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Publications

Journal articles

  • Kholoimov V., Jashal BK., Oyanguren A., Svintozelskyi V., Zhuo J., “A Downstream and vertexing algorithm for Long Lived Particles (LLP) selection at the first High-level trigger (HLT1) of LHCb” Comp. and Soft. for Big Science (2025) Vol 9, 10, https://arxiv.org/abs/2503.13092
  • Rubén López Ruiz, Celia Fernández Madrazo, Sergio Sánchez Cruz, Lara Lloret Iglesias, Pablo Martínez Ruiz del Árbol, “Fast simulation for scattering muography applications using generative adversarial neural networks”, Engineering Applications of Artificial Intelligence, Volume 162, Part A, 2025, 112357, https://doi.org/10.1016/j.engappai.2025.112357
  • B. Lefevre, E. Gozillon, D. Attié, H. Gómez, I. Mandjavidze, P. Mas. “Demultiplexing and Electron-Muon identification in different Micropattern Readout Planes with common U-Net approach”. Engineering Applications of Artificial Intelligence 155 (2025) 110962 https://doi.org/10.1016/j.engappai.2025.110962
  • B. Lefevre, J. Vogel, H. Gómez, D. Attié, L. Gallego, P. Gonzales, B. Lesage, P. Mas, D. Pomarède. “3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection”. PRX Energy 4 (2025) 013002 https://doi.org/10.1103/PRXEnergy.4.013002
  • Saúl Cano Ortiz; Lara Lloret Iglesias; Pablo Martínez Ruiz del Arbol; Pedro Lastra González; Daniel Cano Fresno, "Leveraging a deep learning generative model to enhance recognition of minor asphalt defects." Scientific Reports 14.1 (2024): 28904.
  • Saúl Cano Ortiz; Lara Lloret Iglesias; Pablo Martínez Ruiz del Arbol; Pedro Lastra González; Daniel Cano Fresno, "Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment." Results in Engineering 23 (2024): 102745. DOI: 10.1016/j.rineng.2024.102745
  • P. Martínez Ruiz del Árbol, P. Vischia et al. G. Strong et al, “TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography”, Mach. Learn.: Sci. Technol. 5 (2024) 035002, 10.1088/2632-2153/ad52e7. https://arxiv.org/abs/2309.14027
  • B. Lefevre, D. Attié, R. Bajou, H. Gómez. “2D and 3D analysis improvements with machine learning for muography applications”. Nucl. Ins. and Meth. A 1068 (2024) 169755 https://doi.org/10.1016/j.nima.2024.169755
  • P. Martínez Ruiz del Árbol et al. “An end-to-end computer vision system based on deep learning for pavement distress detection and quantification”. Construction and Building Materials (2024) https://doi.org/10.1016/j.conbuildmat.2024.135036
  • Gorkavenko V. et al., “LHCb potential to discover long-lived new physics particles with lifetimes above 100 ps”, Eur.Phys.J.C 84 (2024) 6, 608; https://arxiv.org/abs/2312.14016
  • J. Hernández-Tello, M.Á. Martínez-del-Amor, D. Orellana-Martín, F.G. Cabarle. “Sparse Spiking Neural-like Membrane Systems on Graphics Processing Units”. International Journal of Neural Systems, 30, 7 (2024) DOI: 10.1142/S0129065724500382. https://arxiv.org/abs/2408.04343
  • D. Cagigas-Muñiz et al. “Comparing the Efficiency of Traffic Simulations Using Cellular Automata”. In: Guisado-Lizar, JL., Riscos-Núñez, A., Morón-Fernández, MJ., Wainer, G. (eds) Simulation Tools and Techn. SIMUtools 2023. (2024).
    https://link.springer.com/chapter/10.1007/978-3-031-57523-5_14
  • P. Martínez Ruiz del Árbol et al. “Improving detection of asphalt distresses with deep learning-based diffusion model for intelligent road maintenance”. Developments in the Built Environment (2023). https://doi.org/10.1016/j.dibe.2023.100315
  • S. Folgueras et al. (MODE collaboration). “The Analytical Method algorithm for trigger primitives generation at the LHCDrift Tubes detector”, NIM in Physics Research, A 1049 (2023) 168103. https://doi.org/10.1016/j.nima.2023.168103
  • A. Valero, L. Fiorini et al., “The PreProcessor module for the ATLAS Tile calorimeter at the HL-LHC”, Front. Detect. Sci. Technol. 1 (2023) 1264123. https://doi.org/10.3389/fdest.2023.1264123
  • Cristina Fernández Bedoya et al. "The Analytical Method algorithm for trigger primitive’s generation at the LHC Drift Tubes detector." CMS Drift Tubes collaboration. 2023. Nuclear Inst. and Methods in Physics Research, A. 1049 168103. https://arxiv.org/abs/2302.01666
  • P. Vischia et al. (MODE collaboration) “Toward the end-to-end optimization of particle physics instruments with differentiable programming”. Reviews in Physics 10 (2023) 1000085. https://doi.org/10.1016/j.revip.2023.100085
  • J. Fernández et al. “Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter”. Sensors 2023, 23, 9679. https://doi.org/10.3390/s23249679
  • Roel Aaij et al, “A Comparison of CPU and GPU implementations for the LHCb Experiment Run 3 Trigger”. Comp. and Soft. for Big Science 6, Article number: 1 (2022) https://arxiv.org/abs/2105.04031
  • A. Oyanguren et al., “Effect of the High Level Trigger for Detecting Long-Lived Particles at LHCb”. Frontiers in Big Data [Sec. Big Data and AI in HEP]. Vol. 5. (2022). DOI: https://doi.org/10.3389/fdata.2022.1008737
  • Aaij R. et al, “Allen: a high-level trigger on GPUs for LHCb”, Comput Softw Big Sci 4, 7 (2020); https://arxiv.org/abs/1912.09161
  • Aaij, R. et al., “Design and performance of the LHCb trigger and full real-time reconstruction in Run 2 of the LHC”, JINST 14 (2019) P04013; https://arxiv.org/abs/1812.10790
  • Aaij, R. et al., “Selection and processing of calibration samples to measure the particle identification performance of the LHCb experiment in Run 2”, EPJ Tech. Instrum. 6 (2019) 1; https://arxiv.org/abs/1803.00824
  • X. Cid Vidal et al. “Unleashing the full power of LHCb to probe Stealth New Physics”. In: Rept. Prog. Phys. 85 (2022),024201. doi: 10.1088/1361-6633/ac4649. https://arxiv.org/abs/2105.12668
  • José Enrique García; Luis M. Fernández-Prieto; Antonio Villaseñor; Verónica Sanz; Jean-Baptiste Ammirati; Eduardo A. Díaz Suárez; Carmen García. “Performance of Deep Learning Pickers in Routine Network Processing Applications Seismological”, Research Letters 2022-09-0, DOI: https://doi.org/10.1785/0220210323
  • Johannes Hirn; José Enrique García; Alicia Montesinos‐Navarro; Ricardo Sánchez‐Martín; Veronica Sanz; Miguel Verdú. “A Deep Generative Artificial Intelligence system to predict species coexistence patterns” Methods in Ecology and Evolution 2022-05,
    DOI: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13827
  • Aiola, S. et al, “HybridSeeding: A standalone track reconstruction algorithm for scintillating fibre tracker at LHCb”. Comput.Phys.Commun. 260 (2021) 107713. https://arxiv.org/abs/2007.02591
  • Valls Canudas, Núria, et al. "Use of Deep Learning to Improve the Computational Complexity of Reconstruction Algorithms in High Energy Physics." Applied Sciences 11.23 (2021): 11467. https://www.mdpi.com/2076-3417/11/23/11467
  • J. Alda, A. Mir, S. Penaranda. "A comparative analysis of results on B-anomalies using a machine learning algorithm". e-Print: 2412.15830 [hep-ph] https://doi.org/10.48550/arXiv.2412.15830
  • J. Alda, J. Guasch, S. Penaranda. "Using Machine Learning techniques in phenomenological studies on flavour physics". JHEP 07 (2022) 115 https://doi.org/10.1007/JHEP07(2022)115
  • K. Altenmüller et al. “REST-for-Physics”. Zenodo (2024) https://doi.org/10.5281/zenodo.11110607
  • I. Coarasa, J. Apilluelo, J. Amaré, S. Cebrián, D. Cintas, E. García, M. Martínez, M.A. Oliván, Y. Ortigoza, A. Ortiz de Solórzano, T. Pardo, J. Puimedón, A. Salinas, M.L. Sarsa and P. Villar. “Improving ANAIS-112 sensitivity to DAMA/LIBRA signal with machine learning techniques”. JCAP11 (2022) 048 https://doi.org/10.1088/1475-7516/2022/11/048
  • I. Coarasa, J. Amaré J. Apilluelo, S. Cebrián, D. Cintas, E. García, M. Martínez, M.Á. Oliván, Y. Ortigoza, A. Ortiz de Solórzano, T. Pardo, J. Puimedón, A. Salinas, M. L. Sarsa, P. Villar. “ANAIS–112 three years data: a sensitive model independent negative test of the DAMA/LIBRA dark matter signal”. Comm Phys ( 2024) 7:345 https://doi.org/10.1038/s42005-024-01827-y
  • F. J. Galindo Guarch, P. Baudrenghien and J. M. Moreno Arostegui. "An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals". IEEE Transactions on Circuits and Systems I: Regular Papers 67:5 (2020) 1653-1666 10.1109/TCSI.2019.2960686
  • F. Javier Galindo Guarch, Philippe Baudrenghien, J. Manuel Moreno Arostegui. “A new beam synchronous processing architecture with a fixed frequency processing clock. Application to transient beam loading compensation in the CERN SPS machine”. Nucl. Inst. And Meth. A 988 (2021) 164894 https://doi.org/10.1016/j.nima.2020.164894
  • F J Galindo Guarch, J M Moreno Aróstegui and P Baudrenghien. “Compensation of Transient Beam Loading in Ramping Synchrotrons using a Fixed Frequency Processing Clock”. J. Phys.: Conf. Ser. 1067 (2018) 072033 https://doi.org/10.1088/1742-6596/1067/7/072033

Reports

  • The LHCb Collaboration, “Computing and software for LHCb Upgrade II - Input to the European Particle Physics Strategy Update 2024–26”; https://doi.org/10.48550/arXiv.2503.24106
  • The LHCb Collaboration, Egede U. et al, “LHCb Data Acquisition Enhancement”, TDR CERN-LHCC-2024-001, LHCB-TDR-025; https://cds.cern.ch/record/2886764
  • The LHCb Collaboration, Batpista J. et al, “LHCb upgrade GPU high level trigger technical design report.” Tech. rep. 2020, CERN-LHCC-2020-006, LHCB-TDR-021;
    http://cds.cern.ch/record/2717938
  • V. Chobanova et al,”Fast simulation of a forward detector at 50 and 100 TeV proton-proton colliders”. https://arxiv.org/abs/2012.02692
  • M. Eriksen et al. “The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning.” Monthly Notices of the Royal Astronomical Society. 2023.
  • L. Cabayol, M. Eriksen, and the PAUS collaboration. “The PAU survey: estimating galaxy photometry with deep learning”. MNRAS, 506(3):4048–4069, September 2021
  • J. Alimena et al, “Review of opportunities for new long-lived particle triggers in Run 3 of the Large Hadron Collider”, CERN-LPCC-2021-01, https://doi.org/10.48550/arXiv.2110.14675
  • M. Eriksen and the PAUS collaboration. “The PAU Survey: Photometric red-shifts using transfer learning from simulations”. MNRAS, 497(4):4565–4579, October 2020
  • A. Oyanguren et al, "A Roadmap for HEP Software and Computing R&D for the 2020s",
    https://arxiv.org/abs/1712.06982

Books

Master and PhD thesis

Proceedings