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

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 ($\lambda2796$, $\lambda2803$) 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 ($\lambda_{\rm{MgII,2796}}$, $\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} = 0.05 - 5.15$ \AA, $\rm{SNR} = 3 - 50$), the algorithm has a robust classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9 \AA. For high signal-to-noise spectra ($\rm{SNR > 20}$), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of $\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} = 0.05$ \AA. For the lowest SNR spectra ($\rm{SNR=3}$), the CNN reliably recovers and localizes EW$_{\rm{MgII,2796}}^{\rm{rest}}$ $\geq$ 0.75 \AA\, 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.