CDM Computing Group

Organisation Team

Lead:

• Matthew Green (UoA)
• Jayden Newstead (UoM)

Local:

• Stephan Meighan-Berger (UoM)
• Ellen Sirks (USyd)
• Lindsey Bignell (ANU)
• Ben McAllister (Swin)
• Maxim Goryachev (UWA)

Mini-workshops

Monte Carlo methods - Jayden Newstead (April 2024)

Recording 1

Recording 2

The workshop notebook is in mini-workshops/Monte_Carlo_methods

Machine Learning: Deep Learning Architectures - Matthew Green (May 2024)

Recording 1

Recording 2

The workshop material is in mini-workshops/ML_architectures

GEANT4 - Lindsey Bignell (June 2024)

Recording 1

Recording 2

The workshop material is in mini-workshops/GEANT4_tut

Developing detector simulations - Stephan Meighen-Berger (July 2024)

Recording 1

Recording 2

The workshop material is in mini-workshops/cdm_detector_example

Kick-off workshop (Feb 2024)

Lecturers:

Lecture	Lecturer	Contact Details
Introduction and DM Freeze out and Relic Density	Jayden Newstead	jayden.newstead@unimelb.edu.au
Git Masterclass	Albert Kong	albert.kong@adelaide.edu.au
"Advanced Python" and Containers	Stephan Meighan-Berger	stephan.meighanberger@unimelb.edu.au
HowToMakeAPlot	Ciaran O'Hare	ciaran.ohare@sydney.edu.au
Bayesian and Frequentist Inference	Jayden Newstead	jayden.newstead@unimelb.edu.au
Introduction to Machine Learning	Matthew Green	matthew.green@adelaide.edu.au

Recordings:

Day 1:

Passcode: q.FC0T@c

Introduction and DM Freeze out and Relic Density

Git Part 1

Git Part 2

Day 2:

Passcode: &dy1+ZkZ

Advanced Python / Containers Part 1

Advanced Python / Containers Part 2

HowToMakeAPlot Part 1

HowToMakeAPlot Part 2

Day 3:

Passcode: d=F!71pR

Bayesian and Frequentist Inference Part 1

Bayesian and Frequentist Inference Part 2

Introduction to Machine Learning Part 1

Introduction to Machine Learning Part 2

Conclusion

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