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

Abstrak

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.

Publikasi
The Astrophysical Journal

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