Learning to see R -parity violating scalar top decays

With this article, we introduce recent, improved machine learning methods from computer vision to the problem of event classification in particle physics. Supersymmetric scalar top decays to top quarks and weak-scale bino-like neutralinos, where the neutralinos decay via the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi>U</a:mi><a:mi>D</a:mi><a:mi>D</a:mi></a:math> operator to three quarks, are difficult to search for and therefore weakly constrained. The jet substructure of the boosted decay products can be used to differentiate signal from background events. We apply the transformer-based computer vision models otet and axi to images built from jet constituents and compare the classification performance to a more classical convolutional neural network (CNN). We find that results from computer vision translate well to physics applications, and both transformer-based models perform better than the CNN. By replacing the CNN with axi, we find an improvement of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mi>S</c:mi><c:mo>/</c:mo><c:msqrt><c:mi>B</c:mi></c:msqrt></c:math> by a factor of almost 2 for some neutralino masses. We show that combining this classifier with additional features results in a strong separation of background and signal. We also find that replacing a CNN with a axi model in a simple mock analysis can push the 95% C.L. exclusion limit of stop masses by about 100 and 60 GeV for neutralino masses of 100 and 500 GeV.