The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning

Abstract

The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph will offer new prospects of using a massive sample of $\sim 1$ million high spectral resolution ($R$ = 20,000) background quasars to map the circumgalactic metal content of foreground galaxies (observed at $R$ = 4000 - 7000), as traced by metal absorption. Such large surveys require specialized analysis methodologies. In the absence of early data, we instead produce synthetic 4MOST high-resolution fibre quasar spectra. To do so, we use the TNG50 cosmological magnetohydrodynamical simulation, combining photo-ionization post-processing and ray tracing, to capture MgII ($\lambda 2796$, $\lambda 2803$) absorbers. We then use this sample to train a Convolutional Neural Network (CNN) which searches for, and estimates the redshift of, MgII absorbers within these spectra. For a test sample of quasar spectra with uniformly distributed properties ($\hat{I}{»}_{\mathrm{MgII},2796}$, ${\mathrm{EW}}_{\mathrm{MgII},2796}^{\mathrm{rest}}=0.05-5.15$ Ã , $\mathrm{SNR}=3-50$), the algorithm has a robust classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9 Ã . For high signal-to-noise spectra ($\mathrm{SNR}>20$), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of ${\mathrm{EW}}_{\mathrm{MgII},2796}^{\mathrm{rest}}=0.05$ Ã . For the lowest SNR spectra ($\mathrm{SNR}=3$), the CNN reliably recovers and localizes EW${}_{\mathrm{MgII},2796}^{\mathrm{rest}}$ $\ge$ 0.75 Ã \, absorbers. This is more than sufficient for subsequent Voigt profile fitting to characterize the detected MgII absorbers. We make the code publicly available through GitHub. Our work provides a proof-of-concept for future analyses of quasar spectra datasets numbering in the millions, soon to be delivered by the next generation of surveys.

13 pages, 9 figures, 1 table. Accepted for publication in MNRAS