Support for annotating and classifying particles detected by Timepix3
| Thesis title in Czech: | Podpora pro anotaci a klasifikaci částic detekovaných detektorem Timepix3 |
|---|---|
| Thesis title in English: | Support for annotating and classifying particles detected by Timepix3 |
| Key words: | elemntární částice|skeletonizace|strojové učení|klaster|Timepix3 |
| English key words: | elementary particles|skeletonization|machine learning|cluster|Timepix3 |
| Academic year of topic announcement: | 2020/2021 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | RNDr. František Mráz, CSc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 23.03.2021 |
| Date of assignment: | 25.03.2021 |
| Confirmed by Study dept. on: | 13.04.2021 |
| Date and time of defence: | 10.09.2021 09:00 |
| Date of electronic submission: | 21.07.2021 |
| Date of submission of printed version: | 22.07.2021 |
| Date of proceeded defence: | 10.09.2021 |
| Opponents: | RNDr. Tomáš Holan, Ph.D. |
| Guidelines |
| TimePix3 is an electronic detector of elementary particles used mainly in particle physics. The thesis aims to develop software analyzing data produced by TimePix3 detectors. The vast data produced by a TimePicx3 detector is preprocessed by an external tool called clusterer that groups detection data from one event in a group of pixel data called a cluster. A cluster is a trace of one particle or several particles when the original particle decays. The support will consist of several interactive and non-interactive tools that enable filtering the clusters, annotating them manually or automatically by adding features that describe properties of the cluster, visualizing the clusters and their features, and finally training classifiers for distinguishing various types of clusters. For the main classification task, the author must suggest and implement a suitable set of features describing the properties of clusters. Afterward, the performance of a proposed classifier will be evaluated on labeled data. |
| References |
| Bergmann, B., Pichotka, M., Pospisil, S., Vycpalek, J., Burian, P., Broulim, P., & Jakubek, J. (2017). 3D track reconstruction capability of a silicon hybrid active pixel detector. The European Physical Journal C, 77(6), 1-9.
Frojdh, E., Campbell, M., De Gaspari, M., Kulis, S., Llopart, X., Poikela, T., & Tlustos, L. (2015). Timepix3: first measurements and characterization of a hybrid-pixel detector working in event driven mode. Journal of Instrumentation, 10(01), C01039. Meduna, L. (2019). Detecting elementary particles with Timepix3 detector. Master thesis, Faculty of Mathematics and Physics, Charles University. Zhang, T. Y., & Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 236-239. |