Implementationof Monte Carlo and Numerical Integration Techniques within an Online Physics Laboratory Environment

A robust and sophisticated online physics laboratory environment has been developed. This environment can handle large data sets and generate realistic experimental results by applying Monte Carlo and numerical integration techniques. The advantages and limitations of both the Flash 5 and Java development environments were explored. Java was chosen for it’s ability to handle large data sets and consequently used to create the Java Laboratory (JLab) environment. Within the online environment two JLabs were created, the ”Online Virtual Nuclear Decay Laboratory” and the ”Online Virtual Stern-Gerlach Laboratory”. These laboratories teach students how to manipulate experimental parameters, take data, and use various analysis tools. These JLabs generate realistic data sets for students to analyze and prove that online laboratories can play a significant role in enhancing physics education

Deep Learning Level-3 Electron Trigger for CLAS12

Fast, efficient and accurate triggers are a critical requirement for modern high-energy physics experiments given the increasingly large quantities of data that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a highly efficient electron trigger to filter the amount of recorded data by requiring at least one electron in each event, at the cost of a low purity in electron identification. Machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this article, we show how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that the AI trigger would achieve a significant data reduction compared to the traditional trigger, whilst preserving a 99.5\% electron identification efficiency. The AI trigger purity as a function of increased luminosity is improved relative to the traditional trigger. As a consequence, this AI trigger can achieve a data recording reduction improvement of 0.33\% per nA when compared to the traditional trigger whilst maintaining an efficiency above 99.5\%. A reduction in data output also reduces storage costs and post-processing times, which in turn reduces the time to the publication of new physics measurements

Status Report of the DPHEP Study Group: Towards a Global Effort for Sustainable Data Preservation in High Energy Physics

Data from high-energy physics (HEP) experiments are collected with significant financial and human effort and are mostly unique. An inter-experimental study group on HEP data preservation and long-term analysis was convened as a panel of the International Committee for Future Accelerators (ICFA). The group was formed by large collider-based experiments and investigated the technical and organisational aspects of HEP data preservation. An intermediate report was released in November 2009 addressing the general issues of data preservation in HEP. This paper includes and extends the intermediate report. It provides an analysis of the research case for data preservation and a detailed description of the various projects at experiment, laboratory and international levels. In addition, the paper provides a concrete proposal for an international organisation in charge of the data management and policies in high-energy physics

The CLAS12 Software Framework and Event Reconstruction

We describe offline event reconstruction for the CEBAF Large Acceptance Spectrometer at 12 GeV (CLAS12), including an overview of the offline reconstruction framework and software tools, a description of the algorithms developed for the individual detector subsystems, and the overall approach for charged and neutral particle identification. We also present the scheme for data processing and the code management procedures

The CLAS12 software framework and event reconstruction

We describe offline event reconstruction for the CEBAF Large Acceptance Spectrometer at 12 GeV (CLAS12), including an overview of the offline reconstruction framework and software tools, a description of the algorithms developed for the individual detector subsystems, and the overall approach for charged and neutral particle identification. We also present the scheme for data processing and the code management procedures

Studies of BONuS12 Radial GEM Detector and TCS Beam Spin Asymmetry in CLAS12

The Barely Offshell Nucleon Structure (BONuS12) experiment adopted the concept of spectator tagging technique to study the nearly-free neutron structure function F2n in the CLAS12 of Jefferson Lab. A novel Radial Time Projection Chamber (RTPC) detector was built, tested and integrated into the CLAS12 system to detect a back-moving low momentum tagged proton in d(e, ep)X deep-inelastic scattering. It was a 40 cm long gaseous detector consisting of 3 layers of cylindrical GEM foils for the charge amplification, with the data readout directly from the surrounding padboard. The RTPC detected the recoiling spectator proton, in coincidence with the scattered electron in the CLAS12. Nucleon structure functions are directly related to the partonic functions, quarks momentum distribution in one dimension. A Generalized Parton Distribution (GPD) came to the lime-light as it encodes the information of both longitudinal momentum and transverse position of partons inside the nucleons. Factorization of hard process such as DVCS allows to access GPDs. Timelike Compton Scattering (TCS), γp → γ∗p, is another process that allows to access the GPDs. TCS is studied experimentally in the CLAS12 of Jefferson lab using the quasi-real photoproduction of time-like photon which eventually decays to lepton pair. This dissertation presents the concept of spectator tagging in BONuS12, and the research and development efforts during the BONuS12 preparation leading up to the successful data-taking during spring and summer 2020. In addition, analysis framework to extract the beam spin asymmetry of TCS events through the CLAS12 Run group A data is presented

Matlab environment potential for cooperation with other applications and programming languages

Tato práce je zaměřena na možnosti prostředí Matlab. Možnosti ve smyslu sdílení nebo zpracování dat mezi Matlabem a jinými programovacími jazyky a programy. Práce si klade za cíl vytvořit návod a přehled v těchto možnostech a dále zviditelňuje možnosti prezentace dat přes webové stránky.This thesis is focused on possibillities of Matlab enviroment. Mainly shows the possibillities of sharing and processing data between Matlab and other programs and programming languages. The main goal is to create an introducement and overview in these possibillities and shows potentialities to present data through web sites.

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.Comment: 27 pages, 11 figures, AI4EIC workshop, tutorials and hackatho