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Example Research Projects

Sample projects for REU and RET participants

 

David Allred

Extreme Ultraviolet Optics for Next Decade's Broadband-Space Observatory

One or two REU students will work with Professor Allred to do the basic material and optical science behind determining what the mirror coating for the next very large NASA flagship space telescope will be and how that coating will be applied and protected. The LUVOIR (large UV-optical-IR) space telescope is in the formulation stage with scientists and engineers around the country contributing their insights. It will be as large as 10 meters in diameter and must meet both the needs of astrophysicists probing the beginnings and endings of stars and galaxies, etc., and the needs of exoplanetary scientists seeking to characterize some of the tens of thousands of planets around other stars that we will discover in the 10 years the space observatory will be used. Professors Allred and Turley's research will look at protecting aluminum in a way that allows its VUV and EUV optical properties to remain intact.

Background Needed

  • Introductory mechanics and electromagnetic theory. Modern physics is helpful.

Skills Developed

  • High vacuum and ultra-high vacuum systems
  • Thin film evaporation and sputtering techniques
  • X-ray diffraction measurements
  • X-ray photoelectron spectroscopy measurements
  • Ellipsometry
  • Atomic force microscopy
  • VUV optical measurements

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Branton Campbell

Materials Science

Students in our group use intense particle beams (e.g. electrons, x-rays, neutrons) to probe atomic structures in useful and exotic materials such as high-temperature superconductors and superionic conductors and their relationships to the interesting material properties.  Group members learn to apply advanced computer algorithms and mathematics to real-world physics problems. See http://www.physics.byu.edu/faculty/campbell/ for more information.

Background Needed

  • introductory physics and/or chemistry courses
  • an introductory computer programming course

Skills Developed

  • drive state-of-the-art diffraction instruments
  • learn Fourier analysis techniques
  • use sophisticated data analysis software such as Maple, MATLAB, or Mathematica

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Karine Chesnel

Nanomagnetism

Our group studies magnetic properties of nanosystems such as nanoparticles and magnetic ultra-thin films. These materials exhibit magnetic structures at the nanometer scale. We use various tools to investigate the properties of these magnetic structures, including magnetic imaging (MFM),  magnetometry (VSM), and synchrotron X-ray scattering techniques. By combining these different experiments we learn about how magnetic domains form, propagate and disappear as we apply an external magnetic field to the material. In the case of magnetic thin films, we also study the ability for the magnetic domain pattern to remember its configuration throughout field cycling. In case of magnetic nanoparticles, we also study magnetic ordering between nanoparticles and dynamics of magnetic fluctuations.  This research is mostly experimental. A REU student would typically be involved in collecting magnetic images or magnetometry data on these magnetic structures after proper training on instrumentation, and in analyzing the data.

Background Needed

  • Interest in material sciences
  • Basic Electricity and Magnetism
  • Introductory Modern Physics
  • Interest in learning experimental techniques

Skills and Knowledge Learned

  • Expertise in Magnetic Force Imaging (MFM)
  • Expertise in Vibrating Sample Magnetometry (VSM)
  • Expertise in X-ray Diffraction (XRD)
  • Advanced knowledge in nanomagnetism
  • Analytical skills in interpreting magnetometry data and analyzing magnetic images
  • Computational skills in processing X-ray scattering images

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John Colton

Optical Studies of Semiconductors

This research involves studying the materials properties of semiconductors through optical methods. We have two main research areas: (1) ferritin as a material for solar energy harvesting, and (2) SiC (silicon carbide) as a material for spintronics/quantum computing. The ferritin project involves chemically synthesizing nanoparticles inside the hollow protein ferritin. The particles are roughly 10 nm in diameter. We are then studying these particles by measuring chemical, structural, and optical properties for use in solar cells and as catalysts for water splitting (hydrogen gas production). The SiC project involves studying the properties of the electronic spin states of defects in the material.  Experimental techniques combine optical spectroscopies such as photoluminescence and reflectivity with magnetic resonance of the electron and sometimes nuclear spins. Experiments are done at very low temperatures (down to 4 K) and large magnetic fields (up to 1.4 T tesla). Students help in all aspects of the experimental work, including things like writing computer programs to control equipment and take data, synthesizing nanoparticles, aligning the lasers and other optics, and plotting and analyzing data.

Background Needed

  • introductory modern physics class would be helpful
  • other skills such as chemical synthesis, computer programming, and/or basic electronics can also be helpful

Skills Developed

  • experience with lasers, microwaves, and cryostats
  • optical spectroscopy techniques
  • fundamental concepts in quantum mechanics and semiconductor physics
  • computer programs to control experiments
  • chemical synthesis and characterization
  • miscellaneous lab skills (basic electronics, plumbing, soldering, etc.)

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Robert Davis

Nanostructure Fabrication and Characterization Our group is working on microscale and nanometer scale fabrication and characterization. Recent advances now allow us to fabricate structures including biological structures with sizes down to a few nanometers across. In our research, we are exploring carbon nanotube composites, nanoscale chemical patterning of surfaces, and nanocrystaline phase change materials. These nanostructures have unique mechanical and electrical properties and will have significant impact in many fields including: solar power conversion, micromachines and microsensors, and biological tissue growth. We perform a host of measurements on these structures to aid in understanding and controlling their structure and physical properties.

Background Needed 

  • introductory mechanics electricity and magnetism
  • modern physics
  • electronics is valuable

Skills Developed 

Nanomaterial preparation and characterization techniques including:

  • chemical vapor
  • deposition of nanotubes
  • atomic force microscopy and manipulation
  • ellipsometry
  • electron microscopy
  • lithography
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Kent Gee, Traci Neilsen, and Scott Sommerfeldt

Acoustics

There is an opportunity for collaborative research with faculty and current graduate students in the area of acoustics. Projects may involve making a variety of acoustical measurements in different types of sound fields. Examples include pressure, intensity, or other energy-based measurements in our anechoic or reverberation chambers, in ducts, or outdoors. Some research may include working with theoretical or numerical models for comparison with experimental data. Other research could involve measurement automation using LabVIEW or another package. Applications of current research involve architectural and audio design, jet and rocket noise simulation, and active noise control.

Background Needed

  • strong interest in acoustics, audio, or noise control
  • aptitude for working with instrumentation (oscilloscopes, analyzers, microphones, etc)
  • familiarity with a numerical mathematics program such as MATLAB or Mathcad
  • an ability to both work with a team and independently
  • knowledge of passive electrical circuits would be helpful
  • a working knowledge of LabVIEW would be helpful

Skills Developed

  • hands-on familiarity with acoustical measurement hardware
  • ability to comprehend relevant technical literature
  • acoustic data analysis and graphical representation
  • data interpretation
  • physical experiment design
  • programming experience in MATLAB, Mathcad, LabVIEW, or another language
  • a knowledge of time series photometry

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Eric Hintz

Astronomy

Participants will have two projects to choose from when working with Dr. Hintz:

Observations of High Mass X-Ray Binaries

These are binary star systems with one very massive star (O or B) and a compact object in orbit about the primary. The compact object could be either a neutron star or a black hole. We are monitoring these systems to watch for short and long term variations that will give us clues about the interaction of the two components.

Background Needed

  • introductory astronomy class helpful
  • basic telescope observing skills
  • IRAF data reduction skills
  • much of this could also be learned while you are here

Skills Developed

  • astronomical observing techniques
  • CCD observing
  • telescope operations
  • data reduction methods using IRAF
  • astronomy background

Period Changes in Medium Amplitude Delta Scuti Variables

In general, researchers consider there to be two groups of delta Scuti variables; the High Amplitude delta Scuti (HADS) and the Low Amplitude delta Scuti (LADS). However, the in between realm is interesting. The Medium Amplitude delta Scuti stars seems to show a range of changes in both amplitude and period. This makes them a very interesting group to monitor. Often we participate with astronomers from around the world in taking data for these projects.

Background Needed

  • skills acquired in Physics 329 are useful, but not entirely required to get started

Skills Developed

  • astronomical observing techniques
  • CCD observing
  • telescope operations
  • data reduction methods using IRAF
  • astronomy background

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Eric Hirschmann

Students will study aspects of compact object systems such as neutron stars and black holes in astrophysical environments. Problems considered include the modeling the physics of neutron stars such as their interior and exterior magnetic field configurations and the effects of rotation, magnetic helicity and equations of state. Dynamical binary systems may also be studied.

Background Needed

  • strong mathematical skills
  • experience with numerical methods, particularly as applied to solving differential equations

Skills and Knowledge Learned

  • differential geometry
  • an introduction to general relativity
  • computational physics
  • skills related to solving PDEs

 

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Michael Joner

Astronomy

My research is focused on the study of time series observations of a wide variety of different astrophysical sources.  These objects include solar system minor bodies such as asteroids and Kuiper belt objects, the detection of planetary sized objects transiting distant stars, the study of both pulsating and eclipsing variable stars, and studies of extragalactic objects such as blazars and active galactic nuclei.  Current studies look for variability on timescales of a few minutes all the way up to several years. These data can be used to detect extrasolar planets, determine fundamental stellar properties, and define the fundamental properties of supermassive black holes in distant galaxies.  REU students will work on a project in one of these fields by making observations at our West Mountain Observatory or by analyzing archival data from previous observing runs.  One interesting bonus gained by doing work at the observatory is that there are often opportunities to help with observations on a wide variety of objects being studied as part of several different ongoing investigations.

Background Needed

  • some background in introductory astronomy
  • a desire to learn something new

Skills Developed

  • astronomical observing techniques
  • CCD observing methods
  • telescope and observatory operations
  • data reduction methods using different astronomical software  (IRAF, AstroImageJ, VPhot)
  • a knowledge of time series photometry

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Darin Ragozzine

Orbits of Exoplanets and Solar System Small Bodies

Despite our powerful telescopes, most objects we discover are so far away that they only appear as a point of light. This includes objects in the far reaches of our own solar system beyond Neptune, known as Kuiper Belt Objects (or KBOs or sometimes Trans-Neptunian Objects or TNOs). Much further away are planets orbiting around other stars -- exoplanets -- which are usually discovered without detecting the light from the planet at all, but only the effect that the planets have on their parent stars. For both exoplanets and KBOs, the majority of the limited information we have is their orbital properties, such as time to complete an orbit or tilt of the orbit relative to some reference plane. As a result, orbital dynamics can be used to investigate both populations. An REU student could choose among multiple projects related to the orbits of KBOs or exoplanets. The goal of the project would be to develop transferable skills, to gain a letter of recommendation, and to contribute to the publication and/or conference proceeding. Dr. Ragozzine has a talent for identifying summer undergraduate projects; he has assisted 5+ undergraduate students in their eventual publication of a first-author journal article, helping to launch them into good graduate schools. 

Background Needed

 - Scientific computing (even at a minimal level)
 - Basic knowledge of math, physics, astronomy, and/or planetary science is helpful.

Skills Developed

 - Better undergraduate research practices
 - Stronger scientific computing
 - Improved insight into KBOs or exoplanets
 - State-of-the-art statistical analysis
 - Written/oral communication

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Denise Stephens

Astronomy

Our research group is currently looking for binary brown dwarf systems using data from the Hubble Space Telescope (HST). An REU student would primarily work with us on refining the binary detection technique, looking for new binary systems, and characterizing the uncertainty in the detection approach and the final magnitudes, separations, etc. of the systems.

We also have a research program searching for transiting planets around nearby bright stars.  Using the 16" telescope on campus and the 0.9-meter telescope at West Mountain, we will teach the REU student how to obtain photometric data for a star that may have a planet.  We will teach the REU student how to reduce that data and run it through a data processing program called AstroImageJ to determine whether or not the star's light curve does show a dip in brightness characteristic of the drop in light expected from a transiting exoplanet.

Background Needed

  • Introductory astronomy class useful, but not required
  • Must be able to write and work with some kind of computer programming language
  • Have enough familiarity with programming to read and understand Fortran programs

Skills and Knowledge Learned

  • Analyzing and Reducing data from HST
  • Creating programs to handle various aspects of data reduction
  • Learning how to program using Monte Carlo techniques
  • Data reduction using the STSDAS package in IRAF
  • Background in astronomy doing some cutting edge research

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Mark Transtrum

Information Theory of Multi-Parameter Models

Mathematical modeling is a central component of nearly all scientific inquiry. Parsimonious representations of physical systems, together with robust methods for interacting with them, is one of the primary engines of scientific progress. Much of the work in our group involves developing new methods, both theoretical and computational, for improving the predictive performance of complex multi-parameter models. Our research explores the mathematical structures that enable predictive modeling. We use information theory, statistics, differential geometry, and topology, as well as relevant physical laws from a variety of fields to better understand data, models, and the relationship between reductionism and emergence.

Computational Methods for Exploring Complex High-dimensional Parameter Spaces

Modern computers enable large models of complex processes. These models often involve a large number of parameters and a relevant question is often how the model's behavior depends on the parameter values. Because the parameter space is high-dimensional, a brute force search will never be possible for models with more than a few parameters. We are developing novel computational methods for efficiently and intelligently exploring these high-dimensional parameter spaces. This project uses theoretical insights based on information theory and applies sophisticated techniques in computational differential geometry, automatic differentiation, and topology with high-performance computing. Our goal is to improve algorithms for fitting models to data, performing statistical sampling, and classifying regimes of distinct model behaviors.

Modeling Complex Energy Systems

Models of energy systems involve a large number of heterogeneous components connected in complex networks. Detailed models of these systems constructed from physical first principles are similarly complicated and involve a large number of unknown parameters. In spite of their detail and complexity, models often have limited predictive capability because it is difficult to identify the model, i.e., find accurate values for all of the parameters. Our goals it develop models that are sufficiently complex to capture the rich behavior of real power systems, but simple enough so that all the parameters can be learned from data.

Modeling Complex Biological Systems

Biological systems are rich in the types of behavior they can exhibit. This is enabled through a complex web of components. In the case of development biology, the relevant components are networks of chemical reactions while in neuroscience, it is a combination of electrical and biochemical signals. In both cases, the complex system responds to external stimuli and performs calculations to formulate an appropriate response. The complexity of these systems is overwhelming. New theoretical and computational tools are needed to organize our knowledge of these processes and compress it into a coherent theory. Our research tries to develop minimal models from these "parts lists" in order to summarize and organize our understanding of biological and neurological processes.

Superconducting Materials for Next Generation Particle Accelerators

Particle accelerators are are foundational technology in modern science, enabling fundamental research in facilities such as the Large Hadron Collider (LHC), as well as providing some of our best sources of coherent x-rays for probing nanoscale structure in materials. The same physical principles underly other technologies such as electron microscopy. Superconducting resonance cavities are the enabling technology that allows subatomic particles to be accelerated to near light speeds. In collaboration with researchers at the Center for Bright Beams (cbb.cornell.edu), we are working to better understand materials properties of superconductors in order to lay the foundation for the next generation of particle accelerators. Our work uses high performance computing to solve equations that describe how specific materials respond to applied magnetic fields, accounting for details such as surface roughness, grain boundaries, and material inhomogeneities.

Machine Learning on Acoustic Data Sets

Sound is one of the fundamental ways we observe our environment. In collaboration with acousticians at BYU and Blue Ridge Research and Consulting, we use machine learning techniques to predict ambient sound levels from environmental parameters (such as the distance to a road or local population densities). Our models will ultimately be useful for a variety of applications including military mission planning, public health, urban development, and ecology. We also use machine learning to predict crowd dynamics from acoustic data sets. Can analysis of acoustic data collected at sporting events be used to infer the shifting mood of a diverse crowd? If so, can acoustic monitoring be used to improve law enforcement responses to crowds before they become violent?

Background Needed

  • Programming experience in Python/Julia/or other scripting language
  • Multivariate Calculus/Ordinary Differential Equations
  • Computational Physics Tools

Skills Developed

  • Information theory
  • Differential Geometry
  • Programming/Scripting/Analysis skills
  • Techniques of mathematical modeling and simulation
  • Theory of specific complex systems (e.g., power systems, developmental biology, neuroscience, materials science)

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Richard Vanfleet

Electron Microscopy

These projects involve the characterization of materials from the micron level down to atomic dimensions. The primary tools are electron microscopes (SEM and TEM). These unique instruments will not only allow students to image nanostructures and new materials but will allow them to probe structure, composition, and chemistry with high resolution.

Background Needed

  • introductory physics
  • some computer experience

Skills Developed

  • materials handling and polishing
  • SEM and TEM sample preparation
  • SEM and TEM basic operation

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Jean-Francois VanHuele and Manuel Berrondo

Quantum Dynamics for Quantum Information Systems

We study the time evolution of quantum systems with time-dependent parameters for which no exact analytic solutions are known. These involve anharmonic and coupled oscillators, quantum optical and condensed matter systems exhibiting nonlinear effects, all of which play a role in experimental implementations of quantum information schemes involving entanglement, interference, and teleportation. We aim for quantum control and watch for the onset of decoherence and dissipation.

Background Needed

  • Exposure to symbolic manipulation and programming (Mathematica and MATLAB will be used)
  • Elementary knowledge of quantum operator formalism
  • Willingness to learn

Skills and Knowledge Learned

  • Analytical and computational skills
  • Lie algebras/differential equations
  • Interpretation of approximate solutions in quantum optical and condensed matter systems

 

 

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Biological systems are rich in the types of behavior they can exhibit.  This is enabled through a complex web of components.  In the case of development biology, the relevant components are networks of chemical reactions while in neuroscience, it is a combination of electrical and biochemical signals.  In both cases, the complex system responds to external stimuli and performs calculations to formulate an appropriate response.  The complexity of these systems is overwhelming.  New theoretical and computational tools are needed to organize our knowledge of these processes and compress it into a coherent theory.  Our research tries to develop minimal models from these "parts lists" in order to summarize and organize our understanding of biological and neurological processes.
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