2023/05/29 by Roland Szakacs, Céline Péroux, Szakacs, Roland +15
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics of Galaxies (astro-ph.GA) #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · pdf · doi:10.48550/arxiv.2305.17970
openalex publication_date 2023/05/29 · openalex created_date 2023/05/31 · openalex updated_date 2026/07/28
The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph will offer new prospects of using a massive sample of ∼ 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 (λ2796, λ2803) 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 (λ_\rmMgII,2796, \rmEW_\rmMgII,2796^\rmrest = 0.05 - 5.15 Å, \rmSNR = 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 (\rmSNR > 20), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of \rmEW_\rmMgII,2796^\rmrest = 0.05 Å. For the lowest SNR spectra (\rmSNR=3), the CNN reliably recovers and localizes EW_\rmMgII,2796^\rmrest ≥ 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.