The final projects are intended to allow you to use concepts learned in the course to investigate scientific data. We have a number of template projects, or you can design your own project.
Some expectations:
* We expect you to work in small groups of 2-3 people.
* The project should incorporate data analysis techniques that we have learned in the course.
* Each person should complete a written report describing the project. Be sure to use your own words.
Here is an example of a report. The report should be 2-3 pages and include any plots and numbers that you produced in the project. Please be sure to explain each plot and the purpose/context behind each step. Please describe the data you used as well. If you don’t have a group, please contact on of the teaching staff as soon as possible.
You are definitely encouraged to design your own project, but please speak to us (the teaching staff) about it at the beginning of class. It should be of similar length to the template projects to receive full credit. We estimate that the project should take 5-6 hours in total, including time to write the report.
For each project, we have provided you with most of the necessary code in some form, which you will need to adapt. Some projects require more programming knowledge than others (explanations below). Please don’t hesitate to ask us for assistance with the code or the concepts!
Here is a description of the four template projects available:
In the first couple weeks of the class we saw that the measurements of the Hubble parameter were not distributed as a Gaussian. This project involves investigating the difference between two techniques used to measure the Hubble parameter, and evaluating if these two techniques are giving consistent results.
This project requires a limited amount of programming, and it will ask you to apply concepts from this course to quantify the significance of how consistent measurements are.
In this project you will go back to the Vela pulsar data, and you will study the process of fitting the model parameters in more mathematical detail. This will help you to understand the mathematical relationships between some of the statistics that we studied in this course.
This project requires you to program a few of your own functions and to take partial derivatives. It will explore the fitting concepts by asking you to analytically solve for the best fit value of a fitted model parameter.
This project is similar to the dark matter search, but instead you will be looking evidence of a new particle in collisions at the Large Hadron Collider.
This project includes understanding a lot of code, but most of it is written for you, so it requires very little programming from scratch. It will explore concepts of statistical significance and how to design a sensitive search for a new particle.
This project will incorporate Fourier techniques to look for exoplanets. You will be asked to explore how noise and limited statistics can affect the strength of a periodic signal.
This project requires a limited amount of programming. It will explore concepts of statistical significance and Fourier transforms.
This project will make use of Fourier techniques to calibrate the response of an optically levitated microsphere to external forces. This is accomplished by configuring the microsphere to have single excess electron and thus a net charge of $q = -e$, and then driving it with a known electric field and observing the response.
This project is reasonably straightforward from a conceptual standpoint, but will require you to think about the Fourier Transform and write some of your own functions and array manipulations
| Grade | Criteria |
|---|---|
| 0 | Project was not turned in or is largely incomplete |
| 1 | Project does not demonstrate thorough application of data analysis techniques learned in the course. Unclear or excessively brief explanations of figures, numbers, and/or statistical conclusions. Explanations may demonstrate a lack of understanding of the data analysis concepts. |
| 2 | Project has clear objectives that incorporate data analysis techniques learned in the course. Report has detailed explanations of figures, numbers, and/or statistical conclusions. Student demonstrates that they understand most (>70%) of concepts covered in the project |
| 3 | Project has clear objectives that incorporate data analysis techniques learned in the course. Report has detailed explanations of figures, numbers, and/or statistical conclusions. Student demonstrates clear understanding of concepts covered in the project |