Amgen Scholars: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty for the Amgen Scholars program.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. For additional tips on identifying a mentor click here.
Please remember:
- Students pursuing Amgen must be U.S. citizens, U.S. permanent residents, or students with DACA status.
- Students pursuing Amgen must complete the 10-week program from June 18 - August 23, 2024. Students must commit to these dates. No exceptions will be made.
- Accepted students must live in provided Caltech housing.
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| Project: | Combining diverse datasets for improved classification | ||||||||
| Disciplines: | Data Science, Astronomy | ||||||||
| Mentor: |
Ashish Mahabal,
Deputy Director, Center for Data Driven Discovery, (PMA),
aam@astro.caltech.edu, |
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| Mentor URL: | http://www.astro.caltech.edu/~aam (opens in new window) | ||||||||
| Background: |
The Zwicky Transient Facility (ZTF) is one of the largest ongoing sky surveys in the Northern Hemisphere designed, among other things, to catch cosmic transients (objects in the night sky that vary in brightness on different time scales). Using a state-of-the art CCD camera mounted on a 48-inch telescope at the Palomar Observatory, ZTF scans the entire Northern sky every two nights. To do so, the ZTF camera is wide-field with each image covering 47 sq deg (equivalent to about 200 full moons). Each night ZTF discovers on the order of 100 K transients such as supernovae, flaring stars, white dwarf binaries, active galactic nuclei and many variable stars. Astronomers have developed multiple techniques to identify different transients using the change in brightness with time (light curve), their color and many more. Groups within ZTF classify transients using various schemes, and others classify variables using archival data. There are always many low-hanging fruit and researchers often go after the most obvious cases. That leaves many nuggets uncaught. We plan to explore machine learning and data science methodology to get to some of the unexplored spaces through systematic exploration of the filtering that different groups use within the Fritz framework used for ZTF. |
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| Description: |
The SURF project will include tasks related to exploring the large parameter space. (1) learn about different kinds of transients and variables (functionally) and the parameter spaces that they occupy, (2) do gap analysis to understand overdensities and unexplored areas in the parameter space, (3) run similarity searches to look for objects in sparse areas that are not uninteresting (e.g. nonvariables are not interesting and would likely occupy an area most groups are not interested in). |
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| References: |
ZTF: https://ztf.caltech.edu/ ACAI filters: https://arxiv.org/abs/2111.12142 SCoPe: https://arxiv.org/abs/2102.11304 Fritz: https://www.ztf.caltech.edu/ztf-fritz.html ZARTH: https://zarth.caltech.edu/ |
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| Student Requirements: |
Knowledge of python, jupyter notebooks (Google Colab), git, and databases like Mongo DB. Conversant with basics of machine learning and statistics, knowledge about linux/unix. Basic astronomy knowledge will be a plus. |
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| Programs: |
This AO can be done under the following programs:
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