The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning - Aix-Marseille Université Accéder directement au contenu
Article Dans Une Revue Monthly Notices of the Royal Astronomical Society Année : 2023

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

Roland Szakacs
  • Fonction : Auteur
Dylan Nelson
  • Fonction : Auteur
Martin A Zwaan
  • Fonction : Auteur
Daniel Grün
  • Fonction : Auteur
Simon Weng
  • Fonction : Auteur
Alejandra Y Fresco
  • Fonction : Auteur
Victoria Bollo
  • Fonction : Auteur
Benedetta Casavecchia
  • Fonction : Auteur

Résumé

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 ($\lambda_{\rm{MgII,2796}}$, $\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} = 0.05 - 5.15$Å, $\rm{SNR} = 3 - 50$), the algorithm has a robust classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9 Å. For high signal-to-noise spectra ($\rm{SNR > 20}$), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of $\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} = 0.05$Å. For the lowest SNR spectra ($\rm{SNR=3}$), the CNN reliably recovers and localizes EW$_{\rm{MgII,2796}}^{\rm{rest}}$$\geq$ 0.75 Å 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.
Fichier principal
Vignette du fichier
stad2431.pdf (1.76 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04128466 , version 1 (24-05-2024)

Identifiants

Citer

Roland Szakacs, Céline Péroux, Dylan Nelson, Martin A Zwaan, Daniel Grün, et al.. The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning. Monthly Notices of the Royal Astronomical Society, 2023, 526 (3), pp.3744-3756. ⟨10.1093/mnras/stad2431⟩. ⟨hal-04128466⟩
11 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More