Related papers: Automatizing the search for mass resonances using …
The $B$-factories and Large Hadron Collider experiments have demonstrated the ability to observe and measure the properties of bottomonium mesons. In order to discover missing states it is useful to know their properties to develop a…
This paper discusses a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large Hadron Collider (LHC). The method requires calculations of $Z$-scores for a large number of…
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…
Resonances are ubiquitous phenomena in nature, and physicists have developed many methods to explore resonant states. Of particular note is the complex momentum representation(CMR) method, which has been developed and widely used in the…
At the Large Hadron Collider, numerous physics processes expected within the standard model and theories beyond it give rise to very high momentum particles decaying to multihadronic final states. Development of algorithms for efficient…
The existence of Beyond Standard Model (BSM) physics is firmly suggested by both experimental observations (Dark Matter, neutrino masses) and theoretical arguments. In the hypothesis that the scale of new physics is considerably higher than…
Two related searches for phenomena beyond the standard model (BSM) are performed using events with hadronic jets and significant transverse momentum imbalance. The results are based on a sample of proton-proton collisions at a…
We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures trained to reconstruct background events via masked-token…
Ultralight bosons are a hypothetical class of particles predicted under various extensions of Standard Model physics. As a result of the superradiance mechanism, we expect ultralight bosons, should they exist in certain mass ranges, to form…
In scenarios beyond the Standard Model (BSM) characterized by charged ($W^\prime$) or neutral ($Z^\prime$) massive gauge bosons with large width, resonant mass searches are not very effective, so that one has to exploit the tails of the…
Experimental tests of the Standard Model of particle physics (SM) find excellent agreement with its predictions. Since the original formation of the SM, experiments have provided little guidance regarding the explanations of phenomena…
Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these…
In high Energy Physics, it is common to look for a localized deviation in data with respect to a given reference. For this task, the well known BumpHunter algorithm allows for a model-independent deviation search with the advantage of…
Experiments will soon start taking data at CERN's Large Hadron Collider (LHC) with high expectations for discovery of new physics phenomena. Indeed, the LHC's unprecedented center-of-mass energy will allow the experiments to probe an energy…
Recent results on searches for physics beyond the Standard Model at Large Hadron Collider are presented, based on early LHC data in proton-proton collisions at $\sqrt{s} = 7$ TeV collected by the CMS experiment. Prospects of early SUSY…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant progress in automatic…
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used…
A model-independent global search for new physics has been performed at the CDF experiment. This search examines nearly 400 final states, looking for discrepancies between the observed data and the standard model expectation in populations,…
High-dimensional feature spaces in particle physics events pose a fundamental challenge to density-estimation-based weakly supervised anomaly detection, whose fidelity degrades rapidly with an increasing number of dimensions. We propose a…