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: | Scaling up binary black hole merger followup for the Rubin Era | ||||||||
| Disciplines: | Astronomy, Data Science | ||||||||
| Mentor: |
Matthew Graham,
Professor, (PMA),
mjg@caltech.edu, |
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| Mentor URL: | https://www.astro.caltech.edu/~mjg (opens in new window) | ||||||||
| Background: | In 2025, the Rubin Observatory will begin its Legacy Survey of Space and Time (LSST), launching a decade of observation. One of LSST's four primary science focuses will be transient astronomy: while scanning the sky each night, the survey will record roughly 10 million changes in the brightness or position of astronomical sources. On occasion such recorded increases in brightness will be due to exotic events classified as compact binary coalescences -- the merging of neutron stars or black holes. These transient signals are targets of multi-messenger astronomy (MMA), with expected gravitational wave signals that can be detected by observatories such as LIGO. Characterization of these events can advance scientific understanding from dense matter physics to measurements of cosmological parameters. LSST continues an era of big data astronomy advanced by other projects such as Caltech’s Zwicky Transient Facility (ZTF). These large data streams call for novel approaches to find interesting objects and optimize rapid follow-up. | ||||||||
| Description: |
The student will work on the infrastructure for binary black hole (BBH) merger follow-up (looking for electromagnetic counterparts) in the Rubin era. This will include focus on the LIGO observing run which will coincide with the first LSST science (LIGO 05 in 2026), and simulations of BBH merger gravitational wave detection in that period. The student can use projections for LSST observations, including predicted kilonovae detections, to develop infrastructure that would benefit similar BBH follow-up. The student will work with members of the NSF Accelerated AI Algorithms for Data-Driven Discovery (A3D3) institute, and have the potential to work on machine learning algorithms as part of the follow-up infrastructure. The student will have the opportunity to collaborate with scientists working on LSST planning as well as ZTF operations. |
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| References: |
https://a3d3.ai https://ui.adsabs.harvard.edu/abs/2023ApJ...942...99G/abstract https://ui.adsabs.harvard.edu/abs/2023ApJS..267...31C/abstract |
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| Student Requirements: | Proficiency in Python programming | ||||||||
| Programs: |
This AO can be done under the following programs:
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