arXiv subjects

Yuan-Sen Ting

Publications and source records attributed to Yuan-Sen Ting.

At least 91 records · Page 5Linked to original sources

arXiv2024

Planets Around Solar Twins/Analogs (PASTA) I.: High precision stellar chemical abundance for 17 planet-hosting stars and the condensation temperature trend

The Sun is depleted in refractory elements compared to nearby solar twins, which may be linked to the formation of giant or terrestrial planets. Here we present high-resolution, high signal-to-noise spectroscopic data for 17 solar-like stars hosting planets, obtained with Magellan II/MIKE, to investigate whether this depletion is related to planet formation. We derive stellar parameters, including stellar atmosphere, age, radius, mass, and chemical abundances for 22 elements from carbon to europium through line-by-line differential analysis. Our uncertainties range from 0.01 dex for Fe and Si to 0.08 dex for Sr, Y, and Eu. By comparing the solar abundances to those of the 17 stars, we investigate the differential abundance ([X/Fe]$_{\rm solar}$ - [X/Fe]$_{\rm star}$) versus condensation temperature ($T_c$) trend. In particular, we apply Galactic chemical evolution corrections to five solar twins within the full sample. Our results conform to previous studies that the Sun is relatively depleted in refractory compared to volatile elements. For both five solar twins and the rest of solar-like stars, we find that all stars hosting known gas giant planets exhibit negative $T_c$ trend slopes, suggesting that the Sun is relatively depleted in refractory elements compared to similar giant-planet-host stars. Additionally, we find no correlation between $T_c$ trend slopes and the total mass of detected terrestrial planets in each system, suggesting that terrestrial planet formation may not be the cause of refractory element depletion in the Sun.

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arXiv2024

AstroMLab 1: Who Wins Astronomy Jeopardy!?

We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future.

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arXiv2024

CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, such models may be affected by miscalibration that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the Contrastive Learning and Adaptive KNN for Photometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, we point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional deep learning methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP.

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arXiv2024

Double-lined Spectroscopic Binaries from the LAMOST Low-Resolution Survey

We report on a data-driven spectral model that we have developed for the identification of double-lined spectroscopic binary stars (SB2s) in the LAMOST low-resolution survey (R$\sim$1800). Employing simultaneous fitting with both single-star and binary-star models, we detected over 4800 SB2 candidates, where both components are detectably contributing to the spectrum, from an initial pool of 2.6 million objects. Tests show that our model favors FGK-type main-sequence binaries with high mass ratio ($q\geq$ 0.7) and large radial velocity separation ($Δ\rm RV \geq$ 100~km$\,$s$^{-1}$). Almost all of these candidates are positioned above the main sequence in the color-magnitude diagram, indicating their binary nature. Additionally, we utilized various observational data, including spectroscopy, photometry, parallax, and extinction, to determine multiple physical parameters such as the effective temperature, age, metallicity, radial velocity, mass, mass ratio, stellar radius, along with their associated uncertainties for these SB2 candidates. For the 44 candidates with seven or more observational epochs, we provide complete orbital solutions. We make available catalogs containing various stellar parameters for identified SB2 systems.

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arXiv2024

AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy

Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality astronomical MCQs, this study aims to quantitatively assess specialized LLMs in astronomy. We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model. We demonstrate that this performance degradation can be partially mitigated by utilizing high-quality data for continual pretraining, such as summarized text from arXiv. Despite the observed catastrophic forgetting in smaller models, our results indicate that continual pretraining on the 70B model can yield significant improvements. However, the current supervised fine-tuning dataset still constrains the performance of instruct models. In conjunction with this study, we introduce a new set of models, AstroLLaMA-3-8B and AstroLLaMA-2-70B, building upon the previous AstroLLaMA series.

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arXiv2024

Uncovering the first-infall history of the LMC through its dynamical impact in the Milky Way halo

The gravitational interactions between the LMC and the Milky Way cause dynamical perturbations in the MW halo, leading to biased distributions of stellar density and kinematics. We run 50 high-resolution N-body simulations exploring varying masses and halo shapes of the MW and LMC to study the evolution of LMC-induced perturbations. By measuring mean velocities of simulated halo stars, we identify a discontinuity between the first-infall and second-passage scenarios of the LMC's orbital history. In the first infall, the Galactocentric latitudinal velocity hovers around 16 km/s for stars at 50-100 kpc, while it subsides to about 8 km/s in the second-passage scenario. We demonstrate that this reduced perturbation magnitude in the second-passage scenario is mainly due to the short dynamical times of the Galactic inner halo and the lower velocity of the LMC during its second infall into the MW. Using a subset of $\sim 1100$ RR Lyrae stars located in the outer halo ($50 \leq R_{\mathrm{GC}} < 100$ kpc) with precise distance estimates from Gaia, we find the mean latitudinal velocity ($v_{b}$) in the Galactocentric frame to be $\langle v_{b} \rangle = 18.1 \pm 4.1$ km/s. The observation supports the first-infall scenario with a massive LMC ($\sim 2.1 \times 10^{11} \mathrm{M}_{\odot}$) at infall, an oblate MW halo with a virial mass $M_{200} < 1.4 \times 10^{12} \mathrm{M}_{\odot}$ and a flattening parameter $q > 0.7$. Our study indicates that LMC-induced kinematic disturbances can reveal its orbital history and key characteristics, as well as those of the MW. This approach shows promise in helping determine fundamental parameters of both galaxies.

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arXiv2024

The Rapid Formation of the Metal Poor Milky Way

Our understanding of the assembly timeline of the Milky Way has been transforming along with the dramatic increase in astrometric and spectroscopic data available over the past several years. Many substructures in chemo-dynamical space have been discovered and identified as the remnants of various galactic mergers. To investigate the timeline of these mergers we select main sequence turn off & subgiant stars (MSTOs) from the H3 survey, finding members in seven metal poor components of the halo: GSE, the Helmi Streams, Thamnos, Sequoia, Wukong/LMS-1, Arjuna, and I'itoi. We also select out the metal poor in situ disk to facilitate comparison to the evolution of the Milky Way itself at these early epochs. We fit individual isochrone ages to the MSTOs in each of these substructures and use the resulting age distributions to infer simple star formation histories. For GSE we resolve an extended star formation history that truncates $\approx10$ Gyr ago, as well as a clear age -- metallicity relation. From this age distribution and measured star formation history we infer that GSE merged with the Milky Way at a time $9.5-10.2$ Gyr ago, in agreement with previous estimates. We infer that the other mergers occurred at various times ranging from $9-13$ Gyr ago, and that the metal poor component of the disk built up within only a few billion years. These results reinforce the emerging picture that both the disk and halo of the Milky Way experienced a rapid assembly.

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arXiv2024

Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural Networks and Lightcone Simulations

We present a proof-of-concept simulation-based inference on $Ω_{\rm m}$ and $σ_{8}$ from the SDSS BOSS LOWZ NGC catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid lightcone simulations, ${\rm L{\scriptsize -PICOLA}}$, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, ${\texttt{Minkowski-PointNet}}$. We also add relatively more accurate ${\rm G{\scriptsize ADGET}}$ mocks to obtain robust and generalizable neural networks. By explicitly learning the representations which reduces the discrepancies between the two different datasets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find $Ω_{\rm m}=0.339 \pm 0.056$ and $σ_{8}=0.801 \pm 0.061$, inferred only from the distribution of galaxies in the lightcone slices without relying on any indirect summary statistics. A single machine that best adapts to the ${\rm G{\scriptsize ADGET}}$ mocks yields a tighter prediction of $Ω_{\rm m}=0.282 \pm 0.014$ and $σ_{8}=0.786 \pm 0.036$. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.

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arXiv2024

TransformerPayne: enhancing spectral emulation accuracy and data efficiency by capturing long-range correlations

Stellar spectra emulators often rely on large grids and tend to reach a plateau in emulation accuracy, leading to significant systematic errors when inferring stellar properties. Our study explores the use of Transformer models to capture long-range information in spectra, comparing their performance to The Payne emulator (a fully connected multilayer perceptron), an expanded version of The Payne, and a convolutional-based emulator. We tested these models on synthetic spectra grids, evaluating their performance by analyzing emulation residuals and assessing the quality of spectral parameter inference. The newly introduced TransformerPayne emulator outperformed all other tested models, achieving a mean absolute error (MAE) of approximately 0.15% when trained on the full grid. The most significant improvements were observed in grids containing between 1000 and 10,000 spectra, with TransformerPayne showing 2 to 5 times better performance than the scaled-up version of The Payne. Additionally, TransformerPayne demonstrated superior fine-tuning capabilities, allowing for pretraining on one spectral model grid before transferring to another. This fine-tuning approach enabled up to a tenfold reduction in training grid size compared to models trained from scratch. Analysis of TransformerPayne's attention maps revealed that they encode interpretable features common across many spectral lines of chosen elements. While scaling up The Payne to a larger network reduced its MAE from 1.2% to 0.3% when trained on the full dataset, TransformerPayne consistently achieved the lowest MAE across all tests. The inductive biases of the TransformerPayne emulator enhance accuracy, data efficiency, and interpretability for spectral emulation compared to existing methods.

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arXiv2024

Galactic-Seismology Substructures and Streams Hunter with LAMOST and Gaia. I. Methodology and Local Halo Results

We present a novel, deep-learning based method -- dubbed Galactic-Seismology Substructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search for substructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies on a combined application of Siamese Neural Networks to transform the phase space information and the K-means algorithm for the clustering. As a validation test, we apply GS$^{3}$ Hunter to a subset of the Feedback in Realistic Environments (FIRE) cosmological simulations. The stellar streams and substructures thus identified are in good agreement with corresponding results reported earlier by the FIRE team. In the same vein, we apply our method to a subset of local halo stars from the Gaia Early Data Release 3 and GALAH DR3 datasets, and recover several, previously known dynamical groups, such as Thamnos 1+2, Hot Thick Disk, ED-1, L-RL3, Helmi 1+2, and Gaia-Sausage-Enceladus, Sequoia, VRM, Cronus, Nereus. Finally, we apply our method without fine-tuning to a subset of K-giant stars located in the inner halo region, obtained from the LAMOST Data Release 5 (DR5) dataset. We recover three, previously known structures (Sagittarius, Hercules-Aquila Cloud, and the Virgo Overdensity), but we also discover a number of new substructures. We anticipate that GS$^{3}$ Hunter will become a useful tool for the community dedicated to the search of stellar streams and structures in the Milky Way (MW) and the Local group, thus helping advance our understanding of the stellar inner and outer halos, and of the assembly and tidal stripping history in and around the MW.

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arXiv2024

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.

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arXiv2024

Birth of the Galactic Disk Revealed by the H3 Survey

We use chemistry ([alpha/Fe] and [Fe/H]), main sequence turnoff ages, and kinematics determined from H3 Survey spectroscopy and Gaia astrometry to identify the birth of the Galactic disk. We separate in-situ and accreted stars on the basis of angular momenta and eccentricities. The sequence of high-alpha in-situ stars persists down to at least [Fe/H]=-2.5 and shows unexpected non-monotonic behavior: with increasing metallicity the population first declines in [alpha/Fe], then increases over the range -1.3<[Fe/H]<-0.7, and then declines again at higher metallicities. The number of stars in the in-situ population rapidly increases above [Fe/H]=-1. The average kinematics of these stars are hot and independent of metallicity at [Fe/H]<-1 and then become increasingly cold and disk-like at higher metallicities. The ages of the in-situ, high-alpha stars are uniformly very old (13 Gyr) at [Fe/H]<-1.3, and span a wider range (8-12 Gyr) at higher metallicities. Interpreting the chemistry with a simple chemical evolution model suggests that the non-monotonic behavior is due to a significant increase in star formation efficiency, which began 13 Gyr ago. These results support a picture in which the first 1 Gyr of the Galaxy was characterized by a "simmering phase" in which the star formation efficiency was low and the kinematics had substantial disorder with some net rotation. The disk then underwent a dramatic transformation to a "boiling phase", in which the star formation efficiency increased substantially, the kinematics became disk-like, and the number of stars formed increased tenfold. We interpret this transformation as the birth of the Galactic disk at z~4. The physical origin of this transformation is unclear and does not seem to be reproduced in current galaxy formation models.

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arXiv2024

Our Halo of Ice and Fire: Strong Kinematic Asymmetries in the Galactic Halo

The kinematics of the stellar halo hold important clues to the assembly history and mass distribution of the Galaxy. In this study, we map the kinematics of stars across the Galactic halo with the H3 Survey. We find a complex distribution that breaks both azimuthal symmetry about the $Z$-axis and mirror symmetry about the Galactic plane. This asymmetry manifests as large variations in the radial velocity dispersion $σ_r$ from as ``cold'' as 70 $\text{km}\text{ s}^{-1}$ to as ``hot'' as 160 $\text{km}\text{ s}^{-1}$. We use stellar chemistry to distinguish accreted stars from in-situ stars in the halo, and find that the accreted population has higher $σ_r$ and radially biased orbits, while the in-situ population has lower $σ_r$ and isotropic orbits. As a result, the Galactic halo kinematics are highly heterogeneous and poorly approximated as being spherical or axisymmetric. We measure radial profiles of $σ_r$ and the anisotropy parameter $β$ over Galactocentric radii $10-80\text{ kpc}$, and find that discrepancies in the literature are due to the nonspherical geometry and heterogeneous nature of the halo. Investigating the effect of strongly asymmetric $σ_r$ and $β$ on equilibrium models is a path forward to accurately constraining the Galactic gravitational field, including its total mass.

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arXiv2024

The Scaling Law in Stellar Light Curves

Analyzing time series of fluxes from stars, known as stellar light curves, can reveal valuable information about stellar properties. However, most current methods rely on extracting summary statistics, and studies using deep learning have been limited to supervised approaches. In this research, we investigate the scaling law properties that emerge when learning from astronomical time series data using self-supervised techniques. By employing the GPT-2 architecture, we show the learned representation improves as the number of parameters increases from $10^4$ to $10^9$, with no signs of performance plateauing. We demonstrate that a self-supervised Transformer model achieves 3-10 times the sample efficiency compared to the state-of-the-art supervised learning model when inferring the surface gravity of stars as a downstream task. Our research lays the groundwork for analyzing stellar light curves by examining them through large-scale auto-regressive generative models.

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arXiv2024

Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery

Identifying and predicting the factors that contribute to the success of interdisciplinary research is crucial for advancing scientific discovery. However, there is a lack of methods to quantify the integration of new ideas and technological advancements in astronomical research and how these new technologies drive further scientific breakthroughs. Large language models, with their ability to extract key concepts from vast literature beyond keyword searches, provide a new tool to quantify such processes. In this study, we extracted concepts in astronomical research from 297,807 publications between 1993 and 2024 using large language models, resulting in a set of 24,939 concepts. These concepts were then used to form a knowledge graph, where the link strength between any two concepts was determined by their relevance through the citation-reference relationships. By calculating this relevance across different time periods, we quantified the impact of numerical simulations and machine learning on astronomical research. The knowledge graph demonstrates two phases of development: a phase where the technology was integrated and another where the technology was explored in scientific discovery. The knowledge graph reveals that despite machine learning has made much inroad in astronomy, there is currently a lack of new concept development at the intersection of AI and Astronomy, which may be the current bottleneck preventing machine learning from further transforming the field of astronomy.

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arXiv2024

A path towards constraining the evolution of the interstellar medium and outflows in the Milky Way using APOGEE

In recent years, the study of the Milky Way has significantly advanced due to extensive spectroscopic surveys of its stars, complemented by astroseismic and astrometric data. However, it remains disjoint from recent advancements in understanding the physics of the Galactic interstellar medium (ISM). This paper introduces a new model for the chemical evolution of the Milky Way that can be constrained on stellar data, because it combines a state-of-the-art ISM model with a Milky Way stellar disc model. Utilizing a dataset of red clump stars from APOGEE, known for their precise ages and metallicities, we concentrate on the last 6 billion years -- a period marked by Milky Way's secular evolution. We examine the oxygen abundance in the low-$α$ disc stars relative to their ages and birth radii, validating or constraining critical ISM parameters that remain largely unexplored in extragalactic observations. The models that successfully reproduce the radius -- metallicity distribution and the age -- metallicity distribution of stars without violating existing ISM observations indicate a need for modest differential oxygen enrichment in Galactic outflows, meaning that the oxygen abundance of outflows is higher than the local ISM abundance, irrespective of outflow mass loading. The models also suggest somewhat elevated ISM gas velocity dispersion levels over the past 6 billion years compared to galaxies of similar mass. The extra turbulence necessary could result from energy from gas accretion onto the Galaxy, supernovae clustering in the ISM, or increased star formation efficiency per freefall time. This work provides a novel approach to constraining the Galactic ISM and outflows, leveraging the detailed insights available from contemporary Milky Way surveys.

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arXiv2024

Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform

Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simulation used to model the noise properties of the galaxy distribution. In a recent paper, we introduce an unsupervised learning approach which offers an alternative method that avoids the dependence on mock catalogs, by learning parity violation directly from observational data. However, the Convolutional Neural Network (CNN) model utilized by our previous unsupervised approach struggles to extend to more realistic scenarios where data is limited. We propose a novel method, the Neural Field Scattering Transform (NFST), which enhances the Wavelet Scattering Transform (WST) technique by adding trainable filters, parameterized as a neural field. We first tune the NFST model to detect parity violation in a simplified dataset, then compare its performance against WST and CNN benchmarks across varied training set sizes. We find the NFST can detect parity violation with $4\times$ less data than the CNN and $32\times$ less than the WST. Furthermore, in cases with limited data the NFST can detect parity violation with up to $6σ$ confidence, where the WST and CNN fail to make any detection. We identify that the added flexibility of the NFST, and particularly the ability to learn asymmetric filters, as well as the specific symmetries built into the NFST architecture, contribute to its improved performance over the benchmark models. We further demonstrate that the NFST is readily interpretable, which is valuable for physical applications such as the detection of parity violation.

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arXiv2024

New Evidence of Binarity in Young α-Rich Turn-off and Subgiant Stars: Fast Rotation and Strong Magnetic Activity

Young α-rich (YAR) stars within the old Galactic thick disk exhibit a dual characteristic of relative youth determined with asteroseismology and abundance enhancement in α elements measured from high-resolution spectroscopy. The youth origin of YAR stars has been proposed to be binary evolution via mass transfer or stellar mergers. If that is the case, YAR stars should spin rapidly and thus be magnetically active, because they are mass and angular momentum gainers. In this study, to seek this binary footprint we select YAR stars on the main-sequence turn-off or the subgiant branch (MSTO-SGB) from APOGEE DR17, whose ages and projected rotation velocities (vsini) can be precisely measured. With APOGEE vsini and LAMOST spectra, we find that YAR stars are indeed fast rotators and magnetically active. In addition, we observe low [C/N] ratios and high Gaia RUWE in some YAR stars, suggesting that these MSTO-SGB stars probably have experienced mass transfer from red-giant companions. Our findings underscore that magnetic activity can serve as a valuable tool for probing the binary evolution for other chemically peculiar stars, such as red giants with lithium anomalies and carbon-enhanced metal-poor stars.

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