SILVERRUSH X: Machine Learning-Aided Selection of $9318$ LAEs at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ from the HSC SSP and CHORUS Survey Data

TL;DR

This paper introduces a new catalog of over 9,300 Lyα emitter candidates at multiple redshifts, using machine learning to efficiently select genuine objects from large imaging surveys, validated by spectroscopic data and consistent with prior results.

Contribution

The study develops a convolutional neural network for LAE selection, achieving high completeness and low contamination, and provides a large, validated LAE catalog from Subaru survey data.

Findings

Achieved 94% completeness in LAE identification.

Contamination rate of only 1% in the selected sample.

Catalog includes 177 spectroscopically confirmed LAEs.

Abstract

We present a new catalog of $9318$ Ly$\alpha$ emitter (LAE) candidates at $z = 2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to $25.0$ deg$^2$) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam (HSC SSP) and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS). We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of $94$% and a contamination rate of $1$%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include $177$ LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are $\simeq 80$-$100$% at bright NB magnitudes of $\lesssim 24$ mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage.