UC Santa Cruz

Name: Amy Furniss
Title: Studying Extragalactic Gamma-ray Photons
Description: Dr. Amy Furniss works on the study of very-high-energy gamma-ray photons from sources outside of our galaxy using the gamma-ray telescope VERITAS. These photons most commonly come from active galaxies known as blazars, which have jets that are pointed along our line of sight. Amy's work concentrates on (1) attempting to optimize the observing strategy of these gamma-ray blazars by triggering VERITAS observations on lower-energy observations such as those made by X-ray telescopes and the space-based Fermi gamma-ray telescope and then (2) studying the gamma-ray photons from these sources in an attempt to understand their interactions with extragalactic photon and magnetic fields as they pass from their origin galaxy to Earth.
Day-to-day work for a summer project working with Amy's group would involve learning astrophysics analysis in either the X-ray or gamma-ray bands, writing of python programs which would automatically query observations in other bands, and the interpretation of the gamma-ray data in the context of photon creation and propagation across huge distances.
Preferred qualifications: Experience with Python programming and linux operating systems is a plus, but not required for a successful summer research project.

Name: Bruce A. Schumm
Title: Research on Fast Sensors R&D
Description: Our group is involved in the development of fast sensor technologies, and their associated electronic readout. Technologies include silicon sensors with graded doping (LGADs), ultrafast diamond sensor systems, and discrete, CMOS, and SiGe electronic readout. Some of the work is pure R&D, focused on breaking through various technical barriers, while other parts of the program focus on applications such as a rare pion decay experiment that is under development, and the Electron Ion Collider.
The participating student could work in a number of areas, to be determined by interest and need. Characterization of sensor or readout performance is one area. Design and analysis of experiments to determine performance characteristics, including a potential trip to an accelerator laboratory, is another. Laboratory training would be required, most of which would ideally be done online prior to starting the program (3-5 hours).
Preferred qualifications: Basic familiarity with lower division E&M, and with Lower Division laboratory apparatus.

Name: Olivier Hervet
Title: What is the origin of gamma rays emitted by the blazar S2 0109+22?
Description: My research focuses on the highest energy phenomena from distant galaxies, where I study the emission, propagation, and detection of high-energy photons. My main topic is what happens in the relativistic plasma jet produced by supermassive black holes in active galactic nuclei (AGN). Blazars are the brightest of AGN, shining through the whole electromagnetic spectrum, from radio to gamma rays. They are excellent distant laboratories to test numerical emission models.
With the student, we will investigate the case of the blazar S2 0109+22. A recent observing campaign with the NASA X-ray space satellite NuStar highlighted a break in the X-ray spectrum that will deeply change our understanding of this source. The day-to-day work will be on building the broadband spectrum of S2 0109+22, including the data analysis of at least one instrument that observed S2 0109+22 between optical and gamma-ray energies. At the end of the internship, the student will run an existing numerical model on our dataset that will provide preliminary answers on the origin of the gamma-ray emission of S2 0109+22.
Preferred qualifications: Experience with Python programming and Linux operating systems is a plus, but not required for a successful summer research project.

Name: Raja GuhaThakurta
Title: Stellar Kinematics of M32 and the Disks of M31 and M33
Description: The Andromeda galaxy (M31) and the dwarf satellite galaxies that orbit it offer a (relatively speaking!) close-up view of galaxy formation and evolution. The ultra-compact dwarf (UCD) satellite M32 is a rare kind of galaxy and its nature and origin remain poorly understood. M32 is superposed against the southern portion of the disk of M31. The Triangulum galaxy (M33) is a somewhat larger (than M32) companion of M31. In Fall 2022 and 2023, the mentor’s research group obtained a large volume of spectra of M31, M32, and M33’s resolved stellar population using the DEIMOS spectrograph on the Keck II 10-meter telescope in Hawaii. These spectra, along with spectra from other parts of M31 and M33’s disk obtained over the last decade or more, will be useful for placing observational constraints on the past, ongoing, and future tidal interaction between M31, M32, and M33.
The Cal-Bridge scholar will: (1) learn to vet the Fall 2022 and Fall 2023 Keck DEIMOS multi object spectra of M31, M32, and M33’s resolved stellar population and classify radial velocity measurements as secure, marginal, or unreliable; (2) analyze the stellar kinematics of M32, M31’s northern and southern disk, and M33's disk and compare them to HI kinematics; and (3) compare and contrast the stellar kinematics (i.e., asymmetric drift, velocity dispersion) of the southern and northern portions of M31’s disk and M33's disk.
Preferred qualifications: Scholars with Python programming skills preferred

Name: Robert Johnson
Title: Low-energy cosmic-ray electrons and positrons
Description: Our charged-particle spectrometer AESOP-Lite, supported by NASA, is scheduled to fly in December 2023 over Antarctica on a long-duration balloon, at an altitude of around 150,000 feet. Our goal is to measure the spectrum of electrons and positrons in primary cosmic rays at energies from about 20 MeV up to 1 GeV. The physics goals mostly relate to understanding propagation of cosmic rays through the heliosphere of the solar system. In the summer of 2024 we will be busy analyzing data from the flight.
Preferred qualifications: Some programming experience, for example with Python or C++.

Name: Ryan Baumbach
Title: Investigations of Complex Magnetic Order and Novel Electronic States in Next Generation Quantum Materials
Description: While just over a century ago most technologies used a handful of materials (e.g., glass, wood, concrete, and some alloys), modern society now relies on thousands of complex crystalline compounds that are optimized for specific tasks. This is largely due to advances in quantum mechanics that were implemented at the interface between physics, chemistry, materials science, and engineering, where superconductors, semiconductors, permanent magnets, and high performance alloys are commonplace examples. The phenomena that they exhibit are indispensable for providing a high quality of life; e.g., for magnetic resonance imaging, motors, energy generation/storage/transmission, and smart phones. However, there remain many challenges relating to sustainability, health, the economy, and security that these ‘classical’ quantum materials have not been able to address. This motivates efforts to develop next generation examples, which hold the promise for the development of new fundamental insights that can drive advanced applications.
The nexus for discovery is amongst systems with protected electronic states and/or strong electronic correlations, which are deep reservoirs for novel structural, electronic, and magnetic phenomena. During this project, we will focus on a particular subset of such materials: magnetically frustrated metals, where the fundamental degrees of freedom (lattice, charge, spin and orbital) often are dynamically intertwined and produce novel many body states of matter. This includes unconventional superconductivity, strange metal behavior, spin liquids/glasses, skyrmion lattices, anomalous ordered states, enhanced thermoelectric properties, and more. Our work will center on materials with the chemical formula LnTAl4Ge2 (Ln = lanthanide and T = transition metal), where we have already shown that chemical variation produces a wealth complex behavior within temperature-magnetic field phase space. We will expand this project by examining existing magnetization, heat capacity, and electrical resistivity data sets that were recently generated by our group. We will also seek to synthesize new examples using advanced crystal growth techniques. If successful, we will characterize their structure and chemical composition, and will investigate their magnetic and electronic behaviors over a wide range of temperatures and magnetic fields.
Preferred qualifications: Some physics, materials science, or chemistry lab experience is preferred but not required.

Name: Stefano Profumo
Title: Formation mechanisms for non-stellar black holes
Description: We will work on different possible formation mechanisms for "primordial" black holes, i.e. black holes that don't originate from stellar collapse. Such mechanism range from dark matter scenarios with self interactions, density perturbations in inflationary models, collapse of "Fermi balls" whose formation is triggered by dark sector interactions etc.
Preferred qualifications: Working knowledge of Python or any other programming language; ability to make graphs.

Name: Tesla Jeltema
Title: Probing Dark Matter and Dark Energy with Clusters of Galaxies
Description: Broadly speaking, Dr. Jeltema's research focuses on observational cosmology and particle astrophysics, including constraints on the nature of dark matter and dark energy and studies of the evolution of galaxies. In particular, we study the formation and evolution of large-scale structure in the universe using observations covering a broad wavelength range and numerical simulations.
Potential research projects include a variety of opportunities to contribute to cosmology with clusters of galaxies in the Dark Energy Survey and Legacy Survey of Space and Time. Additional work in our research group includes probing the nature of dark matter using multiwavelength data and lensing. Specific projects will be assigned based on student interest and skills and considering needs within the research group.
Preferred qualifications: Projects matched to student skills, and there are no specific requirements. At least one programming class and course work through Modern Physics are beneficial.


Name:Yu Zhang
Title: Energy data analytics via Deep Learning
Description: Leverage the state-of the art AI techniques to 1) forecast renewable generation, energy demand, electricity prices; 2) estimate the power system; 3) energy disaggregation; or 4) power system event detection (e.g. electrical faults, cyber attacks)
Preferred qualifications: Python programming skills. Basic knowledge of machine learning and deep learning such as supervised and unsupervised learning, RNN/CNN


Name:Ashesh Chattopadhyay
Title: Hallucinations in deep learning models of physics
Description: Modern scientific machine learning models of complex systems have shown tremendous success in predicting large scale chaotic and multiscale dynamical systems. Prominently today’s AI weather models have surpassed in terms of accuracy, a 100 years of numerical modeling capabilities. Despite their success these models often suffer from unphysical drifts, instabilities, general hallucinations when integrated for a long time. Our proposed research aims to shed light on this by exploiting tools from deep learning theory, theoretical physics, and computational mathematics.
Preferred qualifications: Python and PyTorch programming

Name: Dongwook Lee
Title: Machine Learning for Computational Fluid Dynamics
Description: In this project, we will explore various approaches and options of using machine learning strategies to help enhance numerical methods in computational fluid dynamics. The primary goal is to identify standing issues in modern numerical methods that are inherently nonlinear and challenging, and come up with a way to replace them with a machine learning alternative.
Preferred qualifications: Python programming skills and basic knowledge in differential calculus and differential equations.

Name: Hongyun Wang
Title: Thermal effect of electromagnetic wave on skin
Description: We study the absorption of electromagnetic wave into skin, and the temperature evolution heated by the absorbed electromagnetic energy with heat conduction in multi-layers of skin tissues. The multi-layers of skin tissues have different material properties. Analytical solution or efficient numerical solutions of skin temperature solution are challenging. We will develop semi-analytical and/or efficient numerical solutions using the framework of neural network learning in combination with the traditional mathematical tool of asymptotic analysis. A related problem is the inverse problem in this physical setting. We also study the problem of estimating skin material properties from the measurable data (i.e., a time series of skin surface temperatures record by a thermographic camera)
Preferred qualifications: Some programming skill, preferably in Matlab