29 citations · 29 across the 7 of their papers we have counts for
5 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…
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…
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values
Dylan Green, Stephen Bailey
Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, t…