| 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 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. |
| Authors |
Ashod Khederlarian  , Jeffrey A. Newman , Brett H. Andrews , Biprateep Dey , John Moustakas , Andrew P. Hearin , S. Juneau , Luca Tortorelli , David Gruen , ChangHoon Hahn , Rebecca Canning , Jessica Nicole Aguilar , S. P. Ahlen , David H. Brooks , Todd Claybaugh , Axel de la Macorra , P. Doel , Kevin Fanning , Simone Ferraro , Jaime E. Forero-Romero , E. Gaztañaga , Satya Gontcho A Gontcho , R. Kehoe , T. S. Kisner , Anthony Kremin , Andrew Lambert , Martin Landriau , Marc Manera , Aaron M. Meisner , R. Miquel , Eva-Maria Mueller , A. Muñoz-Gutiérrez , Adam D. Myers  , Jun Nie , Claire Poppett , Francisco Prada , Mehdi Rezaie , Graziano Rossi , E. Sánchez , M. Schubnell , Joseph H. Silber , David Sprayberry , G. Tarlé , B. A. Weaver , Zhimin Zhang , Hu Zou
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