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20062024
most citedDESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations

1.1k citations · 4k across the 30 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

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★ 240 cited

The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument

J. Guy, S. Bailey, A. Kremin +55

We describe the spectroscopic data processing pipeline of the Dark Energy Spectroscopic Instrument (DESI), which is conducting a redshift survey of about 40 million galaxies and qu…

astro-ph.CO2022★ 177 cited

Target Selection and Validation of DESI Emission Line Galaxies

A. Raichoor, J. Moustakas, Jeffrey A. Newman +48

The Dark Energy Spectroscopic Instrument (DESI) will precisely constrain cosmic expansion and the growth of structure by collecting 40 million extra-galactic redshifts across…

astro-ph.CO2022

The DESI Survey Validation: Results from Visual Inspection of Bright Galaxies, Luminous Red Galaxies, and Emission Line Galaxies

Ting-Wen Lan, R. Tojeiro, E. Armengaud +79

The Dark Energy Spectroscopic Instrument (DESI) Survey has obtained a set of spectroscopic measurements of galaxies to validate the final survey design and target selections. To as…

astro-ph.CO2022★ 239 cited

Target Selection and Validation of DESI Luminous Red Galaxies

Rongpu Zhou, Biprateep Dey, Jeffrey A. Newman +46

The Dark Energy Spectroscopic Instrument (DESI) is carrying out a 5-year survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million…

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…