Related papers: Effective LHC measurements with matrix elements an…
We present an extension of the simplified fast detector simulator of MadAnalysis 5 - the SFS framework - with methods making it suitable for the treatment of long-lived particles of any kind. This allows users to make use of intuitive…
Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories…
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection…
The Large Hadron Collider, LHC, though meant for discovery, will provide enough data from early phase to also perform various studies of Standard Model processes in as yet unexplored kinematic regions. Precision measurements of the…
Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and…
High-energy physics is facing increasingly computational challenges in real-time event reconstruction for the near-future high-luminosity era. Using the LHCb vertex detector as a use-case, we explore a new algorithm for particle track…
We propose a new strategy to look for long-lived particles (LLP) at the LHC. The LLPs are produced at one experiment, but its decay products are detected by a detector at another experiment. We use a confining Hidden Valley scenario as a…
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based…
In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. To…
Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding…
Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect. Adjusting sensor bias voltage and…
In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. This is a critical challenge due to the scarcity of…
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the…
With the LHC close to complete its 8 TeV run, the experimental searches have already started to probe the vast beyond-the-standard Model scenery. Providing next-to-leading order (NLO) predictions for the major new physics discovery channels…
Trustworthy classifiers are essential to the adoption of machine learning predictions in many real-world settings. The predicted probability of possible outcomes can inform high-stakes decision making, particularly when assessing the…
This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the…
Modern machine learning embeddings provide powerful compression of high-dimensional data, yet they typically destroy the geometric structure required for classical likelihood-based statistical inference. This paper develops a rigorous…
We investigate the need and prospects for measuring dark matter properties at particle collider experiments. We discuss the connections between the inferred properties of particle dark matter and the physics that is expected to be uncovered…
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC).…
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions…