Search for gluinos in final states with jets and missing transverse momentum in $pp$ collisions at $\sqrt{s}$=13 TeV

Standard Model (SM) provides a successful description in the current particle physics and it is consistent with almost all experimental results. However, there are problems such as an existence of dark matter and a hierarchy problem, which cannot be explained in the SM. Hence, the extension of the SM is needed. One of the promising extension is to introduce Supersymmetry (SUSY), which is a symmetry between fermions and bosons. When SUSY is considered, partners of all the SM particles (super-partners) are introduced and these particles have been searched. This thesis describes a search for gluino, which is the super-partner of gluon at the LHC ATLAS experiment. In the $pp$ collision, the cross section of gluino is relatively large due to strong interaction and the gluino search is one of the most important physics programs. The result of a search for gluinos in final states with jets and missing transverse momentum $\sqrt{s}$ =13 TeV with the ATLAS detector is presented. This thesis uses data collected at in 2015-2018 corresponding to the integrated luminosity of 139 fb$^{-1}$. Compared to the past search, a machine learning technique as a new approach is introduced and allows the sensitivity to heavier gluino which has not been looked into before, to be improved well. No significant excess over the background prediction is observed and the gluino mass is excluded up to 2.2 TeV and the lightest neutralino mass is excluded up to about 1.2 TeV at 95% confidence level. This research shows the first result of the gluino search using the machine learning, which is published from ATLAS. This thesis has been proved that the machine learning technique is applicable to SUSY search.