Modelling the dusty universe I: Introducing the artificial neural network and first applications to luminosity and colour distributions
C. Almeida (1), C. M. Baugh (1), C. G. Lacey (1), C. S. Frenk (1), G., L. Granato (2), L. Silva (3), A. Bressan (2, 3, 4) ((1) Institute for, Computational Cosmology, University of Durham, UK, (2) INAF, Osservatorio, Astronomico di Padova, Italy, (3) INAF

TL;DR
This paper presents a neural network-based method to accurately predict galaxy spectral energy distributions across a broad wavelength range, enabling efficient generation of large galaxy mock catalogs for various observational selections.
Contribution
The authors introduce a neural network technique trained on hybrid galaxy models to predict SEDs, improving computational efficiency while maintaining accuracy for large galaxy samples.
Findings
Neural network reproduces galaxy luminosities within 10% of detailed models.
Method performs best in sub-millimeter wavelengths, reasonably in mid-IR and UV.
Predictions of galaxy overlaps in UV and sub-mm are consistent with expectations.
Abstract
We introduce a new technique based on artificial neural networks which allows us to make accurate predictions for the spectral energy distributions (SEDs) of large samples of galaxies, at wavelengths ranging from the far-ultra-violet to the sub-millimetre and radio. The neural net is trained to reproduce the SEDs predicted by a hybrid code comprised of the GALFORM semi-analytical model of galaxy formation, which predicts the full star formation and galaxy merger histories, and the GRASIL spectro-photometric code, which carries out a self-consistent calculation of the SED, including absorption and emission of radiation by dust. Using a small number of galaxy properties predicted by GALFORM, the method reproduces the luminosities of galaxies in the majority of cases to within 10% of those computed directly using GRASIL. The method performs best in the sub-mm and reasonably well in the…
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