arXiv subjects

Gagik Gavalian

Publications and source records attributed to Gagik Gavalian.

10 recordsLinked to original sources

arXiv2026

Online Electron Reconstruction at CLAS12

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle's type. Of particular interest to electroproduction Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. This approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.

physics.ins-det↗

arXiv2025

3-Dimensional Adaptive Unstructured Tessellated Look-up Tables for the Approximation of Compton Form Factors

We describe an iterative algorithm to construct an unstructured tessellation of simplices (irregular tetrahedra in 3-dimensions) to approximate an arbitrary function to a desired precision by interpolation. The method is applied to the generation of Compton Form Factors for simulation and analysis of nuclear femtography, as enabled by high energy exclusive processes such as electron-proton scattering producing just an electron, proton, and gamma-ray in the final state. While producing tessellations with only a 1% mean interpolation error, our results show that the use of such tessellations can significantly decrease the computation time for Monte Carlo event generation by $\sim23$ times for $10^{7}$ events (and using extrapolation, by $\sim955$ times for $10^{10}$ events).

math.NA↗

arXiv2025

High-Performance Data Format for Scientific Data Storage and Analysis

In this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT and Parquete).

physics.data-an↗

arXiv2024

Real-Time Charged Track Reconstruction for CLAS12

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

physics.ins-det↗

arXiv2023

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.

physics.ins-det↗

arXiv2022

Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12

In this article we describe the development of machine learning models to assist the CLAS12 tracking algorithm by identifying tracks through inferring missing segments in the drift chambers. Auto encoders are used to reconstruct missing segments from track trajectory. Implemented neural network was able to reliably reconstruct missing segment positions with accuracy of $\approx 0.35$ wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$.

cs.CV↗

arXiv2022

CLAS12 Track Reconstruction with Artificial Intelligence

In this article we describe the implementation of Artificial Intelligence models in track reconstruction software for the CLAS12 detector at Jefferson Lab. The Artificial Intelligence based approach resulted in improved track reconstruction efficiency in high luminosity experimental conditions. The track reconstruction efficiency increased by $10-12\%$ for single particle, and statistics in multi-particle physics reactions increased by $15\%-35\%$ depending on the number of particles in the reaction. The implementation of artificial intelligence in the workflow also resulted in a speedup of the tracking by $35\%$.

physics.data-an↗

arXiv2022

Convolutional Auto-Encoders for Drift Chamber data de-noising for CLAS12

In this article, we present the results of using Convolutional Auto-Encoders for de-noising raw data for CLAS12 drift chambers. The de-noising neural network provides increased efficiency in track reconstruction and also improved performance for high luminosity experimental data collection. The de-noising neural network used in conjunction with the previously developed track classifier neural network \cite{Gavalian:2022hfa} lead to a significant track reconstruction efficiency increase for current luminosity ($0.6\times10^{35}~cm^{-2}~sec^{-1}$ ). The increase in experimentally measured quantities will allow running experiments at twice the luminosity with the same track reconstruction efficiency. This will lead to huge savings in accelerator operational costs, and large savings for Jefferson Lab and collaborating institutions.

physics.ins-det↗

arXiv2022

Using Machine Learning for Particle Track Identification in the CLAS12 Detector

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99\% and resulted in an end-to-end speedup of 35\% compared to existing algorithms.

cs.CV↗

arXiv2021

Strange Hadron Spectroscopy with Secondary KL Beam in Hall D

We propose to create a secondary beam of neutral kaons in Hall D at Jefferson Lab to be used with the GlueX experimental setup for strange hadron spectroscopy. The superior CEBAF electron beam will enable a flux on the order of $1\times 10^4~K_L/sec$, which exceeds the flux of that previously attained at SLAC by three orders of magnitude. The use of a deuteron target will provide first measurements ever with neutral kaons on neutrons. The experiment will measure both differential cross sections and self-analyzed polarizations of the produced $Λ$, $Σ$, $Ξ$, and $Ω$ hyperons using the GlueX detector at the Jefferson Lab Hall D. The measurements will span CM $\cosθ$ from $-0.95$ to 0.95 in the range W = 1490 MeV to 2500 MeV. The new data will significantly constrain the partial wave analyses and reduce model-dependent uncertainties in the extraction of the properties and pole positions of the strange hyperon resonances, and establish the orbitally excited multiplets in the spectra of the $Ξ$ and $Ω$ hyperons. Comparison with the corresponding multiplets in the spectra of the charm and bottom hyperons will provide insight into he accuracy of QCD-based calculations over a large range of masses. The proposed facility will have a defining impact in the strange meson sector through measurements of the final state $Kπ$ system up to 2 GeV invariant mass. This will allow the determination of pole positions and widths of all relevant $K^\ast(Kπ)$ $S$-,$P$-,$D$-,$F$-, and $G$-wave resonances, settle the question of the existence or nonexistence of scalar meson $κ/K_0^\ast(700)$ and improve the constrains on their pole parameters. Subsequently improving our knowledge of the low-lying scalar nonet in general.

nucl-ex↗