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Found 24 papers, 5 papers with code
Scaling Laws for Emulation of Stellar Spectra
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.
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
This paper describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory.
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
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.
AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy
We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model.
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.
AstroMLab 1: Who Wins Astronomy Jeopardy!?
This dataset comprises 4, 425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics.
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.
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training.
Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models
Notably, the error remains under 3% for 30-day light curves, whose oscillations are undetectable by a conventional pipeline in 30% cases.
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy.
Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
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.
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.
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.
Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature
We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers using in-context prompting.
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.
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR.
How Many Elements Matter?
Our results demonstrate that cross-element correlations are a much more sensitive probe of hidden structure than dispersion, and they can be measured precisely in a large sample even if star-by-star measurement noise is comparable to the intrinsic scatter.
Astrophysics of Galaxies Solar and Stellar Astrophysics
The GALAH+ Survey: A New Library of Observed Stellar Spectra Improves Radial Velocities and Hints at Motions within M67
GALAH+ is a magnitude-limited survey of high resolution stellar spectra obtained by the HERMES spectrograph at the Australian Astronomical Observatory.
Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
The GALAH+ Survey: Third Data Release
Based on kinematics, 4% are halo stars.
Astrophysics of Galaxies Solar and Stellar Astrophysics
The non-monotonic, strong metallicity dependence of the wide-binary fraction
The negative metallicity correlation at [Fe/H]$>0$ can be inherited from the similar metallicity dependence of close binaries, and radial migration may play a role in enhancing the wide-binary fraction around the solar metallicity.
Solar and Stellar Astrophysics Astrophysics of Galaxies
A Diffuse Metal-Poor Component of the Sagittarius Stream Revealed by the H3 Survey
We speculate that this kinematically diffuse, low metallicity, population is the stellar halo of the Sagittarius progenitor system.
Astrophysics of Galaxies
Interpreting Stellar Spectra with Unsupervised Domain Adaptation
We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation.
Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations.
The Payne: self-consistent ab initio fitting of stellar spectra
We present The Payne, a general method for the precise and simultaneous determination of numerous stellar labels from observed spectra, based on fitting physical spectral models.
Solar and Stellar Astrophysics Astrophysics of Galaxies
