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The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…

Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In particle ABC, an ensemble of particles in the product space of…

We present version 2.3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental…

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…

Till today, although the Standard Model (SM) is the most celebrated theory that explains nature almost completely, there are still some phenomena observed in nature that the SM cannot explain. That is why it is needed to look for theories…

Supersymmetry, which extends the Standard Model (SM) by introducing supersymmetric partners for the SM particles, can provide an elegant solution to the hierarchy problem. One of the most important parameters in supersymmetry is the mass of…

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not…

Our article deals with Bayesian inference for a general state space model with the simulated likelihood computed by the particle filter. We show empirically that the partially or fully adapted particle filters can be much more efficient…

Searches are presented for physics beyond the Standard Model involving top-quark and related signatures. The results are based on proton-proton collision data corresponding to integrated luminosities between 1 fb-1 and 5 fb-1 collected at a…

We present a general procedure to decompose Beyond the Standard Model (BSM) collider signatures presenting a Z2 symmetry into Simplified Model Spectrum (SMS) topologies. Our method provides a way to cast BSM predictions for the LHC in a…

Selected results from searches for new physics with unconventional signatures using the ATLAS and CMS detectors are presented. Such signatures include emerging jets, heavy charged particles, displaced or delayed objects, and disappearing…

We present version 2 of SModelS, a program package for the fast reinterpretation of LHC searches for new physics on the basis of simplified model results. The major novelty of the SModelS v2 series is an extended topology description with a…

Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional…

In 2017, ATLAS has been equipped with a new, dedicated detector system allowing measurements of forward protons scattered at small angles in diffractive and electromagnetic processes. These ATLAS Forward Proton detectors (AFP) can operate…

The exceptionally accurate Standard Model (SM) theory of fundamental interactions is known to be incomplete. Many new theories extend the SM, trying to solve some of the most compelling puzzles of nature. Since the start of LHC experiments,…

The objective of this article is to study the asymptotic behavior of a new particle filtering approach in the context of hidden Markov models (HMMs). In particular, we develop an algorithm where the latent-state sequence is segmented into…

Probabilistic models often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model. These symmetries can lead to strong correlation and multimodality in the posterior distribution over…

The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce \textsc{Albert}, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory…

This paper explores innovations to parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates.…

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…