Publications ¶

Chronological list of machine learning-related papers (published on HAL/arXiv) with significant contribution from IN2P3 members.

Publication list ¶

G. Kane, P. Drobniak, S. Kazamias, V. Kubytskyi, M. Lenivenko, B. Lucas, J. Serhal, K. Cassou, A. Beck, A. Specka, and F. Massimo. Surrogate models studies for laser-plasma accelerator electron source design through numerical optimisation, 2024. [ arXiv | http ]

Louis Vaslin, Vincent Barra, and Julien Donini. GAN-AE: an anomaly detection algorithm for new physics search in LHC data. The European Physical Journal C , 83(11), nov 2023. [ DOI | http ]

M. Treyer, R. Ait-Ouahmed, J. Pasquet, S. Arnouts, E. Bertin, and D. Fouchez. CNN photometric redshifts in the SDSS at r <= 20. , October 2023. [ DOI | arXiv ]

Kirill Grishin, Simona Mei, and Stéphane Ilić. YOLO-CL: Galaxy cluster detection in the SDSS with deep machine learning. , 677:A101, September 2023. [ DOI | arXiv ]

A. Trovato, É. Chassande-Mottin, M. Bejger, R. Flamary, and N. Courty. Neural network time-series classifiers for gravitational-wave searches in single-detector periods. sep 2023. [ arXiv ]

Michaël Dell’aiera, Thomas Vuillaume, Mikaël Jacquemont, and Alexandre Benoit. Deep unsupervised domain adaptation applied to the cherenkov telescope array large-sized telescope. page 133–139, September 2023. [ DOI | http ]

Philippe Bacon, Agata Trovato, and Michał Bejger. Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder. Machine Learning: Science and Technology , 4(3):035024, aug 2023. [ DOI | http ]

R. Aaij et al. Test of lepton flavor universality using B ⁰ -> D ^*- τ ⁺ ν _τ decays with hadronic τ channels. Phys. Rev. D , 108:012018, July 2023. [ DOI | http ]

Anass Bairouk, Marc Chaumont, Dominique Fouchez, Jerome Paquet, Frédéric Comby, and Julian Bautista. Astronomical image time series classification using CONVolutional attENTION (ConvEntion). , 673:A141, May 2023. [ DOI | arXiv ]

Georges Aad, Thomas Calvet, Nemer Chiedde, Robert Faure, Etienne Marie Fortin, Lauri Laatu, Emmanuel Monnier, and Nairit Sur. Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the atlas experiment. JINST , 18(05):P05017, 2023. [ DOI | arXiv ]

C. Allaire, R. Ammendola, E. C. Aschenauer, M. Balandat, M. Battaglieri, J. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W. Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, Jr. au2 M. Finger, E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana Waduge, R. Harish, O. Hassan, P. L. Hegde, R. J. Hernández-Pinto, A. Hiller Blin, T. Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, P. Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, F. Liu, S. Liuti, G. Matousek, M. McEneaney, D. McSpadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B. Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L. Purschke, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy, D. Roy, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, A. N. Tawfik, F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou, and P. Zurita. Artificial intelligence for the electron ion collider (ai4eic), 2023. [ arXiv ]

Corentin Allaire, Françoise Bouvet, Hadrien Grasland, and David Rousseau. Ranking-based neural network for ambiguity resolution in acts, 2023. [ arXiv ]

Rocky Bala Garg, Corentin Allaire, Andreas Salzburger, Hadrien Grasland, Lauren Tompkins, and Elyssa Hofgard. Potentiality of automatic parameter tuning suite available in acts track reconstruction software framework, 2023. [ arXiv ]

Corentin Allaire, Rocky Bala Garg, Hadrien Benjamin Grasland, Elyssa Frances Hofgard, David Rousseau, Rama Salahat, Andreas Salzburger, and Lauren Alexandra Tompkins. Auto-tuning capabilities of the acts track reconstruction suite, 2023. [ arXiv ]

M. Regnier, E. Manzan, J. Ch Hamilton, A. Mennella, J. Errard, L. Zapelli, S. A. Torchinsky, S. Paradiso, E. Battistelli, P. De Bernardis, L. Colombo, M. De Petris, G. D'Alessandro, B. Garcia, M. Gervasi, S. Masi, L. Mousset, N. Miron Granese, C. O'Sullivan, M. Piat, E. Rasztocky, G. E. Romero, C. G. Scoccola, and M. Zannoni. Identifying frequency decorrelated dust residuals in b-mode maps by exploiting the spectral capability of bolometric interferometry, 2023. [ arXiv ]

Biswas, B., Ishida, E. E. O., Peloton, J., Möller, A., Pruzhinskaya, M. V., de Souza, R. S., and Muthukrishna, D. Enabling the discovery of fast transients - A kilonova science module for the Fink broker. A&A , 677:A77, 2023. [ DOI | http ]

Biswajit Biswas, Junpeng Lao, Eric Aubourg, Alexandre Boucaud, Axel Guinot, Emille E. O. Ishida, and Cécile Roucelle. Bayesian multi-band fitting of alerts for kilonovae detection, 2023. [ arXiv ]

Anja Butter, Michael Krämer, Silvia Manconi, and Kathrin Nippel. Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks. JCAP , 07:033, 2023. [ DOI | arXiv ]

Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer, Tilman Plehn, Peter Sorrenson, and Jonas Spinner. Jet Diffusion versus JetGPT -- Modern Networks for the LHC. SciPost Physics , 2023. [ arXiv ]

Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, and Anja Butter. Precision-Machine Learning for the Matrix Element Method. SciPost Physics , 2023. [ arXiv ]

Mathias Backes, Anja Butter, Monica Dunford, and Bogdan Malaescu. Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods, 2023. [ arXiv ]

V. V. Gligorov and V. Reković. Review of real-time data processing for collider experiments. Eur. Phys. J. Plus , 138(11):1005, 2023. [ DOI | arXiv ]

Simon Badger et al. Machine learning and LHC event generation. SciPost Phys. , 14(4):079, 2023. [ DOI | arXiv ]

Louis Vaslin, Samuel Calvet, Vincent Barra, and Julien Donini. pyBumpHunter: A model independent bump hunting tool in Python for high energy physics analyses. SciPost Phys. Codebases , page 15, 2023. [ DOI | http ]

Georges Aad et al. ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset. Eur. Phys. J. C , 83(7):681, 2023. [ DOI | arXiv ]

Q. Lin, D. Fouchez, J. Pasquet, M. Treyer, R. Ait Ouahmed, S. Arnouts, and O. Ilbert. Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods. , 662:A36, June 2022. [ DOI | arXiv ]

Justine Zeghal, François Lanusse, Alexandre Boucaud, Benjamin Remy, and Eric Aubourg. Neural posterior estimation with differentiable simulators, 2022. [ arXiv ]

Georges Aad et al. Measurement of Higgs boson decay into b -quarks in associated production with a top-quark pair in pp collisions at sqrt( s )=13 TeV with the ATLAS detector. JHEP , 06:097, 2022. [ DOI | arXiv ]

Yann Coadou. Boosted decision trees , chapter 2, pages 9--58. World Scientific, 2022. [ DOI | arXiv ]

Georges Aad, Anne-Sophie Berthold, Thomas Calvet, Nemer Chiedde, Etienne Marie Fortin, Nick Fritzsche, Rainer Hentges, Lauri Antti Olavi Laatu, Emmanuel Monnier, Arno Straessner, and Johann Christoph Voigt. Artificial neural networks on FPGAs for real-time energy reconstruction of the ATLAS LAr calorimeters. Computing and Software for Big Science , 5(1), October 2021. [ DOI | http ]

Anna Stakia, Tommaso Dorigo, et al. Advances in multi-variate analysis methods for new physics searches at the large hadron collider. Reviews in Physics , 7:100063, 2021. [ DOI | http ]

X. Fabian, G. Baulieu, L. Ducroux, O. Stézowski, A. Boujrad, E. Clément, S. Coudert, G. de France, N. Erduran, S. Ertürk, V. González, G. Jaworski, J. Nyberg, D. Ralet, E. Sanchis, and R. Wadsworth. Artificial neural networks for neutron/γ discrimination in the neutron detectors of neda. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 986:164750, 2021. [ DOI | http ]

Thomas Vuillaume, Mikaël Jacquemont, Mathieu de Bony de Lavergne, David A Sanchez, Vincent Poireau, Gilles Maurin, Alexandre Benoit, Patrick Lambert, Giovanni Lamanna, and CTA-LST Project. Analysis of the cherenkov telescope array first large-sized telescope real data using convolutional neural networks. arXiv preprint arXiv:2108.04130 , 2021.

Pietro Grespan, Mikael Jacquemont, Rubèn López-Coto, Tjark Miener, Daniel Nieto-Castaño, and Thomas Vuillaume. Deep-learning-driven event reconstruction applied to simulated data from a single large-sized telescope of cta, 2021. [ DOI | http ]

Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoit, Gilles Maurin, Patrick Lambert, and Giovanni Lamanna. First full-event reconstruction from imaging atmospheric cherenkov telescope real data with deep learning. In 2021 International Conference on Content-Based Multimedia Indexing (CBMI) , pages 1--6. IEEE, 2021.

Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoit, Gilles Maurin, and Patrick Lambert. Deep learning for astrophysics, understanding the impact of attention on variability induced by parameter initialization. In International Conference on Pattern Recognition , pages 174--188. Springer, Cham, 2021.

Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoît, Gilles Maurin, and Patrick Lambert. Single imaging atmospheric cherenkov telescope full-event reconstruction with a deep multi-task learning architecture. In Astronomical Data Analysis Software and Systems ADASS XXX , 2020.

Bastien Arcelin, Cyrille Doux, Eric Aubourg, Cécile Roucelle, and The LSST Dark Energy Science Collaboration. Deblending galaxies with variational autoencoders: A joint multiband, multi-instrument approach. Monthly Notices of the Royal Astronomical Society , 500(1):531--547, 2020. [ DOI | arXiv | http ]

D Nieto Castaño, A Brill, Q Feng, M Jacquemont, B Kim, T Miener, and T Vuillaume. Studying deep convolutional neural networks with hexagonal lattices for imaging atmospheric cherenkov telescope event reconstruction. In 36th International Cosmic Ray Conference (ICRC2019) , volume 36, page 753, 2019.

Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoit, Gilles Maurin, Patrick Lambert, Giovanni Lamanna, and Ari Brill. Gammalearn: a deep learning framework for iact data. In 36th International Cosmic Ray Conference , page 705, 2019.

Thomas Vuillaume, Jacquemont Mikael, Luca Antiga, Alexandre Benoit, Patrick Lambert, Gilles Maurin, and Giorgia Silvestri. Gammalearn-first steps to apply deep learning to the cherenkov telescope array data. In EPJ Web of Conferences , volume 214, page 06020. EDP Sciences, 2019.

Mikael Jacquemont, Luca Antiga, Thomas Vuillaume, Giorgia Silvestri, Alexandre Benoit, Patrick Lambert, and Gilles Maurin. Indexed operations for non-rectangular lattices applied to convolutional neural networks. In VISAPP 2019 , 2019.

M. Jacquemont, T. Vuillaume, A. Benoit, G. Maurin, P. Lambert, and G. Lamanna. Deep learning applied to the cherenkov telescope array data analysis. In CHEP 2018 Conference , 2018.

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