ORCID
0000-0002-5640-3824
College
College of Engineering & Technology (Batten)
Department
Computer Science
Graduate Level
Doctoral
Graduate Program/Concentration
Computer Science
Publication Date
2022
DOI
10.25883/xxgn-yn89
Abstract
In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mapping. Data that connects parameters to detector level events is used to train the proposed model. A point cloud is used to describe a series of events that keeps the permutation invariant property and geometric correlations of the events while being flexible with the number of events in the input. The trained Point Cloud-based Variational Autoencoder functions as an effective inverse function from detector level events to parameter space and can be utilized as the final step in inferring QCFs model parameters from experimental detector level events. Using a point cloud-based variational autoencoder, our results show that Sigma1 and Sigma2 are within one standard division of the predicted Sigma1 (pc) and Sigma2 (pc). The suggested model can be extended to high-dimensional events with permutation invariant features in future work.
Keywords
Inverse problems, Variational autoencoder, Point cloud, Latent space analysis
Disciplines
Computer Sciences | Nuclear | Quantum Physics
Files
Download Poster (519 KB)
Recommended Citation
Alghamdi, Tareq; Alanazi, Yasir; Almaeen, Manal; Sato, Nobuo; and Li, Yaohang, "Point Cloud-based Mapper for QCD Analysis" (2022). The Graduate School Posters. 1.
https://digitalcommons.odu.edu/gradposters2022_gradschool/1