Tag: Oral Session 2S

A Computational Approach To Tangled String

Presenter(s): Nathaniel Schieber − Mathematics

Faculty Mentor(s): Robert Lipshitz

Oral Session 2S

Research Area: Knot Theory (Mathematics)

Funding: Mercer Family Foundation Scholarship, UO Department of Mathematics Juilfs Scholarship

Knot theory is exactly what it sounds like. It studies how pieces of string can be tied around themselves and around each other. From this tangible starting point, a wealth of abstract mathematics has arisen. My research in knot theory has had two main goals: to study a specific tangle and to classify tangles up to small complexity. Both have centered on encoding and manipulating the three dimensional geometry of knots within a computer program. The specific tangle I am studying is known as Krebes’s Tangle, named for the mathematician who first asked if it were possible to connect the ends of this specific tangle to the ends of a second tangle in order to form a single un-knotted circle of string. My method in approaching this question has been computational, writing code which generates random tangles, accomplishes the gluing process, and then computes a knot invariant known as the Alexander polynomial. In order to classify tangles, my code takes these randomly generated tangles and organizes them into equivalence classes based on what are known as quantum invariants. Both projects are still on–going.

Knot theory has found applications across mathematics as well as in data analysis and DNA research. However, the software for generating and manipulating generic knots directly has remained relatively limited. Along with working toward generalizing the Alexander polynomial, my work adds to the computational resources available to mathematicians studying knots. I hope it to prove of experimental benefit.

Nanoparticles! 

Presenter(s): Makenna Pennel − Chemistry

Faculty Mentor(s): Jim Hutchison, Kenyon Plummer

Oral Session 2S

Research Area: Materials Science

Funding: Hutchison Lab, Alden Research Award

Whether we realize it or not, the emerging field of nanoengineering is continually revolutionizing the world around us. From smartphones to sunscreen, engineered nanoparticles are everywhere in our day-to-day lives. Scientists are constantly discovering new properties and applications—the possibilities of this incredibly small realm seem endless! This talk will feature a general introduction to the fascinating world of nanoparticles, in addition to a brief overview of my research on the topic in regard to metal oxides. Currently my work revolves around synthetic and mechanistic inquires, with emphasis on particle morphology and plasmon tunability. Some of the goals: improving the novel synthetic approach for metal oxide nanoparticles developed by the Hutchison Lab here at the University of Oregon, and creating new structures with enhanced optical properties. These are ongoing interests of mine, but significant progress has been made in both cases. Microscope images of things that are 7 nanometers wide will abound!

Clustering Algorithm Performance Studies for the ATLAS Trigger System at the HL-LHC

Presenter(s): Taylor Contreras − Physics

Faculty Mentor(s): Stephanie Majewski

Oral Session 2S

Research Area: Physics

Funding: PURS, McNair

The Large Hadron Collider (LHC) at CERN is a particle accelerator providing massive amounts of data which can reveal new physics about fundamental particles and forces. An upgrade to the LHC that will increase the luminosity will be enacted in 2026, called the High-Luminosity LHC (HL-LHC). The higher luminosity will increase the rate of proton-proton interactions in detectors like ATLAS, thus these detectors must increase the speed of sorting through data. This sorting is performed by the ATLAS Trigger System, which decides whether an interaction is interesting enough to keep within about ten microseconds. Our group is studying the efficiency of different algorithms that cluster energy for implementation on a Field Programmable Gate Array (FPGA) in the Global Trigger. These algorithms cluster the most energetic cells in multiple layers of the detector to reconstruct particle showers. We have implemented the algorithms used on the FPGA in python in order to validate the performance of the FPGA, analyze the background rejection and trigger efficiency of the clustering algorithms, and compare these quantities between different algorithms.

Using Machine Learning for Source Detection at the First G-APD Cherenkov Telescope (FACT)

Presenter(s): Jacob Bieker – Physics, Computer and Information Science

Faculty Mentor(s): Tim Cohen

Poster 37

Oral Session 2S

Research Area: Physical Science

Funding: DAAD Research Internships in Science and Engineering (RISE)

Finding gamma-ray sources is of paramount importance for Imaging Air Cherenkov Telescopes (IACT). This study looks at using deep neural networks on data from the First G-APD Cherenkov Telescope (FACT) as a proof-of-concept of finding gamma-ray sources with deep learning for the upcoming Cherenkov Telescope Array (CTA). In this study, FACT’s individual photon level observation data from the last 5 years was used with convolutional neural networks to determine if one or more sources were present. The output from the neural networks were compared using the default method of finding sources as a baseline. The neural networks used various architectures to determine which architectures were most successful in finding sources. Neural networks offer a promising method for finding gamma-ray sources for IACTs. With further improvement and modifications, they offer a compelling method for source detection for the next generation of IACTs.