Dark Matter particles could potentially be detected at the Large Hadron Collider (LHC) using the monojet channel, where at least one high pT jet recoils against missing transverse momentum. However, these searches pose a challenge as they require distinguishing subtle differences among similar jets. One way to improve this is by using Machine Learning (ML) methods to analyze correlations...
The Data-Directed paradigm (DDP) is a new physics search strategy for efficiently detecting anomalies in a large number of spectra with smoothly-falling SM backgrounds. Unlike the traditional analysis strategy, DDP avoids the need for a simulated or functional-form based background estimate by directly predicting the statistical significance using a convolutional neural network trained to...
Training a neural network is challenging when the training dataset is contaminated by labelling errors, which are commonly referred to as label noise. This challenge often coexists with the challenge of predicting confidence, allowing one to flag low-confidence predictions for the main task. Existing techniques tackle one of the two challenges but not both, neglecting their interdependency. We...
Traffic safety systems especially in critical infrastructure such as road tunnels have garnered the interest of researchers for many years. While traffic managers have used traffic surveillance cameras for some time now, recent advances in computer vision and understanding have enabled more sophisticated automatic incident detection systems. Most state of the art systems consist of modern...
Anomaly detection refers to the identification of rare events that differ significantly from the normal trend observed in the data distribution. When the number of variables to analyze is large, it can be difficult to understand the detected anomaly without explanation. In this work, we present the prototype of an explainable and real-time anomaly detection system, based on measurements from a...
The unsupervised classification of images is a conceptually simple problem, yet it remains a significant challenge in the field of Machine Learning. In fact, despite the development of models that have demonstrated excellent performance on benchmark datasets in recent years, reproducing equally satisfactory results in real-world cases is often very difficult. This study presents a real-world...
