Métodos de Análise e Simulação em Física de Altas Energias

Objectives

To learn the basic analysis techniques used in High Energy Physics (HEP). To get experienced in the use of simulation and analysis software packages and tools currently used in HEP.

Syllabus

Introduction and review of previous knowledge (cross section, luminosity, Monte Carlo simulation, particles detection and identification). Simulation in physics. Monte Carlo generators. Detector response simulation. Signal and background processes. Discriminating variables and experimental signatures. Methods for optimisation of the signal/background separation. Machine Learning methods. Data-driven in-situ calibration/correction methods. Determination and application of Monte Carlo corrections (weights, background normalization). Deconvolution or unfolding. Basic methods (bin by bin, inverse matrix method). Use of Machine Learning for deconvolution. Fits: the likelihood method. Nuisance parameters and constraints. Analysis strategy: definition of observables, blind analysis.

Prerequisites

To accomplish the objectives of the Curricular Unit the students should have basic knlowledge of programming in C, C++ or python as well as knowledge of basic particle Physics

Cross Competence Component

Strategies of problem resolution, critical thinking, proactive learning and initiative, writen/oral communication, use of computing tools from the point of view of the user, structuring and formatting of documents and presentations.

Laboratorial Component

The CU includes a large lab component, designed to implement and practice the techniques discussed in the theoretical/practical lectures. The lab courses will use public data from HEP experiments as well as simulated data samples.

Programming And Computing Component

The lab component will mainly consist of programming analysis techniques, using state of the art tools and simulation packages in HEP (such as ROOT, MadGraph or DELPHES) from the point of view of the user. It will also require development and implementation of analysis programs or algorithms to test the techniques under study. The programming languages will mainly be C++ or python.

Ethical Principles

All members of a group are responsible for the group’s work In any assessment, every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.