research
Ten Million Galaxies Get Multi-Band Structural Posteriors
A transfer-learned network estimates bulge fraction, radius and flux across the Hyper Suprime-Cam wide survey, with full coverage for about 3.9 million objects.
Summary
A transfer-learned network estimates bulge fraction, radius and flux across the Hyper Suprime-Cam wide survey, with full coverage for about 3.9 million objects.
The authors report calibration within about six percent mean deviation after adapting with roughly 0.5 percent of the data. They exclude high-redshift g-band measurements where rest-frame ultraviolet light degrades performance.
Why it matters
A transfer-learned network estimates bulge fraction, radius and flux across the Hyper Suprime-Cam wide survey, with full coverage for about 3.9 million objects.
Limits and context
No additional limitation was separately recorded.
Key claims
A transfer-learned network estimates bulge fraction, radius and flux across the Hyper Suprime-Cam wide survey, with full coverage for about 3.9 million objects.
Evidence: source-2026-10-26-021
Sources
- arXiv preprint 2610.32913, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.