SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2018 can be found here.
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.
Announcements for external summer programs are listed here.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Characterizing LIGO Envioronmental Channels|
|Disciplines:||Data Science, Astronomy|
Sr. Research Scientist, (PMA),
|Mentor URL:||http://www.astro.caltech.edu/~aam (opens in new window)|
The 2017 Physics Nobel was related to observations by the Laser Interferometric Gravitational Observatory, LIGO. LIGO has detected several BH-BH events and a NS-NS event. There are likely a few fainter low significance events that have escaped attention due to low SNR relative to the noise background. To help diagnose noise sources that contribute to the astrophysical search backgrounds, there are a large number of environmental channels that register all kinds of data continuously. Wind speed and ground motion which can indicate periods of data contamination are a few key examples. Understanding the characteristics of these channels, and how they contribute to the noise in the LIGO channels is an important task if one hopes to increase the sensitivity of the astrophysical searches.
The project will involve characterizing about a dozen environmental channels by understanding the time-scales/durations over which the corresponding phenomena are active, and obtaining possible signatures for them. The goal is to use these environmental channels to accurately predict the contamination response in the gravitational wave strain data, and identify complex patterns in the data that will allow instrumentalists to improve the astrophysical search backgrounds. The environmental channel signatures can be obtained as an intensity histogram in the dt-intensity space (same as the dmdt method used for periodic stars - see references). The channels will have to be properly averaged. One can then do something akin to matched filtering and/or use convolutional neural networks on streaming data (we will simulate the streaming using archival data). The analysis can be extended to additional channels in order to understand their contribution to the noise in LIGO data, and explore using them to automatically tune the interferometer state to maximize astrophysical reach.
Deep Learning: http://deeplearning.net/tutorial/
dmdt method: https://arxiv.org/pdf/1709.06257.pdf
Detector characterization overview: https://arxiv.org/abs/1602.03844
|Student Requirements:||Proficiency in python, conversant with statistics basics, deep learning, using GPUs and AWS, knowledge about linux/unix, git and related software engineering techniques. Basic astronomy knowledge will be a plus.|
This AO can be done under the following programs:
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