Quantum Chebyshev probabilistic models for fragmentation functions

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.

This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed quantum architecture demonstrates an advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to the faster convergence, the QCNN achieves a greater test accuracy compared to CNNs. Based on our results from numerical simulations, it is a promising direction to apply QCNN and other quantum machine learning models to high energy physics and other scientific fields.

Solid-State-Drives are currently introduced in storage facilities for High Energy Physics (HEP) experiments and their performances are measured and compared to standard magnetic disks. For this paper a typical HEP data analysis is performed and used as a test to measure computing performances. The tests exploit the features provided by PROOF-Lite which allows to distribute a huge number of events among different CPU cores, thus reducing the overall time needed to complete the analysis task. These tests are carried on few computational devices typically hosted at a current Tier-2/Tier-3 facility. The performance results are provided in terms of figures of merit and the main issue is scalability described in terms of speed up factor and processing event rate. The obtained results can be used as guideline for both the typical HEP analyst and the Tier-2/Tier-3 manager: the former in the configuration of his own analysis task while dealing with increasing data sizes, the latter in the implementation of an interactive data analysis facility for HEP experiments while facing solutions that concern both technological and economical aspects.

One burden of high energy physics data analysis is uncertainty within the measurement, both systematically and statistically. Even with sophisticated neural network techniques that are used to assist in high energy physics measurements, the resulting measurement may suffer from both types of uncertainties. Fortunately, most types of systematic uncertainties are based on knowledge from information such as theoretical assumptions, for which the range and behaviour are known. It has been proposed to mitigate such systematic uncertainties by using a new type of neural network called adversarial neural network (ANN) that would make the discriminator less sensitive to these uncertainties, but this has not yet been demonstrated in a real-life LHC analysis. This work investigates ANNs using as a benchmark the search for the production of four top quarks, an extremely rare physics process at the LHC and one of the important processes that can prove or disprove the Standard Model. The search for four top quarks in some cases is sensitive to large systematic uncertainties. The expected cross section upper limit for four top quark production is calculated using traditional neural networks and adversarial neural networks based on simulated proton-proton collisions within the Compact Muon Solenoid detector within Large Hadron Collider, and are compared to existing results. The improvement and further considerations to the search for rare processes at the LHC will be discussed.

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.

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.

We apply a genetic programming technique to search for the doubly Cabibbo suppressed decays Λc+→pK+π− and Ds+→K+K+π−. We normalize these decays to their Cabibbo favored partners and find BR(Λc+→pK+π−)/BR(Λc+→pK−π+)=(0.05±0.26±0.02)% and BR(Ds+→K+K+π−)/BR(Ds+→K−K+π+)=(0.52±0.17±0.11)% where the first errors are statistical and the second are systematic. Expressed as 90% confidence levels (CL), we find <0.46 and <0.78%, respectively. This is the first successful use of genetic programming in a high energy physics data analysis.

We apply a genetic programming technique to search for the doubly Cabibbo suppressed decays Λc+ → pK+π− and Ds+ → K+K+π−. We normalize these decays to their Cabibbo favored partners and find BR(Λc+ → pK+π−)/BR(Λc+ → pK−π+) < 0.46% and BR(Ds+ → K+K+π−)/BR(Ds+ → K−K+π+) < 0.78% at the 90% confidence level (CL). This is the first successful use of genetic programming in a high energy physics data analysis.

Grid is one of the most effective new paradigms in large scale distributed computing. Only recently Petri nets have been adopted as a formal modeling framework for describing some features related to Grid infrastructures: mostly related to the description of large-scale dynamic workflows. In this paper we describe a Grid tool for High Energy Physics data analysis, and we show how modeling its architecture with nets-within-nets has led us to identify and solve a number of defects affecting the current implementation.KeywordsGridPetri netsnets-within-netshypernetvalidation

This study presents a quantum chromodynamics (QCD) analysis of fragmentation functions (FFs) for neutral pions (π0), neutral kaons (KS0), and eta mesons (η), utilizing world data of single-inclusive hadron production in e+e− annihilation involving the most recent BESIII data with low collision energy, to test the operational region of QCD collinear factorization for single-inclusive hadron production. We found that the QCD-based analysis at next-to-next-to-leading order in perturbative QCD with parametrized higher-twist effects can explain both existing high-energy world data and the BESIII new measurements, while the latter cannot be explained with existing FFs extracted with high-energy data. To investigate the higher-twist contributions to this discrepancy, a direct functional approach is employed, providing a testing framework for characterizing the experimental results over a wide range of energy scales, from low to high, thus extending the classical theoretical models to the BESIII domain.

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.

Upgrades to the LHCb computing infrastructure in the first long shutdown of the LHC have allowed for high quality decay information to be calculated by the software trigger making a separate offline event reconstruction unnecessary. Furthermore, the storage space of the triggered candidate is an order of magnitude smaller than the entire raw event that would otherwise need to be persisted. Tesla is an application designed to process the information calculated by the trigger, with the resulting output used to directly perform physics measurements.

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.