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Abstract AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, cosmology, and astronomical instrumentation. Trained on the complete collection of astronomy-related arXiv papers from 2007 to 2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable proficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9% on the AstroMLab-1 benchmark, greatly outperforming all models—proprietary and open-weight—in the 8-billion parameter class, and performing on par with GPT-4o. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. AstroSage-Llama-3.1-8B is freely available, enabling widespread access to advanced AI capabilities for astronomical education and research. 

ABSTRACT Chemical abundance anomalies in twin stars have recently been considered tell-tale signs of interactions between stars and planets. While such signals are prevalent, their nature remains a subject of debate. On the one hand, exoplanet formation may induce chemical depletion in host stars by locking up refractory elements. On the other hand, exoplanet engulfment can result in chemical enrichment, and both processes potentially produce similar differential signals. In this study, we aim to observationally disentangle these processes by using the Ca ii infrared triplet to measure the magnetic activity of 125 co-moving star pairs with high signal-to-noise ratio, and high-resolution spectra from the Magellan, Keck, and VLT (Very Large Telescope) telescopes. We find that co-natal star pairs in which the two stars exhibit significant chemical abundance differences also show differences in their magnetic activity, with stars depleted in refractories being magnetically more active. Furthermore, the strength of this correlation between differential chemical abundances and differential magnetic activity increases with condensation temperature. One possible explanation is that the chemical anomaly signature may be linked to planet formation, wherein refractory elements are locked into planets, and the host stars become more active due to more efficient contraction during the pre-main-sequence phase or star–planet tidal and magnetic interactions. 

Abstract 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 turnoff and subgiant stars (MSTOs) from the H3 survey, finding members in seven metal-poor components of the halo: Gaia-Sausage/Enceladus (GSE), the Helmi Streams, Thamnos, Sequoia, Wukong/LMS-1, Arjuna, and I’itoi. We also select out a metal-poor in situ population 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 (SFHs). For GSE, we resolve an extended SFH that truncates ≈10 Gyr ago, as well as a clear age–metallicity relation. From this age distribution and measured SFH 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 to 13 Gyr ago, and that the metal-poor in situ Galaxy 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. 

Abstract We present a proof-of-concept simulation-based inference on Ω_mandσ₈from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-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,Minkowski-PointNet. We also add relatively more accurate Gadgetmocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets 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_m= 0.339 ± 0.056 andσ₈= 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadgetmocks yields a tighter prediction of Ω_m= 0.282 ± 0.014 andσ₈= 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data. 

Abstract We presentAugustus, a catalog of distance, extinction, and stellar parameter estimates for 170 million stars from 14 mag <r< 20 mag and with ∣b∣ > 10° drawing on a combination of optical to near-infrared photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from Gaia DR2 and 3D dust extinction maps. After applying quality cuts, we find 125 million objects have “high-quality” posteriors with statistical distance uncertainties of ≲10% for objects with well-constrained stellar types. This is a substantial improvement over the distance estimates derived from Gaia parallaxes alone and in line with the recent results from Anders et al. We find the fits are able to reproduce the dereddened Gaia color–magnitude diagram accurately, which serves as a useful consistency check of our results. We show that we are able to detect large, kinematically coherent substructures in our data clearly relative to the input priors, including the Monoceros Ring and the Sagittarius Stream, attesting to the quality of the catalog. Our results are publicly available at doi:10.7910/DVN/WYMSXV. An accompanying interactive visualization can be found athttp://allsky.s3-website.us-east-2.amazonaws.com. 

Neural network-based emulators for the inference of stellar parameters and elemental abundances represent an increasingly popular methodology in modern spectroscopic surveys. However, these approaches are often constrained by their emulation precision and domain transfer capabilities. Greater generalizability has previously been achieved only with significantly larger model architectures, as demonstrated by Transformer-based models in natural language processing. This observation aligns with neural scaling laws, where model performance predictably improves with increased model size, computational resources allocated to model training, and training data volume. In this study, we demonstrate that these scaling laws also apply to Transformer-based spectral emulators in astronomy. Building upon our previous work with TransformerPayne and incorporating Maximum Update Parametrization techniques from natural language models, we provide training guidelines for scaling models to achieve optimal performance. Our results show that within the explored parameter space, clear scaling relationships emerge. These findings suggest that optimal computational resource allocation requires balanced scaling. Specifically, given a tenfold increase in training compute, achieving an optimal seven-fold reduction in mean squared error necessitates an approximately 2.5-fold increase in dataset size and a 3.8-fold increase in model size. This study establishes a foundation for developing spectral foundational models with enhanced domain transfer capabilities. 

Dwarf galaxy star formation histories are theoretically expected to be bursty, potentially leaving distinct imprints on their chemical evolution. We propose that episodic starbursts with quiescent periods longer than ~100 Myr should lead to discontinuous tracks in a dwarf galaxy’s [ $\alpha$/Fe]-[Fe/H] chemical abundance plane, with metallicity gaps as large as 0.3-0.5 dex at [Fe/H] = -2. This occurs due to continued Fe production by Type Ia supernovae during quiescent periods. We demonstrate that Gaussian mixture models can statistically distinguish discontinuous and continuous tracks based on the Akaike Information Criterion. Applying this method to APOGEE observations of the Sculptor dSph galaxy suggests an episodic star formation history with ~300 Myr quiescent periods. While current dwarf galaxy datasets are limited by small spectroscopic sample sizes, future surveys and extremely large telescopes will enable determining large numbers of precise chemical abundances, opening up the investigation of very short timescales in early dwarf galaxy formation. This unprecedentedly high time resolution of dwarf galaxy formation in the early Universe has important implications for understanding both reionization in the early Universe and the episodic star formation cycle of dwarf galaxies.