Emission line predictions for mock galaxy catalogues: a new differentiable and empirical mapping from DESI

Khederlarian, Ashod; Newman, Jeffrey A.; Andrews, Brett H.; Dey, Biprateep; Moustakas, John; Hearin, Andrew; Juneau, Stéphanie; Tortorelli, Luca; Gruen, Daniel; Hahn, ChangHoon; Canning, Rebecca E. A.; Aguilar, Jessica Nicole; Ahlen, Steven; Brooks, David; Claybaugh, Todd; de la Macorra, Axel; Doel, Peter; Fanning, Kevin; Ferraro, Simone; Forero-Romero, Jaime; Gaztañaga, Enrique; Gontcho, Satya Gontcho A.; Kehoe, Robert; Kisner, Theodore; Kremin, Anthony; Lambert, Andrew; Landriau, Martin; Manera, Marc; Meisner, Aaron; Miquel, Ramon; Mueller, Eva-Maria; Muñoz-Gutiérrez, Andrea; Myers, Adam; Nie, Jundan; Poppett, Claire; Prada, Francisco; Rezaie, Mehdi; Rossi, Graziano; Sanchez, Eusebio; Schubnell, Michael; Silber, Joseph Harry; Sprayberry, David; Tarlé, Gregory; Weaver, Benjamin Alan; Zhou, Zhimin; Zou, Hu

doi:10.1093/mnras/stae1189

Title: Emission line predictions for mock galaxy catalogues: a new differentiable and empirical mapping from DESI

Journal Article · 06 May 2024 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stae1189 · OSTI ID:2356917

; Newman, Jeffrey A.; Andrews, Brett H.;

; Moustakas, John;

;

; Canning, Rebecca E. A.; Aguilar, Jessica Nicole; Ahlen, Steven; Brooks, David; Claybaugh, Todd; de la Macorra, Axel; Doel, Peter; Fanning, Kevin; Ferraro, Simone; Forero-Romero, Jaime more »

ABSTRACT 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 brightest optical emission lines (including H α, H β, [O ii], and [O iii]) from a galaxy’s rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman’s rank correlation coefficient ρs > 0.87 between predictions and observations for most lines. Using a non-linear dimensionality reduction technique, we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterize and account for biases in the spectroscopic training sets used for training and calibration of photo-z’s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357

OSTI ID:: 2356917

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 531; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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