Brice Ménard

About

Brice Ménard joined the faculty at Johns Hopkins University in 2010. He received his PhD from both the Institut d’Astrophysique de Paris and the Max Planck Institute for Astrophysics in Germany. His research combines physics and statistics, initially focusing on astrophysics and cosmology. He has worked as a postdoctoral member of the Institute for Advanced Study in Princeton and as a senior research associate at the Canadian Institute for Theoretical Astrophysics in Toronto. His current research interests include the physics of learning and the properties of neural networks in artificial and biological systems. Ménard holds secondary appointments in the Cognitive Science and Computer Science departments. Throughout his career, he has received several awards, including the Johns Hopkins President Frontier Award in 2019, the Packard Fellowship in 2014, the Sloan Research Fellowship in 2012, and the Henri Chrétien grant award by the American Astronomical Society in 2011.

Research topics

• Artificial Intelligence
• Computer Science
• Physics
• Statistical physics
• Mathematics
• Astrophysics
• Quantum mechanics
• Algorithm
• Statistics

Selected publications

• A Cooling Anomaly of High-mass White Dwarfs

  The Astrophysical Journal · 2019-11-25 · 135 citations

  articleOpen accessSenior author

  Abstract Recently, the power of Gaia data has revealed an enhancement of high-mass white dwarfs (WDs) on the Hertzsprung–Russell diagram, called the Q branch. This branch is located at the high-mass end of the recently identified crystallization branch. Investigating its properties, we find that the number density and velocity distribution on the Q branch cannot be explained by the cooling delay of crystallization alone, suggesting the existence of an extra cooling delay. To quantify this delay,…

  PublisherOA PDFDOI

• A new approach to observational cosmology using the scattering transform

  Monthly Notices of the Royal Astronomical Society · 2020 · 130 citations

  ABSTRACT Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring neither training nor tuning. It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized struct…

  PublisherOA PDFDOI

• Weak lensing scattering transform: dark energy and neutrino mass sensitivity

  Monthly Notices of the Royal Astronomical Society · 2021-07-20 · 59 citations

  articleOpen accessSenior author

  ABSTRACT As weak lensing surveys become deeper, they reveal more non-Gaussian aspects of the convergence field which can only be extracted using statistics beyond the power spectrum. In a companion paper, we showed that the scattering transform, a novel statistic borrowing mathematical concepts from convolutional neural networks, is a powerful tool for cosmological parameter estimation in the non-Gaussian regime. Here, we extend that analysis to explore its sensitivity to dark energy and neutrin…

  PublisherOA PDFDOI

• The Cosmic Thermal History Probed by Sunyaev–Zeldovich Effect Tomography

  The Astrophysical Journal · 2020-10-01 · 58 citations

  articleOpen access

  Abstract The cosmic thermal history, quantified by the evolution of the mean thermal energy density in the universe, is driven by the growth of structures as baryons get shock heated in collapsing dark matter halos. This process can be probed by redshift-dependent amplitudes of the thermal Sunyaev–Zeldovich (SZ) effect background. To do so, we cross-correlate eight sky intensity maps in the Planck and Infrared Astronomical Satellite missions with two million spectroscopic redshift references in…

  PublisherOA PDFDOI

• Black hole mass estimation for active galactic nuclei from a new angle

  Monthly Notices of the Royal Astronomical Society · 2019-06-05 · 55 citations

  articleOpen accessSenior author

  Abstract The scaling relations between supermassive black holes and their host galaxy properties are of fundamental importance in the context black hole-host galaxy co-evolution throughout cosmic time. In this work, we use a novel algorithm that identifies smooth trends in complex data sets and apply it to a sample of 2000 type 1 active galactic nuclei (AGNs) spectra. We detect a sequence in emission line shapes and strengths which reveals a correlation between the narrow L([O iii])/L(H β) line…

  PublisherOA PDFDOI

Frequent coauthors

• Guangtun Zhu

  26 shared

• Ryan Scranton

  University of California, Davis

  24 shared

• M. Fukugita

  Kavli Institute for the Physics and Mathematics of the Universe

  24 shared

• Daniel B. Nestor

  University of California, Los Angeles

  24 shared

• Eiichiro Komatsu

  18 shared

• David A. Turnshek

  15 shared

• S. Zibetti

  14 shared

• Sihao Cheng

  Institute for Advanced Study

  14 shared

Awards & honors

• Johns Hopkins President Frontier Award (2019)
• Packard Fellowship (2014)
• Sloan Research Fellowship (2012)
• 2012 Outstanding Young Scientist of Maryland
• 2011 Henri Chrétien grant award by the American Astronomical…

Similar researchers at Johns Hopkins University

• Colin Wilsonh-71
• Benjamin Van Durmeh-54
• Jason Eisnerh-53