Neural physical engines for inferring the halo mass distribution function |
Tom Charnock, Guilhem Lavaux, Benjamin D. Wandelt, Supranta Sarma Boruah, Jens Jasche, Michael J. Hudson |
arxiv:1909.06379 |
The Quijote simulations |
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, et al. |
arxiv:1909.05273 |
Painting halos from 3D dark matter fields using Wasserstein mapping networks |
Doogesh Kodi Ramanah, Tom Charnock* and Guilhem Lavaux: *Project supervisor |
Physical Review D 100, 0435151 (2019) |
arxiv:1903.10524 |
Fast likelihood-free cosmology with neural density estimators and active learning |
Justin Alsing, Tom Charnock, Stephen Feeney and Benjamin Wandelt |
Monthly Notices of the Royal Astronomical Society, 488 (2019) |
arxiv:1903.00007 |
Towards online triggering for the radio detection of air showers using deep neural networks |
Florian Führer, Tom Charnock*, Anne Zilles and Matias Tueros: *Supervisory role |
Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2018) (C18-06-12) |
arxiv:1809.01934 |
Automatic physical inference with information maximising neural networks |
Tom Charnock, Guilhem Lavaux and Benjamin D. Wandelt |
Physical Review D 97, 083004 (2018) |
arxiv:1802.03537 |
Planck versus large scale structure: methods to quantify discordance |
Tom Charnock, Richard A. Battye and Adam Moss |
Physical Review D 95, 123535 (2017) |
arxiv:1703.05959 |
Deep recurrent neural networks for supernovae classification |
Tom Charnock and Adam Moss |
The Astrophysical Journal Letters, 837 L28 (2017) |
arxiv:1606.07442 |
CMB constraints on cosmic strings and superstrings |
Tom Charnock, Anastasios Avgoustidis, Edmund J. Copeland and Adam Moss |
Physical Review D 93, 123503 (2016) - Editor's Suggestion |
arxiv:1603.0.1275 |
Tension between the power spectrum of density perturbations measured on large and small scales |
Richard A. Battye, Tom Charnock and Adam Moss |
Physical Review D 91, 103508 (2015) |
arxiv:1409.2769 |
Novel uses of machine learning: Predicting probabilities and trusting outputs | |
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March 2019 | École doctorale - Observatoire de Paris |
Beginner's guide to neural networks: Tips and tricks | |
July 2017 | Institut d'Astrophysique de Paris |
Beginner's guide to neural networks | |
June 2017 |
Bayesian deep learning for cosmology and gravitational waves | |
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March 2020 | Bayesian deep learning for cosmology and gravitation waves, APC, Paris |
Machine learning for cosmology and astronomy | |
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February 2020 | Cosmology and astroparticle physics group, University of Geneva, Switzerland |
Machine learning for cosmology and astronomy | |
February 2020 | Physique théorique : gravitation et cosmologie, Institut d'Astrophysique de Paris, Paris |
Neural networks: How they work and their future in science | |
January 2020 | Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy |
Parameter inference using neural networks | |
December 2019 | Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK |
Bayesian methods for performing inference using neural networks | |
November 2019 | Département d'Informatique, Ecole normale supérieure, Paris |
Parameter inference using neural networks | |
November 2019 | Scuola Normale Superiore, Pisa, Italy |
Neural physical engines for inferring the halo mass distribution function | |
October 2019 | Laboratoire Astroparticle & Cosmologie, Université Paris Diderot, Paris |
Machine learning and its application in astronomy and cosmology | |
November 2016 | Queen Mary University of London |
Parameter inference using neural networks | |
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January 2020 | Machine learning meets astrophysics, Saclay, France |
Modern machine learning methods for trustworthy science | |
December 2019 | Machine Learning Tools for Research in Astronomy, Ringberg Castle, Germany |
Lossless data compression for cosmological surveys (and everything else!) | |
June 2019 | AICosmo2019, Ascona |
Information maximising neural networks for dimensionality reduction | |
March 2019 | LFI TaskForce, Flatiron Institute, New York |
Machine learning based (likelihood-free) statistical inference | |
October 2018 | IHP statistics, Paris |
Statistics and novel uses of machine learning | |
March 2018 | IAP Postdoc day, Paris |
Generating galaxies with DCGAN | |
January 2018 | DEEPVIS, Paris |
Information maximising non-linear data compression | |
November 2017 | BLSS, Munich |
Recurrent neural networks and the classification of supernovae | |
May 2017 | ICAP, Paris |
Machine learning and its application in supernovae classification | |
November 2016 | Nottingham |
Monte Carlo parameter estimation | |
January 2016 | HPC conference, Nottingham |
Automatic physical inference with information maximising neural networks | |
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May 2018 | CosmoStat21, Valencia |
Automatic physical inference with information maximising neural networks | |
April 2018 | StatLSS, Oxford |
The state of the tension between the CMB and LSS | |
November 2015 | UKCosmo, Edinburgh |
September 2015 | COSMO15, Warsaw |
Tension between the power spectrum of density perturbation measured on large and small scales | |
December 2014 | YTF2014, Durham |
September 2014 | BUSSTEPP2014, Southampton |
Cosmic string constraints | |
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April 2016 | BritGrav2016 |
February 2016 | Tessella competition |
Tension between the power spectrum of density perturbations measured on large and small scales | |
April 2015 | BritGrav2015 |
February 2015 | Tessella competition |
September 2016 | ICIC Data Analysis |
May 2016 | Astronomical Data Analysis (ADA8) |
September 2014 | BUSSTEPP2014 |
2017-present | GRAND collaboration advisor, Neural triggers for radio showers |
2018-present | GRAND collaboration advisor, Bayesian pruning of deep networks for reduced computational architectures |
2019-present | Euclid collaboration advisor, Statistical understanding of CCD defects and automatic recalibration |
2018-2019 | PhD project supervisor, Painting halos using novel physically motivated networks |
2019 | Masters thesis supervisor, Using conditional generative networks to understand CCD detector defects |
2019 | Masters thesis advisor, Statistical understanding of families in classification networks |
ANR |
The Astrophysical Journal |
Journal of Astronomy and Computing |
Journal of Cosmology and Astroparticle Physics |
Monthly Notices of the Royal Astronomical Society |
Computers in Biology and Medicine |
2018-present | AstroPePs | xk€ | Member |
2019 | HENON | xk€ | Member |
2017-present | Machine learning journal club (neural networking) |
2018-present | Machine learning workshop organiser |
2015-2016 | BritGrav16 organising committee |
2015-2016 | Journal club organiser |
2014-2015 | Student journal club organiser |
2013-2017 | Website manager |
June 2016 | Open day particle physics talk |
October 2015 | Particle physics masterclass |
June 2015 | Open day particle physics talk |
October 2014 | Particle physics masterclass |
September 2014 | Open day particle physics talk |
June 2014 | Open day particle physics talk |