Tesla: An application for real-time data analysis in High Energy Physics

Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficiently learning and sampling these distributions remains challenging. We propose a quantum protocol for a bivariate probabilistic model based on shifted Chebyshev polynomials, trained as a circuit-based representation of two correlated variables, with sampling performed via quantum Chebyshev transforms. As a key application, we study fragmentation functions (FFs) of charged pions and kaons from single-inclusive hadron production in electron-positron annihilation. We learn the joint distribution of momentum fraction z and energy scale Q, and infer their correlations from the entanglement structure. Building on the generalization capabilities of the quantum model and extended register architecture, we perform fine-grid multivariate sampling for FF dataset augmentation. Our results highlight the growing potential of quantum generative modeling to advance data analysis and scientific discovery in high-energy physics. Quantum computing holds great promise for advancing data analysis in science, including high-energy physics. This work presents a quantum protocol for learning and sampling multivariate distributions, using Chebyshev polynomials and quantum Chebyshev transforms to study fragmentation functions and energy-momentum correlations in hadron production.

Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior performance in some tasks. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques. In this work a quantum support vector machine (QSVM) is implemented for signal-background classification. We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. For this same dataset the best classical method tested, a classical Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of 0.793. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.

In high-energy physics experiments, traditional data analysis methods increasingly show limitations when handling large-scale datasets, highlighting the urgent need for a transition to automated and intelligent analysis techniques to improve accuracy and efficiency. To address this challenge, we utilized an open dataset from CERN and proposed a novel Z-Bagging model, an ensemble learning approach designed to distinguish between two decay processes of the Z boson: Z [Formula: see text]+[Formula: see text] (Zmumu) and Z [Formula: see text] e+e[Formula: see text] (Zee). Initially, we tested several classical machine learning models to evaluate their performance in distinguishing these two decay modes. While these models showed some improvements in accuracy, further optimization was identified. Consequently, we introduced the Z-Bagging model, which combines multiple weak learners, such as Random Forest, Support Vector Machine and K-Nearest Neighbors, into a single strong learner. By integrating diverse models, Z-Bagging achieves more accurate and stable predictions. Experimental results demonstrate that the Z-Bagging model significantly outperforms existing models in classification accuracy for distinguishing Zmumu and Zee decay processes. This improvement provides a more precise tool for data analysis in high-energy physics.

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables.

With the development of the new generation of High Energy Physics (HEP) experiments, huge amounts of data are being generated. Efficient parallel algorithms/frameworks and High IO throughput are key to meet the scalability and performance requirements of HEP offline data analysis. Though Hadoop has gained a lot of attention from scientific community for its scalability and parallel computing framework for large data sets, it’s still difficult to make HEP data processing tasks run directly on Hadoop. In this paper we investigate the application of Hadoop to make HEP jobs run on it transparently. Particularly, we discuss a new mechanism to support HEP software to random access data in HDFS. Because HDFS is streaming data stored only supporting sequential write and append. It cannot satisfy HEP jobs to random access data. This new feature allows the Map/Reduce tasks to random read/write on the local file system on data nodes instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop diverse MapReduce model for HEP jobs such as Corsika simulation, ARGO detector simulation and Medea++ reconstruction. And we develop a toolkit for users to submit/query/remove jobs. In addition, we provide cluster monitoring and account system to benefit to the system availability. This work has been in production for HEP experiment to gain about 40,000 CPU hours per month since September, 2016.

SAM (Sequential Access through Meta-data) is the data access system for the D0 high energy physics (HEP) experiment at Fermilab. The system is being developed and used to handle the Petabyte-scale experiment data. The D0 applications, like virtually all HEP applications, are data-intensive, which poses special problems for the data management and job control facilities in the distributed environment. The fundamental problem is to bring the user applications and the data together, and SAM attacks the problems from both sides. First, we describe how the system moves the data through the distributed disk cache. Second, we describe how SAM interacts with the batch system to synchronize parallel user jobs with the data availability. All the design solutions herein have been implemented in a real system that handles the mission-critical data of the D0 experiment; thus, we present our work from the standpoint of real experience.

High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency.Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their performance and characteristics. These techniques, if used together with multiple streams data access, can also be effective in allowing to efficiently and transparently deal with data repositories accessible via a Wide Area Network.

Grid enabled physics analysis requires a workload management system (WMS) that takes care of finding suitable computing resources to execute data intensive jobs. A typical example is the WMS available in the LCG2 (also referred to as EGEE-0) software system, used by several scientific experiments. Like many other current grid systems, LCG2 provides a file level granularity for accessing and analysing data. However, application scientists such as high energy physicists often require a higher abstraction level for accessing data, i.e. they prefer to use datasets rather than files in their physics analysis. We have improved the current WMS (in particular the Matchmaker) to allow physicists to express their analysis job requirements in terms of datasets. This required modifications to the WMS and its interface to potential data catalogues. As a result, we propose a simple data location interface that is based on a Web service approach and allows for interoperability of the WMS with new dataset and file catalogues. We took a particular high energy physics experiment as the source for our study and show that physics analysis can be improved by our modifications to the current grid system.

Yoshio Yamaguchi, an icon of postwar Japanese high-energy physics and a strong advocate of international collaboration, died of pneumonia on 12 August 2016 in Tokyo. Yoshio Yamaguchi AKIKO YAMAGUCHI, 2010PPT|High resolutionBorn on 29 January 1926 in Takefu, Japan, Yamaguchi received his bachelor’s degree in physics in 1947 and a doctor of science degree in physics in 1953 from the University of Tokyo. His dissertation was entitled “Phenomenological analysis of meson processes.”In 1949 Yamaguchi joined Osaka City University as a cofounder of its particle-physics theory group. His proposal that the newly discovered particles in cosmic-ray collisions are created in pairs led to the eventual understanding of strange particles. Yamaguchi started his international career in 1953 at the University of Illinois at Urbana-Champaign; during his two years there, he proposed a separable nuclear potential.In 1957 Yamaguchi was invited to the theory division at CERN. At the time, CERN was in its infancy, and the newly created theory division was attracting numerous superb physicists, with whom Yamaguchi had deep and fruitful interactions and developed close, lasting friendships. His experience during that period greatly influenced Yamaguchi’s passion for international collaboration throughout the rest of his life.Yamaguchi played a vital role in evaluating and consulting on experimental programs at CERN. According to local legend, anyone seeking to get a new research experiment approved first had to convince him to support it. His four years at the organization convinced him that experimental study and verification by means of accelerators were crucial to advancing high-energy physics. While at CERN, he also laid the groundwork for a theory on SU(3) and other flavor symmetries in particle physics.After leaving CERN, Yamaguchi went to the Institute for Nuclear Study (INS) at the University of Tokyo as head of the theory group. In 1968 he moved to the university’s physics department, where he taught and supervised many students. He was not only an excellent researcher but also a dedicated educator. His enthusiasm for and deep knowledge of high-energy physics inspired many young scientists. As one of his students, I witnessed lively discussions every week during and after his High Energy Physics lectures, which attracted many students and staff members.Yamaguchi strongly believed that significant progress can be achieved only if experimental and theoretical physics researchers work hand in hand. He made great efforts to establish experimental high-energy physics by introducing high-energy proton accelerators in Japan. He was instrumental in the creation of the National Laboratory for High Energy Physics and of KEK, the High Energy Accelerator Research Organization.An excellent manager, Yamaguchi returned to the INS as director in 1983 until his retirement in 1986. He was a cofounder and chair of the International Committee for Future Accelerators, which promoted a worldwide network of collaborations. As a member of the Scientific Policy Committee at CERN, he was responsible for, among other things, further promoting international collaborations. He served as president of the Physical Society of Japan in 1986–87. In 1993 he became the first person from Japan to be elected president of the International Union of Pure and Applied Physics. Yamaguchi contributed a great deal to the creation of the Association of Asia Pacific Physical Societies and the Asia Pacific Center for Theoretical Physics.Yamaguchi had a tasteful knowledge of the culture of Japan and the world, especially of the classical period. He often impressed colleagues with his memory of his many intellectual conversations with them. His talks were full of wit and humor, which was somewhat exceptional for a Japanese person. Some years ago his friends and students had a chance to listen to his stories about physics and physicists from the early days of postwar Japan; the stories were so vivid that they left a lasting impression. Many of us pray sincerely that Yamaguchi’s hope and enthusiasm for international collaboration and high-energy physics will inspire the coming generation of researchers in Japan and around the world.© 2017 American Institute of Physics.

Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system. The resulting selection of events are reconstructed and pass through a number of software filters before arriving at a final offline analysis where hard physical constants are extracted. Although sophisticated statistical data analysis techniques are routinely employed high energy physics, the use of statistical signal processing in the field is has until now been rare. Our paper will begin with an overview of a typical high energy physics data acquisition system, outlining the technologies and tradeoffs involved at each stage. We will then proceed to argue that the dominant roles of model dependence and systematic errors in final physics analyses render statistical signal processing techniques largely inapplicable at this level. We observe, however, that at the low-level pattern recognition and event reconstruction levels, statistical signal processing techniques have been making inroads in high energy physics for a number of years, and examples from the literature will be cited. The viability of the technique for second level triggers will be assessed. Parallels to other other approaches, such as neural networks, will also be drawn. It will be argued that the falling cost of computing hardware favors the growth of statistical signal processing methods in high energy physics

At present, data generated by high-energy physical devices has reached the PB or EB level as it is constantly updated and running. Therefore, the technical requirements for physical analysis are constantly increasing with the mass of physical events generated by high-energy physical colliders. The physical analysis for high-energy physics events refers to the selection of thousands of meaningful events from massive physical events. The analysis process relies on different attribute parameters and different operators to obtain the final selection criteria. Because the traditional method of high-energy physics events analysis is less efficient and more complex, it is very important to design an efficient and convenient operator library for high-energy physics events processing. This paper proposes a new operator library system based on hbase coprocessor for high-energy physics events processing. It uses storage structure and endpoint coprocessor of the hbase as the framework of the physical analysis operator library, uses protocol buffer to customize the client and server RPC communication and encapsulates the operators in the object-oriented data analysis framework ROOT to hbase coprocessor. In this way, the complexity of the high-energy physics events analysis is reduced and the efficiency of the calculation is improved. In the experiment, we take the zc3900 analysis process as an example and apply it to the 10 nodes experimental cluster. The test results show that the proposed system is more efficient and avoids the complexity of the events processing.

This research program in high energy physics is carried out under the general supervision of a committee which is composed of G.W. Brandenburg, G.J. Feldman, M.E. Franklin, R.J. Glauber, K. Kinoshita, F.M. Pipkin, K. Strauch, R. Wilson, and H. Yamamoto. Professor G.J. Feldman currently serves as chair of this committee. Dr. Brandenburg is the Director of the High Energy Physics Laboratory and administers the DOE high energy physics contract. In the fall of 1991 S. Mishra will join this committee. Harvard is planning to make one or two additional senior faculty appointments in experimental high energy physics over the next two years. The principal goals of the work described here are to carry out forefront programs in high energy physics research and to provide first rate educational opportunities for students. The experimental program supported through HEPL is carried out at the major accelerator centers in the world and addresses some of the most important questions in high energy physics. Harvard`s educational efforts are concentrated in graduate education, where they are currently supporting thirteen research students. In addition, undergraduate students work in projects at HEPL during the academic year and over summers. These budget projections cover all of the Harvard based high energy physics experimental activities. The {open_quotes}umbrella{close_quotes} nature of this contract greatly simplifies support of essential central technical and computer services and helps the group to take advantage of new physics opportunities and to respond to unexpected needs. The funding for the operation of the HEPL facility is shared proportionally by the experimental groups. Harvard financially supports this high energy physics research program in many ways.

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

In this Final Report, we the researchers of the high energy physics group at Tufts University summarize our works and achievements in three frontier areas of elementary particle physics: (i) Neutrino physics at the Intensity Frontier, (ii) Collider physics at the Energy Frontier, and (iii) Theory investigations of spin structure and quark-gluon dynamics of nucleons using quantum chromodynamics. With our Neutrino research we completed, or else brought to a useful state, the following: Data-taking, physics simulations, physics analysis, physics reporting, explorations of matter effects, and detector component fabrication. We conducted our work as participants in the MINOS, NOvA, and LBNE neutrino oscillation experiments and in the MINERvA neutrino scattering experiment. With our Collider research we completed or else brought to a useful state: Data-taking, development of muon system geometry and tracking codes, software validation and maintenance, physics simulations, physics analysis, searches for new particles, and study of top-quark and B-quark systems. We conducted these activities as participants in the ATLAS proton-proton collider experiment at CERN and in the CDF proton-antiproton collider experiment at Fermilab. In our Theory research we developed QCD-based models, applications of spin phenomenology to fundamental systems, fitting of models to data, presenting and reporting of new concepts and formalisms. The overarching objectives of our research work have always been: 1) to test and clarify the predictions of the Standard Model of elementary particle physics, and 2) to discover new phenomena which may point the way to a more unified theoretical framework.

High energy physics (HEP) experiments are facing unprecedented challenges to deal with huge volumes of data. Grid is the excellent technology for it. However, Incompatibility of a variety of grid middewares is an obstacle to exploit the resources on multiple grid infrastructures. This paper firstly introduces HEP experiments and HEP grid, and then reviews the solutions for grid interoperability and interoperations. Because current HEP applications are tightly with gLite middleware, this paper highlights a gateway approach to interoperate with gLite and described it in detail. Direct access to gLite storage system is very important, and a dedicated tool for HEP data sharing is then proposed and implemented. Finally, the implementation of the interoperability of GOS and gLite is introduced.