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Administrative data

David Richard Harvey

david.harvey@epfl.ch +41 21 693 37 87 https://www.epfl.ch/research/domains/astrophysics/home/research-groups/david-harvey/

Nationality: British

EPFL SB IPHYS LASTRO
Observatoire de Sauverny
1290 Versoix

EPFL SB IPHYS LASTRO
SAUV 344 (Sauverny)
Ch. Pegasi 51
1290 Versoix

Expertise

Cosmology Dark Matter Galaxy Clusters Strong Gravitational Lensing Weak Gravitational Lensing
I am an observational and theoretical astrophysicist at EPFL. My main area of research is dark matter and trying to understand its properties and dynamics.  I love to use deep learning in my work and understand how fondation models and generative AI can help us understand the Universe. I am a member of Euclid and ARRAKIHS and the lead of the self interacting dark matter Key Project in Euclid.  

Outside of my research I have worked for Terres des Hommes, TruthEngine, Prophy and Kaggle as machine learning consultants. I love to apply different statistical tools to different problems in science and society.

Selected publications

On the cross-section of dark matter using substructure infall into galaxy clusters

Harvey, David; Tittley, Eric; Massey, Richard; Kitching, Thomas D.; Taylor, Andy; Pike, Simon R.; Kay, Scott T.; Lau, Erwin T.; Nagai, Daisuke
Published in MNRAS, Volume 441, Issue 1, p.404-416 in

Origins of weak lensing systematics, and requirements on future instrumentation (or knowledge of instrumentation)

Massey, Richard; Hoekstra, Henk; Kitching, Thomas; Rhodes, Jason; Cropper, Mark; Amiaux, Jérôme; Harvey, David; Mellier, Yannick; Meneghetti, Massimo; Miller, Lance; Paulin-Henriksson, Stéphane; Pires, Sandrine; Scaramella, Roberto; Schrabback, Tim
Published in MNRAS, Volume 429, Issue 1, p.661-678 in

Dark matter astrometry: accuracy of subhalo positions for the measurement of self-interaction cross-sections

Harvey, David; Massey, Richard; Kitching, Thomas; Taylor, Andy; Jullo, Eric; Kneib, Jean-Paul; Tittley, Eric; Marshall, Philip J.
Published in MNRAS, Volume 433, Issue 2, p.1517-1528 in

Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping

Harvey, D.; Kitching, T. D.; Noah-Vanhoucke, J.; Hamner, B.; Salimans, T.; Pires, A. M.
Published in Astronomy and Computing, Volume 5, p. 35-44. in

The nongravitational interactions of dark matter

The nongravitational interactions of dark matter in colliding galaxy clusters

D. Harvey; R. Massey; T. Kitching; A. Taylor; E. Tittley

Science. 2015. DOI : 10.1126/science.1261381.

Detailed record

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First signs of particle dark matter?

The behaviour of dark matter associated with four bright cluster galaxies in the 10 kpc core of Abell 3827

R. Massey; L. Williams; R. Smit; M. Swinbank; T. D. Kitching  et al.

Monthly Notices Of The Royal Astronomical Society. 2015. DOI : 10.1093/mnras/stv467.

Detailed record

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Systematic or signal?

Systematic or signal? How dark matter misalignments can bias strong lensing models of galaxy clusters

D. Harvey; J. P. Kneib; M. Jauzac

Monthly Notices Of The Royal Astronomical Society. 2016. DOI : 10.1093/mnras/stw295.

Detailed record

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Hubble Frontier Fields

Hubble Frontier Fields: the geometry and dynamics of the massive galaxy cluster merger MACSJ0416.1-2403

M. Jauzac; E. Jullo; D. Eckert; H. Ebeling; J. Richard  et al.

Monthly Notices Of The Royal Astronomical Society. 2015. DOI : 10.1093/mnras/stu2425.

Detailed record

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Weak lensing study

A weak lensing comparability study of galaxy mergers that host AGNs

D. Harvey; F. Courbin

Monthly Notices Of The Royal Astronomical Society. 2015. DOI : 10.1093/mnrasl/slv073.

Detailed record

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Teaching & PhD

PhD Students

Ethan Daniel Tregidga, Felix Francisco Vecchi

Courses

Lecture series on scientific machine learning

PHYS-754

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.