Student Research Papers Volume 30

2019 NSF/REU RESEARCH EXPERIENCE FOR UNDERGRADUATES IN PHYSICS STUDENT RESEARCH PAPERS VOLUME 30 REU DIRECTOR UMESH GARG, PH.D. REU Student Research Papers – Summer 2019 University of Notre Dame – Department of Physics Page Student Research Title No. Winter Allen Exploring the Optical Region of Stellar Spectra for 1 Arkansas Technical University Metallicity Dependence Samantha Berek Project AMIGA: Circumgalactic Gas about 11 Yale University Andromeda and its Dwarf Satellites Adaptive Optics: Phase Unwrapping Hannah Blue 21 University of Hawaii at Hilo Alyssa Davis Determining the astrophysical 20Ne(α,p)23Na Swarthmore College reaction rate from measurements with the Notre 31 Dame 5U accelerator Jack Enright Characterization of CeBr3 Scintillation Detectors for 41 University College Cork use in Coincidence Measurements Yadira Gaibor Reducing the uncertainty of the white dwarf spin- 51 Missouri State University down rate in AR Scorpii Arianna García Caffaro Implementation of Tilted Module Bend Corrections 59 University of Notre Dame in the CMS Detector Track Trigger Curtis Goss Radial Velocity Measurements of Stellar Bodies Indiana Univeristy Bloomington Observed by the Automated Planet Finder and Their 69 Implications on Exoplanet Exploration Kathleen Halloran The First Extended Look at an Eclipsing Polar: 79 Saint Mary's College V1309 Orionis Isabella Ianora Teaching Through the Chart of Nuclides 89 University of San Diego Sean Kelly The High Efficiency Total Absorption Spectrometer University of Notre Dame (HECTOR) and Correcting for Inconsistencies in 99 27A1(p,γ)28Si Kinsey Lee Dependence of Helium Atmospheric-Pressure Scripps College Plasma Jet (APPJ)-Induced DNA Damage on 109 Voltage Pulse Duty Cycle and Irradiation Time Emily McGill Detecting PFAS in Water Samples Using PIGE 119 Dublin City University Austin Mitchell 23 20Ne(α,p) Na 129 University of Southern Indiana Adil Mubarak Development of the Electrical Feedthroughs for the 137 University of Minnesota at Duluth ND-cube Active-Target Detector Yohan Musle Synthesis of (BiSb) Te Thin Films on Mica 2 3 147 Florida Atlantic University Through Chemical Vapor Deposition A model for marine species distribution in advective- Ha Cong Nguyen 157 Hanoi National Univ.of Education diffusive ecosystems Mary-Ellen Phillips Investigating CMS Binary Chip Data Losses in the Fort Lewis College Phase II High Luminosity Upgrade to the CMS 167 Jason Prochaska Improving the Notre Dame MR-TOF and using 179 Drake University Photodiode Detectors for Beam Analysis Rachel Procter-Murphy Energy Reconstruction of Low Energy Supernova 189 Arizona State University Neutrinos Bingcheng Qing Measurements on Magnetic Properties and 199 Xi'an Jiaotong University Transport Properties of Spintronic Materials Melissa Quevedo Low-energy electron interactions with triatomic 209 University of Atlántico molecules: CO2 and H2O Zariff Rahman Characterization of Clover Detectors for use in 219 University of Wisconsin-La Crosse fIREBall Detectors Austin Smith Extraction of Optical Model Parameters for Point Loma Nazerene University 90Zr(6Li,6Li) at 60 MEV / U Using a Markov Chain 229 Monte Carlo Algorithm Nicholas Tusay Adaptive Optics Instrumentation: Building a Better 239 The College of New Jersey Wave Front Sensor Tianyi Wang Study of Stub Momentum Resolution and Hardware 249 University of Notre Dame Benchmarking for CMS Track Trigger Hiroka Warren The Effect of Finite Organism Size and Contact 259 University of Southern Mississippi Forces in Population Dynamics Lexanne Weghorn 14 Implanted N Target Preparation and Evaluation 269 University of Wisconsin-La Crosse Sierra Weyhmiller A Method to Account for Hydroxide Contamination University of Notre Dame in Characterizing the Giant Monopole Resonance to 279 Determine an Accurate Kτ Jingxu Xie Mean Filter Method to Find and Identify Peaks for Xi'an Jiaotong University PIGE and PIXE and Signal Sensitivity Analysis for 289 2.4-5.1 MeV Protons Haobo Yan Simulation of the elastic scattering in the HIPPO 299 Xi'an Jiaotong University gas-jet target of the St. George recoil separator Yihao Zhou Influence of Carbon Abundance on Stellar Xi'an Jiaotong University Photometric Temperature Estimates 309 Exploring the Optical Region of Stellar Spectra for Metallicity Dependence Winter Allen 2019 NSF/REU Program Physics Department, University of Notre Dame Advisors: Prof. Timothy C. Beers Prof. Vinicius M. Placco 1 Abstract Metal-poor stars are fossil records of the chemical evolution of the Galaxy and the Universe. They are rare and therefore hard to find. The goal of this work is to facilitate the identification of such objects by using photometric filter response curves, designed to be sensitive to metallicity changes. This can make the process of finding the metal-poor stars faster and easier. Using several mathematical methods, a filter response curve has been designed to measure theoretical metallicity sensitivity over the optical region of the electromagnetic spectrum of stars in the Milky Way. Introduction In the early universe, the first stars were composed primarily of hydrogen. Elements heavier than lithium were formed in stellar nucleosynthesis. Over time, a portion of these stars ended as supernova explosions, which enriched the surrounding environment with the heavier elements. The subsequent stars to form were composed of those heavier elements. As generations of stars formed, the stars became enriched with heavier and heavier elements. (Welsh, et al., 2019) In the search for old stars, metal-poor stars are of great interest. The abundance of one metal compared to another in a star relative to solar abundance ratio is defined by Equation 1. Τ Τ Τ (െ݈݋݃ଵ଴ሺܰ஺ ܰ஻ሻٖ (1 כሿ ؠ݈݋݃ଵ଴ሺܰ஺ ܰ஻ሻܤܣሾ The variables ܰ஺and ܰ஻ represent the numbers of atoms in the elements A and B, respectively. ,The * symbol refers to star of interest, while the ۨ symbol refers to the sun. (Beers & Christlieb 2005) For the purposes of this project, the metallicity of a star is referred to by the iron to hydrogen ratio, [Fe/H]. The classifications defined by Beers & Christlieb (2005) for metal-poor stars are shown in Table 1. 2 [Fe/H] Term Acronym >+0.5 Super metal-rich SMR ~0.0 Solar - <-1.0 Metal-poor MP <-2.0 Very metal-poor VMP <-3.0 Extremely metal-poor EMP <-4.0 Ultra metal-poor UMP <-5.0 Hyper metal-poor HMP <-6.0 Mega metal-poor MMP Table 1. Classifications for metal-poor stars (Beers&Christlieb, 2005). When stars have low metallicities, they tend to have increased levels of carbon abundances. These are referred to as Carbon-Enhanced Metal-Poor (CEMP) stars. (Yoon, et al., 2016) The abundance of carbon can affect the optical spectrum, so it is important to keep the absolute carbon abundance constant. The carbon abundance is measured in the outer atmosphere of the star where the absorption lines under study are collected. (Beers & Christlieb, 2005) For this project we held the absolute carbon abundance constant and varied the metallicities. The absolute carbon is determined by the relation shown in Equation 2. (ሻۨ (2ܥሺܣሿ൅ܪȀ݁ܨሿ൅ሾ݁ܨȀܥሻ ൌሾܥሺܣ ,.ሻۨ is the solar absolute carbon abundance, which has a value of 8.43 . (Yoon, et alܥሺܣ Where 2016) The focus of this project is to determine regions in the optical spectrum that are sensitive to metallicity. Currently, when observing the sky, observers must either scan a large portion of the sky for less detailed information of stars, or they can focus on individual stars and collect more information. Developing a filter response curve may aid in the determination for which stars are 3 metal-poor using the less detailed information. Old stars could hold the important insights into the early universe. Metal-poor stars are rare, which is why a photometric filter response curve developed specifically for metal-poor stars is needed to aid in the search. Data The methods for this project were developed using synthetic spectroscopic stellar data from the library developed by Whitten, et al.(2018). The library was developed using model atmospheres computed with the MARCS code. From the model atmospheres, they generated the synthetic spectra using the Turbospectrum routine. The library consists of wavelengths from 3000 to 10,000 AngstromsሺՀሻ. Also, the parameters in the library consist of 3500K ൑ Teff ൑8000K in 250K increments, 0.0൑݃൑ͷǤͲ in increments of 0.5 dex, െͶǤͷ ൑ ሾܨ݁Τ ܪሿ ൑൅ͲǤͷ in increments of 0.025 dex, and െͳǤͷ ൑ ሾܥܨ݁Τ ሿ ൑൅ͶǤͷ in increments of 0.25 dex. (Whitten, et al., 2018) The synthetic data from this library used on this project consisted of thirty-two files for four different temperatures. The temperatures were 5000K ൑ Teff ൑6500K in 500K increments. For each temperature, the absolute carbon abundance was held constant at 6.93. Since the surface gravity and temperature are dependent, its value changed with temperature. The metallicity values ranged from െͶǤͲ ൑ ሾܨ݁Τ ܪሿ ൑െͲǤͷ in increments of 0.5 dex. The data consisted of the wavelengths from 3000 Հ to 10,000 Հ and the corresponding normalized flux and the actual flux. All computation was performed using the actual flux. As a result of the synthetic data used to develop the filter, it is only applicable to the Sloan Extension for Galactic Understanding and Exploration (SEGUE) spectra and Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectra. 4 Methods The computational work performed in this project was done using Spyder 3.2.8. The first step was to compute the variance of the eight different metallicities for each temperature using the NumPy package. The formula for variance is shown by Equation 3. σሺ௑ି௑തሻమ ߪଶ ൌ (3) ேିଵ The variables X and ܺത represent the actual flux values and the average of the actual flux values for each corresponding row. The variable N represents the eight values. This resulted in four different sets of data of the actual flux variance. These sets were plotted versus the wavelength using Matplotlib. This was done to determine and visualize where the actual flux varies most with metallicity.

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