Showing 1–20 of 33 results
Harrison B. Prosper
After making some general remarks, I consider two examples that illustrate the use of Bayesian Probability Theory. The first is a simple one, the physicist's favorite "toy," that provides a forum for a discussion of the key conceptual issue of Bayesian analysis: the assignment of prior probabilities. The other example illustrates the use of Bayesian ideas in the real world of experimental physics.
Rutger Dungan, Harrison B. Prosper
The observation that Type Ia supernovae are fainter than expected given their red shifts has led to the conclusion that the expansion of the universe is accelerating. The widely accepted hypothesis is that this acceleration is caused by a cosmological constant or, more generally, some dark energy field that pervades the universe. This hypothesis presents a challenge to physics so severe that one is motivated to explore alternative explanations. In this paper, we explore whether the data from Type Ia supernovae can be explained with an idea that is almost as old as that of the cosmological constant, namely, that the strength of gravity varies on a cosmic timescale. This topic is an ideal one for investigation by an undergraduate physics major because the entire chain of reasoning from models to data analysis is well within the mathematical and conceptual sophistication of a motivated undergraduate.
Harrison B. Prosper
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
Maurizio Pierini, Harrison B. Prosper, Sezen Sekmen, Maria Spiropulu
The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our idea is to initiate the chain of inference with the reference prior for a likelihood function that depends on a single parameter of interest that is a function of the parameters of the physics model. The reference posterior density of the parameter of interest induces on the parameter space of the physics model a class of posterior densities. We propose to continue the chain of inference with a particular density from this class, namely, the one for which indistinguishable models are equiprobable and use it as the prior for subsequent analysis. We illustrate our method by applying it to the constrained minimal supersymmetric Standard Model and two non-universal variants of it.
Harrison B. Prosper
We introduce a few of the key ideas of statistical analysis using two real-world examples to illustrate how these ideas are used in practice.
Michele Gallinaro, Kenneth Long, Jürgen Reuter, Richard Ruiz, Dinos Bachas, Liron Barak, Fady Bishara, Ilaria Brivio, Diogo Buarque Franzosi, Giacomo Cacciapaglia, Farida Fassi, Eirini Kasimi, Henning Kirschenmann, Chara Petridou, Harrison Prosper, Jorge Romão, Ignasi Rosell, Ennio Salvioni, Rui Santos, Magdalena Slawinska, Giles Chatham Strong, Michał Szleper
The high-energy scattering of massive electroweak bosons, known as vector boson scattering (VBS), is a sensitive probe of new physics. VBS signatures will be thoroughly and systematically investigated at the LHC with the large data samples available and those that will be collected in the near future. Searches for deviations from Standard Model (SM) expectations in VBS facilitate tests of the Electroweak Symmetry Breaking (EWSB) mechanism. Current state-of-the-art tools and theory developments, together with the latest experimental results, and the studies foreseen for the near future are summarized. A review of the existing Beyond the SM (BSM) models that could be tested with such studies as well as data analysis strategies to understand the interplay between models and the effective field theory paradigm for interpreting experimental results are discussed. This document is a summary of the EU COST network "VBScan" workshop on the sensitivity of VBS processes for BSM frameworks that took place December 4-5, 2019 at the LIP facilities in Lisbon, Portugal. In this manuscript we outline the scope of the workshop, summarize the different contributions from theory and experiment, and discuss the relevant findings.
Braden Kronheim, Michelle Kuchera, Harrison Prosper, Alexander Karbo
One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Hadron Collider (LHC) at CERN. The searches for new physics are often guided by BSM theories that depend on many unknown parameters, which, in some cases, makes testing their predictions difficult. In this paper, machine learning is used to model the mapping from the parameter space of the phenomenological Minimal Supersymmetric Standard Model (pMSSM), a BSM theory with 19 free parameters, to some of its predictions. Bayesian neural networks are used to predict cross sections for arbitrary pMSSM parameter points, the mass of the associated lightest neutral Higgs boson, and the theoretical viability of the parameter points. All three quantities are modeled with average percent errors of 3.34% or less and in a time significantly shorter than is possible with the supersymmetry codes from which the results are derived. These results are a further demonstration of the potential for machine learning to model accurately the mapping from the high dimensional spaces of BSM theories to their predictions.
Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata
Abdulhakim Alnuqaydan, Sergei Gleyzer, Harrison Prosper
The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of QCD and QED processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.
Howard Baer, Harrison Prosper, Heaya Summy
Traditional searches for SUSY at hadron colliders rely heavily on the presence of large missing transverse energy (ME_T) to reject background compared to signal. On the other hand, initial searches for new physics at the LHC may not be able to rely on ME_T due to a variety of detector calibration issues. We show that much of SUSY parameter space is accessible to discovery even {\it without} using ME_T, and with rather low integrated luminosities 0.1-1 fb^{-1}. A key role is played by isolated lepton multiplicity which arises from gluino and squark cascade decays. Requiring \ge 3 isolated leptons plus jets yields a high rate of background rejection compared to signal. We find an LHC reach in m(gluino) of about 700-750 GeV for just 0.1 fb^{-1} of integrated luminosity by requiring events with \ge 4 jets plus \ge 3 isolated leptons but {\it without} using ME_T. If a large enough event sample is assembled, then kinematic reconstruction of sparticle mass properties should be possible just as in the case where large ME_T is required. SUSY without ME_T can also be seen in opposite-sign/same flavor {\it dilepton plus jets} events when a characteristic invariant mass edge stands out against background.
Nick Smith, Daniel Spitzbart, Jennet Dickinson, Jon Wilson, Lindsey Gray, Kelci Mohrman, Saptaparna Bhattacharya, Andrea Piccinelli, Titas Roy, Garyfallia Paspalaki, Duarte Fontes, Adam Martin, William Shepherd, Sergio Sánchez Cruz, Dorival Goncalves, Andrei Gritsan, Harrison Prosper, Tom Junk, Kyle Cranmer, Michael Peskin, Andrew Gilbert, Jonathon Langford, Frank Petriello, Luca Mantani, Andrew Wightman, Charlotte Knight, Prasanth Shyamsundar, Aashwin Basnet, Giacomo Boldrini, Kevin Lannon
The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of truncation and validity in interpretation of results with an eye towards practicality; "reconstruction-level results" visits the question of how best to design analyses directly targeting inference of EFT parameters; "logistics of combining likelihoods" addresses the challenges of bringing a diverse array of measurements into a cohesive whole; "unfolded results" tackles the question of designing fiducial measurements for later use in EFT interpretations, and the benefits and limitations of unfolding; and "building a sample library" addresses how best to generate simulation samples for use in data analysis. This document serves as a summary of presentations, subsequent discussions, and actionable items identified over the course of the workshop.
Braden Kronheim, Michelle Kuchera, Harrison Prosper
TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is represented as a point cloud sampled using Hamiltonian Monte Carlo. The TensorBNN package leverages TensorFlow's architecture and training features as well as its ability to use modern graphics processing units (GPU) in both the training and prediction stages.
Nobuo Sato, J. F. Owens, Harrison Prosper
Two different techniques for adding additional data sets to existing global fits using Bayesian reweighting have been proposed in the literature. The derivation of each reweighting formalism is critically reviewed. A simple example is constructed that conclusively favors one of the two formalisms. The effects of this choice for global fits is discussed.
Maurizio Pierini, Harrison Prosper, Sezen Sekmen, Maria Spiropulu
We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively.
Jon Butterworth, Sabine Kraml, Harrison Prosper, Andy Buckley, Louie Corpe, Cristinel Diaconu, Mark Goodsell, Philippe Gras, Martin Habedank, Clemens Lange, Kati Lassila-Perini, André Lessa, Rakhi Mahbubani, Judita Mamužić, Zach Marshall, Thomas McCauley, Humberto Reyes-Gonzalez, Krzysztof Rolbiecki, Sezen Sekmen, Giordon Stark, Graeme Watt, Jonas Würzinger, Shehu AbdusSalam, Aytul Adiguzel, Amine Ahriche, Ben Allanach, Mohammad M. Altakach, Jack Y. Araz, Alexandre Arbey, Saiyad Ashanujjaman, Volker Austrup, Emanuele Bagnaschi, Sumit Banik, Csaba Balazs, Daniele Barducci, Philip Bechtle, Samuel Bein, Nicolas Berger, Tisa Biswas, Fawzi Boudjema, Jamie Boyd, Carsten Burgard, Jackson Burzynski, Jordan Byers, Giacomo Cacciapaglia, Cécile Caillol, Orhan Cakir, Christopher Chang, Gang Chen, Andrea Coccaro, Yara do Amaral Coutinho, Andreas Crivellin, Leo Constantin, Giovanna Cottin, Hridoy Debnath, Mehmet Demirci, Juhi Dutta, Joe Egan, Carlos Erice Cid, Farida Fassi, Matthew Feickert, Arnaud Ferrari, Pavel Fileviez Perez, Dillon S. Fitzgerald, Roberto Franceschini, Benjamin Fuks, Lorenz Gärtner, Kirtiman Ghosh, Andrea Giammanco, Alejandro Gomez Espinosa, Letícia M. Guedes, Giovanni Guerrieri, Christian Gütschow, Abdelhamid Haddad, Mahsana Haleem, Hassane Hamdaoui, Sven Heinemeyer, Lukas Heinrich, Ben Hodkinson, Gabriela Hoff, Cyril Hugonie, Sihyun Jeon, Adil Jueid, Deepak Kar, Anna Kaczmarska, Venus Keus, Michael Klasen, Kyoungchul Kong, Joachim Kopp, Michael Krämer, Manuel Kunkel, Bertrand Laforge, Theodota Lagouri, Eric Lancon, Peilian Li, Gabriela Lima Lichtenstein, Yang Liu, Steven Lowette, Jayita Lahiri, Siddharth Prasad Maharathy, Farvah Mahmoudi, Vasiliki A. Mitsou, Sanjoy Mandal, Michelangelo Mangano, Kentarou Mawatari, Peter Meinzinger, Manimala Mitra, Mojtaba Mohammadi Najafabadi, Sahana Narasimha, Siavash Neshatpour, Jacinto P. Neto, Mark Neubauer, Mohammad Nourbakhsh, Giacomo Ortona, Rojalin Padhan, Orlando Panella, Timothée Pascal, Brian Petersen, Werner Porod, Farinaldo S. Queiroz, Shakeel Ur Rahaman, Are Raklev, Hossein Rashidi, Patricia Rebello Teles, Federico Leo Redi, Jürgen Reuter, Tania Robens, Abhishek Roy, Subham Saha, Ahmetcan Sansar, Kadir Saygin, Nikita Schmal, Jeffrey Shahinian, Sukanya Sinha, Ricardo C. Silva, Tim Smith, Tibor Šimko, Andrzej Siodmok, Ana M. Teixeira, Tamara Vázquez Schröder, Carlos Vázquez Sierra, Yoxara Villamizar, Wolfgang Waltenberger, Peng Wang, Martin White, Kimiko Yamashita, Ekin Yoruk, Xuai Zhuang
Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita
Mark S. Neubauer, Todd Adams, Jennifer Adelman-McCarthy, Gabriele Benelli, Tulika Bose, David Britton, Pat Burchat, Joel Butler, Timothy A. Cartwright, Tomáš Davídek, Jacques Dumarchez, Peter Elmer, Matthew Feickert, Ben Galewsky, Mandeep Gill, Maciej Gladki, Aman Goel, Jonathan E. Guyer, Bo Jayatilaka, Brendan Kiburg, Benjamin Krikler, David Lange, Claire Lee, Nick Manganelli, Giovanni Marchiori, Meenakshi Narain, Ianna Osborne, Jim Pivarski, Harrison Prosper, Graeme A Stewart, Eduardo Rodrigues, Roberto Salerno, Marguerite Tonjes, Jaroslav Trnka, Vera Varanda, Vassil Vassilev, Gordon T. Watts, Sam Zeller, Yuanyuan Zhang
The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive development, the timeline and pathway to "normality" is uncertain and the "new normal" we will settle into is anyone's guess. Particle physics, like many other scientific fields, has more than a year of experience in holding virtual meetings, workshops, and conferences. A great deal of experimentation and innovation to explore how to execute these meetings effectively has occurred. Therefore, it is an appropriate time to take stock of what we as a community learned from running virtual meetings and discuss possible strategies for the future. Continuing to develop effective strategies for meetings with a virtual component is likely to be important for reducing the carbon footprint of our research activities, while also enabling greater diversity and inclusion for participation. This report summarizes a virtual two-day workshop on Virtual Meetings held May 5-6, 2021 which brought together experts from both inside and outside of high-energy physics to share their experiences and practices with organizing and executing virtual workshops, and to develop possible strategies for future meetings as we begin to emerge from the COVID-19 pandemic. This report outlines some of the practices and tools that have worked well which we hope will serve as a valuable resource for future virtual meeting organizers in all scientific fields.
Sezen Sekmen, Philippe Gras, Lindsey Gray, Benjamin Krikler, Jim Pivarski, Harrison B. Prosper, Andrea Rizzi, Gokhan Unel, Gordon Watts
An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. It is designed for use by anyone with an interest in, and knowledge of, LHC physics, i.e., experimentalists, phenomenologists and other enthusiasts. Adopting analysis description languages would bring numerous benefits for the LHC experimental and phenomenological communities ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Here, we introduce the analysis description language concept and summarize the current efforts ongoing to develop such languages and tools to use them in LHC analyses.
Harrison B. Prosper, Sezen Sekmen, Gokhan Unel, Arpon Paul
This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domain specific, declarative language that describes the physics content of an analysis in a standard and unambiguous way, independent of any computing frameworks. It also describes infrastructures that render ADL executable, namely CutLang, a direct runtime interpreter (originally also a language), and adl2tnm, a transpiler converting ADL into C++ code. In ADL, analyses are described in human readable plain text files, clearly separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Recent studies demonstrate that adapting the ADL approach has numerous benefits for the experimental and phenomenological HEP communities. These include facilitating the abstraction, design, optimization, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents and long term preservation of the analyses beyond the lifetimes of experiments. Here we also discuss some of the current ADL applications in physics studies and future prospects based on static analysis and differentiable programming.
Andrew Askew, Prerit Jaiswal, Takemichi Okui, Harrison B. Prosper, Nobuo Sato
The search for a new source of CP violation is one of the most important endeavors in particle physics. A particularly interesting way to perform this search is to probe the CP phase in the $hττ$ coupling, as the phase is currently completely unconstrained by all existing data. Recently, a novel variable $Θ$ was proposed for measuring the CP phase in the $hττ$ coupling through the $τ^\pm \to π^\pm π^0 ν$ decay mode. We examine two crucial questions that the real LHC detectors must face, namely, the issue of neutrino reconstruction and the effects of finite detector resolution. For the former, we find strong evidence that the collinear approximation is the best for the $Θ$ variable. For the latter, we find that the angular resolution is actually not an issue even though the reconstruction of $Θ$ requires resolving the highly collimated $π^\pm$'s and $π^0$'s from the $τ$ decays. Instead, we find that it is the missing transverse energy resolution that significantly limits the LHC reach for measuring the CP phase via $Θ$. With the current missing energy resolution, we find that with $\sim 1000\,\textrm{fb}^{-1}$ the CP phase hypotheses $Δ= 0^\circ$ (the standard model value) and $Δ= 90^\circ$ can be distinguished, at most, at the 95\% confidence level.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental collaborations that obtain them, it is imperative that the collaborations and the wider particle physics community publish and preserve sufficient information to ensure that this impact can be realised, now and into the future. The information to be published and preserved includes the algorithms, statistical information, simulations and the recorded data. This publication and preservation requires significant resources, and should be a strategic priority with commensurate planning and resource allocation from the earliest stages of future facilities and experiments.
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.