# ProMage: fast galaxy magnitudes emulation combining SED forward-modelling and machine learning

## TL;DR

ProMage is a neural network that rapidly emulates galaxy magnitude calculations from physical properties, achieving a 10,000-fold speed-up with high accuracy, facilitating large-scale galaxy surveys and modeling.

## Contribution

It introduces a neural network model that significantly accelerates galaxy magnitude computations from physical parameters, enabling efficient analysis for large surveys.

## Key findings

- Achieves a 10,000x speed-up in magnitude computation.
- Maintains per-mille accuracy for 99% of sources.
- Applicable to next-generation galaxy surveys.

## Abstract

We present ProMage, a feed-forward neural network that emulates the computation of observer- and rest-frame magnitudes from the generative galaxy SED package ProSpect. The network predicts magnitudes conditioned on input galaxy physical properties, including redshift, star formation history, gas and dust parameters. ProMage accelerates magnitude computation by a factor of $10^4$ compared to ProSpect, while achieving per-mille relative accuracy for $99\%$ of sources in the test set across the $g,r,i,z,y$ Hyper Suprime-Cam bands. This acceleration is key to enabling fast inference of galaxy physical properties in next-generation Stage IV surveys and to generating large catalogue realisations in forward-modelling frameworks such as GalSBI-SPS.