6 citations · 10 across the 8 of their papers we have counts for
8 papers
High-Performance Data Format for Scientific Data Storage and Analysis
Gagik Gavalian
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 fo…
Real-Time Charged Track Reconstruction for CLAS12
Gagik Gavalian
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…
Deep Learning Level-3 Electron Trigger for CLAS12
Richard Tyson, Gagik Gavalian, David Ireland +1
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 C…
Convolutional Auto-Encoders for Drift Chamber data de-noising for CLAS12
Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos +1
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 eff…
CLAS12 Track Reconstruction with Artificial Intelligence
Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos +3
In this article we describe the implementation of Artificial Intelligence models in track reconstruction software for the CLAS12 detector at Jefferson Lab. The Artificial Intellige…
Jas4pp -- a Data-Analysis Framework for Physics and Detector Studies
S. V. Chekanov, G. Gavalian, N. A. Graf
This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java…
Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12
Gagik Gavalian
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…
Using Machine Learning for Particle Track Identification in the CLAS12 Detector
Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian +1
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively te…