Papers › The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers...
The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning
Roland Szakacs, Céline Péroux, Dylan Nelson, Martin A. Zwaan, Daniel Grün, Simon Weng, Alejandra Y. Fresco, Victoria Bollo, Benedetta Casavecchia
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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_(MgII,2796), EW_(MgII,2796)ʳᵉˢᵗ = 0.05 - 5.15 \AA, 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 (SNR > 20), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of EW_(MgII,2796)ʳᵉˢᵗ = 0.05 \AA. For the lowest SNR spectra (SNR=3), the CNN reliably recovers and localizes EW_(MgII,2796)ʳᵉˢᵗ ≥ 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.
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