Open AccessIMPROVEMENTS OF THE LOOT MODEL FOR PRIMARY VERTEX FINDING BASED ON THE ANALYSIS OF DEVELOPMENT RESULTS
Author(s) -
E. Rezvaya,
Pavel Goncharov,
Y. Nefedov,
G. Ososkov,
A. Zhemchugov
Publication year - 2021
Publication title -
9th international conference "distributed computing and grid technologies in science and education"
Language(s) - English
Resource type - Conference proceedings
DOI - 10.54546/mlit.2021.76.95.001
Subject(s) - vertex (graph theory) , convolutional neural network , event (particle physics) , computer science , artificial intelligence , metric (unit) , detector , algorithm , pattern recognition (psychology) , mathematics , theoretical computer science , physics , graph , telecommunications , operations management , quantum mechanics , economics
The recognition of particle trajectories (tracks) from experimental measurements plays a key role in the reconstruction of events in experimental high-energy physics. Knowledge about the primary vertex of an event can significantly improve the quality of track reconstruction. To solve the problem of primary vertex finding in the BESIII inner tracking detector we applied the LOOT program which is a deep convolutional neural network that processes all event hits at once, like a three-dimensional image. We used mean absolute error to measure the quality of the trained model, but a thorough analysis of the results showed that this metric by itself is inadequate without considering output distributions of the vertex coordinates. Correcting all errors allowed us to propose special corrections to the loss function that gave quite acceptable results. The process of our problem investigation and itsoutcomes are presented.