Synthesis models in a probabilistic framework: metrics of fitting

TL;DR

This paper proposes a new metric based on the mean-averaged dispersion from synthesis models to improve the accuracy of physical parameter inference from observational data.

Contribution

It introduces a novel approach using the theoretical mean-averaged dispersion as a fitting metric in synthesis models, addressing limitations of traditional methods.

Findings

The proposed metric enhances the accuracy of physical parameter estimation.

It provides a more reliable measure of fit quality than mean values alone.

The method improves the interpretation of synthesis model results.

Abstract

In general, synthesis models provide the mean value of the distribution of possible integrated luminosities, this distribution (and not only its mean value) being the actual description of the integrated luminosity. Therefore, to obtain the closest model to an observation only provides confi- dence about the precision of such a fit, but not information about the accuracy of the result. In this contribution we show how to overcome this drawback and we propose the use of the theoretical mean-averaged dispersion that can be produced by synthesis models as a metric of fitting to infer accurate physical parameters of observed systems.