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Publications

A chronological list of machine learning-related papers, software or dataset (published on plateforms with unique identifiers such as HAL, arXiv, Zenodo, Software Heritage, etc...) with significant contribution from IN2P3 members.

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Publications by year

Publication list

  1. Wahid Bhimji, Ragansu Chakkappai, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Aishik Ghosh, Cristina Giordano, Isabelle Guyon, Chris Harris, Yota Hashizume, Shih-Chieh Hsu, Elham E Khoda, Claudius Krause, Ang Li, Benjamin Nachman, David Rousseau, Robert Schöfbeck, Maryam Shooshtari, Dennis Schwarz, Ihsan Ullah, Daohan Wang, and Yulei Zhang. FAIR universe higgsML uncertainty dataset and competition. In The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 2026. URL: https://openreview.net/forum?id=F48NfH4NFE. ↩

  2. Corentin Allaire, Vera Maiboroda, and David Rousseau. Interdisciplinary digital twin engine intertwin for calorimeter simulation. 2026. URL: https://arxiv.org/abs/2509.26527, arXiv:2509.26527. ↩

  3. Biwei Dai, Po-Wen Chang, Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Isabelle Guyon, Chris Harris, Elham E Khoda, Benjamin Nachman, David Rousseau, Uroš Seljak, Ihsan Ullah, and Yulei Zhang. Fair universe weak lensing ml uncertainty challenge: handling uncertainties and distribution shifts for precision cosmology. 2026. URL: https://arxiv.org/abs/2604.14451, arXiv:2604.14451. ↩

  4. Sylvain Caillou, Christophe Collard, Charline Rougier, Jan Stark, Hubert Torres, and Alexis Vallier. Novel fully-heterogeneous GNN designs for track reconstruction at the HL-LHC. EPJ Web Conf., 295:09028, 2024. doi:10.1051/epjconf/202429509028. ↩

  5. Sylvain Caillou, Paolo Calafiura, Xiangyang Ju, Daniel Murnane, Tuan Pham, Charline Rougier, Jan Stark, and Alexis Vallier. Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain. EPJ Web Conf., 295:03030, 2024. doi:10.1051/epjconf/202429503030. ↩

  6. Charline Rougier, Alexis Vallier, Daniel Thomas Murnane, Jan Stark, Paolo Calafiura, Steven Farrell, Sylvain CAILLOU, and Xiangyang Ju. Ctd2022: atlas itk track reconstruction with a gnn-based pipeline. June 2023. URL: https://doi.org/10.5281/zenodo.8187248, doi:10.5281/zenodo.8187248. ↩

  7. Catherine Biscarat, Sylvain Caillou, Charline Rougier, Jan Stark, and Jad Zahreddine. Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC. EPJ Web Conf., 251:03047, 2021. arXiv:2103.00916, doi:10.1051/epjconf/202125103047. ↩

  8. Gage DeZoort, Peter W. Battaglia, Catherine Biscarat, and Jean-Roch Vlimant. Graph neural networks at the Large Hadron Collider. Nature Rev. Phys., 5(5):281–303, 2023. doi:10.1038/s42254-023-00569-0. ↩

  9. ATLAS Collaboration. Optimizations of the ATLAS ITk GNN reconstruction pipeline. Technical Report, CERN, Geneva, 2025. URL: https://cds.cern.ch/record/2948192. ↩

  10. ATLAS Collaboration. Computational Performance of the ATLAS ITk GNN Track Reconstruction Pipeline. Technical Report, CERN, Geneva, 2024. URL: https://cds.cern.ch/record/2914282. ↩

  11. Torres, Heberth. Energy-efficient graph-based algorithm for tracking at the HL-LHC. EPJ Web Conf., 337:01301, 2025. doi:10.1051/epjconf/202533701301. ↩

  12. Heberth Torres, Jared Burleson, Sylvain Caillou, Paolo Calafiura, Jay Chan, Christophe Collard, Xiangyang Ju, Daniel Murnane, Mark Neubauer, Tuan Pham, Charline Rougier, Jan Stark, and Alexis Vallier. Physics performance of the atlas GNN4ITk track reconstruction chain. mar 2024. URL: https://doi.org/10.5281/zenodo.15178159, doi:10.5281/zenodo.15178159. ↩

  13. M. Rejmund and A. Lemasson. Analysis of atomic charge state and atomic number for vamos++ magnetic spectrometer using deep neural networks and fractionally labelled events. Journal of Instrumentation, 20(08):P08022, aug 2025. URL: https://doi.org/10.1088/1748-0221/20/08/P08022, doi:10.1088/1748-0221/20/08/P08022. ↩

  14. Adnan Ghribi, Kevin Cassou, Barbara Dalena, Annika Eichler, Hayg Guler, Andrew K Mistry, Adrian Oeftiger, Thomas Shea, Gianluca Valentino, and Carsten P Welsch. Artificial intelligence for advancing particle accelerators. Europhysics News, 56(1):15–19, 2025. ↩

  15. Adnan Ghribi, Muhammad Aburas, Pierre-Emmanuel Bernaudin, Patrick Bonnay, François Bonne, Antoine Corbel, Marco Di-Giacomo, François Millet, Auriol Ngueguim Tsafak, Arnaud Trudel, and Quentin Tura. Advanced cryogenic process control and monitoring for the spiral2 superconducting linac. 2022. URL: https://arxiv.org/abs/2209.00896, arXiv:2209.00896. ↩

  16. Adrien Vassal, Adnan Ghribi, François Millet, François Bonne, Patrick Bonnay, and Pierre-Emmanuel Bernaudin. Spiral2 cryomodule models: a gateway to process control and machine learning. Frontiers in Physics, 2022. URL: https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.875464, doi:10.3389/fphy.2022.875464. ↩

  17. Denise Lanzieri, Justine Zeghal, T. Lucas Makinen, Alexandre Boucaud, Jean-Luc Starck, and François Lanusse. Optimal neural summarisation for full-field weak lensing cosmological implicit inference. 2024. URL: https://arxiv.org/abs/2407.10877, arXiv:2407.10877. ↩

  18. Biswajit Biswas, Eric Aubourg, Alexandre Boucaud, Axel Guinot, Junpeng Lao, Cécile Roucelle, and the LSST Dark Energy Science Collaboration. Madness deblender: maximum a posteriori with deep neural networks for source separation. 2024. URL: https://arxiv.org/abs/2408.15236, arXiv:2408.15236. ↩

  19. Justine Zeghal, Denise Lanzieri, François Lanusse, Alexandre Boucaud, Gilles Louppe, Eric Aubourg, Adrian E. Bayer, and The LSST Dark Energy Science Collaboration. Simulation-based inference benchmark for lsst weak lensing cosmology. 2024. URL: https://arxiv.org/abs/2409.17975, arXiv:2409.17975. ↩

  20. Alessio Spagnoletti, Alexandre Boucaud, Marc Huertas-Company, Wassim Kabalan, and Biswajit Biswas. Bayesian deconvolution of astronomical images with diffusion models: quantifying prior-driven features in reconstructions. 2024. URL: https://arxiv.org/abs/2411.19158, arXiv:2411.19158. ↩

  21. 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, October 2021. URL: https://doi.org/10.1007/s41781-021-00066-y, doi:10.1007/s41781-021-00066-y. ↩

  22. 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. ↩

  23. 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. URL: https://doi.org/10.1093/mnras/staa3062, arXiv:https://academic.oup.com/mnras/article-pdf/500/1/531/34292544/staa3062.pdf, doi:10.1093/mnras/staa3062. ↩

  24. Yann Coadou. Boosted decision trees, chapter 2, pages 9–58. World Scientific, 2022. arXiv:2206.09645, doi:10.1142/9789811234033_0002. ↩

  25. 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. ↩

  26. 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, 06020. EDP Sciences, 2019. ↩

  27. 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, 705. 2019. ↩

  28. 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, 753. 2019. ↩

  29. 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, 174–188. Springer, Cham, 2021. ↩

  30. 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. ↩

  31. 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), 1–6. IEEE, 2021. ↩

  32. 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. URL: https://arxiv.org/abs/2109.14262, doi:10.48550/ARXIV.2109.14262. ↩

  33. 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. ↩

  34. 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/\gamma 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. URL: https://www.sciencedirect.com/science/article/pii/S0168900220311475, doi:https://doi.org/10.1016/j.nima.2020.164750. ↩

  35. Michaël Dell’aiera, Thomas Vuillaume, Mikaël Jacquemont, and Alexandre Benoit. Deep unsupervised domain adaptation applied to the cherenkov telescope array large-sized telescope. Proceedings of the 20th International Conference on Content-based Multimedia Indexing, pages 133–139, September 2023. URL: http://dx.doi.org/10.1145/3617233.3617279, doi:10.1145/3617233.3617279. ↩

  36. Georges Aad and others. ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset. Eur. Phys. J. C, 83(7):681, 2023. arXiv:2211.16345, doi:10.1140/epjc/s10052-023-11699-1. ↩

  37. Georges Aad and others. 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. arXiv:2111.06712, doi:10.1007/JHEP06(2022)097. ↩

  38. R. Aaij and others. Test of lepton flavor universality using $B^0\ensuremath \rightarrow D^*\ensuremath -\ensuremath \tau ^+\ensuremath \nu _\ensuremath \tau $ decays with hadronic $\ensuremath \tau $ channels. Phys. Rev. D, 108:012018, July 2023. URL: https://link.aps.org/doi/10.1103/PhysRevD.108.012018, doi:10.1103/PhysRevD.108.012018. ↩

  39. 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. \aap , 662:A36, June 2022. arXiv:2202.09964, doi:10.1051/0004-6361/202142751. ↩

  40. Anass Bairouk, Marc Chaumont, Dominique Fouchez, Jerome Paquet, Frédéric Comby, and Julian Bautista. Astronomical image time series classification using CONVolutional attENTION (ConvEntion). \aap , 673:A141, May 2023. arXiv:2304.01236, doi:10.1051/0004-6361/202244657. ↩

  41. M. Treyer, R. Ait-Ouahmed, J. Pasquet, S. Arnouts, E. Bertin, and D. Fouchez. CNN photometric redshifts in the SDSS at r \ensuremath \leq 20. \mnras , October 2023. arXiv:2310.02173, doi:10.1093/mnras/stad3171. ↩

  42. 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, pages 15, 2023. URL: https://scipost.org/10.21468/SciPostPhysCodeb.15, doi:10.21468/SciPostPhysCodeb.15. ↩

  43. 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, nov 2023. URL: https://doi.org/10.1140%2Fepjc%2Fs10052-023-12169-4, doi:10.1140/epjc/s10052-023-12169-4. ↩

  44. Anna Stakia, Tommaso Dorigo, and others. Advances in multi-variate analysis methods for new physics searches at the large hadron collider. Reviews in Physics, 7:100063, 2021. URL: https://www.sciencedirect.com/science/article/pii/S2405428321000095, doi:https://doi.org/10.1016/j.revip.2021.100063. ↩

  45. Simon Badger and others. Machine learning and LHC event generation. SciPost Phys., 14(4):079, 2023. arXiv:2203.07460, doi:10.21468/SciPostPhys.14.4.079. ↩

  46. V. V. Gligorov and V. Reković. Review of real-time data processing for collider experiments. Eur. Phys. J. Plus, 138(11):1005, 2023. arXiv:2310.04756, doi:10.1140/epjp/s13360-023-04599-6. ↩

  47. Mathias Backes, Anja Butter, Monica Dunford, and Bogdan Malaescu. Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods. 2023. arXiv:2310.17037. ↩

  48. Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, and Anja Butter. Precision-Machine Learning for the Matrix Element Method. SciPost Physics, 2023. arXiv:2310.07752. ↩

  49. 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:2305.10475. ↩

  50. 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. arXiv:2304.00032, doi:10.1088/1475-7516/2023/07/033. ↩

  51. 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. URL: https://dx.doi.org/10.1088/2632-2153/acd90f, doi:10.1088/2632-2153/acd90f. ↩

  52. 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:2307.09268. ↩

  53. Kirill Grishin, Simona Mei, and Stéphane Ilić. YOLO-CL: Galaxy cluster detection in the SDSS with deep machine learning. \aap , 677:A101, September 2023. arXiv:2301.09657, doi:10.1051/0004-6361/202345976. ↩

  54. 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:2311.04845. ↩

  55. Justine Zeghal, François Lanusse, Alexandre Boucaud, Benjamin Remy, and Eric Aubourg. Neural posterior estimation with differentiable simulators. 2022. arXiv:2207.05636. ↩

  56. 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. URL: https://doi.org/10.1051/0004-6361/202245340, doi:10.1051/0004-6361/202245340. ↩

  57. 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:2309.02957. ↩

  58. 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:2312.05123. ↩

  59. 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:2309.12422. ↩

  60. Corentin Allaire, Françoise Bouvet, Hadrien Grasland, and David Rousseau. Ranking-based neural network for ambiguity resolution in acts. 2023. arXiv:2312.05070. ↩

  61. 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:2307.08593. ↩

  62. 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. arXiv:2302.07555, doi:10.1088/1748-0221/18/05/P05017. ↩

  63. 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. URL: https://arxiv.org/abs/2408.15845, arXiv:2408.15845. ↩

  64. T. Bossis, M.-A. Verdier, C. Trigila, L. Pinot, F. Bouvet, A. Blot, H. Ramarijaona, T. Beaumont, D. Broggio, O. Caselles, S. Zerdoud, and L. Ménard. A high-resolution portable gamma-camera for estimation of absorbed dose in molecular radiotherapy. IEEE Transactions on Radiation and Plasma Medical Sciences, 8(5):556–570, 2024. doi:10.1109/TRPMS.2024.3376826. ↩

  65. T. Bossis, M.-A. Verdier, L. Pinot, F. Bouvet, T. Beaumont, D. Broggio, O. Caselles, S. Zerdoud, and L. Ménard. Optimized reconstruction of the position of interaction in high-performances γ-cameras. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1048:167907, 2023. URL: https://www.sciencedirect.com/science/article/pii/S0168900222011998, doi:https://doi.org/10.1016/j.nima.2022.167907. ↩

  66. ATLAS Collaboration. Measurement of off-shell higgs boson production in the $h^*\rightarrow zz\rightarrow 4\ell $ decay channel using a neural simulation-based inference technique in 13 tev $pp$ collisions with the atlas detector. 2024. URL: https://arxiv.org/abs/2412.01548, arXiv:2412.01548. ↩

  67. ATLAS Collaboration. An implementation of neural simulation-based inference for parameter estimation in atlas. 2024. URL: https://arxiv.org/abs/2412.01600, arXiv:2412.01600. ↩

  68. M. Rejmund and A. Lemasson. Seven-dimensional trajectory reconstruction for vamos++. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1076:170445, 2025. URL: https://www.sciencedirect.com/science/article/pii/S0168900225002463, doi:https://doi.org/10.1016/j.nima.2025.170445. ↩

  69. Bruno Khelifi, Arache Djannati-Ataï, Lea Jouvin, Julien Lefaucheur, Anne Lemiere, Santiago Pita, Thomas Tavernier, and Regis Terrier. HAP-Fr, a pipeline of data analysis for the HESS-II experiment. In PoS, volume ICRC2015, 837. The Hague, Netherlands, July 2015. URL: https://hal.science/hal-01584586, doi:10.22323/1.236.0837. ↩

  70. M. Abushawish, G. Baulieu, J. Dudouet, and O. Stezowski. Neural networks for 3d characterisation of agata crystals. The European Physical Journal A, 2026. URL: https://hal.science/hal-05208731, doi:https://doi.org/10.1140/epja/s10050-026-01793-9. ↩