Title

Data-Driven Approaches to Gravitational Wave Polarizations of Colliding Blue Stragglers

School Name

South Carolina Governor's School for Science and Mathematics; Fort Mill High School

Grade Level

11th Grade

Presentation Topic

Physics

Presentation Type

Non-Mentored

Abstract

Blue stragglers are main sequence stars that appear bluer and more luminous than stars at their corresponding main sequence turnoff points. They possess indefinite origins, with suppositions based on circumstantial evidence. It is assumed that these bodies are formed as the single product of binary star collisions. Due to the intangibility of gravitational waves, these emissions receive inattention during interactional studies. However, understanding such properties would expand insight on associated formative distinctions. This research characterizes the linearly polarized components of a collision instigating blue stragglers. JavaScript code processed datasets from the NASA Open Data Portal. These contained such values during differing times for four simulated collisions. One trial was chosen to represent the studied interactivity due to conditional similarities. The 18,654 data points per trial were arbitrarily distributed; thus, the initial results of each study were implemented in graphs of polarization values vs time. R code used an autoregressive integrated moving average model to fulfill functional discrepancies. The Minitab software acquired the values from prior methods, creating plots for the principal simulation. Fourier transforms converted their domains to frequency and the graphs were divided by similar waveforms. For each region, cosinor regression models produced fit line equations. These would allow the prediction of polarization values during times of standard simulations. While physical studies of gravitational waves are onerous, their statistically-driven models can be computationally manipulated. The only required equipment is a laptop with inexpensive softwares. Hence, the process for acquiring developmental data would increase efficiency with certain envisaged frameworks.

Location

HSS 206

Start Date

4-2-2022 11:30 AM

Presentation Format

Oral Only

Group Project

No

COinS
 
Apr 2nd, 11:30 AM

Data-Driven Approaches to Gravitational Wave Polarizations of Colliding Blue Stragglers

HSS 206

Blue stragglers are main sequence stars that appear bluer and more luminous than stars at their corresponding main sequence turnoff points. They possess indefinite origins, with suppositions based on circumstantial evidence. It is assumed that these bodies are formed as the single product of binary star collisions. Due to the intangibility of gravitational waves, these emissions receive inattention during interactional studies. However, understanding such properties would expand insight on associated formative distinctions. This research characterizes the linearly polarized components of a collision instigating blue stragglers. JavaScript code processed datasets from the NASA Open Data Portal. These contained such values during differing times for four simulated collisions. One trial was chosen to represent the studied interactivity due to conditional similarities. The 18,654 data points per trial were arbitrarily distributed; thus, the initial results of each study were implemented in graphs of polarization values vs time. R code used an autoregressive integrated moving average model to fulfill functional discrepancies. The Minitab software acquired the values from prior methods, creating plots for the principal simulation. Fourier transforms converted their domains to frequency and the graphs were divided by similar waveforms. For each region, cosinor regression models produced fit line equations. These would allow the prediction of polarization values during times of standard simulations. While physical studies of gravitational waves are onerous, their statistically-driven models can be computationally manipulated. The only required equipment is a laptop with inexpensive softwares. Hence, the process for acquiring developmental data would increase efficiency with certain envisaged frameworks.