Related papers: Detecting New Physics as Novelty -- Complementarit…
Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140 fb$^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV recorded by ATLAS at the Large…
Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpreted as generalization across various distributions of…
We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that it learns to map "normal" events back to themselves, but…
Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning…
The study of particle correlations is an important instrument to understand the nature of relativistic heavy ion collisions. Using a wealth of new data available from the recent heavy ion runs of Large Hadron Collider at CERN it becomes…
Dynamical theories of dark energy predict new degrees of freedom with particular environmental sensitivity to avoid constraints on fifth forces. We show that the similar, yet complementary multi-purpose detector setup of the ATLAS and CMS…
The new CERN proton-proton collider, the LHC, is about to start in 2007 its data taking. Millions of top quarks will be available out of these data, allowing to perform a wide range of precision measurements and searches for new physics. An…
To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general, search analyses are not statistically orthogonal, so…
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of…
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instances of an unlabeled test set while allowing for the presence…
We briefly review the current status and future prospects for supersymmetry searches at colliders, and discuss strategies by which further information about sparticle properties may be obtained at the LHC.
Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where…
Many autonomous systems, such as driverless taxis, perform safety critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for the environment perception. Engineers cannot completely test or…
Numerous non-standard dynamics are described by contact-like effective interactions that can manifest themselves through deviations of the cross sections from the Standard Model predictions. If one such deviation were observed, one should…
Model-independent searches in particle physics aim at completing our knowledge of the universe by looking for new possible particles not predicted by the current theories. Such particles, referred to as signal, are expected to behave as a…
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the…
In this paper, we estimate the number of event topologies that have the potential to be produced in $pp$ collisions at the Large Hadron Collider (LHC) without violating kinematic and other constraints. We use numerical calculations and…
We study signals for beyond standard model physics and consider the virtues of single photon signals or associated photons in the final states in identifying different scenarios of new physics models in a very efficient and novel way.
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of…
Measurements at particle collider experiments, even if primarily aimed at understanding Standard Model processes, can have a high degree of model independence, and implicitly contain information about potential contributions from physics…