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About

Arnaud de Mattia is a key figure in the analysis of large-scale structure from the Dark Energy Spectroscopic Instrument (DESI), specializing in the critical challenge of correcting for observational selection effects. His primary research focuses on mitigating the impact of fiber assignment incompleteness—a systematic bias introduced when robotic positioners cannot observe every target galaxy simultaneously. De Mattia’s most cited work (2025, 25 citations) details the production of alternate fiber assignment realizations, a method that creates unbiased clustering measurements by simulating the selection process. His follow-up study (2025, 20 citations) provides an in-depth characterization of these incompleteness effects on two-point clustering for DESI Data Release 1, establishing essential mitigation strategies. Additionally, he contributed to the overview of DESI instrumentation (2022, 10 citations), the ambitious survey mapping 40 million galaxies and quasars to probe dark energy through Baryon Acoustic Oscillations. By developing robust statistical corrections for observational biases, de Mattia ensures that DESI’s cosmological measurements remain accurate and reliable, directly enabling the survey’s core science goals. His work is foundational for any researcher seeking to understand and correct systematic effects in spectroscopic galaxy surveys.