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