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Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics

Julia Gonski ^1,*, Jennifer Ott ^2,*, Shiva Abbaszadeh³, Sagar Addepalli ¹, Matteo Cremonesi ⁴, Jennet Dickinson ⁵, Giuseppe Di Guglielmo^6,7, Yigit Erdem Ertorer ⁴, Lindsey Gray ⁶ et al.

Ryan Herbst¹, Christian Herwig ⁸, Tae Min Hong ⁹, Benedikt Maier ¹⁰, Maryam Bayat Makou ¹¹, David W. Miller ¹², Mark S. Neubauer ¹³, Cristián Peña ⁶, Dylan Rankin ¹⁴, Seon-Hee Seo ⁶, Giordon Stark ¹⁵, Alexander Tapper ¹⁰, Audrey Corbeil Therrien ¹⁶, Ioannis Xiotidis ¹⁷, Keisuke Yoshihara ¹⁸, Nural Akchurin ²⁰, Carlos Argüelles²¹, Saptaparna Bhattacharya ¹¹, Lorenzo Borella ^22,23, Christian Boutan ²⁴, Tom Braine ²⁴, James Brau²⁵, Marco Carminati²⁹, Antonio Chahine ¹⁰, Yuan-Tang Chou ²⁷, Talal Ahmed Chowdhury ²⁶, Seokju Chung²⁸, Ryan Coffee¹, Alberto Coppi ^22,23, Mariarosaria D’Alfonso³⁰, Abhilasha Dave ¹, Chance Desmet²⁴, Angela Di Fulvio ³¹, Karri DiPetrillo ¹², Javier Duarte ¹⁹, Auralee Edelen¹, Jan Eysermans ³⁰, Yongbin Feng ²⁰, Emmett Forrestel ³², Julián García Pardiñas³⁰, Loredana Gastaldo ³³, Lino Gerlach³⁴, Abarajithan Gnaneswaran¹⁹, Loukas Gouskos ³², Katya Govorkova ³⁰, Carl Grace ³⁵, Christopher Grant³⁶, Philip Harris³⁰, Ciaran Hasnip ¹⁷, Timon Heim ³⁵, Abraham Holtermann ³⁰, Gian Michele Innocenti³⁰, Koji Ishidoshiro ³⁷, Miaochen Jin ²¹, Jyothisraj Johnson ³⁵, Stephen Jones²⁴, Andreas Jung ³⁸, Nicholas Kamp ²¹, Georgia Karagiorgi²⁸, Ryan Kastner ¹⁹, Doojin Kim ³⁹, Kyoungchul Kong ²⁶, Katie Kudela ³⁰, Bo-Cheng Lai ⁴⁰, Yun-Tsung Lai ⁴¹, Jelena Lalic³⁰, Tommy Lam ⁴², Jeffrey Lazar ⁴³, Aobo Li ¹⁹, Zepeng Li¹⁸, Haoyun Liu ¹⁰, Qibin Liu¹, Vladimir Lončar³⁰, Zhenghua Ma ¹⁹, Luca Macchiarulo⁴⁴, Christopher Madrid ²⁰, Prashansa Mukim ⁴⁵, Victoria Nguyen³⁰, Sungbin Oh ⁶, Isobel Ojalvo³⁴, Hideyoshi Ozaki⁴⁶, Simone Pagan Griso ⁴⁹, Santosh Parajuli ¹³, Myeonghun Park ⁴⁷, Benjamin Parpillon⁶, Christoph Paus ³⁰, Sara Pozzi⁵², Ema Puljak ¹⁷, Benjamin Ramhorst ⁴⁸, Amy Roberts⁵⁰, Larry Ruckman ¹, Sebastian Schmitt³⁰, Kate Scholberg ⁵¹, Noah Singer³⁸, Eluned Anne Smith ³⁰, Alexandre Sousa ⁵³, Michael Spannowsky⁵⁴, Kaito Sugizaki ¹⁴, Sioni Summers¹⁷, Yanwen Sun¹, Daniel Tapia Takaki²⁶, Antonino Tumeo ²⁴, Caterina Vernieri¹, Belina von Krosigk ⁵⁵, Yash Vora ³², Linyan Wan ⁶, Michael H. L. S. Wang ⁶, Amanda Weinstein ⁵⁶, Andy White ⁵⁷, Simon Williams ⁵⁴, Liangyu Wu¹, and Felix Yu ²¹

¹SLAC National Accelerator Laboratory, Menlo Park, California, USA
²Department of Electrical and Computer Engineering, University of Hawai'i Mānoa, Honolulu, Hawaii, USA
³Santa Cruz Institute for Particle Physics, UC Santa Cruz, Santa Cruz, USA
⁴Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
⁵Department of Physics (also CLASSE) Cornell University, Ithaca, New York, USA
⁶Fermi National Accelerator Laboratory, Batavia, Illinois, USA
⁷Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, USA
⁸Department of Physics, University of Michigan, Ann Arbor, Michigan, USA
⁹Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
¹⁰Imperial College London, London, United Kingdom
¹¹Department of Physics, Southern Methodist University, University Park, Texas, USA
¹²Department of Physics, Enrico Fermi Institute, Kavli Institute for Cosmological Physics, University of Chicago, Chicago, Illinois, USA
¹³Department of Physics, University of Illinois, Urbana-Champaign, USA
¹⁴Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
¹⁵Santa Cruz Institute for Particle Physics (SCIPP), University of California Santa Cruz, Santa Cruz, California, USA
¹⁶Département de Génie Électrique et de Génie Informatique, Université de Sherbrooke, Sherbrooke, Québec, Canada
¹⁷CERN, Geneva, Switzerland
¹⁸Department of Physics and Astronomy, University of Hawai'i Mānoa, Honolulu, Hawaii, USA
¹⁹University of California San Diego, La Jolla, California, USA
²⁰Department of Physics and Astronomy, Texas Tech University, Lubbock, Texas, USA
²¹Department of Physics, Harvard University, Cambridge, Massachusetts, USA
²²Department of Physics and Astronomy, Istituto Nazionale di Fisica Nucleare (INFN), Padova, Italy
²³University of Padua, Padova, Italy
²⁴Nuclear and Particle Physics, Pacific Northwest National Laboratory, Richland, Washington, USA
²⁵University of Oregon, Eugene, Oregon, USA
²⁶Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas, USA
²⁷Department of Physics, University of Washington, Seattle, Washington, USA
²⁸Department of Physics, Columbia University, New York, New York, USA
²⁹Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
³⁰Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
³¹Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois, Urbana-Champaign, USA
³²Department of Physics, Brown University, Providence, Rhode Island, USA
³³Kirchhoff Institute for Physics, Heidelberg University, Heidelberg 69120, Germany
³⁴Department of Physics, Princeton University, Princeton, New Jersey 08544, USA
³⁵Lawrence Berkeley National Laboratory, Berkeley, California, USA
³⁶Department of Physics, Boston University, Boston, Massachusetts, USA
³⁷Research Center for Neutrino Science (RCNS), Tohoku University, Sendai, Miyagi, Japan
³⁸Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana, USA
³⁹Department of Physics, University of South Dakota, Vermillion, South Dakota, USA
⁴⁰Department of Electronics Engineering, National Yang Ming Chiao Tung University, Taiwan
⁴¹High Energy Accelerator Research Organization, Japan
⁴²Virginia Tech, Blacksburg, Virginia, USA
⁴³Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université Catholique de Louvain, Louvain-la-Neuve, Belgium
⁴⁴Nalu Scientific LLC, USA
⁴⁵Brookhaven National Laboratory, Upton, New York, USA
⁴⁶Department of Physics and Astronomy, Tokyo University of Science, Shinjuku City, Tokyo 162-8601, Japan
⁴⁷School of Liberal Arts, Seoul National University of Science and Technology, South Korea
⁴⁸ETH Zurich, Switzerland
⁴⁹Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
⁵⁰Department of Physics, University of Colorado Denver, Denver, Colorado, USA
⁵¹Department of Physics, Duke University, Durham, North Carolina, USA
⁵²Dept. of Nuclear Engineering & Radiological Sciences (joint w/ Physics), University of Michigan, Ann Arbor, Michigan, USA
⁵³Department of Physics University of Cincinnati, Cincinnati, Ohio, USA
⁵⁴Institute for Particle Physics Phenomenology (IPPP), Dept. of Physics, Durham University, Durham, United Kingdom
⁵⁵Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, Germany
⁵⁶Department of Physics and Astronomy, Iowa State University, Ames, Iowa, USA
⁵⁷Department of Physics, University of Texas Arlington, Arlington, Texas, USA

^*Contact author: jgonski@slac.stanford.edu

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PRX Intelligence 1, 021201 – Published 13 October, 2026

DOI: https://doi.org/10.1103/bjx3-vlj8

Abstract

The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.

Physics Subject Headings (PhySH)

Article Text

References (362)

J. N. Butler et al., Report of the 2021 U.S. community study on the future of particle physics (Snowmass 2021) summary chapter, arXiv:2301.06581.
H. Murayama et al., Exploring the quantum universe: Pathways to innovation and discovery in particle physics, Technical Report, Office of Scientific and Technical Information (OSTI), 2023, http://dx.doi.org/10.2172/2368847.
Basic research needs for high energy physics detector research & development, 2019, https://science.osti.gov/-/media/hep/pdf/Reports/2020/DOE%5FBasic%5FResearch%5FNeeds%5FStudy%5Fon%5FHigh%5FEnergy%5FPhysics.pdf.
T. K. Aarrestad et al., Building an AI-native research ecosystem for experimental particle physics: A community vision, arXiv:2602.17582.
C. S. Lindsey, B. Denby, H. Haggerty, and K. Johns, Real time track finding in a drift chamber with a VLSI neural network, Nucl. Instrum. Methods Phys. Res. Sect. A 317, 346 (1992).
B. Denby, C. S. Lindsey, M. Dickson, J. Konigsberg, G. Pauletta, W. Badgett, K. Burkett, and M. Campbell, Performance of the CDF neural network electron isolation trigger at Fermilab, Nucl. Instrum. Methods Phys. Res., Sect. A 356, 485 (1995).
https://genesis.energy.gov/.
https://indico.phys.hawaii.edu/event/2506/.
A. McCarn Deiana, N. Tran, J. Agar, M. Blott, G. Di Guglielmo, J. Duarte, P. Harris, S. Hauck, M. Liu, M. S. Neubauer, J. Ngadiuba, S. Ogrenci-Memik, M. Pierini, et al., Applications and techniques for fast machine learning in science, Front. Big Data 5, 787421 (2022).
https://cpad-dpf.org/.
https://higgsfactory.slac.stanford.edu/
S. Mandal, G. W. Deptuch, D. S. Gorni, P. Maj, and P. Mukim, A smart readout ASIC with real-time digital waveform processing and machine learning, in Proceedings of the 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD) (IEEE, New York, 2024), pp. 1–1.
G. W. Deptuch, G. A. Carini, D. S. Gorni, S. Mandal, S. Miryala, and G. Pinaroli, Pixel detectors with built-in signal processing and bandwidth-efficient data transmission, in Proceedings of the 10th International Workshop on Semi- conductor Pixel Detectors for Particles and Imaging (SISSA, Trieste, Italy, 2022), Vol. 420.
S. Mandal, S. Miryala, G. W. Deptuch, and G. A. Carini, A 12-bit 50 MS/s hybrid ADC for waveform sampling, data streaming, and sparse readout, in Proceedings of the 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (IEEE, New York, 2022), pp. 1–2.
S. Miryala, Md A. Zaman, S. Mittal, Y. Ren, G. Deptuch, G. Carini, S. Zohar, S. Yoo, J. Fried, J. Huang, and S. Katkoori, Peak prediction using multi layer perceptron (MLP) for edge computing ASICs targeting scientific applications, in Proceedings of the 2022 23rd International Symposium on Quality Electronic Design (ISQED) (IEEE, New York, 2022), pp. 1–6.
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, Deep learning with limited numerical precision, in Proceedings of the 32nd International Conference on Machine Learning, Lille, France, Vol. 37 (PMLR, 2015), pp. 1737–1746.
S. R. Kharel, P. Mukim, P. Maj, G. W. Deptuch, S. Yoo, Y. Ren, and S. Mandal, Automated and holistic co-design of neural networks and ASICs for enabling in-pixel intelligence, arXiv:2407.14560.
The phase-2 upgrade of the CMS endcap calorimeter, Technical Report, CERN, Geneva, 2017, https://cds.cern.ch/record/2293646.
Sandvine, The global internet phenomena report, 2024, https://www.applogicnetworks.com/hubfs/Sandvine_Redesign_2019/Downloads/2024/GIPR/GIPR%202024.pdf, accessed 9 March 2025.
A. M. Sirunyan et al., Particle-flow reconstruction and global event description with the CMS detector, J. Inst. 12, P10003 (2017).
G. Di Guglielmo et al., A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC, IEEE Trans. Nucl. Sci. 68, 2179 (2021).
K. Sohn, H. Lee, and X. Yan, Learning structured output representation using deep conditional generative models, in Advances in Neural Information Processing Systems, edited by C. Cortes et al. (Curran Associates, Inc., Red Hook, 2015), Vol. 28.
R. Shenoy, J. Duarte, C. Herwig, J. Hirschauer, D. Noonan, M. Pierini, N. Tran, and C. M. Suarez, Differentiable Earth mover's distance for data compression at the high-luminosity LHC, Mach. Learn.: Sci. Technol. 4, 045058 (2023).
B. Parpillon, A. R. Trivedi, and F. Fahim, Readout IC with 40 MSPS in-pixel ADC for future vertex detector upgrades of Large Hadron Collider, in Proceedings of the 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, New York, 2023), pp. 1–5.
B. Parpillon, Radiation-hard smart-pixel detector ASIC readout with digital AI in 28 nm, Poster presentation at TWEPP 2024, Topical Workshop on Electronics for Particle Physics (2024), 30 September–4 October 2024, https://indico.cern.ch/event/1381495/contributions/5988566/.
B. Parpillon, C. Syal, J. Yoo, J. Dickinson, M. Swartz, G. Di Guglielmo, A. Bean, D. Berry, M. B. Valentin, K. DiPetrillo, A. Badea, L. Gray, P. Maksimovic, C. Mills, M. S. Neubauer, G. Pradhan, N. Tran, D. Wen, and F. Fahim, Smart pixels: In-pixel AI for on-sensor data filtering, arXiv:2406.14860.
FastML Team, fastmachinelearning/hls4ml, 2024, https://github.com/fastmachinelearning/hls4ml.
J. Duarte et al., Fast inference of deep neural networks in FPGAs for particle physics, J. Inst. 13, P07027 (2018).
Siemens EDA, Catapult HLS, online, 2025, https://eda.sw.siemens.com/en-US/ic/catapult-high-level-synthesis.
T. Ajayi et al., INVITED: Toward an open-source digital flow: First learnings from the OpenROAD Project, in Proceedings of the 56th Annual Design Automation Conference (DAC) (ACM Press, New York, NY, USA, 2019), pp. 1–4.
A. B. Kahng and T. Spyrou, The OpenROAD Project: Unleashing hardware innovation, in Proceedings of the Government Microcircuit Applications and Critical Technology Conference (GOMACTech) (2021), pp. 1–6.
N. B. Agostini, S. Curzel, J. J. Zhang, A. Limaye, C. Tan, V. Amatya, M. Minutoli, V. G. Castellana, J. Manzano, D. Brooks, G.-Y. Wei, and A. Tumeo, Bridging Python to silicon: The SODA toolchain, IEEE Micro 42, 78 (2022).
Pacific Northwest National Laboratory, End-to-end co-design for performance, energy efficiency, and security in AI-enabled computational science (ENCODE), in DOE Advanced Scientific Computing Research Competitive Portfolio Program (2025), https://www.pnnl.gov/projects/encode.
J.-F. Schulte et al., hls4ml: A flexible, open-source platform for deep learning acceleration on reconfigurable hardware, ACM Trans. Reconfigurable Technol. Syst. 19, 1 (2026).
C. N. Coelho, A. Kuusela, S. Li, H. Zhuang, T. Aarrestad, V. Loncar, J. Ngadiuba, M. Pierini, A. A. Pol, and S. Summers, Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors, Nat. Mach. Intell. 3, 675 (2021).
C. Sun, Z. Que, T. Årrestad, V. Loncar, J. Ngadiuba, W. Luk, and M. Spiropulu, HGQ: High granularity quantization for real-time neural networks on FPGAs, arXiv:2405.00645.
B. Ramhorst, V. Lončar, and G. A. Constantinides, FPGA resource-aware structured pruning for real-time neural networks, in Proceedings of the 2023 International Conference on Field Programmable Technology (ICFPT) (IEEE, New York, 2023), p. 282.
A. Pappalardo et al., QONNX: Representing arbitrary-precision quantized neural networks, in Proceedings of the 4th Workshop on Accelerated Machine Learning at the High-performance Embedded Architecture and Compilation 2022 Conference (2022).
C. M. S. Collaboration, 2024 data collected with AXOL1TL anomaly detection at the CMS Level-1 trigger, CMS detector performance summary CMS-DP-2024-059, 2024, https://cds.cern.ch/record/2904695.
C. M. S. Collaboration, Model-independent real-time anomaly detection at the CMS Level-1 calorimeter trigger with CICADA, CMS detector performance summary CMS-DP-2024-121, 2024, https://cds.cern.ch/record/2917884.
E. SpAIce, Novel edge-AI system for accurate and near real-time plastic detection and monitoring in marine environment, 2024, https://edgespaice.eu/.
N. Ghielmetti et al., Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml, Mach. Learn.: Sci. Technol. 3, 045011 (2022).
G. Di Guglielmo et al., End-to-end workflow for machine-learning-based qubit readout with QICK and hls4ml, arXiv:2501.14663.
F. A. Mian and S. Zafar, SoC-based implementation of 1-D convolutional neural network for 3-channel ECG arrhythmia classification via hls4ml, IEEE Embedded Syst. Lett. 16, 429 (2024).
J. Yoo et al., Smart pixel sensors: Towards on-sensor filtering of pixel clusters with deep learning, Mach. Learn.: Sci. Technol. 5, 035047 (2024).
B. Parpillon et al., Smart pixels: In-pixel AI for on-sensor data filtering, in Proceedings of the 2024 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room-Temperature Semiconductor Detectors Symposium (IEEE, New York, 2024).
G. Abarajithan, Z. Ma, R. Munasinghe, F. Restuccia, and R. Kastner, CGRA4ML: A hardware/software framework to implement neural networks for scientific edge computing, ACM Trans. Reconfigurable Technol. Syst. 19, 1 (2026).
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. Leong, M. Jahre, and K. Vissers, FINN: A framework for fast, scalable binarized neural network inference, in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (ACM Press, New York, 2017), pp. 65–74.
lowRISC, Ibex RISC-V Core, 2025, https://github.com/lowRISC/ibex.
R. Herbst, R. Coffee, N. Fronk, K. Kim, K. Kim, L. Ruckman, and J. J. Russell, Implementation of a framework for deploying AI inference engines in FPGAs, 2023, arXiv:2305.19455.
A. Dave, C. Wang, J. Russell, R. Herbst, and J. Thayer, FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging, Front. High Perform. Comput. 3, 1520151 (2025).
H. E. Rahali, A. Dave, L. Ruckman, M. M. Rahimifar, A. C. Therrien, J. J. Russel, and R. T. Herbst, Neural network acceleration on MPSoC board: Integrating SLAC's SNL, Rogue software and Auto-SNL, arXiv:2508.21739.
H. Jia, A. Dave, J. Gonski, and R. Herbst, Analysis of hardware synthesis strategies for machine learning in collider trigger and data acquisition, arXiv:2411.11678.
A. Dave, J. Russell, M. Mishra, L. Ruckman, K. Erickson, S. Kim, S. Joung, J. Butt, R. Herbst, R. Coffee, D. Smith, and E. Kolemen, FPGA-accelerated real-time beam emission spectroscopy diagnostics at DIII-D using the SLAC neural network library for ML inference, arXiv:2511.21924.
I. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit, M. Lucic, and A. Dosovitskiy, MLP-Mixer: An all-MLP architecture for vision, Advances in Neural Information Processing Systems, Vol. 34 (Curran Associates, Inc., 2021), pp. 24261–24272.
D. McElfresh, S. Khandagale, J. Valverde, V. Prasad C., G. Ramakrishnan, M. Goldblum, and C. White, When do neural nets outperform boosted trees on tabular data? in Advances in Neural Information Processing Systems, edited by A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Curran Associates, Inc., Red Hook, NY, 2023), Vol. 36, pp. 76336–76369.
L. Grinsztajn, E. Oyallon, and G. Varoquaux, Why do tree-based models still outperform deep learning on tabular data? in Advances in Neural Information Processing Systems, Vol. 35 (Curran Associates, Inc., 2022), pp. 507–520.
T. Finke, M. Hein, G. Kasieczka, M. Krämer, A. Mück, P. Prangchaikul, T. Quadfasel, D. Shih, and M. Sommerhalder, Tree-based algorithms for weakly supervised anomaly detection, Phys. Rev. D 109, 034033 (2024).
S. Summers, G. Di Guglielmo, J. Duarte, P. Harris, D. Hoang, S. Jindariani, E. Kreinar, V. Loncar, J. Ngadiuba, M. Pierini, D. Rankin, N. Tran, and Z. Wu, Fast inference of boosted decision trees in FPGAs for particle physics, J. Instrum. 15, P05026 (2020).
T. M. Hong, B. T. Carlson, B. R. Eubanks, S. T. Racz, S. T. Roche, J. Stelzer, and D. C. Stumpp, Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics, J. Instrum. 16, P08016 (2021).
B. T. Carlson, Q. Bayer, T. M. Hong, and S. T. Roche, Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics, J. Instrum. 17, P09039 (2022).
S. T. Roche, Q. Bayer, B. T. Carlson, W. C. Ouligian, P. Serhiayenka, J. Stelzer, and T. M. Hong, Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays, Nat. Commun. 15, 3527 (2024).
V. V. Gligorov and M. Williams, Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree, J. Inst. 8, P02013 (2013).
D. Acosta et al., Boosted decision trees in the level-1 muon endcap trigger at CMS, J. Phys.: Conf. Ser. 1085, 042042 (2018).
D. Reikher, Leveraging machine learning for enhanced FPGA-based tau triggering and combined $H \to (b b / c c)$ analysis in the ATLAS experiment, Ph.D. thesis, Tel Aviv University, School of Physics and Astronomy, 2025, https://doi.org/10.17181/93sta-ptr08.
C. Brown, CMS level-1 track finder for the phase-2 ipgrade, in Proceedings of the 32nd International Workshop on Vertex Detectors—PoS(VERTEX2023) (2024), Vol. 448, p. 022.
P. E. Meiring, Low energy leptons in high energy physics: A search for physics beyond the Standard Model with compressed mass-spectra and new algorithms for triggering on electrons at the high luminosity LHC, Ph.D. thesis, Dissertation, University of Zurich, 2024.
N. C. Hall and N. Konstantinidis, DECADE: Decorrelated anomaly detection triggers to enhance the low-mass discovery potential of the LHC, arXiv:2508.10224.
F. T. L. Liu, K. M. Ting, and Z.-H. Zhou, Isolation forest, in Proceedings of the 2008 8th IEEE International Conference on Data Mining (IEEE, New York, 2008), pp. 413–422.
Y.-T. Lai, Implementation of small-scale ML in Belle II chamber drift chamber front-end electronics for cross-talk noise reduction, in ML4FE Workshop (University of Hawaii, Honolulu, HI, USA, 2025).
J. Gonski, A. Gupta, H. Jia, H. Kim, L. Rota, L. Ruckman, A. Dragone, and R. Herbst, Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout, J. Instrum. 19, P08023 (2024).
J. Johnson, B. Boxer, T. Prakash, C. Grace, P. Sorensen, and M. Tripathi, Investigating resource-efficient neutron/gamma classification ML models targeting eFPGAs, J. Instrum. 19, P07034 (2024).
J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
A. Di Meglio et al., Quantum computing for high-energy physics: State of the art and challenges, PRX Quantum 5, 037001 (2024).
R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
D. González-Cuadra, M. Hamdan, T. V. Zache, B. Braverman, M. Kornjača, A. Lukin, S. H. Cantú, F. Liu, S.-T. Wang, A. Keesling, M. D. Lukin, P. Zoller, and A. Bylinskii, Observation of string breaking on a $(2 + 1) D$ Rydberg quantum simulator, Nature (London) 642, 321 (2025).
T. A. Cochran et al., Visualizing dynamics of charges and strings in $(2 + 1) D$ lattice gauge theories, Nature (London) 642, 315 (2025).
B. Nachman, D. Provasoli, W. A. de Jong, and C. W. Bauer, Quantum algorithm for high energy physics simulations, Phys. Rev. Lett. 126, 062001 (2021).
J. Q. Toledo-Marín, S. Gonzalez, H. Jia, I. Lu, D. Sogutlu, A. Abhishek, C. Gay, E. Paquet, R. G. Melko, G. C. Fox, M. Swiatlowski, and W. Fedorko, Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions, npj Quantum Inf. 11, 114 (2025).
E. Puljak, M. Pierini, and A. Garcia-Saez, Tensor network for anomaly detection in the latent space of proton collision events at the LHC, Mach. Learn.: Sci. Technol. 6, 045001 (2025).
C. Duffy, M. Hassanshahi, M. Jastrzebski, and S. Malik, Unsupervised beyond-standard-model event discovery at the LHC with a novel quantum autoencoder, Quantum Mach. Intell. 7, 41 (2025).
M. Kjaergaard, M. E. Schwartz, J. Braumüller, et al., Superconducting qubits: Current state of play, Annu. Rev. Condens. Matter Phys. 11, 369 (2020).
C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, Trapped-ion quantum computing: Progress and challenges, Appl. Phys. Rev. 6, 021314 (2019).
A. Browaeys and T. Lahaye, Many-body physics with individually controlled Rydberg atoms, Nat. Phys. 16, 132 (2020).
F. A. Zwanenburg et al., Silicon quantum electronics, Rev. Mod. Phys. 85, 961 (2013).
J. L. O’Brien, A. Furusawa, and J. Vučković, Photonic quantum technologies, Nat. Photon. 3, 687 (2009).
M. Schuld and N. Killoran, Quantum machine learning in feature Hilbert spaces, Phys. Rev. Lett. 122, 040504 (2019).
S. Endo, S. C. Benjamin, and Y. Li, Practical quantum error mitigation for near-future applications, Phys. Rev. X 8, 031027 (2018).
T. Kadowaki and H. Nishimori, Quantum annealing in the transverse Ising model, Phys. Rev. E 58, 5355 (1998).
E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda, A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem, Science 292, 472 (2001).
H. Okawa, Q.-G. Zeng, X.-Z. Tao, and M.-H. Yung, Quantum-annealing-inspired algorithms for track reconstruction at high-energy colliders, Comput. Software Big Sci. 8, 16 (2024).
S. Das, A. J. Wildridge, and A. Jung, Track clustering with a quantum annealer for primary vertex reconstruction at hadron colliders, arXiv:1903.08879.
J. L. Scott, Z. Dong, T. Kim, K. Kong, and M. Park, Hybrid quantum-classical approach for combinatorial problems at hadron colliders, arXiv:2410.22417.
A. Mott, J. Job, J.-R. Vlimant, D. Lidar, and M. Spiropulu, Solving a Higgs optimization problem with quantum annealing for machine learning, Nature (London) 550, 375 (2017).
W. Huggins, P. Patil, B. Mitchell, K. B. Whaley, E. M. Stoudenmire, Towards quantum machine learning with tensor networks, Quantum Sci. Technol. 4, 024001 (2019).
S.-J. Ran and G. Su, Tensor networks for interpretable and efficient quantum-inspired machine learning, Intell. Comput. 2, 0061 (2023).
A. Coppi, E. Puljak, L. Borella, D. Jaschke, E. Rico, M. Pierini, J. Pazzini, A. Triossi, and S. Montangero, Towards tensor network models for low-latency jet tagging on FPGAs, Mach. Learn.: Sci. Technol 7, 045065 (2026).
D. Perez-Garcia, F. Verstraete, M. M. Wolf, and J. I. Cirac, Matrix product state representations, Quant. Inf. Comput. 7, 401 (2001).
I. Cirac, D. Perez-Garcia, N. Schuch, and F. Verstraete, Matrix product states and projected entangled pair states: Concepts, symmetries, and theorems, Rev. Mod. Phys. 93, 045003 (2021).
Y.-Y. Shi, L.-M. Duan, and G. Vidal, Classical simulation of quantum many-body systems with a tree tensor network, Phys. Rev. A 74, 022320 (2006).
A. Coppi, E. Puljak, L. Borella, D. Jaschke, E. Rico, M. Pierini, J. Pazzini, A. Triossi, and S. Montangero, Towards tensor network models for low-latency jet tagging on FPGAs, arXiv:2601.10801.
E . Miles Stoudenmire and D. J. Schwab, Supervised learning with quantum-inspired tensor networks, in Advances in Neural Information Processing Systems, Vol. 29 (Curran Associates, Inc., 2016), pp. 4799–4807.
T. Felser, M. Trenti, L. Sestini, A. Gianelle, D. Zuliani, D. Lucchesi, and S. Montangero, Quantum-inspired machine learning on high-energy physics data, npj Quantum Inf. 7, 111 (2021).
A. Gianelle, P. Koppenburg, D. Lucchesi, D. Nicotra, E. Rodrigues, L. Sestini, J. de Vries, and D. Zuliani, Quantum machine learning for $b$ -jet charge identification, J. High Energy Phys. 08 (2022) 014.
S. Montangero, E. Rico, and P. Silvi, Loop-free tensor networks for high-energy physics, Phil. Trans. A. Math. Phys. Eng. Sci. 380, 20210065 (2021).
J. Y. Araz and M. Spannowsky, Quantum-inspired event reconstruction with tensor networks: Matrix product states, J. High Energy Phys. 08 (2021) 112.
S. Addepalli, P. Bhattarai, A. Dave, and J. Gonski, Hardware-aware tensor networks for real-time quantum-inspired anomaly detection at particle colliders, arXiv:2603.26604.
L. Borella, A. Coppi, A. Triossi, J. Pazzini, M. Zanetti, A. Stanco, and M. Trenti, Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA, Mach. Learn.: Sci. Technol. 6, 045062 (2025).
M. C. Peixoto, N. F. Castro, M. C. Romão, M. G. J. Oliveira, and I. Ochoa, Fitting a collider in a quantum computer: Tackling the challenges of quantum machine learning for big datasets, Front. Artif. Intell. 6, 1268852 (2023).
V. Belis, K. A. Woźniak, E. Puljak, P. Barkoutsos, G. Dissertori, M. Grossi, M. Pierini, F. Reiter, I. Tavernelli, and S. Vallecorsa, Quantum anomaly detection in the latent space of proton collision events at the LHC, Commun. Phys. 7, 334 (2024).
V. Belis, P. Odagiu, M. Grossi, F. Reiter, G. Dissertori, and S. Vallecorsa, Guided quantum compression for high dimensional data classification, Mach. Learn.: Sci. Technol. 5, 035010 (2024).
A. Bal, M. Klute, B. Maier, M. Oughton, E. Pezone, and M. Spannowsky, One particle-one qubit: Particle physics data encoding for quantum machine learning, Phys. Rev. D 112, 076004 (2025).
B. Maier, M. Spannowsky, and S. Williams, Continuous-variable photonic quantum extreme learning machines for fast collider-data selection, arXiv:2510.13994.
V. Bergholm et al., PennyLane: Automatic differentiation of hybrid quantum-classical computations, arXiv:1811.04968.
M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol. 4, 043001 (2019).
M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, and N. Killoran, Evaluating analytic gradients on quantum hardware, Phys. Rev. A 99, 032331 (2019).
D. Wierichs, J. Izaac, C. Wang, and Cedric Yen-Yu Lin, General parameter-shift rules for quantum gradients, Quantum 6, 677 (2022).
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nat. Commun. 9, 4812 (2018).
D. Koch, N. Dao, B. Healy, J. Yu, and A. Attwood, Fabulous: An embedded FPGA framework, in The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA’21 (ACM Press, New York, NY, USA, 2021), pp. 45–56.
X. Tang, E. Giacomin, B. Chauviere, A. Alacchi, and P.-E. Gaillardon, OpenFPGA: An open-source framework for agile prototyping customizable FPGAs, IEEE Micro 40, 41 (2020).
J. J. Manfredi, E. Adamek, J. A. Brown, E. Brubaker, B. Cabrera-Palmer, J. Cates, R. Dorrill, A. Druetzler, J. Elam, P. L. Feng, M. Folsom, A. Galindo-Tellez, B. L. Goldblum, P. Hausladen, N. Kaneshige, K. Keefe, T. A. Laplace, J. G. Learned, A. Mane, and P. Marleau, The single-volume scatter camera, in Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXII, edited by Arnold Burger, Stephen A. Payne, and Michael Fiederle (International Society for Optics and Photonics, SPIE, 2020), Vol. 11494, p. 114940V.
B. Boxer, B. Godfrey, C. Grace, J. Johnson, R. Khandwala, and M. Tripathi, Studies in pulse shape discrimination for an optimized ASIC design, J. Instrum. 18, P01020 (2023).
B. Murmann, M. Verhelst, and Y. Manoli, Analog-to-information conversion, in NANO-CHIPS 2030, edited by Bernhard Murmann and Bernd Hoefflinger (Springer, 2020).
D. A. Villamizar, D. G. Muratore, J. B. Wieser, and B. Murmann, An 800 nW switched-capacitor feature extraction filterbank for sound classification, IEEE Trans. Circuits Syst. I 68, 1578 (2021).
C. Young, A. Omid-Zohoor, P. Lajevardi, and B. Murmann, A data-compressive 1.5/2.75-bit log-gradient QVGA image sensor with multi-scale readout for always-on object detection, IEEE J. Solid-State Circuits 54, 2932 (2019).
Q. Lu and B. Murmann, Enhancing the energy efficiency and robustness of TinyML computer vision using log-gradient images, in ACM Transactions on Embedded Computing Systems (ACM Press, New York, 2024)Vol. 23, pp. 1–20.
M. Verhelst and B. Murmann, Machine learning at the edge, in NANO-CHIPS 2030, edited by B. Murmann and B. Hoefflinger (Springer, 2020).
B. Murmann, Mixed-signal computing for deep neural network inference, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 29, 3 (2021).
Doshi et al., Medusa: A 0.83/4.6 $µ J$ /Frame 86/91.6%-CIFAR-10 TinyML processor with pipelined pixel streaming of bottleneck layers in 28 nm CMOS, in 2024 IEEE Symposium on VLSI Technology and Circuits (IEEE, 2024), pp. 1–2.
Pacific Northwest National Laboratory, Democratization of co-design for energy-efficient heterogeneous computing (decode), in DOE Advanced Scientific Computing Research, Microelectronics Science Research Center Project (2025), https://www.pnnl.gov/projects/decode.
C. Lattner, M. Amini, U. Bondhugula, A. Cohen, A. Davis, J. Pienaar, R. Riddle, T. Shpeisman, N. Vasilache, and O. Zinenko, MLIR: Scaling compiler infrastructure for domain specific computation, in Proceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) (IEEE, New York, 2021), pp. 2–14.
E. Mutlu, R. Tian, B. Ren, S. Krishnamoorthy, R. Gioiosa, J. Pienaar, and G. Kestor, Comet: A domain-specific compilation of high-performance computational chemistry, in Languages and Compilers for Parallel Computing, edited by B. Chapman and José Moreira (Springer International Publishing, Cham, 2022), pp. 87–103.
R. Tian, L. Guo, J. Li, B. Ren, and G. Kestor, A high performance sparse tensor algebra compiler in MLIR, in Proceedings of the 2021 IEEE/ACM 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) (IEEE, New York, 2021), pp. 27–38.
N. B. Agostini, S. Curzel, V. Amatya, C. Tan, M. Minutoli, V. G. Castellana, J. Manzano, D. Kaeli, and A. Tumeo, An MLIR-based compiler flow for system-level design and hardware acceleration, in Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD’22 (ACM Press, New York, NY, 2022).
F. Ferrandi, V. G. Castellana, S. Curzel, P. Fezzardi, M. Fiorito, M. Lattuada, M. Minutoli, C. Pilato, and A. Tumeo, Invited: Bambu: An open-source research framework for the high-level synthesis of complex applications, in Proceedings of the 2021 58th ACM/IEEE Design Automation Conference (DAC) (IEEE, New York, 2021), pp. 1327–1330.
T. Ajayi et al., Toward an open-source digital flow: First learnings from the OpenROAD project, in Proceedings of the 56th Annual Design Automation Conference 2019, DAC’19 (ACM Press, New York, NY, USA, 2019).
C. Tan, C. Xie, A. Li, K. J. Barker, and A. Tumeo, OpenCGRA: An open-source unified framework for modeling, testing, and evaluating CGRAs, in Proceedings of the 2020 IEEE 38th International Conference on Computer Design (ICCD) (IEEE, New York, 2020), pp. 381–388.
C. Tan, N. B. Agostini, T. Geng, C. Xie, J. Li, A. Li, K. J. Barker, and A. Tumeo, DRIPS: Dynamic rebalancing of pipelined streaming applications on CGRAs, in Proceedings of the 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) (IEEE, New York, 2022), pp. 304–316.
D. Ciangottini et al., Analysis facilities for the HL-LHC white paper, Comput. Software Big Sci. 9, 14 (2025).
J. Weitz, D. Demler, L. McDermott, N. Tran, and J. Duarte, Neural architecture codesign for fast physics applications, Mach. Learn.: Sci. Technol. 6, 035009 (2025).
J. Weitz, D. Demler, B. Hawks, N. Tran, and J. Duarte, Surrogate neural architecture codesign package (SNAC-Pack), in Proceedings of the 39th Annual Conference on Neural Information Processing Systems: Includes Machine Learning and the Physical Sciences (ML4PS), arXiv:2512.15998.
J. Weitz, D. Demler, B. Hawks, A. Wang, N. Tran, and J. Duarte, Surrogate neural architecture codesign package (SNAC-Pack), arXiv:2605.16138.
B. Hawks et al., wa-hls4ml: A benchmark and surrogate models for hls4ml resource and latency estimation, ACM Trans. Reconfigurable Technol. Syst. 19, 20 (2026).
NVIDIA corporation, triton inference server: An optimized cloud and edge inferencing solution, https://github.com/triton-inference-server/server.
D. Kondratyev, B. Riedel, Y.-T. Chou, M. Cochran-Branson, N. Paladino, D. Schultz, M. Liu, J. Duarte, P. Harris, and S.-C. Hsu, Supersonic: Cloud-native infrastructure for ML inferencing, in Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration, PEARC’25 (ACM Press, New York, NY, 2025), pp. 29:1–29:5.
T. Aarrestad et al., Fast convolutional neural networks on FPGAs with hls4ml, Mach. Learn.: Sci. Technol. 2, 045015 (2021).
A. Buckley, L. Corpe, M. Habedank, and T. Procter, Enabling stable preservation of ML algorithms in high-energy physics with PetriFyML, arXiv:2509.11830.
S. Astrand, L. Boggia, M. Borsato, L. Bozianu, C. E. C. Toapaxi, F. I. Giasemis, J. Hansen, P. Inkaew, K. E. Iversen, P. Jawahar, H. P. Monteagudo, M. Olocco, and S. Schramm, Perspective on machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb, Mach. Learn.: Sci. Technol. 7, 013001 (2026).
L. Nguyen, J. Jamilkowski, K. Kulmatycski, and P. Bachek, Plans and strategy for edge AI/ML at the Electron-Ion Collider at Brookhaven National Laboratory (ICALEPCS) (JACoW, Publishing, Geneva, Switzerland, 2025), paper WEMR003, https://doi.org/10.18429/JACoW-ICALEPCS2025-WEMR003.
J. Dickinson et al., SmartPixels: Towards on-sensor inference of charged particle track parameters and uncertainties, arXiv:2312.11676.
B. Parpillon et al., In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors, arXiv:2510.07485.
D. Shekar et al., Sensor co-design for smartpixels, arXiv:2510.06588.
J. Yoo et al., Smart pixel sensors: Towards on-sensor filtering of pixel clusters with deep learning, arXiv:2310.02474.
A. Accardi et al., Electron Ion Collider: The next QCD frontier: Understanding the glue that binds us all, Eur. Phys. J. A 52, 268 (2016).
The A LICE Collaboration, Technical design report for the ALICE inner tracking system 3—ITS3: A bent wafer-scale monolithic pixel detector, Technical Report, CERN, Geneva, 2024, https://cds.cern.ch/record/2890181, Co-project Manager: Magnus Mager.
G. Alimonti et al., RD53 pixel readout integrated circuits for ATLAS and CMS HL-LHC upgrades, J. Inst. 20, P03024 (2025).
Technical design report for the ATLAS inner tracker pixel detector, Report number CERN-LHCC-2017-021; ATLAS-TDR-030, 2017, https://doi.org/10.17181/CERN.FOZZ.ZP3Q.
W. Adam et al., The CMS Phase-1 pixel detector upgrade, J. Inst. 16, P02027 (2021).
P. Dorosz, Data transmission architecture of the ALICE ITS3 stitched sensor prototype MOSAIX, J. Inst. 19, C04050 (2024).
Integration time is defined as the time interval during which a pixel is active and integrates charge.
M. Abbrescia et al., The IDEA detector concept for FCC-ee, arXiv:2502.21223.
C. Accettura et al., Towards a muon collider, Eur. Phys. J. C 83, 864 (2023).
S. Jindariani et al., Promising technologies and R&D directions for the future muon collider detectors, arXiv:2203.07224.
C. Bell et al., MAIA: A new detector concept for a 10 TeV muon collider, arXiv:2502.00181.
P. Andreetto et al., MUSIC: A multi-purpose detector concept for physics at the 10 TeV muon collider, arXiv:2511.23273.
M. Swartz, A detailed simulation of the CMS pixel sensor, Report No. CMS-NOTE-2002-027 (CERN, 2002).
D. Yilmaz, L. Wu, and J. Gonski, Edge machine learning for cluster counting in next-generation drift chambers, in Proceedings of the 39th Annual Conference on Neural Information Processing Systems: Includes Machine Learning and the Physical Sciences (ML4PS) (2025), arXiv:2511.10540.
N. Akchurin, K. Carrell, J. Hauptman, H. Kim, H. P. Paar, A. Penzo, R. Thomas, and R. Wigmans, Hadron and jet detection with a dual-readout calorimeter, Nucl. Instrum. Methods Phys. Res., Sect. A 537, 537 (2005).
S. Lee, M. Livan, and R. Wigmans, Dual-readout calorimetry, Rev. Mod. Phys. 90, 025002 (2018).
L. Wu, Q. Liu, M. T. Lucchini, J. Gonski, M. Campajola, and S. Moneta, Machine learning enables real-time waveform decomposition for dual-readout calorimetry, arXiv:2604.26090.
N. Akchurin, C. Cowden, J. Damgov, A. Hussain, and S. Kunori, On the use of neural networks for energy reconstruction in high-granularity calorimeters, J. Inst. 16, P12036 (2021).
J. Brau, M. Breidenbach, A. Dragone, A. Habib, L. Rota, M. Vassilev, and C. Vernieri, The SiD digital ECAL based on monolithic active pixel sensors, EPJ Web Conf. 315, 03005 (2024).
M. Breidenbach, J. Brau, P. Burrows, T. Markiewicz, M. Stanitzki, J. Strube, and A. White, Updating the SiD detector concept, arXiv:2110.09965.
Framework TDR for the LHCb Upgrade II, Technical Report number CERN-LHCC-2021-012 ; LHCB-TDR-023, CERN, Geneva, 2021, https://doi.org/10.17181/CERN.NTVH.Q21W.
F. Fahim et al., hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices, TinyML Research Symposium, arXiv:2103.05579.
M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015, https://www.tensorflow.org/.
F. Chollet et al., Keras, 2015, https://github.com/fchollet/keras.
W. McDonald and D. Gedecke, A constant fraction of pulse height trigger for optimum time resolution, Nucl. Instrum. Methods 55, 1 (1967).
C. M. S. Collaboration, Improving missing transverse momentum estimation with a deep neural network, Phys. Rev. D 113, 072010 (2026).
E. Yigitbasi, Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS endcap muon track finder, arXiv:2509.21062.
Trigger monitoring and rate predictions using Enhanced Bias data from the ATLAS detector at the LHC, Technical Report, CERN, Geneva, 2016, https://cds.cern.ch/record/2223498, All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-DAQ-PUB-2016-002.
K. L. Unger, J. Becker, T. Forsthofer, S. Hiesl, C. Kiesling, M. Neu, E. Schmidt, F. Meggendorfer, T. Mi, and Z. Ou, A multi-Hough-based displaced vertex track trigger for the Belle II experiment, J. Instrum. 20, C02051 (2025).
CMS Collaboration, The CMS trigger system, J. Inst. 12, P01020 (2017).
M. Pierini, J. M. Duarte, N. Tran, and M. Freytsis, HLS4ML LHC jet dataset (150 particles), 2020, https://zenodo.org/records/3602260.
C. Sun et al., Fast jet tagging with MLP-Mixers on FPGAs, Mach. Learn.: Sci. Technol. 6, 035025 (2025).
L. Laatu, C. Sun et al., Sub-microsecond transformers for jet tagging on FPGAs, arXiv:2510.24784.
J. C. Martens, J. P. Ralston, and J. D. T. Takaki, Quantum tomography for collider physics: Illustrations with lepton pair production, Eur. Phys. J. C 78, 5 (2018).
I. B. Alonso et al., High-luminosity large hadron collider (HL-LHC): Technical design report, Technical Report, CERN-2020-010, CERN, 2020.
X. Ju et al., Graph neural networks for particle reconstruction in high energy physics detectors, in 2nd Machine Learning and the Physical Sciences Workshop, 33rd Conference on Neural Information Processing Systems (NeurIPS, 2020).
P. W. Battaglia et al., Interaction networks for learning about objects, relations and physics, in Advances in Neural Information Processing Systems, Vol. 29 (Curran Associates, Inc., 2016), pp. 4502–4510.
J. Biamonte et al., Quantum machine learning, Nature (London) 549, 195 (2017).
S. Parajuli, Quantum-enhanced graph neural networks for particle tracking in high-energy physics, in Proceedings of the Connecting the Dots Workshop (CTD 2025) (CERN, 2026).
S. Amrouche et al., The tracking machine learning challenge: Accuracy phase, The NeurIPS ‘18 Competition (Springer International Publishing, Cham, 2019), pp. 231–264.
M. J. Atkinson et al., ACORN: A charged object reconstruction network, Zenodo, 2024, https://doi.org/10.5281/zenodo.14526454.
C. Peña et al., High energy particle detection with large area superconducting microwire array, J. Inst. 20, P03001 (2025).
C. Wang et al., Towards high-efficiency particle detection using superconducting microwire arrays, J. Inst. 20, P12033 (2025).
D. S. Akerib et al., Snowmass2021 cosmic frontier dark matter direct detection to the neutrino fog, arXiv:2203.08084.
K. Freese, M. Lisanti, and C. Savage, Colloquium: Annual modulation of dark matter, Rev. Mod. Phys. 85, 1561 (2013).
J. H. Davis, Dark matter vs. neutrinos: The effect of astrophysical uncertainties and timing information on the neutrino floor, J. Cosmol. Astropart. Phys. 03 (2015) 012.
Y. Zhuang, L. E. Strigari, Jin Lei, and S. Sinha, Prospects for measuring the time variation of astrophysical neutrino sources at dark matter detectors, Phys. Rev. D 110, 043037 (2024).
J. S. Wilson, H. M. zu Theenhausen, B. von Krosigk, E. Azadbakht, R. Bunker, J. Hall, S. Hansen, B. Hines, B. Loer, J. T. Olsen, S. M. Oser, R. Partridge, M. Pyle, J. Sander, B. Serfass, D. Toback, S. L. Watkins, and X. Zhao, The level-1 trigger for the SuperCDMS experiment at SNOLAB, J. Instrum. 17, P07010 (2022).
H. M. zu Theenhausen, B. von Krosigk, and J. S. Wilson, Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB, J. Inst. 18, P06012 (2023).
M. R. Anderson, V. Basu, R. D. Martin, C. Z. Reed, N. J. Rowe, M. Shafiee, and T. Ye, Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals, Eur. Phys. J. C 82, 1084 (2022).
T. Pang, H. Zheng, Y. Quan, and H. Ji, Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising, in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2021), pp. 2043–2052.
A. Engel, T. Braine, and C. Boutan, Identifying environmentally induced calibration changes in cryogenic RF axion detector systems using deep neural networks, Rev. Sci. Instrum. 96, 123102 (2025).
Z. Dong, D. Kim, K. Kong, M. Park, and M. A. S. Alcaraz, Quantum sensing radiative decays of neutrinos and dark matter particles, arXiv:2508.09139.
K. Nakazono, S. Chen, H.e Fukuda, Y. Iiyama, T. Inada, T. Moroi, T. Nitta, A. Noguchi, R. Sawada, S. Shirai, T. Sichanugrist, K. Terashi, and K. Watanabe, Search for dark photon dark matter with a mass around 36.1 $µ eV$ using a frequency-tunable cavity controlled through a coupled superconducting qubit, arXiv:2505.15619.
J. I. Crespo-Anadón, The MicroBooNE continuous readout stream for detection of supernova neutrinos, J. Phys.: Conf. Ser. 1312, 012006 (2019).
A. Earle, K. Manolopoulos, and I. Hristova, Trigger primitive generation using FELIX FPGA system: A case study for DUNE, in Proceedings of the 42nd International Conference on High Energy Physics–PoS(ICHEP2024) (SISSA, 2024), Vol. 476, p. 1139.
P. Abratenko et al., Demonstration and performance of an online data selection algorithm for liquid argon time projection chambers using MicroBooNE, arXiv:2602.11138.
M. D. Kistler, W. C. Haxton, and H. Yüksel, Tomography of massive stars from core collapse to supernova shock breakout, Astrophys. J. 778, 81 (2013).
S. Al Kharusi et al., SNEWS 2.0: A next-generation supernova early warning system for multi-messenger astronomy, New J. Phys. 23, 031201 (2021).
Note that there may be no bright supernova for the case of a black hole formation.
A. A. Abud et al., Supernova pointing capabilities of DUNE, Phys. Rev. D 111, 092006 (2025).
Michael H. L. S. Wang, Machine learning-based extreme data reduction for prompt supernova pointing at DUNE, IEEE Trans. Nucl. Sci. 72, 678 (2025).
Y.-J. Jwa, G. Di Guglielmo, L. Arnold, L. Carloni, and G. Karagiorgi, Real-time inference with 2D convolutional neural networks on field programmable gate arrays for high-rate particle imaging detectors, Front. Artif. Intell. 5, 855184 (2022).
L. Uboldi, D. Ruth, M. Andrews, M. H. L. S. Wang, H. J. Wenzel, W. Wu, and T. Yang, Extracting low energy signals from raw LArTPC waveforms using deep learning techniques—A proof of concept, Nucl. Instrum. Methods Phys. Res., Sect. A 1028, 166371 (2022).
DUNE Collaboration, B. Abi et al., Deep underground neutrino experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics, arXiv:2002.03005.
L. Manzanillas, Status of ProtoDUNE-II, in Proceedings of 42nd International Conference on High Energy Physics—PoS(ICHEP2024) (SISSA, 2024), Vol. 476, p. 219.
P. Coloma, J. López-Pavón, L. Molina-Bueno, and S. Urrea, New physics searches using ProtoDUNE and the CERN SPS accelerator, J. High Energy Phys. 01 (2024) 134.
C. Touramanis, NP04 annual status report to the SPS, Technical Report, CERN, Geneva, 2025, https://cds.cern.ch/record/2932871.
T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16 (ACM Press, New York, NY, 2016), pp. 785–794.
E. L. Snider and G. Petrillo, LArSoft: Toolkit for simulation, reconstruction and analysis of liquid argon TPC neutrino detectors, J. Phys.: Conf. Ser. 898, 042057 (2017).
S. Seo, Anomaly detection & smart TPC, DUNE AI/ML Kick-off Workshop, April 2025, FERMILAB-SLIDES-25-0081-PPD; see also S. Seo, “Smart TPC: Feasibility Study” Fermilab LDRD pre-proposal (2024).
E. Novello, S. Seo, and M. Wang, Real-time anomaly detection in DUNE raw data, Technical Report FERMILAB-PUB-25-0505-STUDENT, Fermilab, 2025.
Y.-J. Jwa, G. Di Guglielmo, L. P. Carloni, and G. Karagiorgi, Accelerating deep neural networks for real-time data selection for high-resolution imaging particle detectors, in Proceedings of the 2019 New York Scientific Data Summit: Data-Driven Discovery in Science and Industry (IEEE, New York, 2019), pp. 1–10.
J. Mitrevski, Low energy LArTPC signal detection using anomaly detection, Fast Machine Learning for Science Workshop (2023), https://indico.cern.ch/event/1283970/contributions/5550632/.
V. Lian et al., Denoising autoencoder for raw wireplane waveforms in DUNE's LArTPC detector, NSF HDR Postbaccalaureate Workshop (2023), https://indico.cern.ch/event/1253923/contributions/5443551/.
S. Chung, J. Cleeve, A. Malige, G. Karagiorgi, L. Gerlach, A. A. Pol, and I. Ojalvo, Real-time anomaly detection for liquid argon time projection chambers, arXiv:2509.21817.
R. Acciarri et al., A proposal for a three detector short-baseline neutrino oscillation program in the Fermilab Booster Neutrino Beam, arXiv:1503.01520.
B. Abi et al., The single-phase ProtoDUNE technical design report, arXiv:1706.07081.
A. Yankelevich, The I CEBERG test stand for DUNE cold electronics development, in 25th International Workshop on Neutrinos from Accelerators (2025), arXiv:2501.14838.
A. Badea et al., Intelligent pixel detectors: Towards a radiation hard ASIC with on-chip machine learning in 28 nm CMOS, Proceedings of 42nd International Conference on High Energy Physics—PoS ICHEP2024 (SISSA, 2025), p. 1074.
D. A. Dwyer et al., LArPix: Demonstration of low-power 3D pixelated charge readout for liquid argon time projection chambers, JINST 13, P10007 (2018).
Q-Pix, https://www.qpix.work/, accessed 23 February 2026.
A. Paudel et al., Modeling light signals using data from the first pulsed neutron source program at the DUNE vertical drift ColdBox test facility at CERN neutrino platform, arXiv:2512.10790.
H.-H. Chin, R.-S. Tsay, and H.-I. Wu, A high-performance adaptive quantization approach for edge CNN applications, arXiv:2107.08382.
C. J. S. Schaefer, S. Joshi, S. Li, and R. Blazquez, Edge inference with fully differentiable quantized mixed precision neural networks, arXiv:2206.07741.
K. Hoffpauir, J. Simmons, N. Schmidt, R. Pittala, I. Briggs, S. Makani, and Y. Jararweh, A survey on edge intelligence and lightweight machine learning support for future applications and services, ACM J. Data Inf. Quality 15, 1 (2023).
M. Jin, Y. Hu, and C. A. Argüelles, Two Watts is all you need: Enabling in-detector real-time machine learning for neutrino telescopes via edge computing, J. Cosmol. Astropart. Phys. 06 (2024) 026.
N. P. Jouppi, C. Young, N. Patil, et al., In-datacenter performance analysis of a tensor processing unit, in Proceedings of the 44th Annual International Symposium on Computer Architecture. Toronto, ON, Canada (ACM, 2017), pp. 1–12.
N. P. Jouppi, G. Kurian, S. Li, P. Ma, R. Nagarajan, L. Nai, N. Patil, S. Subramanian, A. Swing, B. Towles, C. Young, X. Zhou, Z. Zhou, and D. Patterson, TPU v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings, in Proceedings of the 50th Annual International Symposium on Computer Architecture, Orlando, FL (ACM, 2023), pp. 82:1–82:14.
G. Coral, The edge TPU dev board, https://coral.ai/products/dev-board, retrieved 25 May 2023.
J. Lazar, S. Meighen-Berger, C. Haack, D. Kim, S. Giner, and C. A. Argüelles, Prometheus: An open-source neutrino telescope simulation, arXiv:2304.14526.
Tensorflow lite library, https://www.tensorflow.org/lite, retrieved 24 April 2024.
Pycoral edgetpu compiler, https://coral.ai/docs/edgetpu/compiler/, retrieved 25 May 2023.
Pytorch executorch, https://pytorch.org/executorch-overview, retrieved 20 October 2023.
A. Søgaard et al., GraphNeT: Graph neural networks for neutrino telescope event reconstruction, JOSS 8, 4971 (2023).
M. Huennefeld, Reconstruction techniques in IceCube using convolutional and generative neural networks, EPJ Web Conf. 207, 05005 (2019).
IceCube Collaboration, M. G. Aartsen, et al., The IceCube Neutrino Observatory: Instrumentation and online systems, J. Inst. 12, P03012 (2017).
S. Adrián-Martínez et al., Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources, Astropart. Phys. 111, 100 (2019).
The IceCube Collaboration et al., IceCube-Gen2: The window to the extreme Universe, J. Phys. G: Nucl. Part. Phys. 48, 060501 (2021).
IceCube Collaboration, M. G. Aartsen, et al., Multimessenger observations of a flaring blazar coincident with high-energy neutrino IceCube-170922A, Science, 361, eaat1378 (2018).
F. J. Yu, J. Lazar, and C. A. Argüelles, Trigger-level event reconstruction for neutrino telescopes using sparse submanifold convolutional neural networks, Phys. Rev. D 108, 063017 (2023).
B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.
P. Abratenko et al., Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE, Phys. Rev. D 103, 052012 (2021).
L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102, 012005 (2020).
M. Hünnefeld, Online reconstruction of muon-neutrino events in IceCube using deep learning techniques, Master's thesis, TU Dortmund, 2017.
M. Agostini et al., The pacific ocean neutrino experiment, Nat. Astron. 4, 913 (2020).
F. J. Yu, N. Kamp, and C. A. Argüelles, Learning efficient representations of neutrino telescope events, J. Inst. 20, P12010 (2025).
R. Abbasi et al., A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors, J. Inst. 16, P08034 (2021).
D. Akimov et al., Observation of coherent elastic neutrino-nucleus scattering, Science 357, 1123 (2017)).
D. Akimov et al., First measurement of coherent elastic neutrino-nucleus scattering on Argon, Phys. Rev. Lett. 126, 012002 (2021).
S. Adamski et al., First detection of coherent elastic neutrino-nucleus scattering on germanium, arXiv:2406.13806.
D. Akimov et al., The COHERENT experimental program, arXiv:2204.04575.
A. Mirizzi, I. Tamborra, H.-T. Janka, N. Saviano, K. Scholberg, R. Bollig, L. Hudepohl, and S. Chakraborty, Supernova neutrinos: Production, oscillations and detection, Riv. Nuovo Cimento 39, 1 (2016).
A. Abusleme et al., JUNO physics and detector, Prog. Part. Nucl. Phys. 123, 103927 (2022).
K. Abe et al., Hyper-Kamiokande design report, arXiv:1805.04163.
B. Abi et al., Supernova neutrino burst detection with the deep underground neutrino experiment, Eur. Phys. J. C 81, 423 (2021).
C. Su, L. Kong, Q. Liu, S. Chen, W. Huang, K. Moharrami, Y. Zheng, and J. Li, Performance of a large-volume cryogenic pure CsI detector for $CE ν NS$ and low-energy rare-event searches, arXiv:2509.17528.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, PointNet: Deep learning on point sets for 3D classification and segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2017), pp. 652–660.
S. Abe et al., Search for Majorana neutrinos with the complete KamLAND-Zen dataset, Phys. Rev. Lett. 135, 262501 (2025).
R. Nakamura, H. Sambonsugi, K. Shiraishi, and Y. Wada, Research and development toward KamLAND2-Zen, J. Phys.: Conf. Ser. 1468, 012256 (2020).
A. Migala, E. Ku, Z. Li, and A. Li, Real-time position reconstruction for the KamLAND-Zen experiment using hardware-AI co-design, arXiv:2410.02991.
Z. Fu, C. Grant, D. M. Krawiec, A. Li, and L. A. Winslow, Generative models for simulation of KamLAND-Zen, Eur. Phys. J. C 84, 651 (2024).
S. Abe et al., Measurement of cosmic-ray muon spallation products in a xenon-loaded liquid scintillator with KamLAND, Phys. Rev. C 107, 054612 (2023).
J. St. John, C. Herwig, D. Kafkes, J. Mitrevski, W. A. Pellico, G. N. Perdue, A. Quintero-Parra, B. A. Schupbach, K. Seiya, N. Tran, M. Schram, J. M. Duarte, Y. Huang, and R. Keller, Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster, Phys. Rev. Accel. Beams 24, 104601 (2021).
L. Rota et al., The SparkPix-S ASIC for the sparsified readout of 1 MHz frame-rate X-ray cameras at LCLS-II, J. Inst. 19, C01010 (2024).
Y. Sun et al., Nonuniform flow dynamics probed by nanosecond x-ray speckle visibility spectroscopy, Phys. Rev. Lett. 127, 058001 (2021).
Y. Sun et al., Realizing split-pulse x-ray photon correlation spectroscopy, Phys. Rev. Res. 2, 023099 (2020).
Y. Sun et al., Dynamics of nanoscale phase decomposition in laser ablation, arXiv:2407.10505.
M. Oquab et al., DINOv2: Learning robust visual features without supervision, Trans. Mach. Learn. Res. (2024), arXiv:2304.07193.
A. Radford et al., Learning transferable visual models from natural language supervision (CLIP), Proc. ICML (2021), arXiv:2103.00020.
R. Rombach et al., High-resolution image synthesis with latent diffusion models, in Proceedings of the IEEE CVPR Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2022), pp. 10674–10685.
Y. Lipman et al., Flow matching for generative modeling, in ICLR (2023), arXiv:2210.02747.
H. Chen et al., Reblur2deblur: Deblurring videos via self-supervised learning, in Proceedings of the 2018 IEEE International Conference on Computational Photography (IEEE, New York, 2018), pp. 1–9.
J.-B. Alayrac et al., Flamingo: A visual language model for few-shot learning, Adv. Neural Inf. Process. Syst. 35, 23716 (2022).
J. Li et al., BLIP-2: Bootstrapping language-image pre-training, in Proceedings of the 40th International Conference on Machine Learning (JMLR.org, 2023).
L. Berthier and G. Biroli, Theoretical perspective on the glass transition and amorphous materials, Rev. Mod. Phys. 83, 587 (2011).
G. Biroli and J. P. Garrahan, Perspective: The glass transition, J. Chem. Phys. 138, 12A301 (2013).
T. Fujita et al., Directly observing atomic-scale relaxations of a glass-forming liquid using femtosecond XPCS, arXiv:2312.08613.
P. Niknejadi et al., Free-electron laser inverse-Compton interaction x-ray source, Phys. Rev. Accel. Beams 22, 040704 (2019).
K. Floettmann, A space charge tracking algorithm ASTRA, https://www.desy.de/~mpyflo/.
M. J. de Loos and S. B. van der Geer, General particle tracer: A new 3D code for accelerator and beamline design, in Proceedings of the 5th European Particle Accelerator Conference (1996), Vol. 1241.
M. Borland, Elegant: A flexible SDDS-compliant code for accelerator simulation, Technical Report L S-287, Argonne National Lab., IL, US, 2000.
W. M. Fawley, Algorithm for loading shot noise microbunching in multidimensional, free-electron laser simulation codes, Phys. Rev. ST Accel. Beams 5, 070701 (2002).
S. Reiche, Genesis 1.3: A fully 3D time-dependent FEL simulation code, Nucl. Instrum. Methods Phys. Res., Sect. A 429, 243 (1999).
N. Bidault, A. Weinberg, H. Purwar, and S. Li, Recommissioning of the University of Hawai’i LINAC and free electron laser, in Proceedings of the 16th International Particle Accelerator Conference (JACoW) (2025).
A. Weinberg, N. Bidault, and S. Li, Research plans for the University of Hawai’i accelerator and free-electron laser lab, in Proceedings of the 16th International Particle Accelerator Conference (JACoW) (2025).
A. L. Edelen et al., Neural networks for modeling and control of particle accelerators, IEEE Trans. Nucl. Sci. 63, 878 (2016).
A. Edelen and X. Huang, Machine learning for design and control of particle accelerators: A look backward and forward, Annu. Rev. Nucl. Part. Sci. 74, 557 (2024).
R. Roussel et al., Bayesian optimization algorithms for accelerator physics, Phys. Rev. Accel. Beams 27, 084801 (2024).
K. Rajput et al., Outlook towards deployable continual learning for particle accelerators, Mach. Learn.: Sci. Technol. 6, 031001 (2025).
J. Duris et al., Bayesian optimization of a free electron laser, Phys. Rev. Lett. 124, 124801 (2020).
R. Roussel et al., Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning, Nat. Commun. 12, 5612 (2021).
A. Scheinker, A. Edelen et al., Demonstration of model-independent control of the longitudinal phase space of electron beams in the linac-coherent light source with femtosecond resolution, Phys. Rev. Lett. 121, 044801 (2018).
T. Boltz et al., Leveraging prior mean models for faster Bayesian optimization of particle accelerators, Sci. Rep. 15, 12232 (2025).
A. Hanuka et al., Physics-model informed Gaussian process for online optimization of particle accelerators, Phys. Rev. Accel. Beams 24, 072802 (2021).
S. Miskovich et al., Multipoint-BAX: A new approach for efficiently tuning particle accelerator emittance via virtual objectives, Mach. Learn.: Sci. Technol. 5, 015004 (2024).
A. Edelen et al., Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems, Phys. Rev. Accel. Beams 23, 044601 (2020).
L. Gupta, Improving surrogate model accuracy for the LCLS-II injector frontend using convolutional neural networks and transfer learning, Mach. Learn.: Sci. Technol. 2, 045025 (2021).
A. Edelen et al., Experience integrating online modeling, adaptive digital twin infrastructure, and ML-based tuning for accelerator control at SLAC, in Proceedings of the 20th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS’25) (JACoW Publishing, Geneva, Switzerland, 2025), pp. 80–86.
R. Roussel et al., Xopt and Badger: A machine learning ecosystem for real-time accelerator control and optimization, in Proceedings of the 20th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS’25) (JACoW Publishing, Geneva, Switzerland, 2025), pp. 1271–1274.
C. Mayes et al., Lightsource unified modeling environment (LUME), a start-to-end simulation ecosystem, in Proceedings of the 12th International Particle Accelerator Conference (IPAC’21) (JACoW Publishing, Geneva, Switzerland, 2021), pp. 4212–4215.
S. Liuzzo et al., Optimisation of the Touschek lifetime in synchrotron light sources using Badger, in Proceedings of the 18th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2023) (JACoW Publishing, Geneva, Switzerland, 2023), pp. 108–115.
A. Mishra et al., A start to end machine learning approach to maximize scientific throughput from the LCLS-II-HE, Synchrotron Radiat. News 38, 10 (2025).
https://ichep2026.org.
https://cpad-dpf.org/.
AI for muon colliders virtual workshop, https://indico.muoncollider.us/event/41/June2026.
Next Generation Triggers, Next generation triggers—Public proposal, 2023, https://doi.org/10.17181/9jcgn-r3690.
Fast machine learning, in Fast Machine Learning Techniques for Scientific Data Analysis (Fermi National Accelerator Laboratory, Batavia, IL, USA, 2019), https://indico.cern.ch/event/822126/.
Fast machine learning for science workshop 2023, in Workshop on Accelerated Machine Learning and Inference (Imperial College London, UK, 2023), https://indico.cern.ch/event/1283970/.
Fast machine learning for science conference 2024, in Hybrid Workshop-Style Conference on Machine Learning for Science (Purdue University, West Lafayette, IN, USA, 2024), https://indico.cern.ch/event/1387540/.
Fast machine learning for science conference 2025, in Annual Conference on Accelerated Machine Learning Methods and Scientific Applications (ETH, Zurich, Switzerland, 2025), https://indico.cern.ch/event/1496673/.
CERN, CERN quantum technology initiative, 2024, https://quantum.cern/, accessed 2025
Y. Umuroglu, Y. Akhauri, N. J. Fraser, and M. Blott, LogicNets: Co-designed neural networks and circuits for extreme-throughput applications, in Proceedings of the 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) (IEEE, New York, 2020), pp. 291–297.
J. Duarte et al., Low-latency machine learning inference on FPGAs, in NeurIPS Workshop on Machine Learning and the Physical Sciences (ML4PS) (2019).
A. Sanaullah, C. Yang, Y. Alexeev, K. Yoshii, and M. C Herbordt, Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks, BMC Bioinf. 19, 490 (2018).
R. M. Imenabadi, G. R. Thoreson, K. G. Brown, and D. Bhatia, FPGA-accelerated CNN reconstruction for low-power sparse-array ultrasound imaging, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 72, 1618 (2025).
J. Leland, R. Chappidi, M. Fang, and A. Di Fulvio, Image reconstruction algorithms on an FPGA, in Proceedings of the 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD) (IEEE, New York, 2024), pp. 1–1.
S.Di Giacomo, M. Ronchi, G. Borghi, D. R. Schaart, M. Carminati, and C. Fiorini, Implementing an integrated neural network for real-time position reconstruction in emission tomography with monolithic scintillators, IEEE Trans. Radiat. Plasma Med. Sci. 8, 501 (2024).
S.Di Giacomo, M. Ronchi, M. Amadori, G. Borghi, M. Carminati, and C. Fiorini, Experimental validation of ANNA: Analog neural network ASIC for event positioning in monolithic scintillation detectors, IEEE Trans. Radiat. Plasma Med. Sci. 9, 542 (2025).
M. Ronchi, S.Di Giacomo, M. Amadori, G. Borghi, M. Carminati, and C. Fiorini, Design, implementation, and analysis of an integrated switched capacitor analog neuron for edge computing AI accelerators, IEEE Trans. Circuits Syst. I Regul. Pap. 72, 3947 (2025).
L. Buonanno, D. Di Vita, M. Carminati, and C. Fiorini, Gamma: A 16-channel spectroscopic ASIC for SiPMs readout with 84-dB dynamic range, IEEE Trans. Nucl. Sci. 68, 2559 (2021).
I. D’Adda, C. Riboldi, A. Saporito, G. Borghi, M. Carminati, and C. Fiorini, SITH: A SiPM readout ASIC for prompt gamma-ray detection in hadrontherapy applications, IEEE Trans. Nucl. Sci. 71, 2140 (2024).
M. Amadori, S.Di Giacomo, M. Ronchi, M. Carminati, G. Borghi, and C. Fiorini, A compact neuron implementation for a capacitive analog neural network for in-sensor processing, in 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS) (IEEE, New York, 2024), pp. 1–4.
S. Balliet, K. Gholami, A. Farsoni, and S. Abbaszadeh, Harnessing FPGA for real-time AI-driven PET imaging, in Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling (SPIE, Bellingham, WA, 2025), Vol. 13408, pp. 439–444.
P. G. Bharathi, G. Romanchek, G. Shoop, M. King, M. Kupinski, L. Furenlid, and S. Abbaszadeh, Machine learning-assisted event classification in cadmium zinc telluride positron emission tomography detectors leveraging entanglement-informed angular correlations, Sci. Rep. 16, 3116 (2025).
Y. Yan et al., Innovative approaches in precision radiation oncology: Advanced imaging technologies and challenges which shape the future of radiation therapy, Front. Med. 12, 1686593 (2025).
Z. Hu et al., Image-mode performance characterisation of a positron emission tomography subsystem designed for biology-guided radiotherapy (BgRT), Br. J. Radiol. 96, 20220387 (2023).
H. Shirato et al., Physical aspects of a real-time tumor-tracking system for gated radiotherapy, Int. J. Radiat. Oncol., Biol., Phys. 48, 1187 (2000).
G. C. Sharp, S. B. Jiang, S. Shimizu, and H. Shirato, Prediction of respiratory tumour motion for real-time image-guided radiotherapy, Phys. Med. Biol. 49, 425 (2004).
S. M. Shirvani, C. J. Huntzinger, T. Melcher, P. D. Olcott, Y. Voronenko, J. Bartlett-Roberto, and S. Mazin, Biology-guided radiotherapy: Redefining the role of radiotherapy in metastatic cancer, Br. J. Radiol. 94, 20200873 (2021).
M. Surucu et al., Commissioning of a novel PET-LINAC for biology-guided radiotherapy (BgRT), Med. Phys. 51, 4389 (2024).
Y. Xin, Z. Wei, T. Xinzhi, C. Jun, X. Siwei, L. Qi, P. Hao, and P. Qiyu, Application of PET-LINAC in biology-guided radiotherapy, Chin. J. Med. Instrum. 47, 237 (2023).
O. M. Oderinde, S. M. Shirvani, P. D. Olcott, G. Kuduvalli, S. Mazin, and D. Larkin, The technical design and concept of a PET/CT LINAC for biology-guided radiotherapy, Clin. Transl. Radiat. Oncol. 29, 106 (2021).
Q. Liu, T. Liang, Z. Huang, and V. Dinavahi, Real-time FPGA-based hardware neural network for fault detection and isolation in more electric aircraft, IEEE Access 7, 159831 (2019).
A. Vasilache, S. Nitzsche, D. Floegel, T. Schuermann, S. von Dosky, T. Bierweiler, M. Mußler, F. Kälber, S. Hohmann, and J. Becker, Low-power vibration-based predictive maintenance for Industry 4.0 using neural networks: A survey, arXiv:2408.00516.
J. Caba, J. Barba, M. Díaz, J. L. Mira, S. López, and J. C. López, Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices, J. Real-Time Image Process. 22, 112 (2025).
M. Memarzadeh, B. Matthews, I. Avrekh, and D. Weckler, Unsupervised anomaly detection in high-dimensional flight data using convolutional variational auto-encoder, in Proceedings of the 26th SIGKDD Conference on Knowledge Discovery and Data Mining (NASA Technical Reports Server, 2020), ARC-E-DAA-TN77606.
N. M. Michels, A. J. Jinia, S. D. Clarke, H.-S. Kim, S. A. Pozzi, and D. D. Wentzloff, Real-time classification of radiation pulses with piled-up recovery using an FPGA-based artificial neural network, IEEE Access 11, 78074 (2023).
Z. Que, Y. Liu, C. Guo, X. Niu, Y. Zhu, and W. Luk, Real-time anomaly detection for flight testing using autoencoder and LSTM, in Proceedings of the 2019 International Conference on Field-Programmable Technology (ICFPT) (IEEE, New York, 2019), pp. 379–382.
A. Das, D. Nguyen, J. Zambreno, G. Memik, and A. Choudhary, An FPGA-based network intrusion detection architecture, IEEE Trans. Inf. Forensics Secur. 3, 118 (2008).
D.-M. Ngo, D. Lightbody, A. Temko, C. Pham-Quoc, N.-T. Tran, C. C. Murphy, and E. Popovici, HH-NIDS: Heterogeneous hardware-based network intrusion detection framework for IoT security, Future Internet 15, 9 (2022).
M. A. Farooq, S. M. F. U. Hassan, M. U. Farooq, and A. Rafique, High throughput neural network for network intrusion detection on FPGAs: An algorithm-architecture interaction, in ICISSP (2024), pp. 423–429.
M. Wu and M. Kondo, A high-throughput network intrusion detection system using on-device learning on FPGA, in Proceedings of the 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (IEEE, New York, 2024), pp. 426–433.
C. Pham-Quoc, T. H. Q. Bao, and T. N. Thinh, FPGA/AI-powered architecture for anomaly network intrusion detection systems, Electronics 12, 668 (2023).
E. Singh, C. Barrett, and S. Mitra, E-QED: Electrical bug localization during post-silicon validation enabled by quick error detection and formal methods, in International Conference on Computer Aided Verification (Springer, Berlin, 2017), pp. 104–125.
P. Girard, S. Blanton, and L.-C. Wang, Machine Learning Support for Fault Diagnosis of System-on-Chip (Springer, 2023).
A. DeOrio, Q. Li, M. Burgess, and V. Bertacco, Machine learning-based anomaly detection for post-silicon bug diagnosis, in Proceedings of the 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE) (IEEE, New York, 2013), pp. 491–496 .
P. Wagner, T. Wild, and A. Herkersdorf, DiaSys: Improving SoC insight through on-chip diagnosis, J. Syst. Archit. 75, 120 (2017).
Y. Cao, H. Zheng, and S. Ray, A communication-centric observability selection for post-silicon system-on-chip integration debug, in Proceedings of the 20th International Symposium on Quality Electronic Design (ISQED) (IEEE, New York, 2019), pp. 278–283.

PRX Intelligence 1, 021201– Published 13 October, 2026

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Received 10 March 2026

DOI: https://doi.org/10.1103/bjx3-vlj8

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Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics

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