A Neural Network Model for the Cosmic Dispersion Measure in the CAMELS Simulations

TL;DR

This paper develops a neural network model to emulate how galaxy feedback affects the distribution of cosmic dispersion measures in FRBs, aiding future cosmological and feedback parameter constraints.

Contribution

It introduces a neural network approach to model feedback effects on the DM distribution across redshifts using CAMELS simulations, highlighting complex feedback influences.

Findings

F parameter does not depend monotonically on feedback parameters.

Largest F values are smaller than current observational constraints.

Model limitations due to small simulation box sizes.

Abstract

The probability distribution, $p(\mathrm{DM})$ of cosmic dispersion measures (DM) measured in fast radio bursts (FRBs) encodes information about both cosmology and galaxy feedback. In this work, we study the effect of feedback parameters in the $p(\mathrm{DM})$ calculated from the full Latin Hypercube of parameters sampled by the CAMELS hydrodynamical simulation suite, building a neural network (NN) model that performs well in emulating the effect of feedback on $p(\mathrm{DM})$ at arbitrary redshifts at $z\leq1$. Using this NN model, we further study the parameter $F\equiv \sigma_{\rm DM} \, z^{1/2}$, which is commonly used to summarize the scatter on $p(\mathrm{DM})$. We find that $F$ does not depend monotonically on every feedback parameter; instead each feedback mechanism jointly influences the final feedback strength in non-trivial ways. Even the largest values of $F$ that we find in our entire parameter space are small compared to the current constraints from observed FRB DMs by Baptista et al. 2024, pointing at the limitations of the CAMELS suite due to the small simulation box sizes. In the future, with larger box-sizes from CAMELS-like suites, similar models can be used to constrain the parameters governing galaxy feedback in the increasing observational samples of FRBs.