Related papers: The Application of Bayesian Technique for Particle…
These three lectures provide an introduction to the main concepts of statistical data analysis useful for precision measurements and searches for new signals in High Energy Physics. The frequentist and Bayesian approaches to probability…
A widely used method to create a continuous representation of a discrete data-set is regression analysis. When the regression model is not based on a mathematical description of the physics underlying the data, heuristic techniques play a…
Identification of particles generated by ion collisions in the NICA collider is one of the basic functions of the Multipurpose Detector (MPD). The main means of identification in MPD are the time-of-flight system (TOF) and the…
The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several…
This work introduces the software tool Comprehensive Particle Identification (CPID). It is a modular approach to combined PID for future Higgs factories and implemented in the Key4hep framework. Its structure is explained, the current…
The working principle and performance of the BELLE particle identification device (PID), based on a hybrid system consisting of an array of high precision scintillator Time of Flight and silica Aerogel Counters, is discussed. The…
As part of the investigation done by the IEEE Standards Association P2957 Working Group, called Big Data Governance and Metadata Management, the use of persistent identifiers (PIDs) is looked at for tackling the problem of reproducible…
The numerical solution of differential equations can be formulated as an inference problem to which formal statistical approaches can be applied. However, nonlinear partial differential equations (PDEs) pose substantial challenges from an…
We apply the Bayesian model selection method (based on the Bayes factor) to optimize $\sqrt{s_\mathrm{NN}}$-dependence in the phenomenological parameters of the (3+1)-dimensional hybrid framework for describing relativistic heavy-ion…
We report on the charged particle identification (PID) systems for the upcoming Belle II experiment. The time of propagation counter in the central region and the proximity focusing ring imaging Cherenkov counters with aerogel radiator in…
A problem of a new physical model test given observed experimental data is a typical one for modern experiments of high energy physics (HEP). A solution of the problem may be provided with two alternative statistical formalisms, namely…
The Bayesian Block algorithm, originally developed for applications in astronomy, can be used to improve the binning of histograms in high energy physics. The visual improvement can be dramatic, as shown here with two simple examples. More…
This primer is a brief introduction to the technologies used in particle detectors designed for high-energy particle physics experiments. The intended readers are students, especially undergraduates, starting laboratory work.
Many statistical issues arise in the analysis of Particle Physics experiments. We give a brief introduction to Particle Physics, before describing the techniques used by Particle Physicists for dealing with statistical problems, and also…
There are many interesting physical processes which involve the generation of high density plasmas in large volumes. However, when modeling these systems numerically, the large densities and volumes present a significant computational…
In this paper a new method of experimental data analysis, the Particle-Set Identification method, is presented. The method allows to reconstruct moments of multiplicity distribution of identified particles. The difficulty the method copes…
Particle IDentification (PID) is a central requirement of the experiments at the future EIC. Hadron PID at high momenta by RICH techniques requires the use of low density gaseous radiators, where the challenge is the limited length of the…
We present a Bayesian methodology for infinite as well as finite dimensional parameter identification for partial differential equation models. The Bayesian framework provides a rigorous mathematical framework for incorporating prior…
Source separation problems are ubiquitous in the physical sciences; any situation where signals are superimposed calls for source separation to estimate the original signals. In this tutorial I will discuss the Bayesian approach to the…
This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as:…