Berkeley Physics is proud to announce the Physics Innovators Initiative (Pi2) Scholars for Summer, 2022
These undergraduates will have the opportunity to do research, learn to design the tools that enable such research, develop their scientific independence, and realize their potential as physicists. Each Pi2 scholar will work closely with dedicated graduate student and/or postdoc mentors on their projects. Pi2 Scholars will also participate in a number of activities with their cohorts which could include lectures, roundtable discussions, and hiking excursions. Final projects will require a written report and a poster presentation open to the whole department at the end of the summer. Meet our Pi2 Summer Scholars and their mentors below!
Aarabhi Achanta and mentor Tiancong Zhu
Gate tunable TMDs on graphene device
Two-dimensional transition metal dichalcogenides(TMDs) exhibit rich electronic and topological properties. By integrating TMDs with a gate tunable graphene device, one can continuously change the carrier density in the TMDs and study its impact on the materials' properties with scanning tunneling microscopy. In this project, the student will perform sample fabrication and assist with local probe characterization of the samples
Aarabhi will work as part of the Micheal Crommie group and will be mentored by postdoctoral researcher Tiancong Zhu.
Matthew Bilotta and mentor Scott Eustice
Rapid feedback control of a neutral atom quantum information processor
Precise control of experimental parameters, such as laser intensity or magnetic field, is crucial for quantum gas experiments. We aim to develop a digital feedback system to stabilize transient signals that are too fast to be controlled by the traditional PID circuits.
Matthew will work as part of the Dan Stamper-Kurn group and will be mentored by graduate student Scott Eustice.
Jiu Chang and mentor Eric Y. Ma
An FPGA-based open-source high-speed lock-in amplifier with external reference
We propose to develop an FPGA-based high-speed lock-in amplifier (LIA) that can take external references. LIAs are indispensable tools in experimental physics, but commercial solutions have remained pricey despite vast cost reductions in high-performance analog-digital conversion and high-speed digital signal processing. Open-source solutions do exist, but to the best of our knowledge none of the reported designs can take external reference signals at tens of kHz and above – a critical capability because the modulation is often controlled by another master device in complex experiments. We aim to fill this gap and create a truly versatile yet affordable open-source solution that may democratize LIAs for good.
Jiu will work as part of the Eric Y. Ma group and will be mentored by assistant professor Eric Y. Ma.
Emilie Cote and mentor Antonella Palmese
Machine Learning classification of astronomical transients
A main challenge in time domain astronomy is that of classifying different types of high-energy transients from large datasets solely based on imaging data or sparse spectroscopic observations. As part of this project, the student will use state-of-the-art machine learning techniques to identify different classes of transients in Dark Energy Camera (DECam) and Dark Energy Spectroscopic Instrument (DESI) data.
Emilie will work as part of the Saul Perlmutter group and will be mentored by postdoctoral fellow Antonella Palmese.
Yuan Feng and Maria Foti
Multiple projects on the measurement of the Higgs Boson properties
Student researchers will be analyzing data collected by the ATLAS experiment at the Large Hadron Collider (LHC) to determine the properties of the Higgs boson. The Higgs boson was discovered in 2012 at CERN's LHC. Potential physics beyond the Standard Model may be covered through measurements of the properties of the Higgs boson. In the data analysis project, the student researcher will design a key component of the measurement by developing a machine learning based classifier. Details of the project will be developed with the selected applicant after the interview. Selected applicant would be working with a team of graduate student and postdoc and will have the opportunity to present their research result at the ATLAS Collaboration meetings.
Yuan will work as part of the Haichen Wang group and will be mentored by Maria Foti.
Rav Kaur and mentor Antonella Palmese
Gravitational wave cosmology
Gravitational waves from merging compact object binaries (binary black holes, binary neutron stars and neutron star-black hole systems) can be used to infer cosmological parameters. As part of this project, the student will explore different methods to estimate the Hubble constant in preparation of the next observing run by the gravitational wave detectors LIGO/Virgo/KAGRA.
Rav will work as part of the Saul Perlmutter group and will be mentored by postdoctoral fellow Antonella Palmese.
Yoonsang Kim and mentor Erin Hansen
Phonon propogation simulations
Development of phonon propogation simulations through lithium molybdate and tellurium dioxide crystals. CUPID searches for rare nuclear decays using cryogenic calorimeters; this method uses phonon and photon signals to reconstruct energies on the order of MeV. The demonstrator project DEMETER seeks to reconstruct these energies at the crystalline scale, and will require phonon propogation simulations as well as reconstruction algorithms. You will work on modifications of existing programs and development of new tools, assess the feasibility and sensitivity of such a study, and contribute to the design and development of the DEMETER project.
Yoonsang will work as part of the Yury Kolomensky group and will be mentored by postdoctoral researcher Erin Hansen.
Bingxu (Howard) Meng and mentor Sajant Anand
Tensor Network methods to simulate 2D quantum many-body systems
Tensor networks states (TNS) are an indespensible tool for numerically studying quantum many-body systems, and numerical tools developed in the past 30 years are incredibly effective for 1D systems. Simulating higher dimensional systems continues to be challenging, so we have been developing TNS algorithms properly suited for 2D systems. A student will apply algorithms for optimization and manipulation of 2D TNS to different condensed matter systems and compare performance to existing algorithms.
Bingxu (Howard) will work as part of the Michael Zaletel group and will be mentored by graduate student Sajant Anand.
Runzhe (Ricardo) Mo and mentor Irina Ene
Higgs physics with machine learning
Many properties of the recently discovered Higgs boson remain a mystery. The focus of this project is the application of ML techniques to enable the detection of decay of the Higgs boson to charm quarks. A student on this project will have the opportunity to contribute to R&D associated with this project, learning about charm tagging with ML, Monte Carlo simulation techniques, or data analysis also with ML.
Runzhe (Ricardo) will work as part of the Heather Gray group and will be mentored by graduate student Irina Ene.
Christopher Palacios and mentor Jon Kruppe
Symmetry and magnetism play a critical role in novel quantum technologies like spintronics. In this project, the student will be synthesizing new classes of monolayer materials, specializing in nanofabrication of quantum device prototypes.
Christopher will work as part of the James Analytis group and will be mentored by graduate student Jon Kruppe.
Troy Tsubota and mentors Chang Liu and Benjamin Foster
Stability and dynamics on time-dependent domains
Pattern formation in growing or moving domains is relevant to a wide range of physical problems particularly in fluid dynamics, soft matter physics and biophysics. The time-dependent domain introduces a number of new processes that remain poorly understood. For example, as the domain grows, new stripes have to be added, and the mechanism and location of this process are not well studied. Likewise the notion of stability must be reexamined. In this project you will perform direct numerical simulations of simple partial differential equations to study dynamics on growing domains and to develop new characterizations of stability of patterns on such domains. Your results will be compared with existing approaches including those based on energy and the quasi-steady assumption.
Troy will work as part of the Edgar Knobloch group and will be mentored by postdoc Chang Liu and graduate student Benjamin Foster.