Education

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School 2021

Exploration Series

  • Overview: pdf

  • Meeting time: 9 AM EDT / 8 AM CDT / 3 PM CEST / 9 PM Beijing Time

June 3

  • Coherent Elastic Neutrino-Nucleus Scattering
  • Points to Ponder: pdf
  • Papers:
    • Observation of coherent elastic neutrino-nucleus scattering, COHERENT Collaboration, Science, 357, 1123 (2017). https://science.sciencemag.org/content/357/6356/1123 NOTE: Optional supplemental document: https://arxiv.org/pdf/1708.01294.pdf
    • Coherent effects of a weak neutral current, Daniel Z. Freedman, Physical Review D, 9, 1389 (1974).
  • Facilitators: Phil Barbeau, Long Li and Connor Awe
  • Lead organizer: Mary Kidd
  • Link to recorded session: mp4

June 17

  • Searching for Dark Matter with a Superconducting Qubit
  • Background Material:
    • Watch the following 6 min video: https://www.youtube.com/watch?v=cb_f9KpYipk
    • An Introduction to Superconducting Qubits and Circuit Quantum Electrodynamics, Nicholas Materise, https://arxiv.org/abs/1708.07000v1
  • Core Papers:
    • Physics Article: https://physics.aps.org/articles/v14/s45
    • Searching for Dark Matter with a Superconducting Qubit, Phys.Rev.Lett. 126 (2021). https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.141302
    • Physics Magazine Synopsis: www
    • Background: TBD
  • Points to Ponder: https://drive.google.com/file/d/1Wqwmt65gi_M02YMGJqe5dvFnXgcysmYg/view?usp=sharing
  • Lead Facilitators: Akash Dixit and Ankur Agrawal
  • Lead organizer: Reyco Henning
  • Link to recorded session: mp4

July 1

  • Optimization of Amorphous Germanium Electrical Contacts and Surface Coatings on High Purity Germanium Radiation Detectors
  • Core paper: https://arxiv.org/abs/1809.03046
  • Points to Ponder: https://docs.google.com/document/d/1f2USCIppYoUCHSjpKMCw0v2S7RzIBHwqbZKVo1umbMg
  • Lead Facilitators: Rusty Harris, Kyler Kooi, and Rajendra Panth
  • Lead organizer: Joel Sander
  • Link to recorded session: mp4

July 15

  • Paper: Neutrino observatory based on archaeological lead, Physical Review D, 102, 063001 (2020).
  • Points to Ponder: https://drive.google.com/file/d/1Nyj0sRLGKlXjXpRvqk-P0JU5bZTLeqsN/view?usp=sharing
  • Lead Facilitators: Kate Scholberg, Diane Markoff, Baran Bodur and Adryanna Major
  • Lead organizer: Mary Kidd
  • Link to recorded session: mp4

August 5

  • Paper and other materials:
    • YouTube
    • pdf (pages 1-10)
    • pdf (Supplemental Information sections optional, but you should skim and try to understand what content is being presented in the plots)
  • Points to Ponder: pdf
  • Lead Facilitators: Amy Nicholson
  • Lead Organizer: Reyco Henning
  • Link to recorded session: mp4

Last updated September 28, 2021

School 2021

Machine learning

Abstract

Nowadays, the Big Data revolution has enabled machine learning to play a key role in many different aspects of our life. From the recommendation algorithm on YouTube to the reinforcement learning based AlphaGo, the power and versatility of machine learning has never failed to impress us. The particle physics community has also been utilizing machine learning algorithms to boost the possibility of scientific breakthrough. Neutrino physics experiment is naturally compatible with ML algorithms because of the large dataset size and well-established MC simulation tools. The purpose of this course is to expose frontier machine learning research results to students with a background in particle physics. We will start from some of the most basic concepts and models in ML, and gradually evolve to some of the research frontiers, including computer vision, time series processing, and advanced learning algorithms. Although most ML algorithms have a solid mathematical foundation, this course will aim at the application part and avoid unnecessary mathematical derivation. Thus it gets its name - practical machine learning.

Useful resources

  • PyTorch Official Websites
  • NERSC JupyterHub
  • NERSC Help Documentation
  • Course Homewrok GitHub Repository: https://github.com/aobol/PMLHomework
    • The first two homeworks have been uploaded. The other two homeworks will be uploaded as the course progress.
    • After getting access to NERSC JupyterHub, clone this GitHub Repo to your local directory.
    • Double click to open the .ipynb file

    • JupyterHub will ask you to choose a kernel environment for this notebook file. If not, on the upper right corner, you will find a kernel environment button: Screen Shot 2021-07-19 at 10 50 18 AM, single click it to choose kernel

    • Select pytorch-1.7.1-cpu or pytorch-1.7.1-gpu, whichever is available

Modules

Module 1: The Basics of Machine Learning

Module 2: Convolutional Neural Network

Module 3: Time Series I

Module 4: Time Series II

School 2021

Geant4 Case Studies

An ten-week online seminar on Geant4 that covers basic ideas and common usages of Geant4 through concrete examples and case studies, which help demonstrate the whole simulation process, from modeling the detector geometry to statistical analysis of the Geant4 output data. The learning of basic ideas and usage of Geant4 will be self-paced, following a series of tutorial videos on YouTube. Participants who have a simulation task in hand can describe their projects and the instructor will pick 4 to 6 of them as examples to demonstrate the whole process in weekly bases.

Course Information

Cases

How to choose an OS for Geant4 simulation

YouTube

Text Reference:

Video Reference:

How to find and use existing Geant4 installations

YouTube

How to run Geant4 examples

YouTube

How to write your very own Geant4 main function

YouTube

  • Contact Details

    Email: physics@usd.edu

    Postal Address:

    Department of Physics

    Akeley-Lawrence Science Center

    414 E. Clark St.

    Vermillion, SD 57069

    Phone: 605-677-5649

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