SCREAM adapts the CATHODE method to treat stellar streams as feature-space over-densities, incorporates measurement uncertainties into neural network training, and achieves F1=0.745 on GD-1 while recovering faint members and a diffuse cocoon missed by prior methods.
David Shih, Matthew R
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
astro-ph.GA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Characterizing Stellar Streams with Error-Aware Machine Learning
SCREAM adapts the CATHODE method to treat stellar streams as feature-space over-densities, incorporates measurement uncertainties into neural network training, and achieves F1=0.745 on GD-1 while recovering faint members and a diffuse cocoon missed by prior methods.