Hind Al Ali, Nima Arkani-Hamed, Ian Banta, Sean Benevedes, Dario Buttazzo, Tianji Cai, Junyi Cheng, Timothy Cohen, Nathaniel Craig, Majid Ekhterachian, JiJi Fan, Matthew Forslund, Isabel Garcia Garcia, Samuel Homiller, Seth Koren, Giacomo Koszegi, Zhen Liu, Qianshu Lu, Kun-Feng Lyu, Alberto Mariotti, Amara McCune, Patrick Meade, Isobel Ojalvo, Umut Oktem, Diego Redigolo, Matthew Reece, Filippo Sala, Raman Sundrum, Dave Sutherland, Andrea Tesi, Timothy Trott, Chris Tully, Lian-Tao Wang, Menghang Wang
We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advantages of such a collider over proposed alternatives. We show how one can leverage both the point-like nature of the muons themselves as well as the cloud of electroweak radiation that surrounds the beam to blur the dichotomy between energy and precision in the search for new physics. The physics case is buttressed by a range of studies with applications to electroweak symmetry breaking, dark matter, and the naturalness of the weak scale. Furthermore, we make sharp connections with complementary experiments that are probing new physics effects using electric dipole moments, flavor violation, and gravitational waves. An extensive appendix provides cross section predictions as a function of the center-of-mass energy for many canonical simplified models.
Sridhara Dasu, Emilio A. Nanni, Michael E. Peskin, Caterina Vernieri, Tim Barklow, Rainer Bartoldus, Pushpalatha C. Bhat, Kevin Black, Jim Brau, Martin Breidenbach, Nathaniel Craig, Dmitri Denisov, Lindsey Gray, Philip C. Harris, Michael Kagan, Zhen Liu, Patrick Meade, Nathan Majernik, Sergei Nagaitsev, Isobel Ojalvo, Christoph Paus, Carl Schroeder, Ariel G. Schwartzman, Jan Strube, Su Dong, Sami Tantawi, LianTao Wang, Andy White, Graham W. Wilson
A program to build a lepton-collider Higgs factory, to precisely measure the couplings of the Higgs boson to other particles, followed by a higher energy run to establish the Higgs self-coupling and expand the new physics reach, is widely recognized as a primary focus of modern particle physics. We propose a strategy that focuses on a new technology and preliminary estimates suggest that can lead to a compact, affordable machine. New technology investigations will provide much needed enthusiasm for our field, resulting in trained workforce. This cost-effective, compact design, with technologies useful for a broad range of other accelerator applications, could be realized as a project in the US. Its technology innovations, both in the accelerator and the detector, will offer unique and exciting opportunities to young scientists. Moreover, cost effective compact designs, broadly applicable to other fields of research, are more likely to obtain financial support from our funding agencies.
Meenakshi Narain, Laura Reina, Alessandro Tricoli, Michael Begel, Alberto Belloni, Tulika Bose, Antonio Boveia, Sally Dawson, Caterina Doglioni, Ayres Freitas, James Hirschauer, Stefan Hoeche, Yen-Jie Lee, Huey-Wen Lin, Elliot Lipeles, Zhen Liu, Patrick Meade, Swagato Mukherjee, Pavel Nadolsky, Isobel Ojalvo, Simone Pagan Griso, Christophe Royon, Michael Schmitt, Reinhard Schwienhorst, Nausheen Shah, Junping Tian, Caterina Vernieri, Doreen Wackeroth, Lian-Tao Wang, Dmitri Denisov, Sergey Gleyzer, Peter Onyisi, Manuel Franco Sevilla, Maksym Titov, Daniel Whiteson
This report, as part of the 2021 Snowmass Process, summarizes the current status of collider physics at the Energy Frontier, the broad and exciting future prospects identified for the Energy Frontier, the challenges and needs of future experiments, and indicates high priority research areas.
Charles Bell, Daniele Calzolari, Christian Carli, Karri Folan Di Petrillo, Micah Hillman, Tova R. Holmes, Sergo Jindariani, Kiley E. Kennedy, Ka Hei Martin Kwok, Anton Lechner, Lawrence Lee, Thomas Madlener, Federico Meloni, Isobel Ojalvo, Priscilla Pani, Rose Powers, Benjamin Rosser, Leo Rozanov, Kyriacos Skoufaris, Elise Sledge, Alexander Tuna, Junjia Zhang
Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $μμ$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which enables standalone track reconstruction for high-momentum muons. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background (BIB) simulations generated in FLUKA are overlaid with single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even with BIB, reconstruction efficiencies exceed 95% for energetic tracks, photons, and neutrons in the central region of the detector. This paper outlines promising avenues of future work, including forward region optimization and opportunities for enhanced flavor/boosted object tagging, and addresses the technological assumptions needed to achieve the desired detector performance.
Lino Gerlach, Thore Gerlach, Liv Våge, Elliott Kauffman, Isobel Ojalvo
Fast and efficient AI inference is increasingly important, and recent models that directly learn low-level logic operations have achieved state-of-the-art performance. However, existing logic neural networks incur high training costs, introduce redundancy or rely on approximate gradients, which limits scalability. To overcome these limitations, we introduce WAlsh Relaxation for Probabilistic (WARP) logic neural networks -- a novel gradient-based framework that efficiently learns combinations of hardware-native logic blocks. We show that WARP yields the most parameter-efficient representation for exactly learning Boolean functions and that several prior approaches arise as restricted special cases. Training is improved by introducing learnable thresholding and residual initialization, while we bridge the gap between relaxed training and discrete logic inference through stochastic smoothing. Experiments demonstrate faster convergence than state-of-the-art baselines, while scaling effectively to deeper architectures and logic functions with higher input arity.
Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang, Javier Duarte, Markus Atkinson, Gage DeZoort, Peter Elmer, Scott Hauck, Jin-Xuan Hu, Shih-Chieh Hsu, Bo-Cheng Lai, Mark Neubauer, Isobel Ojalvo, Savannah Thais, Matthew Trahms
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph -- nodes represent hits, while edges represent possible track segments -- and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called $\texttt{hls4ml}$, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.
Gage DeZoort, Savannah Thais, Javier Duarte, Vesal Razavimaleki, Markus Atkinson, Isobel Ojalvo, Mark Neubauer, Peter Elmer
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.
HEP Software Foundation, :, Thea Aarrestad, Simone Amoroso, Markus Julian Atkinson, Joshua Bendavid, Tommaso Boccali, Andrea Bocci, Andy Buckley, Matteo Cacciari, Paolo Calafiura, Philippe Canal, Federico Carminati, Taylor Childers, Vitaliano Ciulli, Gloria Corti, Davide Costanzo, Justin Gage Dezoort, Caterina Doglioni, Javier Mauricio Duarte, Agnieszka Dziurda, Peter Elmer, Markus Elsing, V. Daniel Elvira, Giulio Eulisse, Javier Fernandez Menendez, Conor Fitzpatrick, Rikkert Frederix, Stefano Frixione, Krzysztof L Genser, Andrei Gheata, Francesco Giuli, Vladimir V. Gligorov, Hadrien Benjamin Grasland, Heather Gray, Lindsey Gray, Alexander Grohsjean, Christian Gütschow, Stephan Hageboeck, Philip Coleman Harris, Benedikt Hegner, Lukas Heinrich, Burt Holzman, Walter Hopkins, Shih-Chieh Hsu, Stefan Höche, Philip James Ilten, Vladimir Ivantchenko, Chris Jones, Michel Jouvin, Teng Jian Khoo, Ivan Kisel, Kyle Knoepfel, Dmitri Konstantinov, Attila Krasznahorkay, Frank Krauss, Benjamin Edward Krikler, David Lange, Paul Laycock, Qiang Li, Kilian Lieret, Miaoyuan Liu, Vladimir Loncar, Leif Lönnblad, Fabio Maltoni, Michelangelo Mangano, Zachary Louis Marshall, Pere Mato, Olivier Mattelaer, Joshua Angus McFayden, Samuel Meehan, Alaettin Serhan Mete, Ben Morgan, Stephen Mrenna, Servesh Muralidharan, Ben Nachman, Mark S. Neubauer, Tobias Neumann, Jennifer Ngadiuba, Isobel Ojalvo, Kevin Pedro, Maurizio Perini, Danilo Piparo, Jim Pivarski, Simon Plätzer, Witold Pokorski, Adrian Alan Pol, Stefan Prestel, Alberto Ribon, Martin Ritter, Andrea Rizzi, Eduardo Rodrigues, Stefan Roiser, Holger Schulz, Markus Schulz, Marek Schönherr, Elizabeth Sexton-Kennedy, Frank Siegert, Andrzej Siódmok, Graeme A Stewart, Malik Sudhir, Sioni Paris Summers, Savannah Jennifer Thais, Nhan Viet Tran, Andrea Valassi, Marc Verderi, Dorothea Vom Bruch, Gordon T. Watts, Torre Wenaus, Efe Yazgan
Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.
Seokju Chung, Jack Cleeve, Akshay Malige, Georgia Karagiorgi, Lino Gerlach, Adrian A. Pol, Isobel Ojalvo
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.
Aneesh Heintz, Vesal Razavimaleki, Javier Duarte, Gage DeZoort, Isobel Ojalvo, Savannah Thais, Markus Atkinson, Mark Neubauer, Lindsey Gray, Sergo Jindariani, Nhan Tran, Philip Harris, Dylan Rankin, Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Mia Liu, Edward Kreinar, Zhenbin Wu
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.
Lino Gerlach, Liv Våge, Thore Gerlach, Elliott Kauffman, Isobel Ojalvo
Fast and efficient machine learning is of growing interest to the scientific community and has spurred significant research into novel model architectures and hardware-aware design. Recent hard? and software co-design approaches have demonstrated impressive results with entirely multiplication-free models. Differentiable Logic Gate Networks (DLGNs), for instance, provide a gradient-based framework for learning optimal combinations of low-level logic gates, setting state-of-the-art trade-offs between accuracy, resource usage, and latency. However, these models suffer from high computational cost during training and do not generalize well to logic blocks with more inputs. In this work, we introduce Walsh-Assisted Relaxation for Probabilistic Look-Up Tables (WARP-LUTs) - a novel gradient-based method that efficiently learns combinations of logic gates with substantially fewer trainable parameters. We demonstrate that WARP-LUTs achieve significantly faster convergence on CIFAR-10 compared to DLGNs, while maintaining comparable accuracy. Furthermore, our approach suggests potential for extension to higher-input logic blocks, motivating future research on extremely efficient deployment on modern FPGAs and its real-time science applications.
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos, Nadezda Chernyavskaya, Philip Harris, Maurizio Pierini, Isobel Ojalvo, Peter Elmer
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. We present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint.
Sally Dawson, Patrick Meade, Isobel Ojalvo, Caterina Vernieri, S. Adhikari, F. Abu-Ajamieh, A. Alberta, H. Bahl, R. Barman, M. Basso, A. Beniwal, I. Bozovi-Jelisav, S. Bright-Thonney, V. Cairo, F. Celiberto, S. Chang, M. Chen, C. Damerell, J. Davis, J. de Blas, W. Dekens, J. Duarte, D. Egana-Ugrinovic, U. Einhaus, Y. Gao, D. Goncalves, A. Gritsan, H. Haber, U. Heintz, S. Homiller, S. C. Hsu, D. Jean, S. Kawada, E. Khoda, K. Kong, N. Konstantinidis, A. Korytov, S. Kyriacou, S. Lane, I. M. Lewis, K. Li, S. Li, Z. Liu, J. Luo, L. Mandacar-Guerra, C. Mantel, J. Monroy, M. Narain, R. Orr, R. Pan, A. Papaefstathiou, M. Peskin, M. T. Prim, F. Rajec, M. Ramsey-Musolf, J. Reichert, L. Reina, T. Robens, J. Roskes, A. Ryd, A. Schwartzman, P. Scott, J. Strube, Su Dong, W. Su, M. Sullivan, T. Tanabe, J. Tian, A. Tricoli, E. Usai, J. Vavra, Z. Wang, G. White, M. White, A. G. Williams, A. Woodcock, Y. Wu, C. Young, Y. Zhang, X. Zhu, R. Zou
A future Higgs Factory will provide improved precision on measurements of Higgs couplings beyond those obtained by the LHC, and will enable a broad range of investigations across the fields of fundamental physics, including the mechanism of electroweak symmetry breaking, the origin of the masses and mixing of fundamental particles, the predominance of matter over antimatter, and the nature of dark matter. Future colliders will measure Higgs couplings to a few per cent, giving a window to beyond the Standard Model (BSM) physics in the 1-10 TeV range. In addition, they will make precise measurements of the Higgs width, and characterize the Higgs self-coupling. This report details the work of the EF01 and EF02 working groups for the Snowmass 2021 study.
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles Cranmer, Sridhara Dasu, Peter Elmer, Philip Harris, Isobel Ojalvo, Maurizio Pierini
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches the equation space to discover algebraic relations approximating a dataset. We use PySR (a software to uncover these expressions based on an evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimize the top metric by pinning the network size because the vast hyperparameter space prevents an extensive search for neural architecture. Conversely, SR selects a set of models on the Pareto front, which allows for optimizing the performance-resource trade-off directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our method on a physics benchmark: the multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider. We show that our approach can approximate a 3-layer neural network using an inference model that achieves up to a 13-fold decrease in execution time, down to 5 ns, while still preserving more than 90% approximation accuracy.
Lino Gerlach, Elliott Kauffman, Liv Helen Våge, Isobel Ojalvo
The increasing data rates and complexity of detectors at the Large Hadron Collider (LHC) necessitate fast and efficient machine learning models, particularly for rapid selection of what data to store, known as triggering. Building on recent work in differentiable logic gates, we present a public implementation of a Convolutional Differentiable Logic Gate Neural Network (CLGN). We apply this to detecting anomalies at the Level-1 Trigger at CMS using public data from the CICADA project. We demonstrate that the CLGN achieves physics performance on par with or superior to conventional quantized neural networks. We also synthesize an LGN for a Field-Programmable Gate Array (FPGA) and show highly promising FPGA characteristics, notably zero Digital Signal Processor (DSP) resource usage. This work highlights the potential of logic gate networks for high-speed, on-detector inference in High Energy Physics and beyond.
Julia Gonski, Jenni Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Dickinson, Giuseppe Di Guglielmo, Erdem Yigit Ertorer, Lindsey Gray, Ryan Herbst, Christian Herwig, Tae Min Hong, Benedikt Maier, Maryam Bayat Makou, David Miller, Mark S. Neubauer, Cristián Peña, Dylan Rankin, Seon-Hee, Seo, Giordon Stark, Alexander Tapper, Audrey Corbeil Therrien, Ioannis Xiotidis, Keisuke Yoshihara, G Abarajithan, Sagar Addepalli, Nural Akchurin, Carlos Argüelles, Saptaparna Bhattacharya, Lorenzo Borella, Christian Boutan, Tom Braine, James Brau, Martin Breidenbach, Antonio Chahine, Talal Ahmed Chowdhury, Yuan-Tang Chou, Seokju Chung, Alberto Coppi, Mariarosaria D'Alfonso, Abhilasha Dave, Chance Desmet, Angela Di Fulvio, Karri DiPetrillo, Javier Duarte, Auralee Edelen, Jan Eysermans, Yongbin Feng, Emmett Forrestel, Dolores Garcia, Loredana Gastaldo, Julián García Pardiñas, Lino Gerlach, Loukas Gouskos, Katya Govorkova, Carl Grace, Christopher Grant, Philip Harris, Ciaran Hasnip, Timon Heim, Abraham Holtermann, Tae Min Hong, Gian Michele Innocenti, Koji Ishidoshiro, Miaochen Jin, Jyothisraj Johnson, Stephen Jones, Andreas Jung, Georgia Karagiorgi, Ryan Kastner, Nicholas Kamp, Doojin Kim, Kyoungchul Kong, Katie Kudela, Jelena Lalic, Bo-Cheng Lai, Yun-Tsung Lai, Tommy Lam, Jeffrey Lazar, Aobo Li, Zepeng Li, Haoyun Liu, Vladimir Lončar, Luca Macchiarulo, Christopher Madrid, Benedikt Maier, Zhenghua Ma, Prashansa Mukim, Mark S. Neubauer, Victoria Nguyen, Sungbin Oh, Isobel Ojalvo, Hideyoshi Ozaki, Simone Pagan Griso, Myeonghun Park, Christoph Paus, Santosh Parajuli, Benjamin Parpillon, Sara Pozzi, Ema Puljak, Benjamin Ramhorst, Amy Roberts, Larry Ruckman, Kate Scholberg, Sebastian Schmitt, Noah Singer, Eluned Anne Smith, Alexandre Sousa, Michael Spannowsky, Sioni Summers, Yanwen Sun, Daniel Tapia Takaki, Antonino Tumeo, Caterina Vernieri, Belina von Krosigk, Yash Vora, Linyan Wan, Michael H. L. S. Wang, Amanda Weinstein, Andy White, Simon Williams, Felix Yu