Redshift is a fundamental observable that allows us to access the three-dimensional information on objects. Efficiently deriving accurate redshift measurements is an important task for the large photometric and spectroscopic survey era. We develop a new Python package, RVSNUpy, to estimate spectroscopic redshift based on the cross-correlation technique. Based on the extensive test using synthetic and observed spectra, we demonstrate that RVSNUpy yields robust redshift measurements. RVSNUpy can be an efficient tool for deriving redshifts from large-scale spectroscopy. In addition to the development of RVSNUpy, we also built the photometric redshift estimation tool based on machine learning (ML), particularly based on the random forest model. The combination of these photometric and spectroscopic redshift measurements will prepare us for studying extragalactic science and cosmology in the large survey era.

For Zoom connection information, please contact Emanuele Paolo Farina (emanuele.farina_at_noirlab.edu).

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