Narrow Results

Using the ATLAS 4.7 fb^-1 data on new physics search in the jets + channel, we obtain new limits on the lighter top squark considering all its decay modes assuming that it is the next to lightest supersymmetric particle (NLSP). If the decay dominates and the production of dark matter relic density is due to NLSP–lightest supersymmetric particle (LSP) co-annihilation then the lower limit on is 240 GeV. The limit changes to 200 GeV if the decay dominates. Combining these results it follows that NLSP induced baryogenesis is now constrained more tightly.

In this paper, a study of four different machine learning (ML) algorithms is performed to determine the most suitable ML technique to disentangle a hypothetical supersymmetry (SUSY) signal from its corresponding Standard Model (SM) backgrounds and to establish their impact on signal significance. The study focuses on the production of SUSY top squark pairs (stops), in the mass range of $500<m_{{\tilde{t}}_1}<800$GeV, from proton–proton collisions with a center of mass energy of 13TeV and an integrated luminosity of $140{\mathrm{\text{fb}}}^{-1}$, emulating the data-taking conditions of the run II Large Hadron Collider (LHC) accelerator. In particular, the semileptonic channel is analyzed, corresponding to final states with a single isolated lepton (electron or muon), missing transverse energy, and four jets, with at least one tagged as $b$-jet. The challenging compressed spectra region is targeted, where the stop decays mainly into a $W$ boson, a $b$-jet, and a neutralino (${\tilde{t}}_1\to W+b+{\tilde{\chi }}_1^0$), with a mass gap between the stop and the neutralino of about 150GeV. The ML algorithms are chosen to cover different mathematical implementations and features in ML. We compare the performance of a logistic regression (LR), a Random Forest (RF), an eXtreme Gradient Boosting, XGboost (XG) and a Neural Network (NN) algorithm. Our results indicate that XG and NN classifiers provide the highest improvements (over 17%) in signal significance, when compared to a standard analysis method based on sequential requirements of different kinematic variables. The improvement in signal significance provided by the NN increases up to 31% for the highest stop mass considered in this study (800GeV). The RF algorithm presents a smaller improvement that decreases with stop mass. On the other hand, the LR algorithm shows the worst performance in signal significance which even does not compete with the results obtained by an optimized cut and count method.