Project:	Observational Studies of Exoplanets
Disciplines:	Astrophysics, Astronomy
Mentor:	Andrew Howard, Professor of Astronomy, (PMA), ahoward@caltech.edu, Phone: 626-395-8747
Mentor URL:	https://exoplanets.caltech.edu/about-andrew/  (opens in new window)
Background:	The Howard Research Group investigates extrasolar planets using data from instruments on several telescopes around the world and in space, including the Keck Planet Finder (KPF) spectrometer on the Keck I Telescope. In general, we are interested in the orbits, mass, compositions, and system architectures of planets and their planetary systems. The work primarily relates to the analysis and planning of astronomical observations, especially high-resolution optical spectra of stars.
Description:	SURF projects offered this year are listed below and will be principally mentored by graduate students and postdocs in the group. Caltech students will be prioritized, however we welcome applications from students at other universities. Interested students should email ahoward@caltech.edu, lhandley@caltech.edu, and giacalone@astro.caltech.edu listing which research projects they are most interested in (in order of preference), a statement about their experience in research (if any), and a CV. Please email as soon as possible, and at the latest by February 12. We expect to carry out interviews with prospective candidates and then work with those selected in preparation for the SURF application deadline on February 22, 2025. Project 1 – Atomic abundances of rapidly rotating stars: The student will write code capable of calculating atomic abundances of rapidly rotating stars by leveraging the transit spectroscopy technique, which involves acquiring spectra of a star while an exoplanet passes in front of it. These abundances, which are challenging to calculate without this technique, are important for understanding planet formation. The code will be based on novel but well-established methods and will be applied to data from several new high-resolution spectrographs, including KPF and NEID. The student will be working primarily in Python and should be familiar with the fundamentals of spectroscopy and statistical modeling. Project 2 – Observation planning with KPF: The timing and cadence of radial velocity (RV) observations with KPF affects our sensitivity to planets and their orbits. Using injection and recovery tests for a variety of planets (e.g. small terrestrial planets, long period giant planets, and others), the student will characterize how different observing strategies maximize the yield of KPF surveys. The code may be used to inform the manner by which we schedule observations with KPF and motivate future proposals by forecasting sensitivity to certain planets. The student should have some experience with Python and a basic understanding of statistical methods. Project 3 – Development of algorithms for KPF data analysis: The Keck Planet Finder (KPF) spectrometer measures time-series radial velocities (RVs) to determine the masses and orbits of planets that surround target stars. Measuring RVs at a the 10 centimeter-per-second level (a shift of one ten-thousandth of a detector pixel) requires sophisticated algorithms that are encapsulated in KPF’s data reduction pipeline (DRP). For this project, a student will develop a new algorithm for the DRP. Possible algorithms include modeling and subtraction of sky spectra from stellar spectra, modeling of telluric spectra from the Earth’s atmosphere, characterization of CCD detector systematics and construction of algorithms to undo them, and more. Some experience with Python programming will be helpful for this project. Project 4 – Development of a spectrum-matching code for KPF: High-resolution spectra of stars enable the calculation of fundamental stellar properties, such as effective temperature, surface gravity, and metallicity. These quantities are important for understanding the properties and histories of exoplanets orbiting these stars. This project will involve the development of code capable of performing this calculation for KPF data, using both pre-existing and new techniques. Some experience in Python and data analysis techniques (e.g., fitting data via least squares regression) will be helpful for this project.
References:	https://arxiv.org/abs/2409.15951 https://arxiv.org/pdf/2411.02521 https://arxiv.org/pdf/2408.16830 https://arxiv.org/abs/2409.06795 ​​https://ui.adsabs.harvard.edu/abs/2024SPIE13096E..09G/abstract https://arxiv.org/abs/1701.00922 https://arxiv.org/abs/2407.19016
Student Requirements:	No explicit requirements. Students with experience in Python programming and an interest in astrophysics will be able to progress the most quickly through the projects.
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.