STUDENT RESEARCH SHOWCASE 2020 PRESENTATIONS

Physics and Astronomy

Determining the distance to the close binary star AL Sculptoris
Maya Beleznay, Pine Crest School

This project uses the light and radial velocity data of an eclipsing binary star to determine its distance from earth. This technique improves upon the geometric technique of parallax, and has various application in modern astrophysics, including being used to determine the Hubble Constant.

Quantum Machine Learning Frameworks for Improved SiD Calorimetry and Higgs Boson Analysis
Lucas Braun, School of Science and Technology

Quantum neural networks (QNNs) can solve classically-intractable particle physics problems, but current research ignores future Higgs-boson-centered colliders. I investigated QNN applications for International Linear Collider Higgs analysis. QNNs flexibly accommodated data compression and different quantum computer designs, facilitating future QNN development and showing that Higgs experiments can readily utilize QNNs.

Atomic Force Microscopic Study of Carbon Catalysts for Rechargeable Metal-Air Battery
Alan Chen, Santa Cruz High School

Isolated metal atoms dispersed in a carbon matrix can serve as high-performance catalysts for oxygen electrocatalysis in rechargeable metal-air battery, a new energy technology. For example, porous carbon aerogels embedded with isolated iron atoms in the form of FeN4 are found to exhibit excellent and reversible oxygen electrocatalytic activity, and can be used as bifunctional cathode catalysts in rechargeable zinc–air batteries. However, the distribution of the FeN4 sites within the carbon aerogels is mostly unknown. To acquire such information, we need a tool that can unravel the material structures at the atomic scale. Therefore, in my SIP project, I carried out AFM studies, under the supervision of Professor Jairo Velasco Jr. and his graduate student Mr. John Davenport, to acquire topographic and conductivity images of the samples, and the results showed that the metal sites are mostly located in the high adhesion and high conductivity area of the porous carbon aerogels. These two characteristics are conducive to the oxygen reaction processes and should be the focus in future design and engineering of effective oxygen catalysts for rechargeable metal-air battery.

Anomalous Broad-Line Region Responses to Continuum Variability in Active Galactic Nuclei
Julia Deffner, Menlo School

A current method used to find the mass of black holes is called reverberation mapping, which relies on the concept that emission line fluxes vary in response to the continuum. However, there are times when this does not occur and emission line fluxes do not follow the continuum. In this project, we investigate how often these discrepancies occur and how long they last and show that reverberation mapping must be re-evaluated to take anomalous responses into account.

Manipulating Fluid Shear Tension to Mimic Microgravity and Analyze the Effects on the Pathogenicity and Antibiotic Resistance of Staphyloccocus Aureues and Staphyloccocus Epidermis
Rhea Iyer, American Heritage School Boca Delray

A study to quantify the magnitude to which microgravity affects the virulence and antibiotic resistance of Staphylococcus Aureus and Staphyloccocus Epidermis.

Characteristics and Identification of an Unknown HI emission
Karen Lei, Saratoga High School

In this research, a homemade horn radio telescope is used to conduct observations of the 21 cm neutral hydrogen emission line with software that performs Fast Fourier Transform. The goal was to identify the unknown source that emitted the the hydrogen that was detected. Values such as V_lsr and brightness temperature were calculated to compare with those of objects found in SIMBAD and in VizieR to identify the unknown source. Ultimately, the source was found to be a combination of the objects FVW 173.0+0.0 and FVW 173.0+3.0.

Random Forest Classifier-Aided Candidate Selection of Photometrically Variable Milky Way Halo RR Lyrae in the NGVS Data Set
Jeffrey Munsell, Bronx High School of Science

I developed candidate selection methodologies for identifying RR Lyrae stars through variability analysis of data from the Next Generation Virgo Cluster Survey.

Bassoon - Dinosaur or Innovation?
Zoe Williams, Athens High School

The bassoon was invented in the 17th century, however its predecessors were seen throughout the Middle Ages. In comparison to the majority of modern wind instruments, the bassoon often feels antique and over-complicated. Additionally, functional bassoons rarely cost below $10,000, making them inaccessible to many musicians around the world. I set out to both simplify the modern bassoon model and provide a way to create a reproducible one with much cheaper materials.

Novel Surveys of Substructure in Pulsar Glitch Morphology and Glitching Pulsar Populations
Christine Ye, Eastlake High School

In this project, I present an in-depth substructure analysis of morphology of individual pulsar timing glitches, populations of glitching and non-glitching pulsar, individual glitching pulsar behavior and statistics, and potential future observations, with applications in neutron star theory and pulsar timing.

Calculating Arrival Times at the Center of a Bose-Einstein Condense
Quincy Webb, Smith College

Using measurements of the shape of the ground state wavefunction of a Bose-Einstein condensate before it has been released from the trapping potential, we work to estimate the time at which interference patterns will occur.

Automated Search for Globular Clusters in Virgo Cluster Dwarf Galaxies
Emily Zhou, The Harker School

In this project, we developed a novel automation flow to efficiently process and accurately analyze galaxy images from the Next Generation Virgo Cluster Survey (NGVS) to search for Globular Clusters (GCs). We implemented and verified deep learning Convolutional Neural Networks (CNN) modeling to remove human involvement in the GC detection process and automated the process from fitting background isophotes to the final GC recognition. This new, flexible flow integrates deep learning Convolutional Neural Networks, computation algorithms, mathematical analysis, and astrophysics-based modules. It significantly enhances an otherwise manual process and facilitates the production of unique and bigger GC datasets in low-luminosity dwarf galaxies, contributing to a better understanding of galaxy formation.

STUDENT RESEARCH SHOWCASE 2020 PRESENTATIONS

Physics and Astronomy

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