Experimental High Energy Physics Research Articles

Constructing model-agnostic likelihoods, a method for the reinterpretation of particle physics results

Experimental High Energy Physics has entered an era of precision measurements. However, measurements of many of the accessible processes assume that the final states’ underlying kinematic distribution is the same as the Standard Model prediction. This assumption introduces an implicit model-dependency into the measurement, rendering the reinterpretation of the experimental analysis complicated without reanalysing the underlying data. We present a novel reweighting method in order to perform reinterpretation of particle physics measurements. It makes use of reweighting the Standard Model templates according to kinematic signal distributions of alternative theoretical models, prior to performing the statistical analysis. The generality of this method allows us to perform statistical inference in the space of theoretical parameters, assuming different kinematic distributions, according to a beyond Standard Model prediction. We implement our method as an extension to the pyhf software and interface it with the EOS software, which allows us to perform flavor physics phenomenology studies. Furthermore, we argue that, beyond the pyhf or HistFactory likelihood specification, only minimal information is necessary to make a likelihood model-agnostic and hence easily reinterpretable. We showcase that publishing such likelihoods is crucial for a full exploitation of experimental results.

Experimental high-energy physics without computer simulations

This paper argues against the claim that high-energy physics experiments done so far could not be carried out without computer simulations. We show that it would be possible to completely dispense with computer simulations for experiments conducted to date, and that computer simulations up to now are mostly used for practical reasons. Our investigation covers all elements of experimental research in which computer simulations have been used. Dispensing with simulations would yield an advantage with regard to the complex theory dependence of experiments. We also point out that computer simulations may play a more essential role for the complex measurements foreseen at the Large Hadron Collider, where subtle dependencies between final state objects in high-energy physics experiments must be accurately described. Therefore, the conceivable complete replacement of computer simulations may have come to an end, and the theory dependence of high-energy physics experiments through computer simulations may be entering a new phase.

Invariant mass reconstruction of heavy gauge bosons decaying to τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document} leptons using machine learning techniques

Many analyses are performed by the LHC experiments to search for heavy gauge bosons, which appear in several new physics models. The invariant mass reconstruction of heavy gauge bosons is difficult when they decay to τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document} leptons due to missing neutrinos in the final state. Machine learning techniques are widely utilized in experimental high-energy physics, in particular in analyzing the large amount of data produced at the LHC. In this paper, we study various machine learning techniques to reconstruct the invariant mass of Z′→ττ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z^{\\prime }~\\rightarrow ~\ au \ au $$\\end{document} and W′→τν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W^{\\prime }~\\rightarrow ~\ au \ u $$\\end{document} decays, which can improve the sensitivity of these searches.

Heavy hexaquarks in the flux tube model

Hexaquarks are one of the currently emerging topics in both experimental and theoretical high energy physics. Hexaquarks have been examined in relation to particle physics, however, there are still some research and theoretical conjectures surrounding their relationship to dark matter. Due to some experimental discoveries, it has attracted much interest and also resulted in new theoretical models to study the properties of these states. In this work, Regge trajectories of some hexaquark states are compared with tetraquark and pentaquark states. The study is mainly concentrated on fully heavy hexaquark states. The mass spectra of these hexaquark states have also been investigated and the results are compared with other theoretical works. Our findings agree well with those of other researchers.

FAIR AI models in high energy physics

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

Fifty Years of Experimental High Energy Physics

Fifty Years of Experimental High Energy Physics

Data driven background estimation in HEP using generative adversarial networks

Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the gamma + textrm{jets} background of the textrm{H}rightarrow gamma gamma analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event.

Deployment of High Energy Physics software with a standard method

The installation and maintenance of scientific software for research in experimental, phenomenological, and theoretical High Energy Physics (HEP) requires a considerable amount of time and expertise. While many tools are available to make the task of installation and maintenance much easier, many of these tools require maintenance on their own, have little documentation and very few are used outside of HEP community.For the installation and maintenance of the software, we rely on the well tested, extensively documented, and reliable stack of software management tools with the RPM Package Manager (RPM) at its core. The precompiled HEP software packages can be deployed easily and without detailed Linux system knowledge and are kept up-to-date through the regular system update process. The precompiled packages were tested on multiple installations of openSUSE, RHEL clones, and Fedora. As the RPM infrastructure is adopted by many Linux distributions, the approach can be used on more systems.In this contribution, we discuss our approach to software deployment in detail, present the software repositories for multiple RPM-based Linux distributions to a wider public and call for a collaboration for all the interested parties.

Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

Calorimeters play an important role in high-energy physics experiments. Their design includes electronic instrumentation, signal processing chain, computing infrastructure, and also a good understanding of their response to particle showers produced by the interaction of incoming particles. This is usually supported by full simulation frameworks developed for specific experiments so that their access is restricted to the collaboration members only. Such restrictions limit the general-purpose developments that aim to propose innovative approaches to signal processing, which may include machine learning and advanced stochastic signal processing models. This work presents the Lorenzetti Showers, a general-purpose framework that mainly targets supporting novel signal reconstruction and triggering strategies using segmented calorimeter information. This framework fully incorporates developments down to the signal processing chain level (signal shaping, energy estimation, and noise mitigation techniques) to allow advanced signal processing approaches in modern calorimetry and triggering systems. The developed framework is flexible enough to be extended in different directions. For instance, it can become a tool for the phenomenology community to go beyond the usual detector design and physics process generation approaches. Program summaryProgram Title: Lorenzetti ShowersCPC Library link to program files:https://doi.org/10.17632/sy64367452.1Developer's repository link:https://github.com/lorenzetti-hep/lorenzettiLicensing provisions: GPLv3Programming language: Python, C++.Nature of problem: In experimental high-energy physics, simulation is essential for experiment preparation, design and interpretations of ongoing acquisitions. Especially for calorimeters, an accurate simulation that can describe detector geometry, behavior to different physics processes and signal generation close to the readout electronics and data acquisition levels is required to properly develop signal processing and computational methods. Such detectors may face very challenging demands arising from the new designs, such as pileup mitigation and noise reduction tasks under unprecedented levels. In this sense, simulation requirements continuously increase in complexity and performance, because new physics searches require large datasets and accurate modeling to experimental effects.Solution method: The Lorenzetti Showers is an integrated software framework that provides complete calorimeter information close enough to the electronic readout chain. Thus, the proposed framework allows users to access cell readout values, configurable sensor pulse-shapes, crosstalk modeling, and different energy estimation methods. It aims at supporting designs that target low or high pileup operation conditions in an easy-to-use modular structure. The developed framework is based on Pythia 8 (particle generation) and Geant4 (interactions with the calorimeter technique under analysis). An efficient data recording structure was used to allow full access to the Lorenzetti Showers outputs. In summary, the Lorenzetti Showers tool provides to the scientific community a user-friendly, flexible, user-oriented, and low-level calorimeter simulation framework.Additional comments including restrictions and unusual features: The framework current version provides the implementation of a generic segmented calorimeter (electromagnetic and hadronic sections), which may be modified by the user, if desired. It allows the generation of particles interactions using Pythia 8 (native) or any generator compatible with the HepMC format (which may be integrated using an external input file) and propagation through a user-configurable calorimeter using Geant4.

Experimental layout for the direct measurement of electromagnetic shower acceleration in an oriented crystal scintillator

Progress in experimental high-energy physics has been closely tied to developments of high-performance calorimeters. Since their invention, crystal calorimeters have consistently achieved the best resolution for measurements of the energies of electromagnetic (e.m.) particles (electrons and photons). Recently, we experimentally demonstrated the possibility of significantly accelerating the e.m. shower development inside lead tungstate (PWO) crystal when the incident beam is aligned with the crystal axes within some tenths of a degree. Here, we present the innovative photodetection system, based on Silicon PhotoMultipliers, implemented for the direct measurement of the scintillation light enhancement in case of beam aligned to the main crystal axes, along with its characterization performed with cosmic rays at the Insulab laboratory (Insubria University, Como). In 2021 we performed a test at H2 beam line of CERN SPS with a hundred-GeV electron beam with two PWO samples (1 and 2 X0 thick) directly coupled with SiPMs. Since the angular acceptance of the crystal strong field depends weakly on particle energy, while instead the decreasing of the shower length remains pronounced at very high-energy, a crystal calorimeter based on oriented crystals would feature a consistent compactness enhancement while rivaling the current state of the art in terms of resolution in the range of interest of present and future forward detectors, beam dumps for light dark matter search and source-pointing space-borne γ-ray telescopes.

Gaseous Detectors for Field Applications: Quality Control, Thermal and Mechanical Stability

A cosmic muon imaging system is essentially a particle tracking detector as known from experimental High Energy Physics. The Multiwire Proportional Chamber (MWPC) once revolutionized this field of science, and as such it is a viable choice as the core element of an imaging system. Long term construction and operation experience was gathered from a Japanese–Hungarian collaboration that gave rise to the MWPC-based Muon Observatory System (MMOS), and is being used in Japan at the Sakurajima volcano. The present paper attempts to draw conclusions on the thermal and mechanical limits of the system, based on controlled measurements and detailed simulations. High temperature behavior and effects of thermal cycling and conditioning are presented, which appear to consistently allow one to propose quality control criteria. Regarding mechanical stability, the relation between gluing quality (tensile strength) and expected stress from vibration (during transportation) determines the safety factor to avoid damages. Both of these are presented and quantified in the paper using a conservative and austere approach, with mechanical simulations validated with experimental modal testing data. One can conclude that mechanical stress during industrial standard air freight shipping conditions is nearly a factor of three below the calculated maximum stress.

Advanced scintillation materials for calorimetry at circular colliders

The most probable scenario for the development of experimental high-energy physics in the next 50 years is the creation of a family of Future Circular Colliders (FCC) at CERN, a Circular Electron–Positron Collider at China, and a Future Electron-Ion Collider at Brookhaven (USA), which continue the Large Hadron Collider (LHC) scientific program within the framework of the Standard Model and beyond it. The first generation of colliders to be put into operation will utilize the electron beam as one of the colliding species to provide precise mass spectroscopy in a wide energy range. Similarly to the measurements at the high luminosity phase of the LHC operation, the most important property of the detectors to be used in the experimental setup is a combination of the short response of the detectors and their high time resolution. The radiation tolerance to a harsh irradiation environment remains mandatory but not the main factor of the collider’s experiments using electronic beams. A short response in combination with high time resolution ensures minimization of the influence of the pile-up and spill-over effects at the high frequency of collisions (higher than 50 MGz). The radiation hardness of the materials maintains the long-term high accuracy of the detector calibration. This paper discusses the prospects for using modern inorganic scintillation materials for calorimetric detectors at future colliders.

R&D of gas filled detectors for high energy physics experiments


 
 
 Bose Institute is Asia’s first modern research centre devoted to interdisciplinary research and bears a century old tradition of research excellence. In the experimental high-energy physics (EHEP) detector laboratory of Bose Institute, Kolkata, we are working on the R&D of Gas Electron Multiplier (GEM), straw tube detector for future heavy ion physics experiments and also developing low resistive bakelite Resistive Plate Chamber (RPC), keeping in mind high particle rate handling capacity. The main goal of our research program is the stability study and ageing study of gaseous detectors mentioned above. In this review article, the details of the R&D program of GEM detector, straw tube and RPC detectors carried out during the last five years is reported. 
 
 

Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds.

We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. We have developed REANA, a platform for reproducible data analyses, that supports several such DAG workflow specifications. The REANA platform parses the analysis workflow and dispatches its computational steps to various supported computing backends (Kubernetes, HTCondor, Slurm). The focus on declarative rather than imperative programming enables researchers to concentrate on the problem domain at hand without having to think about implementation details such as scalable job orchestration. The declarative programming approach is further exemplified by a multi-level job cascading paradigm that was implemented in the Yadage workflow specification language. We present two recent LHC particle physics analyses, ATLAS searches for dark matter and CMS jet energy correction pipelines, where the declarative approach was successfully applied. We argue that the declarative approach to data analyses, combined with recent advancements in container technology, facilitates the portability of computational data analyses to various compute backends, enhancing the reproducibility and the knowledge preservation behind particle physics data analyses.

A GPU based multidimensional amplitude analysis to search for tetraquark candidates

The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the B^0 rightarrow J/psi K pi decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

Quantum machine learning and its supremacy in high energy physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

Signal detection using biphotons and potential application in axion-like particle search

This paper presents a new optical system for detecting light signals associated with the change in incoming photon number. The system employs quantum correlation of photon pairs created via spontaneous parametric down-conversion (SPDC). The signal, if present, will perturb the flux of the incident photon stream. The perturbed photon stream is first projected through a birefringent crystal where SPDC occurs, converting a single high-energy photon into a pair of low-energy photons. The photons in each pair eventually arrive at separate detectors. By examining the biphoton correlation using the probability distribution of the photons at the detectors, which varies depending on the displacement of the main “pump” photon stream and the change in the number of photons, the small optical displacement of the photon stream and its variance can be determined. The change in incident photon number, in other words, the presence of light signal does not influence the average of the measured optical displacement values. Nevertheless, the change in optical displacement measurement variance when the number of incident photons has changed detects the light signal. This optical setup enables the detection of light signals with low noise and remarkably high precision and sensitivity using quantum correlation. The proposed technique has potential application for axion-like particle search in experimental high energy physics.

On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PYTORCH called LUMIN is presented, which incorporates all of the techniques studied.

Profile of Edward H. Egelman

Edward H. Egelman has had a long and distinguished career as a biophysicist, but he took a fairly circuitous path into science. Egelman graduated high school in 1968, a particularly tumultuous year for the United States. “This was the peak of the Vietnam war and my main interests at that point were focused on opposition to the Vietnam war and issues of social justice, racism, and inequality,” he says. After starting an undergraduate degree at Brandeis University, Egelman dropped out and was actively involved in the antiwar movement. Edward H. Egelman. Image credit: UVA Health/Kay Taylor. When Egelman eventually went back to Brandeis, an interest in philosophy led him to pursue a degree in physics. “I thought that maybe physics held the answer to all these interesting philosophical questions,” he says. After completing a physics major within a year and a half and graduating in 1976, Egelman started a doctorate in experimental high-energy physics at Harvard University. “I quickly became pretty disillusioned with that type of physics; it was sort of industrial scale with [dozens of] authors on each paper,” he says. As he considered his next steps, Egelman recalled enjoying his undergraduate honors research project with David DeRosier, whose work lay at the intersection of physics and biology. “I had so much fun doing that project, and I thought perhaps biophysics might be as much fun,” he says. Egelman returned to Brandeis to work with DeRosier for his doctorate in biophysics. “In biophysics, a person could do something on a benchtop that was both interesting and potentially relevant to public health,” says Egelman. “Everything I’ve done since then is sort of a continuation of that work,” he says. During his career, Egelman has deciphered the structure and function of protein polymers and has made key contributions to the use …

Using machine learning to speed up and improve calorimeter R&D

Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Frontier R&Ds are used to squeeze the maximum physics performance using cutting edge detector technologies. The evaluation of physics performance for particular configuration includes sketching this configuration in Geant, simulating typical signals and backgrounds, applying reasonable reconstruction procedures, combining results in physics performance metrics. Since the best solution is a trade-off between different kinds of limitations, a quick turn over is necessary to evaluate physics performance for different techniques in different configurations. Two typical problems which slow down evaluation of physics performance for particular approaches to calorimeter detector technologies and configurations are: Emulating particular detector properties including raw detector response together with a signal processing chain to adequately simulate a calorimeter response for different signal and background conditions. This includes combining detector properties obtained from the general simulation with properties obtained from different kinds of bench and beam tests of detector and electronics prototypes. Building an adequate reconstruction algorithm for physics reconstruction of the detector response which is reasonably tuned to extract the most of the performance provided by the given detector configuration. Being approached from the first principles, both problems require significant development efforts. Fortunately, both problems may be addressed by using modern machine learning approaches, that allow a combination of available details of the detector techniques into corresponding higher level physics performance in a semi-automated way. In this paper, we discuss the use of advanced machine learning techniques to speed up and improve the precision of the detector development and optimisation cycle, with an emphasis on the experience and practical results obtained by applying this approach to epitomising the electromagnetic calorimeter design as a part of the upgrade project for the LHCb detector at LHC.