Related papers: A SModelS interface for pyhf likelihoods
Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. We here present version 2.0 of Lilith together with an updated XML database which includes the current ATLAS and CMS Run 2 Higgs results…
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this letter, we introduce a modular physics guided…
Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of…
We report the preliminary results of a meta-analysis conducted to examine possible biases in the uncertainty values published in papers by ATLAS and CMS experiments. We have performed this analysis using two independent techniques; a…
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…
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…
Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of…
We perform a parameter fit in the Standard Model Effective Field Theory (SMEFT) with an emphasis on using regularized linear regression to tackle the issue of the large number of parameters in the SMEFT. In regularized linear regression a…
Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that…
I give in this lecture an overview of the features of the Mathematica package SARAH, and explain how it can be used together with other codes to study all aspects of a BSM model. The focus will be on the description of the analytical…
We investigate new physics that could be discovered with very little LHC data, beyond the expected sensitivity of the Tevatron. We construct "supermodels", for which the LHC sensitivity with 10 pb-1 luminosity is already greater than that…
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…
A new algorithm designed to reduce the model dependence in future SUSY searches at the LHC is described. This algorithm can dynamically adapt itself to a wide range of possible SUSY final states thus reducing the need for detailed…
We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies…
As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn…
Temperature scaling is a popular technique for tuning the sharpness of a model distribution. It is used extensively for sampling likely generations and calibrating model uncertainty, and even features as a controllable parameter to many…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
After the Higgs boson discovery, LHC can be used as a precision machine to explore its properties. Indeed, in case new resonances will not be found, the only access to New Physics would be via measuring small deviations from the SM…
Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling. We provide a…
During the LHC Run-II data-taking period, several searches for supersymmetric particles were performed by the ATLAS collaboration. The results from these searches are concisely reviewed. Model-independent and model-dependent limits on new…