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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 2019 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.

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Project:  Machine Learning the Cosmos
Disciplines:  Astronomy/Astrophysics, Computer Science
Mentor:  Olivier Dore, (JPL), Olivier.P.Dore@jpl.nasa.gov, Phone: (818) 354-0690
Mentor URL:  http://olivierdore.net  (opens in new window)
Background:  The past decade has seen the emergence of cosmology as a precision science. While the era of precision cosmology was heralded by the advent of measurements of anisotropies in the cosmic microwave background radiation of unprecedented accuracy, galaxy redshift surveys are arguably the strongest cosmological probe of the near future. The ever increasing precision of coming datasets calls for innovation in data analysis methods and joint modelling of multiple astronomical datasets. In this project, we want to revisit some of these challenges in light of recent spectacular progress in machine learning (ML).
Description:  Two examples that could constitute a starting point from this project are the following. The first challenge, delensing, is to undo the deflection of the CMB due to the gravitational lensing effects by large-scale structures as the CMB light travels to Earth. The lensing induced signal is a few orders of magnitudes larger than the primordial signal we are after. The second challenge, component separation, refers to the removal of contamination emission from non-primordial astrophysical processes which act as foregrounds. Amongst the most important are the polarized emission from our own galaxy or from unresolved external galaxies. These galactic foregrounds need be removed extremely accurately to avoid any residual bias.
References:  Planck Collaboration. Planck 2018 results. I. Overview and the cosmological legacy of Planck. https://arxiv.org/abs/1807.06205. 2018; BICEP2 Collaboration. BICEP2. II. Experiment and three-year Data Set. Astrophysical Journal 792, 22. 2014; A. Manzotti. Future cosmic microwave background delensing with galaxy surveys. https://arxiv.org/abs/1710.11038. 2018; D. Lenz, B. Hensley and O. DorĂ©. A New Large-Scale Map of Interstellar Reddening Derived from HI. Astrophysical Journal 846, 38. 2017
Student Requirements:  Programming: C, Fortran, Python, Linux. Knowledge of classical physics and/or astrophysics. Knowledge of general relativity and/or cosmology a plus, but not required. Good knowledge of probability and statistics, advanced calculus, linear algebra.
Location / Safety:  Project building and/or room locations: . Student will need special safety training: No.
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    both Caltech and non-Caltech students 

Click on a program name for program info and application requirements.


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