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19942026
most citedThe DESI Experiment Part I: Science,Targeting, and Survey Design

1.3k citations · 13.5k across the 139 of their papers we have counts for

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Showing 2022Show all

10 papers · 1 filter

astro-ph.IM2022

25,000 optical fiber positioning robots for next-generation cosmology

Joseph H. Silber, David J. Schlegel, Ricardo Araujo +10

Massively parallel multi-object spectrographs are on the leading edge of cosmology instrumentation. The highly successful Dark Energy Spectroscopic Instrument (DESI) which begun su…

astro-ph.HE2022★ 15 cited

Carnegie Supernova Project-II: Near-infrared spectral diversity and template of Type Ia Supernovae

Jing Lu, Eric Y. Hsiao, Mark M. Phillips +30

We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carne…

astro-ph.SR2022★ 7 cited

Bump Morphology of the CMAGIC Diagram

L. Aldoroty, L. Wang, P. Hoeflich +38

We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric dataset. The currently existing CMAGIC parameters are t…

astro-ph.IM2022

The MegaMapper: A Stage-5 Spectroscopic Instrument Concept for the Study of Inflation and Dark Energy

David J. Schlegel, Juna A. Kollmeier, Greg Aldering +86

In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts a…

astro-ph.CO2022

A Spectroscopic Road Map for Cosmic Frontier: DESI, DESI-II, Stage-5

David J. Schlegel, Simone Ferraro, Greg Aldering +62

In this white paper, we present an experimental road map for spectroscopic experiments beyond DESI. DESI will be a transformative cosmological survey in the 2020s, mapping 40 milli…

astro-ph.CO2022★ 13 cited

A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

George Stein, Uros Seljak, Vanessa Bohm +37

We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. T…