chosen representations in the latent space. STEER demonstrates improved performance over previous synthetic data generation techniques, exhibiting better balance between data diversity and coherency across three distinct tasks: hypothesis generation, toxic and non-toxic comment generation, and commonsense reasoning task generation. We demonstrate how STEER allows for fine-tuned control over the diversity-coherency trade-off via its hyperparameters, highlighting its versatility.

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arXiv2023

Dynamical masses across the Hertzsprung-Russell diagram

We infer the dynamical masses of stars across the Hertzsprung-Russell (H-R) diagram using wide binaries from the Gaia survey. Gaia's high-precision astrometry measures the wide binaries' orbital motion, which contains the mass information. Using wide binaries as the training sample, we measure the mass of stars across the two-dimensional H-R diagram using the combination of statistical inference and neural networks. Our results provide the dynamical mass measurements for main-sequence stars from 0.1 to 2 M$_\odot$, unresolved binaries and unresolved triples on the main sequence, and the mean masses of giants and white dwarfs. Two regions in the H-R diagram show interesting behaviors in mass, where one of them is pre-main-sequence stars, and the other one may be related to close compact object companions like M dwarf-white dwarf binaries. These mass measurements depend solely on Newtonian dynamics, providing independent constraints on stellar evolutionary models and the occurrence rate of compact objects.

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arXiv2023

Distant Echoes of the Milky Way's Last Major Merger

The majority of the Milky Way's stellar halo consists of debris from our Galaxy's last major merger, the Gaia-Sausage-Enceladus (GSE). In the past few years, stars from GSE have been kinematically and chemically studied in the inner $30$ kpc of our Galaxy. However, simulations predict that accreted debris could lie at greater distances, forming substructures in the outer halo. Here we derive metallicities and distances using Gaia DR3 XP spectra for an all-sky sample of luminous red giant stars, and map the outer halo with kinematics and metallicities out to $100$ kpc. We obtain follow-up spectra of stars in two strong overdensities - including the previously identified Outer Virgo Overdensity - and find them to be relatively metal-rich and on predominantly retrograde orbits, matching predictions from simulations of the GSE merger. We argue that these are apocentric shells of GSE debris, forming $60-90$ kpc counterparts to the $15-20$ kpc shells that are known to dominate the inner stellar halo. Extending our search across the sky with literature radial velocities, we find evidence for a coherent stream of retrograde stars encircling the Milky Way from $50-100$ kpc, in the same plane as the Sagittarius stream but moving in the opposite direction. These are the first discoveries of distant and structured imprints from the GSE merger, cementing the picture of an inclined and retrograde collision that built up our Galaxy's stellar halo.

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arXiv2023

Toward a Spectral Foundation Model: An Attention-Based Approach with Domain-Inspired Fine-Tuning and Wavelength Parameterization

Astrophysical explorations are underpinned by large-scale stellar spectroscopy surveys, necessitating a paradigm shift in spectral fitting techniques. Our study proposes three enhancements to transcend the limitations of the current spectral emulation models. We implement an attention-based emulator, adept at unveiling long-range information between wavelength pixels. We leverage a domain-specific fine-tuning strategy where the model is pre-trained on spectra with fixed stellar parameters and variable elemental abundances, followed by fine-tuning on the entire domain. Moreover, by treating wavelength as an autonomous model parameter, akin to neural radiance fields, the model can generate spectra on any wavelength grid. In the case with a training set of O(1000), our approach exceeds current leading methods by a factor of 5-10 across all metrics.

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arXiv2023

Discovery of the Magellanic Stellar Stream Out to 100 Kiloparsecs

The Magellanic Stream (MS) - an enormous ribbon of gas spanning $140^\circ$ of the southern sky trailing the Magellanic Clouds - has been exquisitely mapped in the five decades since its discovery. However, despite concerted efforts, no stellar counterpart to the MS has been conclusively identified. This stellar stream would reveal the distance and 6D kinematics of the MS, constraining its formation and the past orbital history of the Clouds. We have been conducting a spectroscopic survey of the most distant and luminous red giant stars in the Galactic outskirts. From this dataset, we have discovered a prominent population of 13 stars matching the extreme angular momentum of the Clouds, spanning up to $100^\circ$ along the MS at distances of $60-120$ kpc. Furthermore, these kinemetically-selected stars lie along a [$α$/Fe]-deficient track in chemical space from $-2.5 < \mathrm{[Fe/H]} < -0.5$, consistent with their formation in the Clouds themselves. We identify these stars as high-confidence members of the Magellanic Stellar Stream. Half of these stars are metal-rich and closely follow the gaseous MS, whereas the other half are more scattered and metal-poor. We argue that the metal-rich stream is the recently-formed tidal counterpart to the MS, and speculate that the metal-poor population was thrown out of the SMC outskirts during an earlier interaction between the Clouds. The Magellanic Stellar Stream provides a strong set of constraints - distances, 6D kinematics, and birth locations - that will guide future simulations towards unveiling the detailed history of the Clouds.

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arXiv2023

Weisfeiler-Lehman Graph Kernel Method: A New Approach to Weak Chemical Tagging

Stars' chemical signatures provide invaluable insights into stellar cluster formation. This study utilized the Weisfeiler-Lehman (WL) Graph Kernel to examine a 15-dimensional elemental abundance space. Through simulating chemical distributions using normalizing flows, the effectiveness of our algorithm was affirmed. The results highlight the capability of the WL algorithm, coupled with Gaussian Process Regression, to identify patterns within elemental abundance point clouds correlated with various cluster mass functions. Notably, the WL algorithm exhibits superior interpretability, efficacy and robustness compared to deep sets and graph convolutional neural networks and enables optimal training with significantly fewer simulations (O(10)), a reduction of at least two orders of magnitude relative to graph neural networks.

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arXiv2023

Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

This study investigates the application of Large Language Models (LLMs), specifically GPT-4, within Astronomy. We employ in-context prompting, supplying the model with up to 1000 papers from the NASA Astrophysics Data System, to explore the extent to which performance can be improved by immersing the model in domain-specific literature. Our findings point towards a substantial boost in hypothesis generation when using in-context prompting, a benefit that is further accentuated by adversarial prompting. We illustrate how adversarial prompting empowers GPT-4 to extract essential details from a vast knowledge base to produce meaningful hypotheses, signaling an innovative step towards employing LLMs for scientific research in Astronomy.

astro-ph.IM↗

arXiv2023

The Prevalence of the $α$-bimodality: First JWST $α$-abundance Results in M31

We present initial results from our JWST NIRSpec program to study the $α$-abundances in the M31 disk. The Milky Way has two chemically-defined disks, the low-$α$ and high-$α$ disks, which are closely related to the thin and thick disks, respectively. The origin of the two populations and the $α$-bimodality between them is not entirely clear, although there are now several models that can reproduce the observed features. To help constrain the models and discern the origin, we have undertaken a study of the chemical abundances of the M31 disk using JWST NIRSpec, in order to determine whether stars in M31's disk also show an $α$-abundance bimodality. Approximately 100 stars were observed in our single NIRSpec field at a projected distance of 18 kpc from the M31 center. The 1-D extracted spectra have an average signal-to-noise ratio of 85 leading to statistical metallicity precision of 0.016 dex, $α$-abundance precision of 0.012 dex, and a radial velocity precision 8 km/s. The initial results indicate that, in contrast to the Milky Way, there is no $α$-bimodality in the M31 disk, and no low-$α$ sequence. The entire stellar population falls along a single chemical sequence very similar to the MW's high-alpha component which had a high star formation rate. While this is somewhat unexpected, the result is not that surprising based on other studies that found the M31 disk has a larger velocity dispersion than the MW and is dominated by a thick component. M31 has had a more active accretion and merger history than the MW which might explain the chemical differences.

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arXiv2023

Carbon-Enhanced Metal-Poor star candidates from BP/RP Spectra in $Gaia$ DR3

Carbon-enhanced metal-poor (CEMP) stars comprise almost a third of stars with [Fe/H] < --2, although their origins are still poorly understood. It is highly likely that one sub-class (CEMP-$s$ stars) is tied to mass-transfer events in binary stars, while another sub-class (CEMP-no stars) are enriched by the nucleosynthetic yields of the first generations of stars. Previous studies of CEMP stars have primarily concentrated on the Galactic halo, but more recently they have also been detected in the thick disk and bulge components of the Milky Way. $Gaia$ DR3 has provided an unprecedented sample of over 200 million low-resolution ($R\approx$ 50) spectra from the BP and RP photometers. Training on the CEMP catalog from the SDSS/SEGUE database, we use XGBoost to identify the largest all-sky sample of CEMP candidate stars to date. In total, we find 58,872 CEMP star candidates, with an estimated contamination rate of 12%. When comparing to literature high-resolution catalogs, we positively identify 60-68% of the CEMP stars in the data, validating our results and indicating a high completeness rate. Our final catalog of CEMP candidates spans from the inner to outer Milky Way, with distances as close as $r \sim$ 0.8 kpc from the Galactic center, and as far as $r >$ 30 kpc. Future higher-resolution spectroscopic follow-up of these candidates will provide validations of their classification and enable investigations of the frequency of CEMP-$s$ and CEMP-no stars throughout the Galaxy, to further constrain the nature of their progenitors.

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arXiv2023

Disentangling Stellar Age Estimates from Galactic Chemodynamical Evolution

Stellar ages are key for determining the formation history of the Milky Way, but are difficult to measure precisely. Furthermore, methods that use chemical abundances to infer ages may entangle the intrinsic evolution of stars with the chemodynamical evolution of the Galaxy. In this paper, we present a framework for making probabilistic predictions of stellar ages, and then quantify the contribution of both stellar evolution and Galactic chemical evolution to those predictions using SHAP values. We apply this interpretable prediction framework to both a simulated Milky Way sample containing stars in a variety of evolutionary stages and an APOGEE-mocked sample of red clump stars. We find that in the former case, stellar evolution is the dominant driver for age estimates, while in the latter case, the more restricted evolutionary information causes the model to proxy ages through the chemical evolution model. We show that as a result of the use of non-intrinsic Galactic chemical information, trends estimated with the predicted ages, such as the age-metallicity relation, can deviate from the truth.

astro-ph.GA↗

arXiv2023

Stellar Parameters and Chemical Abundances Estimated from LAMOST-II DR8 MRS based on Cycle-StarNet

Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS model atmospheric theoretical synthetic spectra combined with a domain-adaptation method, we estimate the fundamental stellar parameters (Teff, log g, [Fe/H], vmic, and vmac) and 11 chemical abundances for 1.38 million FGKM-type stars of the Medium-Resolution Spectroscopic Survey (MRS) from LAMOST-II DR8. The domain-adaptation method, Cycle-StarNet, is employed to reduce the gap between observed and synthetic spectra, and the L-BFGS algorithm is used to search for the best-fit synthetic spectra. By combining the 2MASS photometric survey data, Gaia EDR3 parallax, and MIST isochrones, the surface gravities of the stars are constrained after estimating their bolometric luminosities. The accuracy of Teff, log g, and [Fe/H] can reach 150 K, 0.11 dex, and 0.15 dex, evaluated by the PASTEL catalog, asteroseismic samples, and other spectroscopic surveys. The precision of these parameters and elemental abundances ([C/Fe], [Na/Fe], [Mg/Fe], [Si/Fe], [Ca/Fe], [Ti/Fe], [Cr/Fe], [Mn/Fe], [Co/Fe], [Ni/Fe], and [Cu/Fe]) is assessed by repeated observations and validated by cluster members. For spectra with signal-to-noise (S/N) ratios greater than 10, the precision of the three stellar parameters and elemental abundances can achieve 76 K, 0.014 dex, 0.096 dex, and 0.04-0.15 dex. For spectra with S/N ratios higher than 100, the precision stabilizes at 22 K, 0.006 dex, 0.043 dex, and 0.01-0.06 dex. The full LAMOST MRS stellar properties catalog is available online.

astro-ph.SR↗

arXiv2023

Spatial metallicity variations of mono-temperature stellar populations revealed by early-type stars in LAMOST

We investigate the radial metallicity gradients and azimuthal metallicity distributions on the Galactocentric $X$--$Y$ plane using mono-temperature stellar populations selected from LAMOST MRS young stellar sample. The estimated radial metallicity gradient ranges from $-$0.015\,dex/kpc to $-$0.07\,dex/kpc, which decreases as effective temperature decreases (or stellar age increases) at $7500 < T_{\rm eff} < 12500$\,K ($τ< $1.5 Gyr). The azimuthal metallicity excess (metallicity after subtracting radial metallicity gradient, $Δ$\,[M/H]) distributions exhibit inhomogeneities with dispersions of 0.04\,dex to 0.07\,dex, which decrease as effective temperature decreases. We also identify five potential metal-poor substructures with large metallicity excess dispersions. The metallicity excess distributions of these five metal-poor substructures suggest that they contain a larger fraction of metal-poor stars compared to other control samples. These metal-poor substructures may be associated with high-velocity clouds that infall into the Galactic disk from the Galactic halo, which are not quickly well-mixed with the pre-existing ISM of the Galactic disk. As a result, these high-velocity clouds produce some metal-poor stars and the observed metal-poor substructures. The variations of metallicity inhomogeneities with different stellar populations indicate that high-velocity clouds are not well mixed with the pre-existing Galactic disk ISM within 0.3\,Gyr.

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arXiv2023

Chasing the impact of the Gaia-Sausage-Enceladus merger on the formation of the Milky Way thick disc

We employ our Bayesian Machine Learning framework BINGO (Bayesian INference for Galactic archaeOlogy) to obtain high-quality stellar age estimates for 68,360 red giant and red clump stars present in the 17th data release of the Sloan Digital Sky Survey, the APOGEE-2 high-resolution spectroscopic survey. By examining the denoised age-metallicity relationship of the Galactic disc stars, we identify a drop in metallicity with an increase in [Mg/Fe] at an early epoch, followed by a chemical enrichment episode with increasing [Fe/H] and decreasing [Mg/Fe]. This result is congruent with the chemical evolution induced by an early-epoch gas-rich merger identified in the Milky Way-like zoom-in cosmological simulation Auriga. In the initial phase of the merger of Auriga 18 there is a drop in metallicity due to the merger diluting the metal content and an increase in the [Mg/Fe] of the primary galaxy. Our findings suggest that the last massive merger of our Galaxy, the Gaia-Sausage-Enceladus, was likely a significant gas-rich merger and induced a starburst, contributing to the chemical enrichment and building of the metal-rich part of the thick disc at an early epoch.

astro-ph.GA↗

arXiv2023

Validating Stellar Abundance Measurements from Multi-Resolution Spectroscopy

Large-scale surveys will provide spectroscopy for $\sim$50 million resolved stars in the Milky Way and Local Group. However, these data will have a high degree of heterogeneity and most will be low-resolution ($R<10000$), posing challenges to measuring consistent and reliable stellar labels. Here, we introduce a framework for identifying and remedying these issues. By simultaneously fitting the full spectrum and Gaia photometry with the Payne, we measure $\sim$40 abundances for 8 red giants in M15. From degraded quality Keck/HIRES spectra, we evaluate trends with resolution and S/N and find that (i) $\sim$20 abundances are recovered consistently within $\lesssim$0.1 dex agreement and with $\lesssim$0.05-0.15~dex systematic uncertainties from $10000\lesssim R\lesssim80000$; (ii) for 9 elements (C, Mg, Ca, Sc, Ti, Fe, Ni, Y, Nd), this systematic precision and accuracy extends down to $R\sim2500$; and (iii) while most elements do not exhibit strong S/N-dependent systematics, there are non-negligible biases for 4 elements (C, Mg, Ca, and Dy) below $\text{S/N}\sim10$ pixel$^{-1}$. We compare statistical uncertainties from MCMC sampling to the easier-to-compute Cramér-Rao bounds and find that they agree for $\sim$75% of elements, indicating the latter to be a reliable and faster way to estimate uncertainties. Our analysis illustrates the great promise of low-resolution spectroscopy for stellar chemical abundance work, and ongoing improvements to stellar models (e.g., 3D-NLTE physics) will only further extend its viability to more elements and to higher precision and accuracy.

astro-ph.SR↗

arXiv2023

Ba-enhanced dwarf and subgiant stars in the LAMOST Galactic surveys

Ba-enhanced stars are interesting probes of stellar astrophysics and Galactic formation history. In this work, we investigate the chemistry and kinematics for a large sample of Ba-enhanced ([Ba/Fe]$>$1.0) dwarf and subgiant stars with $5000 < T_{\rm eff }< 6700$\,K from LAMOST. We find that both stellar internal evolution process and external mass exchange due to binary evolution are responsible for the origins of the Ba-enhancement of our sample stars. About one third of them exhibit C and N enhancement and ultraviolet brightness excess, indicating they are products of binary evolution. The remaining Ba-enhanced stars with normal C and N abundances are mostly warm stars with $T_{\rm eff} > 6000$\,K. They are likely consequences of stellar internal elemental transport processes, but they show very different elemental patterns to the hotter Am/Fm stars. Our results reveal a substantially lack of high-[$α$/Fe] Ba-enhanced stars in the [Fe/H]--[$α$/Fe] plane, which we dub as a {\em high-$α$ desert}. We suggest it is due to a lower efficiency for producing Ba-enhanced stars by low-mass AGB progenitors in binary systems. Our results call for detailed modellings for these Ba-enhanced stellar peculiars, in the context of both stellar internal elemental transport and external mass accretion.

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