Astronomy
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Benchmarks
BIG-bench
Most implemented
Self-Normalizing Neural Networks
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
Prediction-Powered Inference
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Extracting the main trend in a dataset: the Sequencer algorithm
Deep-Learnt Classification of Light Curves
Papers
Exoplanet Classification through Vision Transformers with Temporal Image Analysis
The classification of exoplanets has been a longstanding challenge in astronomy, requiring significant computational and observational resources. Traditional methods demand substantial effort, time, and cost, highlightin…
AstronomyCan AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in la…
AstronomyMorphology classificationobject-detectionObject DetectionCategory-based Galaxy Image Generation via Diffusion Models
Conventional galaxy generation methods rely on semi-analytical models and hydrodynamic simulations, which are highly dependent on physical assumptions and parameter tuning. In contrast, data-driven generative models do n…
AstronomyImage GenerationStatistical Machine Learning for Astronomy -- A Textbook
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data …
AstronomyBayesian InferenceGaussian ProcessesUncertainty QuantificationEmulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations are…
AstronomyUncertainty QuantificationUnsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite it…
Astronomyscientific discovery