Add to ORKGpeer reviewedOne 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 solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
Advances in data analysis techniques may play a decisive role in the discovery reach of particle col...
First-principle simulations are at the heart of the high-energy physics research program. They link ...
An important part of the LHC legacy will be precise limits on indirect effects of new physics, frame...
The prevalence of null results in searches for new physics at the LHC motivates the effort to make t...
Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusio...
AbstractThe prevalence of null results in searches for new physics at the LHC motivates the effort t...
We develop, discuss, and compare several inference techniques to constrain theory parameters in coll...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with ...
In high energy particle physics, machine learning has already proven to be an indispensable techniqu...
High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the La...
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model...
Machine learning entails a broad range of techniques that have been widely used in Science and Engin...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
Advances in data analysis techniques may play a decisive role in the discovery reach of particle col...
First-principle simulations are at the heart of the high-energy physics research program. They link ...
An important part of the LHC legacy will be precise limits on indirect effects of new physics, frame...
The prevalence of null results in searches for new physics at the LHC motivates the effort to make t...
Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusio...
AbstractThe prevalence of null results in searches for new physics at the LHC motivates the effort t...
We develop, discuss, and compare several inference techniques to constrain theory parameters in coll...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with ...
In high energy particle physics, machine learning has already proven to be an indispensable techniqu...
High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the La...
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model...
Machine learning entails a broad range of techniques that have been widely used in Science and Engin...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
Advances in data analysis techniques may play a decisive role in the discovery reach of particle col...
First-principle simulations are at the heart of the high-energy physics research program. They link ...