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Astronomy
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Category-based Galaxy Image Generation via Diffusion Models
Exoplanet Classification through Vision Transformers with Temporal Image Analysis
Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
Anomaly detection in radio galaxy data with trainable COSFIRE filters
AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
Radiometer Calibration using Machine Learning
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
Detecting streaks in smart telescopes images with Deep Learning
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
AstroLLaVA: towards the unification of astronomical data and natural language
AstroClearNet: Deep image prior for multi-frame astronomical image restoration
Unveiling the Power of Uncertainty: A Journey into Bayesian Neural Networks for Stellar dating
When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
DomainCQA: Crafting Expert-Level QA from Domain-Specific Charts
Scaling Laws for Emulation of Stellar Spectra
A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations
Neuromorphic Cameras in Astronomy: Unveiling the Future of Celestial Imaging Beyond Conventional Limits
Open Science and Artificial Intelligence for supporting the sustainability of the SRC Network: The espSRC case
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems
The R2D2 Deep Neural Network Series for Scalable Non-Cartesian Magnetic Resonance Imaging
Reconstruction of muon bundles in KM3NeT detectors using machine learning methods
AI Agents for Ground-Based Gamma Astronomy
Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges
Leveraging Error Resilience of Iterative Algorithms for Energy Efficiency: from Concept to Implementation
Solving Online Resource-Constrained Scheduling for Follow-Up Observation in Astronomy: a Reinforcement Learning Approach
Sparse Data Generation Using Diffusion Models
Gamma/hadron separation in the TAIGA experiment with neural network methods
Machine Learning in Gamma Astronomy
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