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Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder
C. Nicolaou, R. P. Nathan, O. Lahav +43
The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning,…
Using Active Learning to Improve Quasar Identification for the DESI Spectra Processing Pipeline
Dylan Green, David Kirkby, J. Aguilar +51
The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as p…
Validation of the DESI DR2 Ly BAO analysis using synthetic datasets
L. Casas, H. K. Herrera-Alcantar, J. Chaves-Montero +95
The second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI), containing data from the first three years of observations, doubles the number of Lyman- (Ly…
Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument
E. F. Schlafly, J. Guy, K. Honscheid +37
Highly-multiplexed, robotic, fiber-fed spectroscopic surveys are observing tens of millions of stars and galaxies. For many systems, accurate positioning relies on imaging the fibe…
The Optical Corrector for the Dark Energy Spectroscopic Instrument
Timothy N. Miller, Peter Doel, Gaston Gutierrez +44
The Dark Energy Spectroscopic Instrument (DESI) is currently measuring the spectra of 40\,million galaxies and quasars, the largest such survey ever made to probe the nature of cos…
The Dark Energy Survey Six-Year Calibration Star Catalog
E. S. Rykoff, D. L. Tucker, D. L. Burke +64
This Technical Note presents a catalog of calibrated reference stars that was generated by the Forward Calibration Method (FGCM) pipeline (arXiv:1706.01542) as part of the FGCM pho…