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:||Measuring Galaxy Redshifts for the Dark Energy Survey (DES)|
|Disciplines:||Astronomy/Astrophysics, Computer Science|
|Mentor URL:||https://science.jpl.nasa.gov/people/JRhodes/ (opens in new window)|
|Background:||The Dark Energy Survey (DES) is a major international survey designed to map the structure and dynamics of the Universe, with the goal of understanding the nature of the dark energy causing cosmic acceleration. DES uses galaxy weak lensing (warping of background galaxy light due to the gravity of foreground matter) to measure the growth of cosmic structure. Accurate galaxy redshift estimates are a crucial component of DES weak lensing cosmology. Our group is preparing to lead an effort to measure the redshifts of millions of galaxies imaged by DES, using innovative machine learning techniques in combination with extensive deep galaxy spectroscopy we have obtained with the Keck telescopes. In addition to its importance to the DES survey, this project will constitute a testbed for similar analyses that will be crucial in the next decade with data from the planned Stage IV space-based dark energy missions Euclid and WFIRST.|
The goal of this project is to implement and test an innovative method for photometric redshift estimation and weak lensing tomographic bin generation on the DES photometric dataset. The results have the potential to substantially improve cosmological inference from DES imaging data. The project will involve coding in Python as well as using other common astronomical software tools. Ultimately, we hope to devise the best possible galaxy weak lensing shear bins from DES imaging data in four filters, which will strengthen the constraints on the dark energy equation of state.
|Student Requirements:||Candidates with a background in physics and/or astronomy as well as a reasonable level of computational skill (previous experience coding in Python, C, Matlab or similar) are preferred. Students with a background/interest in computer science and machine learning are particularly encouraged to apply.|
|Location / Safety:||Project building and/or room locations: . Student will need special safety training: No.|
This AO can be done under the following programs:
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