In:
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP)
Kurzfassung:
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 Mg ii (λ2796, λ2803) absorbers. We then use this sample to train a Convolutional Neural Network (CNN) which searches for, and estimates the redshift of, Mg ii absorbers within these spectra. For a test sample of quasar spectra with uniformly distributed properties ($\lambda _{\rm {Mg\, \small {II},2796}}$, $\rm {EW}_{\rm {Mg\, \small {II},2796}}^{\rm {rest}} = 0.05\!-\!5.15$ 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 ($\rm {SNR & gt; 20}$), the algorithm robustly detects and localizes Mg ii absorbers down to equivalent widths of $\rm {EW}_{\rm {Mg\, \small {II},2796}}^{\rm {rest}} = 0.05$ Å. For the lowest SNR spectra ($\rm {SNR=3}$), the CNN reliably recovers and localizes EW$_{\rm {Mg\, \small {II},2796}}^{\rm {rest}}$ ≥ 0.75 Å absorbers. This is more than sufficient for subsequent Voigt profile fitting to characterize the detected Mg ii 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.
Materialart:
Online-Ressource
ISSN:
0035-8711
,
1365-2966
DOI:
10.1093/mnras/stad2431
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2023
ZDB Id:
2016084-7
SSG:
16,12
