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 $>99.8\%$

Charged Track Reconstruction with Artificial Intelligence for CLAS12

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program

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

Charged Track Reconstruction with Artificial Intelligence for CLAS12

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program

Level-3 Trigger for CLAS12 with Artificial Intelligence

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 data recorded by requiring at least one electron candidate in each event, at the cost of a low purity in electron identification. However, machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this proceeding we present recently published work that showed how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that this AI trigger would achieve a significant data reduction compared to the conventional CLAS12 electron trigger, whilst preserving a 99.5% electron identification efficiency, at nominal CLAS12 beam currents

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