Related papers: Advances in Multi-Variate Analysis Methods for New…
During the coming decade, high energy physics experiments at the Fermilab Tevatron and around the globe will use very sophisticated equipment to record unprecedented amounts of data in the hope of making major discoveries that may unravel…
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is…
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…
The latest results of high mass searches for new physics in a variety of final states from the CMS and ATLAS collaborations are presented. These searches are based on $\sqrt{s}=13$ TeV proton-proton collisions data at the LHC collected in…
Events containing a pair of high energy hadronic jet can provide clear signatures in the search for new physics at high energy hadron colliders. The ATLAS and CMS experiments collected the data from LHC collisions at $\sqrt{s}$= 13 TeV…
Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify what the goal of a multivariate algorithm should be for the search…
These lectures were presented at the 2019 CERN Latin-American School of High Energy Physics. They were centered on the experimental methods used in hadron colliders to advance our understanding in the field of high energy particle physics.…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
The ATLAS experiment at the Large Hadron Collider at CERN has been successfully taking data since the end of 2009 in proton-proton collisions at centre-of-mass energies of 7 and 8 TeV, and in heavy ion collisions. In these lectures, some of…
The study of particle correlations is an important instrument to understand the nature of relativistic heavy ion collisions. Using a wealth of new data available from the recent heavy ion runs of Large Hadron Collider at CERN it becomes…
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is…
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of…
The potential for top quark physics of the ATLAS and CMS experiments at the Large Hadron Collider is surveyed ranging from top quark "re-discovery" and its use as a calibration tool to initial and later stage measurements.
The ATLAS and CMS experiments have collected data at the CERN Large Hadron Collider (LHC) since December 2009, and with a collision energy \sprts=7 TeV since March 2010. Both detectors work remarkably well at this early stage of operation,…
The upcoming upgrade of the existing LHC facility at CERN is known as the High-Luminosity Large Hadron Collider (HL-LHC). It is designed to extend its physics reach by substantially increasing the integrated luminosity. It will enable more…
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for…
The successful operation of the {\em Large Hadron Collider} (LHC) during the past two years allowed to explore particle interaction in a new energy regime. Measurements of important Standard Model processes like the production of high-\pt\…
This report presents a comprehensive collection of searches for new physics performed by the ATLAS Collaboration during the Run 2 period of data taking at the Large Hadron Collider, from 2015 to 2018, corresponding to about 140 fb$^{-1}$ of…
An event reweighting technique incorporated in multivariate training algorithm has been developed and tested using the Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT). The event reweighting training are compared to that of…
Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning…