Related papers: Maximizing Returns: Optimizing Experimental Observ…
The LHC effective field theory working group gathers members of the LHC experiments and the theory community to provide a framework for the interpretation of LHC data in the context of EFT. In this note we discuss experimental observables…
Information geometry can be used to understand and optimize Higgs measurements at the LHC. The Fisher information encodes the maximum sensitivity of observables to model parameters for a given experiment. Applied to higher-dimensional…
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies…
In phenomenological preparation for new measurements one searches for the carriers of quality signatures. Often, the first approach quantities may be difficult to measure or to provide sufficiently precise predictions for comparisons.…
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset.…
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.…
A machine-learning-based framework for constructing generator-level observables optimized for parameter extraction in particle physics analyses is introduced, referred to as the Optimal Observable Machine (OOM). Unfoldable differential…
In many real world problems, optimization decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of points…
Precise measurements of SM particles properties at the LHC allows to look for heavy New Physics in the context of an Effective Field Theory (EFT). These searches, however, often rely on kinematic regions where the validity of the EFT may be…
We present a new way to define and compute the maximum significance achievable for signal and background processes at the LHC, using all available phase space information. As an example, we show that a light Higgs boson produced in…
High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger systems are typically constructed as modular chains of…
We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the…
We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy $ZW$ production with fully…
A novel model of the data selection, acquisition and analysis for a multi-purpose and multi-component high-energy-physics experiment is presented. Its departure point is the freedom and the responsibility given to the different physics…
In these proceedings we present the main results of [arXiv:2311.04280 [hep-ph]], where we explore the phenomenological implications of multi-Higgs boson production through longitudinal vector boson scattering within the framework of…
We present a global interpretation of Higgs, diboson, and top quark production and decay measurements from the LHC in the framework of the Standard Model Effective Field Theory (SMEFT) at dimension six. We constrain simultaneously 36…
Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while…
We introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven probabilistically using a Deep Neural Network on…
Many domains of high energy physics analysis are starting to explore machine learning techniques. Powerful methods can be used to identify and measure rare processes from previously insurmountable backgrounds. One of the most profound…
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…