XXI Mexican School on Particles and Fields 2026

Programme

Tentative Academic Program

The program combines theoretical foundations, computational tools, data analysis, and hands-on instrumentation to provide participants with an integrated introduction to contemporary high-energy physics.

1. Foundations of the Standard Model of Particle Physics [TBA]

• Fundamental particles and interactions

• Gauge symmetries and conservation laws

• Electroweak and strong interactions

• Experimental tests of the Standard Model

• Open questions and physics beyond the Standard Model

2. The Higgs Sector: Unraveling the Fundamental Interactions of the Standard Model, by Prof. Luis Flores Castillo [The Chinese University of Hong Kong]

• The Higgs mechanism and electroweak symmetry breaking

• Higgs boson production and decay channels

• Experimental discovery and measurements at the LHC

• Higgs couplings to fermions and gauge bosons

• Current results and prospects for future studies

3. Introduction to Gravitation and Modern Physics, by Prof. Iraís Rubalcava [BUAP]

• Foundations of special and general relativity

• Gravity as the geometry of spacetime

• Black holes and gravitational waves

• Connections between gravitation, cosmology, and particle physics

• Open questions concerning gravity and quantum mechanics

4. Hands-on Instrumentation for High-Energy Physics, by Prof. Luis Villaseñor, Prof. Epifanio Ponce, Prof. Guillermo Tejeda, M.I. Emigdio Jiménez [BUAP] and Prof. Mateo Ramírez [IBERO]

• Principles of particle detection

• Introduction to detector components and readout systems

• Assembly and testing of particle detectors

• Construction and characterization of scintillator detectors

• Signal acquisition using a coincidence module

• Detector calibration and performance evaluation

• Analysis of signals and data from currently operating experiments

5. ROOT for High-Energy Physics Data Analysis: From Fundamentals to Advanced Tools, Dr. Stephan Hageböck [CERN]

• Introduction to ROOT

• Histograms, graphs, and data visualization

• Data input and output using TFile and TTree

• Introduction to RDataFrame

• Collections and advanced RDataFrame features

• Interoperability between C++ and Python

• Using ROOT in Jupyter notebooks

• Emerging ROOT tools: RFile, RNTuple, new histogram interfaces, and memory management

• Development of reproducible analysis workflows

6. Practical Machine Learning for High-Energy Physics Data Analysis, Prof. Alfredo Castañeda [Universidad de Sonora]

• Introduction to machine learning in high-energy physics

• Preparation and exploration of LHC Open Data

• Feature selection and data preprocessing

• Supervised learning for classification and regression

• Model training, validation, and performance evaluation

• Interpretation of machine-learning results

• Practical analysis of data from LHC experiments

• Implementation of reproducible workflows in Python and Jupyter

Integrative Activities

Throughout the program, participants will work on practical exercises connecting theory, instrumentation, and data analysis. The course will culminate in a small project in which participants will acquire or examine experimental data, process it using ROOT and Python, and apply statistical or machine-learning techniques to interpret the results.