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Projects


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 may be as large as 16 meters in diameter and will be designed to meet both the needs of astrophysicists probing the beginnings and endings of stars, planetary systems 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. We will also look at designing, fabricating, and testing multilayer mirror coatings under the aluminum which will further extend the mirrors’ reflectance into the EUV.

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Brian Anderson, Tim Leishman, 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.

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Manuel Berrondo and Jean-Francois Van Huele

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.

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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.

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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.

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John Colton

Semiconductor Materials Physics with Optics

This research involves studying the materials properties of semiconductors through primarily optical methods. Right now we’ve got four main research projects going on: 1) Semiconductor nanoparticles in ferritin - Ferritin is a hollow protein about 10 nm in diameter and can be used to create semiconductor nanoparticles which form inside the protein shell. We’re investigating ways to synthesize the nanoparticles and studying their properties once synthesized. 2) Platinum nanoparticles - Ferritin semiconductor nanoparticles can be used to make metallic platinum nanoparticles. Platinum is well known as a catalyst, and nanoparticles are great catalysts because of the extremely high surface area to volume ratio. We’re looking to use them to produce hydrogen gas. 3) ZnO thin films - Zinc oxide is a semiconductor that potentially has good optical properties that should allow it to be used to make semiconductor devices such as LEDs and lasers. However, to make such devices you need both “n-type” and “p-type” material. ZnO tends to naturally form as n-type and it’s traditionally been very hard to make good quality p-type ZnO, but we have a method of doing thin film growth on substrates coated with ZnAs that has promise. 4) Nanoparticles as temperature sensors - We’re trying to use semiconductor nanoparticles as temperature sensors. By characterizing the nanoparticles’ photoluminescence as a function of temperature, both wavelength spectrum and time of emission, we should be able to later measure the optical properties in order to deduce the local temperature. For example, one could use the optical emission from nanoparticles injected into tissue to monitor temperatures as focused ultrasound is used to heat up and destroy tumors.

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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.

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Nanomaterial preparation and characterization techniques including: - chemical vapor - deposition of nanotubes - atomic force microscopy and manipulation - ellipsometry - electron microscopy - lithography

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Dallin Durfee

Lensless Imaging

We are working on a method of imaging without the need of lenses, and with a single-pixel detector. The technique is based on the collection of light scattered from an object by a pair of interfering laser beams. The technique has several advantages over conventional imaging, including an unlimited effective depth of field and field of view which is independent of the resolving power. Also, as it does not require high quality optics or a multi-pixel detector, it could be very useful for things such as x-ray, electron wave, or acoustic imaging. We are working on testing various aspects of the imaging method, including testing effects of shadows in the interfering beams, understanding the effects of wavefront distortion, finding ways to decrease the required detector dynamic range, using the method to realize digital holography, and finding ways to increase speed and resolution.

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Ben Frandsen

Local Structure of Quantum Materials

“Quantum materials” possess fascinating properties such as superconductivity, unconventional magnetism, topological phases, and more. These properties cannot be explained by classical physics, but instead originate from the principles of quantum mechanics playing out in a system with a large number of interacting particles—in this case, the electrons in a solid. In addition to revealing the fundamental workings of quantum mechanics in solids, many of these materials may also have potential for technological application. We use advanced scattering techniques with beams of x-rays and neutrons to study the atomic and magnetic structure of quantum materials and gain insight into their exotic properties. Students will perform sophisticated data analysis and visualization in the Python programming language and may also help synthesize these materials in the laboratory.

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Eric Hintz

Astronomy: Robotic telescopes, Variable Stars

Project 1

I’m currently in the process of installing 5 robotic telescopes to provide follow-up observations for potential transiting planets from the TESS satellite and the KELT project. The student will program the robots to acquire the data each night, process the digital frames, and then produce a light curve for the target and surrounding stars. These observations will then be uploaded to the appropriate research teams.

Project 2

Using the robotic telescopes of the BYU Eyring Science Center I’m monitoring a set of pulsating variable stars to watch for phase jumps that might be associated with changes in the pulsational characteristics of the star. These changes give us clues to the internal structure of the stars. Students will need to program the robotic telescopes, reduce data, generate light curves, and analyze the data.

Project 3

The SCORPIO camera is currently being constructed for installation on the Gemini-South telescope. A student involved in the project will work to model the expected output of this new camera system as it applies to the study of pulsating variable stars such as Cepheids and delta Scuti.

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Eric Hirschmann and David Neilsen

Numerical Relativity

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.

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Michael Joner

Astronomy: Asteroids, Exoplanets, etc.

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.

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Traci Neilsen

Underwater Acoustics

Large arrays of hydrophones in the ocean can be used to locate acoustic sources. The reliability of these localization algorithms depends on the degree to which the ocean environment is correctly parameterized in the models. Machine learning is needed to correctly tackle this problem in real-time.

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Justin Peatross

High Intensity Laser Physics

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Denise Stephens

Astronomy: Brown dwarfs

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.

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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?

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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.

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Contact

Jean-Francois Van Huele
N151 ESC Brigham Young University
Provo UT, 84604
(801) 422-4481

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