  A study of four different machine learning (ML) algorithms is performed to
determine the most suitable ML technique to disentangle a hypothetical
supersymmetry 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 13 TeV and an integrated luminosity of 150 fb$^{-1}$, emulating
the data-taking conditions of the run II 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 ($\tilde{t}_1\rightarrow W+b+\tilde{\chi}_1^0$), with a mass gap
between the stop and the neutralino of about 150 GeV. The ML algorithms are
chosen to cover different mathematical implementations and features in machine
learning. We compare the performance of a logistic regression (LR), a Random
Forest (RF), an XGBoost (XG), and a Neural Network (NN) algorithm. Our results
indicate that all four algorithms provide an improvement in signal significance
calculation when compared to the ones obtained with a standard analysis method
based on sequential requirements of different kinematic variables. The highest
gain in significance is obtained with the NN and XG classifiers with an average
improvement over 20\%, both having compatible statistical performance for the
stop mass range considered, followed by RF(15\%). The LR has the poorest
performance of all ML algorithms studied, but still presents an average
improvement of about 4\%.
