38 citations · 146 across the 73 of their papers we have counts for
6 papers · 1 filter
Quality Assessment of Spectroscopic Data Reduction Pipelines Using Artificial Intelligence: Scrutinizing Data Release 2 from the DESI Survey
V. Torres-Gomez, J. Suarez-Perez, J. E. Forero-Romero +45
Large spectroscopic surveys now collect data at a scale that makes traditional visual inspection impractical. We present an unsupervised pipeline for spectroscopic quality assessme…
Dimensional reduction for sampled priors and application to photometric redshift distributions
Gary Bernstein, William Assignies Doumerg, Michael A. Troxel +35
A typical Bayesian inference on the values of some parameters of interest from some data involves running a Markov Chain (MC) to sample from the posterior $p({\bf q},{\…
Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration
Biprateep Dey, Jeffrey A. Newman, Tianqing Zhang +62
Deep spectroscopic samples can be used to improve photometric redshift (photo-) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the…
Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder
C. Nicolaou, R. P. Nathan, O. Lahav +43
The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning,…
Using Active Learning to Improve Quasar Identification for the DESI Spectra Processing Pipeline
Dylan Green, David Kirkby, J. Aguilar +51
The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as p…
Validation of the DESI DR2 Ly BAO analysis using synthetic datasets
L. Casas, H. K. Herrera-Alcantar, J. Chaves-Montero +95
The second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI), containing data from the first three years of observations, doubles the number of Lyman- (Ly$Î…