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Astronomy

Astronomy is the study of everything in the universe beyond Earth’s atmosphere. That includes objects we can see with our naked eyes, like the Sun, the Moon, the planets, and the stars. It also contains objects we can only see with telescopes or other instruments, like faraway galaxies and tiny particles. And it even includes questions about things we can't see, like dark matter and energy.

Papers

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Showing 51–75 of 395 papers

TitleStatusHype
NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms—0
Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning—0
Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515Code0
Self-supervised learning for radio-astronomy source classification: a benchmarkCode0
Quantized symbolic time series approximationCode2
AstroM^3: A self-supervised multimodal model for astronomy—0
Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry—0
Flow Matching for Posterior Inference with Simulator FeedbackCode1
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Hamiltonian Matching for Symplectic Neural Integrators—0
Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae StarsCode0
Data and models for sunspots detection in solar images captured with smart telescopesCode0
A comparative study of NeuralODE and Universal ODE approaches to solving Chandrasekhar White Dwarf equation—0
Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA—0
CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessmentCode1
What is the Role of Large Language Models in the Evolution of Astronomy Research?—0
AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy—0
Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy—0
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning—0
AstroMAE: Redshift Prediction Using a Masked Autoencoder with a Novel Fine-Tuning Architecture—0
Maven: A Multimodal Foundation Model for Supernova ScienceCode1
Turbulence Strength C_n^2 Estimation from Video using Physics-based Deep LearningCode1
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy—0
Disentangling Dense Embeddings with Sparse Autoencoders—0
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