Neural Network identification of Dark Star Candidates. I. Photometry

TL;DR

This paper develops a neural network approach to efficiently identify Dark Star candidates in JWST photometric data, confirming previous candidates and discovering new ones, thus enhancing the search for early universe dark matter-affected stars.

Contribution

The paper introduces a neural network method for rapid identification of Dark Star candidates in large photometric datasets, improving speed and efficiency over traditional chi-squared minimization techniques.

Findings

Confirmed two known dark star candidates using neural networks.

Discovered six new photometric dark star candidates across redshifts 9 to 14.

Neural network approach is ~10,000 times faster than previous methods.

Abstract

The formation of the first stars in the universe could be significantly impacted by the effects of Dark Matter (DM). Namely, if DM is in the form of Weakly Interacting Massive Particles (WIMPs), it could lead to the formation (at $z\sim 25-10$) of stars that are powered by DM annihilations alone, i.e. Dark Stars (DSs). Those objects can grow to become supermassive ($M\sim 10^6 \Msun$) and shine as bright as a galaxy ($L\sim 10^8 \Msun)$. Using a simple $\chi^2$ minimization, the first three DSs photometric candidates (i.e. \JADESeleven, \JADEStwelve, and \JADESzthirteen) were identified by \cite{Ilie:2023JADES}. Our goal is to develop tools to streamline the identification of such candidates within the rather large publicly available high redshift JWST data sets. We present here the key first step in achieving this goal: the development and implementation of a feed-forward neural network (FFNN) search for Dark Star candidates, using data from the JWST Advanced Deep Extragalactic Survey (JADES) photometric catalog. Our method reconfirms JADES-GS-z13 and JADES-GS-z11 as dark star candidates, based on the chi-squared goodness of fit test, yet they are $\sim10^4$ times faster than the Neadler-Mead $\chi^2$ minimization method used in \cite{Ilie:2023JADES}. We further identify six {\it new photometric} Dark Star candidates across redshifts $z \sim 9$ to $z \sim 14$. These findings underscore the power of neural networks in modeling non-linear relationships and efficiently analyzing large-scale photometric surveys, advancing the search for Dark Stars.