The primary objective of this course is to provide students with hands-on experience with advanced concepts in radiation detection and measurement. In particular, the laboratory portion emphasizes:
- Digital signal processing
- Signal generation & pulse shape analysis
- Multichannel detector systems
- Radiation imaging
Ideally, we'd like for all students to have an opportunity to investigate all of these concepts (and more) with each of the state-of-the-art radiation instruments we have available. Unfortunately, given the complexity of many of the experiments; the limited instrument availability relative to the number of students; and realistic time constraints; it is simply not possible for each student to be able to conduct every proposed experiment to a satisfactory degree. Instead, students will be expected to focus on three predefined laboratory experiments, and propose a fourth to be studied in greater detail for the final project. The first two labs deal with digital signal processing and will be investigated by each lab group. A third lab on multichannel detector systems is planned, but which exact investigation will be done will depend on individual groups and equipment availability. Ultimately, the total number of labs to be done is subject to change, with the goal of allowing students to conduct as many as possible, while maximizing the time students spend on topics they are personally interested in.
List of Labs
Links to the lab writeups will be made available over the course of the semester.
- Digital Signal Processing in HPGe - Spectroscopy
- Digital Signal Processing in HPGe - Pulse Shape and Timing
- Digital Signal Processing in LaBr - Spectroscopy & Timing
- Neutron Detection and Pulse Shape Discrimination in Liquid Scintillation Detectors
- Position Determination in Segmented HPGe
- Charge Transport Properties in Segmented HPGe
- Charge Transport Properties in Pixelated CZT
- Gamma-Ray Imaging with Segmented HPGe
- Neutron Imaging with Liquid Scintillator Array
Reproducibility and Scientific Computing
Another important objective of this course is to give students practical experience conducting scientific research with a reproducible workflow. Reproducibility is a foundational component of scientific investigatiion. Without the ability to effectively communicate technical ideas and test and replicate proposed experiments, the scientific enterprise would not be possible. On a more practical note, reproducibility is also a key component in collaborative efforts. A ubiquitous and utterly demoralizing experience for graduate students in STEM fields is dealing with "legacy" systems and codebases, where "things just used to work". The amount of effort that has been lost/wasted due to corrupted hard drives, impenatrable and unmaintainable codebases, or an experienced post doc leaving for a new job is both staggering and depressing. Fortunately, these hair-loss-inducing impediments to the expansion of human knowledge are avoidable, especially now that so much of the scientific endeavor takes place in the digital domain. The primary antidote is the reproducible workflow. Some key components of a reproducible workflow are:
- Using accessible tools for data analysis, so that other people are able to test your ideas.
- Using tools that are tested to reduce errors and make it easier to integrate modifications and new features.
- A tight coupling between the analysis (e.g. code) and the presentation of the results (e.g. report).
- Version control to keep track of changes to both the analysis and accompanying text/figures for presenting the results.
In this course, students will implement a reproducible workflow for the lab work using the incredibly powerful Python ecosystem for scientific computing, along with Git for version control and LaTeX: the de facto standard for generating technical documents.
N.B. For a much more detailed and compelling description of reproducible
workflows (and scientific computation in general), I refer to the seminal
textbook on the topic:
Effective Computation in Physics by
Kathryn Huff and
Anthony Scopatz.
Lab Report Template
In order to facilitate a "learn-by-doing" approach, a lab report template is available in the course Github organization. Instructions for the basic usage of the template can be found in the README.
Lab Report Submission and Grading
Information about how to structure and submit lab reports can be found in the submission instructions.
A detailed breakdown of how the lab reports will be evaluated can be found in the lab report rubric.