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Addressing Large Hadron Collider Challenges by Machine Learning

Approx. 24 hours to complete

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Course Summary

Learn about the Hadron Collider and its role in particle physics, as well as machine learning techniques to analyze the vast amount of data produced by the experiments.

Key Learning Points

Understand the basics of particle physics and the Hadron Collider
Learn machine learning techniques for analyzing large data sets
Apply machine learning to particle physics data

Job Positions & Salaries of people who have taken this course might have

- USA: $110,000
- India: ₹1,000,000
- Spain: €60,000
- USA: $110,000
- India: ₹1,000,000
- Spain: €60,000
- USA: $120,000
- India: ₹1,500,000
- Spain: €70,000
- USA: $110,000
- India: ₹1,000,000
- Spain: €60,000
- USA: $120,000
- India: ₹1,500,000
- Spain: €70,000
- USA: $140,000
- India: ₹2,000,000
- Spain: €80,000

Learning Outcomes

Understand the role of the Hadron Collider in particle physics
Apply machine learning techniques to large data sets
Analyze data from particle physics experiments

Prerequisites or good to have knowledge before taking this course

Basic knowledge of physics
Programming experience in Python

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Notable People in This Field

Dr. Neil deGrasse Tyson
Dr. Michio Kaku

Related Books

Description

The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.

Outline

Introduction into particle physics for data scientists
About the University
Introduction into the course
Experimental particle physics
Testing hypotheses experimentally
Particle physics simulation
About University
Rules on the academic integrity in the course
Lecture slides

Particle identification
Particle identification
Tracking system
Ring Imaging Cherenkov detector
Calorimeters
Muon system
Machine learning in particle identification
Uniform classifiers
Lecture slides
Particle identification quiz

Search for New Physics in Rare Decays
Physics as a Game of Theories
Lepton Flavour Violation
Classifier Constraints
Data vs Simulation Agreement
Lecture slides

Search for Dark Matter Hints with Machine Learning at new CERN experiment
Dark Matter
Search for Dark Matter at Accelerator Experiment
Getting Data Before Experiment is built
Going Deeper
Looking Ahead
Lecture slides

Detector optimization
Detector optimization
Normal distribution
Gaussian processes for regression
Bayesian optimization. Part 1
Bayesian optimization. Part 2
Optimization examples
Lecture slides
Detector optimization quiz

Summary of User Reviews

Discover the fascinating world of Hadron Collider and Machine Learning with this course. Students have praised the engaging content and knowledgeable instructors, while some have found the course to be challenging for beginners.