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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 351–375 of 395 papers

TitleStatusHype
Photometric Redshift Error EstimatorsCode0
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrogramsCode0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
Identifying Transients in the Dark Energy Survey using Convolutional Neural NetworksCode0
Converting High-Dimensional Regression to High-Dimensional Conditional Density EstimationCode0
Image reconstruction by domain transform manifold learningCode0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
Scalable Cosmic AI Inference using Cloud Serverless Computing with FMICode0
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional ImagesCode0
Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning MethodCode0
Astroalign: A Python module for astronomical image registrationCode0
Interferometric Neural NetworksCode0
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio TelescopesCode0
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priorsCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional DataCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Learning Bayesian posteriors with neural networks for gravitational-wave inferenceCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Input Selection for Bandwidth-Limited Neural Network InferenceCode0
Seeing into Darkness: Scotopic Visual RecognitionCode0
Prediction of soft proton intensities in the near-Earth space using machine learningCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae StarsCode0
Carving out the low surface brightness universe with NoiseChiselCode0
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