Beam spin asymmetry measurements in deeply virtual Compton scattering

Beam Spin Asymmetry (BSA) is studied in the Deeply Virtual Compton Scattering (DVCS) using CLAS detector at Jefferson Lab and longitudinally polarized electron beam with 4.8 GeV energy. This asymmetry is directly proportional to the imaginary part of the scattering amplitude, which relates it to the Generalized Parton distribution functions. Reaction ep → epX is studied. A fit to the line shape of the missing mass squared distribution of (ep) is used to extract the number of single photon final states in each kinematical bin for both helicities of the beam and for the helicity sum. DVCS beam spin asymmetry is measured in several bins of Q 2 and t. The Q 2 and the t-dependences of the sin &phis; moment of the asymmetry is extracted for the first time

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 $>99.8\%$

A Machine Learning Approach to Denoising Particle Detector Observations in Nuclear Physics

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics outcomes faster if combined with a highly efficient track reconstruction process. Thus, a method that provides efficient tracking under high luminosity conditions can significantly reduce the amount of time required to conduct physics experiments. In this poster, we present a machine learning (ML) approach for denoising data from particle tracking detectors to improve the track reconstruction efficiency of the CLAS12 detector at Jefferson Lab (JLab). A noise-reducing Convolutional Autoencoder was used to process data for standard experimental running conditions and showed significant improvements in track reconstruction efficiency (\u3e15%). The studies were extended to synthetically generated data emulating much higher beam intensity and showed that the ML approach outperforms conventional algorithms, providing a significant increase in track reconstruction efficiency of up to 80%. This tremendous increase in reconstruction efficiency allows experiments to run at almost three times higher luminosity, leading to significant savings in time (about three times shorter) and money. The software developed by this work is now part of the CLASS12 workflow, assisting scientists of JLab and collaborating institutions.https://digitalcommons.odu.edu/gradposters2022_sciences/1003/thumbnail.jp

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

ML Track Fitting in Nuclear Physics

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown

ML Track Fitting in Nuclear Physics

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown

Particle Trajectory Classification and Prediction Using Machine Learning

Nuclear physics is a challenging scientific domain where experiments are often expensive due to the high cost of the machinery involved. Experimental setups record terabytes of data each day and process them to identify interacting particles from information provided by a series of detectors. One of the most important parts of data processing is identifying trajectories of charged particles in wire chambers. This process is computationally expensive and comprises about 94% of computational time. Charged particles are identified by combinatorically considering all possible combinations of segments. In this work, we used machine learning to identify possible valid combinations of track segments to reduce the number of combinatorics to be considered and reduced the computational time by a factor of ~6 times. We developed three different models to address this problem: an extremely randomized trees model, a multi-layer perceptron as well as a convolutional neural network (CNN). The models achieved an overall classification accuracy of 96.5%. To further reduce the search space for classification, we developed a supervised recurrent neural network (RNN) using long short-term memory (LSTM) layers capable of predicting particle trajectories based on previous trajectory information. Because the model is trained on only acceptable trajectories (i.e. broken lines), it will help eliminate many unacceptable trajectories that do not align with its predictions. These machine learning models will be employed in the experimental pipeline for the CLAS12 detector in order to filter incoming data, save 6-8x more time and energy compared to current methods used, and help increase experimental accuracy

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

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 $35\%$