Conveners
Data processing in HEP
- Václav Kůs (Department of Mathematics, FNSPE Czech Technical University in Prague)
Data processing in HEP
- Jiří Franc (Czech Technical University (CZ))
In this contribution, we will present a brief introduction to neutrinos physics and will focus on two experiments from Fermi National Laboratory, NOvA and DUNE. We will describe how these collaborations works, how they use machine learning and how these methods can contribute to new discoveries and more accurate measurements.
Convolutional neural networks (CNNs) show outstanding results for problems in the area of image classification. Particle classification as a part of high energy physics is no exception. This presentation will deal with basic concepts of artificial neural networks alongside with CNN aspects. The application of CNN on Monte Carlo samples from neutrino experiment protoDUNE will be later discussed.
Classification task is the crucial step in high energy physics data analysis. As many reconstruction steps in high energy physics are similar to image pattern recognition tasks, we explore the potential of appropriate deep learning techniques utilisation. In particular, convolutional neural networks (CNN) are able to extract characteristic features from image pixelmaps at different scales and...
Binary classification of measured data is a common task in high energy physics (HEP). Precise knowledge of the classification algorithm means an advantage in attempt to reach the best accuracy. This paper examines the effects of data transformation tailored to the classificator properties. HEP dataset and the SDDT algorithm were used for this purpose.
In high energy physics analyses, it is necessary to verify whether measured data have the same distribution as a simulated Monte Carlo sample. One of the methods that are used for verification is homogeneity testing. Monte Carlo samples are usually weighted and therefore modifications of homogeneity tests must be employed. In ROOT, a C++ framework used by high energy physics community, some...
For proper description of heavy nuclei collision products, it is often necessary to solve a binary classification problem using machine learning methods. In this contribution, D meson decay will be analyzed using ensemble learning methods applied to the data from the STAR experiment at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory.
We discuss the latest results on electron neutrino appearance and muon neutrino disappearance in the NOvA experiment. Our primary focus will be processing of the Convolutional Visual Network classifier output.
