Related papers: Detecting New Physics as Novelty -- Complementarit…
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function…
A search for new physics is performed based on events with jets and a pair of isolated, same-sign leptons. The results are obtained using a sample of proton-proton collision data collected by the CMS experiment at a centre-of-mass energy of…
Final states including leptons are most promising to detect early signs of new physics processes when the Large Hadron Collider will start proton-proton collisions at the centre of mass energy of 14\TeV. The reach for Supersymmetry and…
If supersymmetry is discovered at future colliders, what can we learn? While our appreciation of the variety of possible supersymmetric models has grown tremendously in recent years, most attempts to answer this question have been in the…
We address the problem of novelty detection in multiclass scenarios where some class labels are missing from the training set. Our method is based on the initial assignment of confidence values, which measure the affinity between a new test…
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper, we aim to search for new signals that appear as deviations from known Standard Model physics in…
A search for new physics is performed based on events with jets and a pair of isolated, same-sign leptons. The results are obtained using a sample of proton-proton collision data collected by the CMS experiment at a centre-of-mass energy of…
We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that…
In this new era of large data, it is important to make sure we do not miss any signs of new physics. Using the publicly-available open data collected by the arXiv.org experiment in the \texttt{hep-ph} channel, corresponding to a raw total…
The nucleus-nucleus impact parameter and collision geometry of a heavy ion collision are typically characterized by assigning a collision "centrality". In all present heavy ion experiments centrality is measured indirectly, by detecting the…
We propose a search strategy at the HL-LHC for a new neutral particle $X$ that couples to $W$-bosons, using the process $p p \rightarrow W^{\pm} X (\rightarrow W^{+} W^{-})$ with a tri-$W$-boson final state. Focusing on events with two…
Many theories beyond the Standard Model predict new phenomena which decay to well isolated, high-$p_{\text{T}}$ leptons. Searches for new physics with these signatures are performed using the ATLAS experiment at the LHC. The results…
In this paper, we point out a novel signature of physics beyond the Standard Model which could potentially be observed both at the Large Hadron Collider (LHC) and at future colliders. This signature, which emerges naturally within many…
We present a collection of signatures for physics beyond the standard model that need to be explored at the LHC. First, are presented various tools developed to measure new particle masses in scenarios where all decays include an…
Search for new physics events at the LHC mostly rely on the assumption that the events are characterized in terms of standard-reconstructed objects such as isolated photons, leptons, and jets initiated by QCD-partons. While such strategy…
Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the…
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge…
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robust statements about any observed outliers. While there is a…
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect…
We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model…