The BarYon Cycle project (ByCycle): identifying and localizing Mg ii 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 ∼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...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
December 2023
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2023, Jahrgang: 526, Heft: 3, Pages: 3744-3756 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stad2431 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/mnras/stad2431
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| Verfasserangaben: | Roland Szakacs, Céline Péroux, Dylan Nelson, Martin A Zwaan, Daniel Grün, Simon Weng, Alejandra Y Fresco, Victoria Bollo and Benedetta Casavecchia |
| Zusammenfassung: | 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 (SNR) 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 data sets numbering in the millions, soon to be delivered by the next generation of surveys. |
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| Beschreibung: | Veröffentlicht: 20. September 2023 Gesehen am 26.06.2024 |
| Beschreibung: | Online Resource |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stad2431 |