Contributor info: Dr Rob Verheyen

Melissa van Beekveld, Mrinal Dasgupta, Basem Kamal El-Menoufi, Silvia Ferrario Ravasio, Keith Hamilton, Jack Helliwell, Alexander Karlberg, Rok Medves, Pier Francesco Monni, Gavin P. Salam, Ludovic Scyboz, Alba Soto-Ontoso, Gregory Soyez, Rob Verheyen

SciPost Phys. Codebases 31-r0.1 (2024) · published 25 June 2024 | Toggle abstract · src

In this article, we document version 0.1 of the PANSCALES code for parton shower simulations. With the help of a few examples, we discuss basic usage of the code, including tests of logarithmic accuracy of parton showers. We expose some of the numerical techniques underlying the logarithmic tests and include a description of how users can implement their own showers within the framework. Some of the simpler logarithmic tests can be performed in a few minutes on a modern laptop. As an early step towards phenomenology, we also outline some aspects of a preliminary interface to PYTHIA 8.3, for access to its hard matrix elements and its hadronisation modules.

Melissa van Beekveld, Mrinal Dasgupta, Basem Kamal El-Menoufi, Silvia Ferrario Ravasio, Keith Hamilton, Jack Helliwell, Alexander Karlberg, Rok Medves, Pier Francesco Monni, Gavin P. Salam, Ludovic Scyboz, Alba Soto-Ontoso, Gregory Soyez, Rob Verheyen

SciPost Phys. Codebases 31 (2024) · published 25 June 2024 | Toggle abstract · pdf

In this article, we document version 0.1 of the PANSCALES code for parton shower simulations. With the help of a few examples, we discuss basic usage of the code, including tests of logarithmic accuracy of parton showers. We expose some of the numerical techniques underlying the logarithmic tests and include a description of how users can implement their own showers within the framework. Some of the simpler logarithmic tests can be performed in a few minutes on a modern laptop. As an early step towards phenomenology, we also outline some aspects of a preliminary interface to PYTHIA 8.3, for access to its hard matrix elements and its hadronisation modules.

J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche, J. Isaacson, F. Kling, S. Mrenna, J. Reuter, S. Alioli, J. R. Andersen, C. Andreopoulos, A. M. Ankowski, E. C. Aschenauer, A. Ashkenazi, M. D. Baker, J. L. Barrow, M. van Beekveld, G. Bewick, S. Bhattacharya, C. Bierlich, E. Bothmann, P. Bredt, A. Broggio, A. Buckley, A. Butter, J. M. Butterworth, E. P. Byrne, C. M. Carloni-Calame, S. Chakraborty, X. Chen, M. Chiesa, J. T. Childers, J. Cruz-Martinez, J. Currie, N. Darvishi, M. Dasgupta, A. Denner, F. A. Dreyer, S. Dytman, B. K. El-Menoufi, T. Engel, S. Ferrario Ravasio, D. Figueroa, L. Flower, J. R. Forshaw, R. Frederix, A. Friedland, S. Frixione, H. Gallagher, K. Gallmeister, S. Gardiner, R. Gauld, J. Gaunt, A. Gavardi, T. Gehrmann, A. Gehrmann-De Ridder, L. Gellersen, W. Giele, S. Gieseke, F. Giuli, E. W. N. Glover, M. Grazzini, A. Grohsjean, C. Gütschow, K. Hamilton, T. Han, R. Hatcher, G. Heinrich, I. Helenius, O. Hen, V. Hirschi, M. Höfer, J. Holguin, A. Huss, P. Ilten, S. Jadach, A. Jentsch, S. P. Jones, W. Ju, S. Kallweit, A. Karlberg, T. Katori, M. Kerner, W. Kilian, M. M. Kirchgaeßer, S. Klein, M. Knobbe, C. Krause, F. Krauss, J. Lang, J. -N. Lang, G. Lee, S. W. Li, M. A. Lim, J. M. Lindert, D. Lombardi, L. Lönnblad, M. Löschner, N. Lurkin, Y. Ma, P. Machado, V. Magerya, A. Maier, I. Majer, F. Maltoni, M. Marcoli, G. Marinelli, M. R. Masouminia, P. Mastrolia, O. Mattelaer, J. Mazzitelli, J. McFayden, R. Medves, P. Meinzinger, J. Mo, P. F. Monni, G. Montagna, T. Morgan, U. Mosel, B. Nachman, P. Nadolsky, R. Nagar, Z. Nagy, D. Napoletano, P. Nason, T. Neumann, L. J. Nevay, O. Nicrosini, J. Niehues, K. Niewczas, T. Ohl, G. Ossola, V. Pandey, A. Papadopoulou, A. Papaefstathiou, G. Paz, M. Pellen, G. Pelliccioli, T. Peraro, F. Piccinini, L. Pickering, J. Pires, W. Placzek, S. Plätzer, T. Plehn, S. Pozzorini, S. Prestel, C. T. Preuss, A. C. Price, S. Quackenbush, E. Re, D. Reichelt, L. Reina, C. Reuschle, P. Richardson, M. Rocco, N. Rocco, M. Roda, A. Rodriguez Garcia, S. Roiser, J. Rojo, L. Rottoli, G. P. Salam, M. Schönherr, S. Schuchmann, S. Schumann, R. Schürmann, L. Scyboz, M. H. Seymour, F. Siegert, A. Signer, G. Singh Chahal, A. Siódmok, T. Sjöstrand, P. Skands, J. M. Smillie, J. T. Sobczyk, D. Soldin, D. E. Soper, A. Soto-Ontoso, G. Soyez, G. Stagnitto, J. Tena-Vidal, O. Tomalak, F. Tramontano, S. Trojanowski, Z. Tu, S. Uccirati, T. Ullrich, Y. Ulrich, M. Utheim, A. Valassi, A. Verbytskyi, R. Verheyen, M. Wagman, D. Walker, B. R. Webber, L. Weinstein, O. White, J. Whitehead, M. Wiesemann, C. Wilkinson, C. Williams, R. Winterhalder, C. Wret, K. Xie, T-Z. Yang, E. Yazgan, G. Zanderighi, S. Zanoli, K. Zapp

SciPost Phys. 16, 130 (2024) · published 24 May 2024 | Toggle abstract · pdf

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan

SciPost Phys. 14, 079 (2023) · published 21 April 2023 | Toggle abstract · pdf

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip Ilten, Leif Lönnblad, Stephen Mrenna, Stefan Prestel, Christian T. Preuss, Torbjörn Sjöstrand, Peter Skands, Marius Utheim, Rob Verheyen

SciPost Phys. Codebases 8 (2022) · published 10 November 2022 | Toggle abstract · pdf

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision "events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip Ilten, Leif Lönnblad, Stephen Mrenna, Stefan Prestel, Christian T. Preuss, Torbjörn Sjöstrand, Peter Skands, Marius Utheim, Rob Verheyen

SciPost Phys. Codebases 8-r8.3 (2022) · published 10 November 2022 | Toggle abstract · src

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision "events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

Rob Verheyen

SciPost Phys. 13, 047 (2022) · published 2 September 2022 | Toggle abstract · pdf

Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the peripheral features of collision events. Using the framework of Nielsen et al. (2020), we introduce several surjective and stochastic transform layers to a baseline normalizing flow to improve modelling of permutation symmetry, varying dimensionality and discrete features, which are all commonly encountered in particle physics events. We assess their efficacy in the context of the generation of a matrix element-level process, and in the context of anomaly detection in detector-level LHC events.

Helen Brooks, Peter Skands, Rob Verheyen

SciPost Phys. 12, 101 (2022) · published 22 March 2022 | Toggle abstract · pdf

We propose a framework for high-energy interactions in which resonance decays and electroweak branching processes are interleaved with the QCD evolution in a single com- mon sequence of decreasing resolution scales. The interleaved treatment of resonance decays allows for a new treatment of finite-width effects in parton showers. At scales above their offshellness (i.e., typically Q > Γ ), resonances participate explicitly as incom- ing and outgoing states in branching processes, while they are effectively “integrated out” of the description at lower scales. We implement this formalism, together with a full set of antenna functions for branching processes involving electroweak (W/Z/H) bosons in the Vincia shower module in Pythia 8.3, and study some of the consequences

Sascha Caron, Luc Hendriks, Rob Verheyen

SciPost Phys. 12, 077 (2022) · published 25 February 2022 | Toggle abstract · pdf

We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that are not inside the dataset. We quantify these two properties using an ensemble of One-Class Deep Support Vector Data Description models, which quantifies differentness, and an autoregressive flow model, which quantifies rareness. These two parameters are then combined into a single anomaly score using different combination algorithms. We train the models using a dataset containing only simulated collisions from the Standard Model of particle physics and test it using various hypothetical signals in four different channels and a secret dataset where the signals are unknown to us. The anomaly detection method described here has been evaluated in a summary paper [1] where it performed very well compared to a large number of other methods. The method is simple to implement and is applicable to other datasets in other fields as well.

T. Aarrestad, M. van Beekveld, M. Bona, A. Boveia, S. Caron, J. Davies, A. De Simone, C. Doglioni, J. M. Duarte, A. Farbin, H. Gupta, L. Hendriks, L. Heinrich, J. Howarth, P. Jawahar, A. Jueid, J. Lastow, A. Leinweber, J. Mamuzic, E. Merényi, A. Morandini, P. Moskvitina, C. Nellist, J. Ngadiuba, B. Ostdiek, M. Pierini, B. Ravina, R. Ruiz de Austri, S. Sekmen, M. Touranakou, M. Vaškevičiūte, R. Vilalta, J. R. Vlimant, R. Verheyen, M. White, E. Wulff, E. Wallin, K. A. Wozniak, Z. Zhang

SciPost Phys. 12, 043 (2022) · published 28 January 2022 | Toggle abstract · pdf

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Bob Stienen, Rob Verheyen

SciPost Phys. 10, 038 (2021) · published 17 February 2021 | Toggle abstract · pdf

We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.