2017 Research Projects

Research Projects by Subject

Note:
Each research project will involve background reading for the interns provided by their mentors.
Each research project will involve a final presentation by the interns.


Interns are expected to work collaboratively on the same project and/or data set.
This may preclude rising seniors from submitting papers based on such projects to the Intel Science Talent Search competition.

Astronomy & Astrophysics

Code Research Project Descriptions
AST-01 Title:
Surface Compositions of Red Giant Stars in Globular Clusters
Primary mentor: Marie Wingyee Lau
Faculty advisor: Prof. Graeme Smith
Location: UCSC Main Campus
Number of interns: 2

Project description:
Globular clusters are collections of 10^3 to 10^6 old stars in the Milky Way and other galaxies. Within a globular cluster, the stars have usually formed at the same time out of material in the same nebula, with rather small chemical composition variations across stars of similar luminosities. The SIP interns and mentors on this research project will study the chemical compositions of the surface of red giant stars in globular clusters. While some astronomers think that the small chemical composition variations across different red giants were already present in the material that the stars formed from, the mentors on this project contend the stars’ own evolution will also change the chemical compositions on their surfaces.


Tasks:
The SIP interns will download spectroscopic data of red giant stars of ten globular clusters. The interns will then study whether the oxygen and sodium abundances correlate with luminosities of the stars, which will be an evidence of stellar evolution effects. We will make use of the results from the SIP interns from last year, when we studied another ten globular clusters. If the oxygen or sodium abundance seems to depend on luminosity, the SIP interns will further quantify how strong the dependence is through running statistical tests. 


Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis

Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor’s availability: ON ON ON ON ON ON ON ON ON ON
Code Research Project Descriptions
AST-02 Title:
Study of White Dwarf Stars in the Disk and Halo of our Milky Way Galaxy
Primary mentor: Emily Cunningham
Faculty advisor: Prof. Raja GuhaThakurta
Other mentors: Madison Harris
Location: UCSC Main Campus
Number of interns: 3

Project description:
White dwarf (WD) stars represent the final phase in the life of solar-mass stars as they fade into oblivion. The extreme low luminosity of WDs means that most detailed measurements of such stars are limited to samples in our immediate neighborhood in the thin disk of the Milky Way galaxy. The mentor is conducting the HALO7D survey, a survey of unprecedented depth of Sun-line main sequence turnoff stars in the Milky Way’s outer halo using a combination of Hubble Space Telescope (HST) images and Keck DEIMOS spectra. Faint WD stars are rare but useful by-products of this survey. They are identified by their relatively blue colors, large proper motion (both measured from the deep, multi-epoch HST images), and characteristic spectral Balmer absorption features (measured from Keck spectra). The WDs found in the HALO7D survey will yield new insights on the old stellar population associated with the Milky Way’s thick disk and halo.


Tasks:
The SIP interns will learn about HST data and proper motion measurements derived from them. They will also learn about Keck DEIMOS spectra and the spectral characteristics of WD stars. The SIP interns will use the Python programming language to develop data analysis techniques for separating the WD population from the rest of the stars in the HALO7D sample. Finally, they will make radial velocity measurements and study the kinematics and spectral characteristics of the Milky Way thick disk and halo WD populations.


Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis

Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor’s availability: ON ON ON ON REM ON ON ON ON ON

Only out-of-area applicants will be considered
for this virtually-mentored project.

Code Research Project Descriptions
AST-03 Title:
Galaxy Formation and Evolution: Comparing Supercomputer Simulations with Observations
Primary mentor: Christoph Lee
Faculty advisor: Prof. Joel Primack
Location:
Number of interns: 2

Project description:
Recent observations by the Hubble Space Telescope (HST) of galaxies in the process of formation compared with the mentor’s group’s supercomputer simulations have revealed unexpected aspects of galaxy evolution. It was generally thought that galaxies start as disks, that merging disk galaxies make stellar spheroids, and that larger disks can then form around these spheroids – bulges at the centers of disk galaxies like our own Milky Way. However, the HST observations are showing us that most galaxies start not as disks, but rather as elongated systems shaped like zucchinis or sausages. This is consistent with the mentors’ simulations, which show that these elongated galaxies are oriented along the dark matter filaments of the Cosmic Web. Both simulations and observations indicate that most early star-forming galaxies have gigantic clumps of stars, hundreds or thousands of times more massive than the largest star-forming regions or globular clusters in the Milky Way or nearby galaxies, and that star-forming galaxies undergo "compaction" processes that make their centers so bright with new star formation that their visible size decreases. The mentors’ are running many new simulations, and the SIP projects will be to analyze the simulations and compare them with the observations both by HST and ground-based observatories such as Keck. With a grant from Google, the mentors are using a convolutional neural net based machine learning method, also called Deep Learning, to analyze the simulation outputs and compare them with observations. The plan is to use observations to try to determine observational correlates of simulation phenomena, and SIP internship projects will be developed in this area that are at an appropriate level depending on the SIP interns’ capabilities.


Tasks:
The SIP interns working will do analyses of the mentor’s group’s simulation outputs and compare them with astronomical observations. The interns will learn powerful computing and visualization tools, including using the mentors’ Deep Learning code, and the interns will be welcome to use the UCSC Hyades astronomical supercomputer, the mentors’ petabyte astro-data system and 3D AstroVisualization Lab.


Required skills for interns prior to acceptance: Computer programming
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis

Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor’s availability: ON ON ON REM ON ON ON ON ON ON
Code Research Project Descriptions
AST-04 Title:
What Happens Around Supermassive Black Holes
Primary mentor: Dr. Martin Gaskell
Location: UCSC Main Campus
Number of interns: 3

Project description:
Astronomers now believe that every large galaxy contains a supermassive black hole in its center. Because of the tremendous energy released as the black hole grows by swallowing gas, these black holes can be readily detected as so-called “active galactic nuclei” (AGNs) back to very early times in the Universe. The details of how supermassive black holes form and grow and how this is related to the formation of normal galaxies is one of the central mysteries of contemporary astrophysics. The mentor’s research group is analyzing spectra and spectral variability to try to understand how AGNs produce the intense radiation seen, what the structure of material around the black hole is like, and how supermassive black holes grow.


Tasks:
SIP intern involvement in the project will consist of analyzing multi-wavelength spectral observations of relatively nearby actively accreting supermassive black holes to try to understand the emissions and how the black holes grow. This work will involve compiling data sets, applying corrections, making statistical estimates of parameters, and comparing the results with theoretical models of processes going on around black holes.