Detecting neutral hydrogen at z > 3 in large spectroscopic surveys of quasars

TL;DR

This paper develops a random forest pipeline to identify high column-density neutral hydrogen clouds in large quasar surveys, achieving over 90% accuracy and providing a substantial catalog of Lyman limit systems at z > 3.

Contribution

The paper introduces a novel machine learning method for efficiently detecting LLSs in large spectroscopic datasets, validated on mock and real survey data.

Findings

Achieved >90% completeness and purity in LLS detection

Identified ~6600 LLSs in SDSS DR16 data at z~3.1-4.0

Measured LLS incidence rate of 2.32 +/- 0.08 per unit redshift

Abstract

We present a pipeline based on a random forest classifier for the identification of high column-density clouds of neutral hydrogen (i.e. the Lyman limit systems, LLSs) in absorption within large spectroscopic surveys of z>3 quasars. We test the performance of this method on mock quasar spectra that reproduce the expected data quality of the Dark Energy Spectroscopic Instrument (DESI) and the WHT Enhanced Area Velocity Explorer (WEAVE) surveys, finding >90% completeness and purity for N(HI)> 10^17.2 cm^-2 LLSs against quasars of g < 23 mag at z~3.5-3.7. After training and applying our method on 10,000 quasar spectra at z~3.5-4.0 from the Sloan Digital Sky Survey (Data Release 16), we identify ~6600 LLSs with N(HI)>10^17.5 cm^-2 between z~3.1-4.0 with a completeness and purity of >90% for the classification of LLSs. Using this sample, we measure a number of LLSs per unit redshift of 2.32 +/- 0.08 at z=[3.3,3.6]. We also present results on the performance of random forest for the measurement of the LLS redshifts and HI column densities, and for the identification of broad absorption line quasars.