Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor. 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. Click here for more tips on finding a mentor. Announcements for external summer programs are listed here.
*Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
*Students pursuing opportunities at JPL must be U.S. citizens or U.S. permanent residents.
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| Project: | Anomaly detection with recurrence analysis of astronomical time series | ||||||||
| Disciplines: | Astronomy, Data Science | ||||||||
| Mentor: |
Matthew Graham,
Research Professor, (PMA),
mjg@caltech.edu, |
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| Mentor URL: | https://www.astro.caltech.edu/~mjg (opens in new window) | ||||||||
| AO Contact: | Kira Nolan, knolan@caltech.edu | ||||||||
| Background: |
Many signals in nature are nonlinear and unpredictable, posing challenges across domains from neuroscience to astrophysics. Recurrence plots are powerful tools in nonlinear data analysis. These kaleidoscope-like visualizations trace how dynamical systems return to similar areas in phase space, and can characterize nonlinear and chaotic patterns. Distinct from traditional power spectral analysis, analysis of recurrence can add unique understanding of the complex physical mechanisms driving the variability in these signals. Recurrence plots applied on a sliding time window can identify anomalous activity in these signals. The astrophysical time series that will be investigated are: Gravitational wave strain data, or the evenly sampled time series describing the compression of space due to gravitational waves. An ongoing effort lies in identifying glitches, observed phenomena such as compact binary coalescences, predicted phenomena such as supernovae, and unknown signals from the noise in these measurements. Active galactic nuclei (AGN) lightcurves, or the unevenly and often sparsely sampled time series describing the brightness over time of accreting supermassive black holes. These stochastic signals, made up of optical observations from instruments like the Zwicky Transient Facility (ZTF) and the upcoming Vera C. Rubin Observatory, are notoriously complicated to model and have already shown promising results with recurrence analysis. |
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| Description: |
The student will use primarily Python to do recurrence analysis for astrophysical data, including gravitational wave strain data and AGN lightcurves. The project can include work on speed-up of the analysis for low-latency applications. This project will take place in-person at Caltech. The student will be a part of a collaborative group including other summer students working on projects in astrophysics. There can be an opportunity to gain experience observing in-person at Palomar Mountain. The student will work with members of the NSF institute Accelerated AI Algorithms for Data-Driven Discovery (A3D3), and gain exposure to Rubin Observatory preparation as well as current ZTF operations. |
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| References: | https://ui.adsabs.harvard.edu/link_gateway/2020MNRAS.497.3418P/doi:10.1093/mnras/staa2069 | ||||||||
| Student Requirements: | Proficiency in Python. Interest in physics and astrophysics encouraged. | ||||||||
| Programs: |
This AO can be done under the following programs:
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