{"paper":{"title":"Emission Line Predictions for Mock Galaxy Catalogues: a New Differentiable and Empirical Mapping from DESI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Aaron Meisner, Adam Myers, Andrea Mu\\~noz-Guti\\'errez, Andrew Hearin, Andrew Lambert, Anthony Kremin, Ashod Khederlarian, Axel de la Macorra, Benjamin Alan Weaver, Biprateep Dey, Brett H. Andrews, ChangHoon Hahn, Claire Poppett, Daniel Gruen, David Brooks, David Sprayberry, Enrique Gazta\\~naga, Eusebio Sanchez, Eva-Maria Mueller, Francisco Prada, Graziano Rossi, Gregory Tarl\\'e, Hu Zou, Jaime Forero-Romero, Jeffrey A. Newman, Jessica Nicole Aguilar, John Moustakas, Joseph Harry Silber, Jundan Nie, Kevin Fanning, Luca Tortorelli, Marc Manera, Martin Landriau, Mehdi Rezaie, Michael Schubnell, Peter Doel, Ramon Miquel, Rebecca E. A. Canning, Robert Kehoe, Satya Gontcho A Gontcho, Simone Ferraro, St\\'ephanie Juneau, Steven Ahlen, Theodore Kisner, Todd Claybaugh, Zhimin Zhou","submitted_at":"2024-04-03T20:38:47Z","abstract_excerpt":"We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03055","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.03055/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}