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20242026
most citedPredictions of the LSST Solar System Yield: Near-Earth Objects, Main Belt Asteroids, Jupiter Trojans, and Trans-Neptunian Objects

39 citations · 56 across the 12 of their papers we have counts for

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6 papers · 1 filter

astro-ph.IM2026

Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

Argyro Sasli, Maojie Xu, Alexandra Junell +21

Time-domain surveys discover thousands of transients per year, but the spectroscopic identification of rare and physically peculiar objects remains rate-limited by closed-set class…

astro-ph.IM2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

Aritra Ghosh, Drew Oldag, Michael Tauraso +29

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that inc…

astro-ph.IM2026

Diagnosing the Effects of Spectroscopic Training Set Imperfection on Photometric Redshift Performance

Alice Crafford, Alex I. Malz, Tianqing Zhang +11

Most LSST extragalactic science will rely on photometric redshifts (photo-) to extract distance information for the galaxies. However, an incomplete or non-representative traini…

astro-ph.IM20251 cited

Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)

T. Zhang, E. Charles, J. F. Crenshaw +87

We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and…

astro-ph.IM20252 cited

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

The RAIL Team, Jan Luca van den Busch, Eric Charles +30

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…

astro-ph.IM20246 cited

DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST

Grant Merz, Xin Liu, Samuel Schmidt +11

Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for…