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Brett Andrews

Research Associate Professor

University of Pittsburgh

United States

Research Interests

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Articles (9)

Year:

2026

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2026

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2026

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2026

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2026

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2026

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2026

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2025

Year:

2025

Collaborators (19)

Adam Myers

Professor

University of Wyoming

UNITED STATES

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John Moustakas

Siena College

UNITED STATES

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Dirk Scholte

University of Edinburgh

UNITED KINGDOM

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Hee-Jong Seo

Professor

Ohio State University

UNITED STATES

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Ann Lee

Professor

Carnegie Mellon University

UNITED STATES

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T M Stanton

University of Edinburgh

UNITED KINGDOM

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Yijung Kang

Stanford University

UNITED STATES

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Markus Michael Rau

Assistant Professor

Newcastle University

UNITED KINGDOM

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Fergus Cullen

Reader

The University of Edinburgh

UNITED KINGDOM

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Davide Bianchi

Università di Milano

ITALY

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Risa Wechsler

Professor

Stanford University

UNITED STATES

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Vivian Miranda

Assistant Professor

Stony Brook University

UNITED STATES

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Rachel Mandelbaum

Carnegie Mellon University

UNITED STATES

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Tianqing Zhang

Research Assistant Professor

University of Pittsburgh

UNITED STATES

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Christopher Walter

Duke University

UNITED STATES

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Satya Gontcho A Gontcho

Assistant Professor

University of Virginia

UNITED STATES

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Rafael Izbicki

Assistant Professor

Universidade Federal de São Carlos

BRAZIL

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Eusebio Sanchez

Profesor de Investigación

Centro de Investigaciones Energéticas Medioambientales y Tecnológicas

SPAIN

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Amelie Saintonge

University College London

UNITED KINGDOM

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Brett Andrews

Research Interests

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Articles (9)

Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Highly Efficient Selection of High-redshift Emission-line Galaxies for Future DESI-like Surveys with Deep Multiband Imaging

Modeling redshift uncertainties in Roman weak lensing cosmology

Electron temperature relations and the direct N, O, Ne, S, and Ar abundances of 49 959 star-forming galaxies in DESI data release 2

Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS

Deep Learning Improves Photometric Redshifts in All Regions of Color Space

Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)

Forecasting the impact of source galaxy photometric redshift uncertainties on the LSST 3 × 2pt analysis

Collaborators (19)

Adam Myers

John Moustakas

Dirk Scholte

Hee-Jong Seo

Ann Lee

T M Stanton

Yijung Kang

Markus Michael Rau

Fergus Cullen

Davide Bianchi

Risa Wechsler

Vivian Miranda

Rachel Mandelbaum

Tianqing Zhang

Christopher Walter

Satya Gontcho A Gontcho

Rafael Izbicki

Eusebio Sanchez

Amelie Saintonge

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