FMF-seminars

Collection of my seminars presented at FMF during my studies.

2020 - Using machine learning methods for data analysis at the LHC

Strojno učenje z razvojem strojne opreme in same optimalnosti algoritmov postaja vse bolj pomembno orodje v eksperimentalni fiziki delcev, v kateri ne manjka podatkov, na katerih se lahko algoritmi učijo. V seminarju naprej opišem delovanje LHC-ja, ter nato še fiziko, ki jo raziskuje. Sledi uvod v strojno učenje, ter predstavitev globokega učenja preko globokih usmerjenih nevronskih mrež (DNN). Na koncu predstavim še konkreten zgled analize na detektorju ATLAS, ki obravnava nastanek parov Higgsovih bozonov v dileptonskem razpadnem kanalu $WWbb$ s pomočjo nevronskih mrež.

2021 - New physics searches using unsupervised learning in high energy physics

The seminar presents unsupervised machine learning, specifically its use for anomaly detection in high energy physics. First, a description of machine learning and anomaly detection is given. A method of classification without labels is presented in the context of new physics searches. Deep neural networks and their use for binary classification are then discussed. What follows is an illustrative example that is meant to showcase the full method, which combines classification without labels and neural networks with bump hunt and is used for distinguishing between a signal region and sideband regions. At the end, a dijet resonance search using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector is presented.

2022 - Use of normalizing flows in particle physics simulations

Normalizing flows are a family of machine learning methods for constructing learnable probability distributions using neural networks. The seminar presents normalizing flows in the context of fast event generation in High Energy Physics. First, the problem of large scale Monte Carlo simulations at the LHC is presented. After that, the basics of flow models and their mathematical design is discussed. What follows is a presentation of a particular flow design, called realNVP. At the end, a study where event generation is tested on a theoretical Higgs boson production simulated dataset is presented.