Showing 1–20 of 31 results
/ Date/ Name
Nov 2, 2023Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data SetsOct 21, 2024Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens AnalysisNov 1, 2022Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionDec 28, 2021DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology ClassificationJul 30, 2022Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based InferenceNov 10, 2022Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based InferenceOct 23, 2024Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty QuantificationJan 15, 2025Deep inference of simulated strong lenses in ground-based surveysNov 19, 2025Toward Complete Merger Identification at Cosmic Noon with Deep LearningMar 10, 2026First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based InferenceSep 16, 2021DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey ImagesJul 24, 2024Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based InferenceNov 28, 2023Domain Adaptation for Measurements of Strong Gravitational LensesJul 7, 2022Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural NetworksNov 13, 2024DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning MethodsOct 14, 2025Beyond the Brightest: A Deep Learning Approach to Identifying Major and Minor Galaxy Mergers in CANDELS at $z \sim 1$Nov 16, 2022DIGS: Deep Inference of Galaxy Spectra with Neural Posterior EstimationJul 9, 2023The LSST AGN Data Challenge: Selection methodsMar 15, 2022Machine Learning and CosmologyNov 24, 2020DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning