The Physics & Astronomy Department has created a ten-week Undergraduate Summer Research program, open only to UCLA students in the Physics & Astronomy Department, to be held June 17 - August 23, 2024. Please fill out the online application HERE. The application deadline is April 1, 2024. Faculty will define a number of available research projects.
In addition to the online application, you are asked to provide:
AMO, Atomic, Molecular, Optical phsyics
Faculty: David Leibrandt
Project title: Atomic, molecular, and optical (AMO) physics
Project description: The Leibrandt group does experimental AMO research at the intersection of trapped-ion quantum information processing, precision measurement, ultracold chemistry, and searches for physics beyond the Standard Model (https://leibrandtgroup.physics.ucla.edu). Summer projects for undergraduate students can include setting up and characterizing lasers and optical systems, designing and building electronics for controlling the experiments, programming experimental control systems, and performing computational modeling of the dynamics of trapped ions; and can be tailored to the student's interests.
Beam Physics
Faculty: James Rosenzweig
Project description: Development of the world's first very high field cryogenic accelerator; study of cryogenic photoemission. Study of applications to advanced X-ray free-electron lasers used for new imaging methods.
Biophysics/Neurophysics
Experimental Condensed Matter Physics
Faculty: Stuart Brown
Project description: "A number of unconventional superconductors exhibit signatures of chiral states. However, the useful probes of such time reversal symmetry breaking (TRSB) are quite limited and controversial. In this project, we test an alternative method for detecting TRSB: Zeeman-perturbed quadrupole resonance. The project will involve hardware, software, measurement, and analysis components."
Experimental condensed matter physics/quantum information science
High-Energy-Density Plasma Physics
Nuclear Physics/QCD Collider
Particle Physics-Dark Matter
Faculty: Alvine Kamaha
Project 1: the project is to optimize a novel technique for particle detection based on supercooled water.
Project 2: This project has to do with the development of a low-background counting facility as well as the development of cleanliness techniques for future generations of dark matter detectors.
Plasma Physics
Machine Learning and computation for plasma physics
Faculty: Paulo Alves
Project description: The project involves exploring/developing machine learning techniques to develop computationally efficient reduced models of nonlinear plasma dynamics.
High-Energy-Density Plasma Physics
Faculty: Derek Schaeffer
Project 1 description: This project would focus on analyzing synthetic data from theory and numerical simulations that model laser-driven shock experiments on the National Ignition Facility (NIF). Quasi-parallel collisionless shocks are thought to be the source of some of the highest energy cosmic rays observed, but the mechanism by which this happens is not well understood. To help address this question, experiments are underway on the NIF, the world’s largest laser, to create astrophysically-relevant quasi-parallel shocks. The shocks will be measured with Thomson scattering and x-ray imaging diagnostics, and modeled with particle-in-cell numerical simulations. The student would have the opportunity to generate synthetic Thomson scattering and x-ray data using python to help predict the experiments, using both theory and simulations.
Project 2 description: This project would focus on the application of machine learning and neural networks to the analysis of large datasets from high-repetition-rate laser-plasma experiments. In the experiments, a high-powered laser generates a plasma blast wave, which is diagnosed with a Thomson scattering diagnostic over thousands of laser shots. A ML algorithm is then used to process this large dataset to generate 2D images of plasma parameters. The goal of these proof-of-principle experiments is to demonstrate an ability to process experimental data in real-time in order to guide the operation of the experiment, for example by optimizing laser parameters to achieve desired plasma properties. The student would have the opportunity to develop ML algorithms and analyze data from these experiments using python. Depending on progress, the student may have an opportunity to help design and participate in follow-on experiments.
plasma physics/fusion
Faculty: Troy Carter
Project title: Lots of projects from electronics/hardware development (e.g. amplifiers for measurements or RF amplifiers), modeling/simulation (wave propagation and absorption in plasmas), data taking/analysis (e.g. spectroscopic measurements, turbulence, etc).
Solid State
Theoretical/Computational Plasma Physics
Faculty: Paulo Alves
Project title: Machine Learning and computation for plasma physics
Project description: The project involves exploring/developing machine learning techniques to develop computationally efficient reduced models of nonlinear plasma dynamics.
Astronomical instrumentation (in particular, Astrophotonics)
Faculty: Pradip Gatkine
Project description: Prof. Gatkine is developing innovative astrophotonic technologies to build instruments-on-a-chip for future astronomical telescopes. These instruments are miniaturized by several orders of magnitude, making them ideal for high-precision measurements (such as characterizing exoplanets) using space-based and ground-based telescopes, and also allow interesting new applications not possible with conventional bulk optics. Astrophotonics is essentially guiding the light in single-mode optical waveguides and performing precise operations on it such as spectroscopy, interferometry, filtering, and more, to extract scientific measurables ranging from kinematics to chemical composition of various astronomical sources.
Project 1: In this project, we will work on experimentally measuring the performance of high-resolution photonic spectrograph chips. This will include: 1) measuring their efficiency and spectral resolution over a broad near-IR waveband in the lab, and 2) making the chips ready for an on-sky test at a telescope/facility - which we will likely do towards the end of summer or the start of Fall 2024. We aim to publish this work in SPIE proceedings and/or journal articles.
Project 2: In this project, we will specifically work on designing, writing, and testing programs to extract the spectrum from the measurements taken using the chip. This pipeline will be further expanded to new experiments with a combination of low-resolution and high-resolution chips to increase the scientific capabilities of the on-chip spectrographs. This end-to-end pipeline will be crucial for on-sky tests at a telescope that we plan to do towards the end of summer or the start of Fall 2024. We aim to publish this work in SPIE proceedings and/or journal articles.
Machine Learning in Astrophysics
Faculty: Tuan Do
Project description: The UCLA Astrophysics Data lab seeks to use machine learning methods to allow for novel ways of examining and analyzing astronomical data. The scale and complexity of astronomical data are growing exponentially, so it is important that our tools and methods grow as well to enable new discoveries. Our group studies both how machine learning is being used in astronomy and applies machine learning methods to challenging astronomical problems. Potential research projects include machine learning in extragalactic astronomy, image recognition and processing, and the study of stars around the supermassive black hole at the center of our galaxy.
Stellar dynamics at the Galactic Center
Faculty: Andrea GhezProject description: The selected student will work on the Galactic Center Orbits Initiative, which is a long-term project being carried out at Keck Observatory to measure the orbits of stars to learn about the physics and astrophysics of supermassive black holes. We explore methods for extending the time baseline of the existing observations to improve our ability to perform new tests of General Relativity in the relatively unexplored regime near a supermassive black hole.
Faculty: Michael Rich
Project description: The halos and environments of Nearby Galaxies ( HERON) survey is undertaking a deep imaging survey of extended, low surface brightness H-alpha in nearby galaxies, and redshifted H-alpha in the Coma cluster of galaxies. The survey currently has 1 operating 0.7 m telescope, and the student will be remotely observing and gathering data with that telescope, while bringing online operations with two additional 0.5 m telescopes. The student will collect and archive the data and work with team scientists in the reduction and interpretation of the images and construction of multiband images. The student will also work with Patrick Ogle (STScI) in constructing a catalog of candidate galaxies that potentially host AGN driven outflows and excited gas at large radii. A subset of this catalog will be observed with the telescopes.
Questions? Contact the Undergraduate office: Françoise Queval, Student Affairs Officer, 1-707A PAB, 310-825-2453.
Previous REU programs: