Machine learning approaches towards tuning ALICE TRD simulations
| dc.contributor.advisor | Dietel, Thomas | |
| dc.contributor.author | Ramraj, Nikhiel | |
| dc.date.accessioned | 2024-07-05T12:54:34Z | |
| dc.date.available | 2024-07-05T12:54:34Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2024-07-05T12:15:31Z | |
| dc.description.abstract | In this work an exploration of the discrepancies existing between real and simulated data pertaining to the ALICE Transition Radiation Detector is carried out as a motivation to tune the necessary parameters in the ALICE Online-Offline simulation software (O2 ). After such exploration a single parameter namely the Xe gas gain is subjected to modification. A machine learning approach is taken with the use of deep learning discrimination mechanisms namely artificial neural networks and convolutional neural networks to quantify the effect that our tuning has on the improvement of the simulation results and their conformation to the real data. The correspondence of the optimal values suggested by deep learning approaches is investigated with pulse height spectrometry. It is shown that the optimal parameters suggested by our deep learning models through inference of their performance metrics are not clear and in agreement with that suggested by naive pulse height inspections. | |
| dc.identifier.apacitation | Ramraj, N. (2024). <i>Machine learning approaches towards tuning ALICE TRD simulations</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/40362 | en_ZA |
| dc.identifier.chicagocitation | Ramraj, Nikhiel. <i>"Machine learning approaches towards tuning ALICE TRD simulations."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2024. http://hdl.handle.net/11427/40362 | en_ZA |
| dc.identifier.citation | Ramraj, N. 2024. Machine learning approaches towards tuning ALICE TRD simulations. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/40362 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Ramraj, Nikhiel AB - In this work an exploration of the discrepancies existing between real and simulated data pertaining to the ALICE Transition Radiation Detector is carried out as a motivation to tune the necessary parameters in the ALICE Online-Offline simulation software (O2 ). After such exploration a single parameter namely the Xe gas gain is subjected to modification. A machine learning approach is taken with the use of deep learning discrimination mechanisms namely artificial neural networks and convolutional neural networks to quantify the effect that our tuning has on the improvement of the simulation results and their conformation to the real data. The correspondence of the optimal values suggested by deep learning approaches is investigated with pulse height spectrometry. It is shown that the optimal parameters suggested by our deep learning models through inference of their performance metrics are not clear and in agreement with that suggested by naive pulse height inspections. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2024 T1 - Machine learning approaches towards tuning ALICE TRD simulations TI - Machine learning approaches towards tuning ALICE TRD simulations UR - http://hdl.handle.net/11427/40362 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/40362 | |
| dc.identifier.vancouvercitation | Ramraj N. Machine learning approaches towards tuning ALICE TRD simulations. []. ,Faculty of Science ,Department of Statistical Sciences, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40362 | en_ZA |
| dc.language.rfc3066 | Eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | Machine learning approaches towards tuning ALICE TRD simulations | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |