Bulletin of the American Physical Society
2021 Virtual Conference for Undergraduate Women in Physics
Friday–Sunday, January 22–24, 2021; Virtual
Session U07: Astrophysics and Cosmology IInteractive Live
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Chair: Sally Shaw, University of California, Santa Barbara |
Sunday, January 24, 2021 12:00PM - 12:10PM |
U07.00001: Identifying Exoplanets with Deep Learning: Removing Stellar Activity Signals from Radial Velocities Using Neural Networks Zoe L de Beurs, Andrew Vanderburg, Christopher J Shallue Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. Here we show that machine learning techniques (linear regression, neural networks) can effectively remove these activity signals from RV observations. Previous efforts have focused on carefully filtering out activity signals in time using Gaussian process regression (e.g. Haywood et al. 2014). Instead, we separate activity signals from true center-of-mass RV shifts using only changes to the average shape of spectral lines, and no information about when the observations were collected. We demonstrate our technique on simulated data, reducing the RV scatter from 82.0 cm/s to 3.1 cm/s, and on approximately 700 observations taken nearly daily over three years with the HARPS-N Solar Telescope, reducing the RV scatter from 1.47 m/s to 0.78 m/s (a factor of $\sim$ 1.9 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars. In this way, improvements in RV precision could significantly accelerate the characterization of habitable zone Earth-sized exoplanets. [Preview Abstract] |
Sunday, January 24, 2021 12:10PM - 12:20PM |
U07.00002: Exploring the Circumgalactic Medium of Quasars: A Search for Nearby Interacting Galaxies Cynthia Ibrahim, Greg Walth, Sean Johnson, Gwen Rudie, Thomas Cooper, John Mulchaey, Hsiao-Wen Chen A challenge in the study of the circumgalactic medium (CGM) around quasars is pinpointing what causes quasars with cool circumgalactic gas to be more luminous than ones without. The CGM is the gas surrounding a galaxy, which is outside their stellar disks and within their virial radius. It is important because the CGM is a source of fuel for star-formation which \textunderscore can then drive stellar winds that can help recycle metals\textunderscore . Here we explore galaxies near quasars, at redshifts 0.4 \textless z \textless 1.0, to test if they are interacting with the quasar which may dynamically disturb the cool gas in the quasar's CGM. This could possibly lead to gas accretion which would fuel the quasar. One way to test for interaction is by investigating the nebular emission of galaxies near a quasar's environment. We do this by looking for a correlation between intensity of emission lines and distance from quasar. Specifically, we are focusing on nebular emission lines associated with star formation with the goal of searching for enhanced star formation, which could be evidence of an interaction with the nearby quasar. Here we will present the preliminary results of our Magellan/LDSS3 spectroscopic survey of galaxies within quasar environments. From our initial case study, we find no evidence of enhanced star formation for the galaxies near the quasar. Our future work will incoporate the full sample of all 19 quasar fields with optical spectroscopy and determining other physical parameters. [Preview Abstract] |
Sunday, January 24, 2021 12:20PM - 12:30PM |
U07.00003: Uncovering the Relationship between Stars and Black Holes in Nearby Luminous Infrared Galaxies: Probing Lines Below the Noise with Stacked \textit{Spitzer}/IRS Spectra Meredith Stone, Alex Pope, Jed McKinney The co-evolution of stars and supermassive black holes over cosmic time has shaped the history of the Universe, and understanding their relative balance is key to understanding galaxy evolution. Luminous infrared galaxies (LIRGs) in the local Universe are an ideal population to study this relationship thanks to their high star formation rates and a range of emission from active galactic nuclei (AGN, quantified as the AGN fraction). We use \textit{Spitzer}/IRS spectra of LIRGs in the GOALS sample to study mid-infrared spectral lines tracing star formation and black hole accretion. Since many important lines are faint and undetected in the majority of the sample, we constrain the balance of star formation and black hole accretion across GOALS by stacking \textit{Spitzer}/IRS spectra, revealing the relationship between 12.8 micron [NeII], 14.3 micron [NeV], 15.6 micron [NeIII], and 25.9 micron [OIV] luminosity as a function of AGN fraction. This allows us to measure the balance of star formation and black hole accretion across GOALS. We use these results to constrain the contribution of star formation to tracers of black hole accretion in order to improve the accuracy of black hole accretion rate calculations. [Preview Abstract] |
Sunday, January 24, 2021 12:30PM - 12:40PM |
U07.00004: Mapping the Invisible: 21-cm intensity mapping with the Tianlai Array Lily Robinthal 21-cm intensity mapping is a new method of mapping the universe using the 21-cm spectral line of neutral hydrogen. Because hydrogen is so prevalent in the universe, this method allows astronomers to image large swaths of the universe and periods from before stars were formed in the radio region of the electromagnetic spectrum, as well as compare to optical surveys. This can ultimately be used to investigate the large-scale structure of the universe, and to study dark energy. I am modeling optical data from a spectroscopic galaxy redshift survey of the North Celestial Cap. This data will be compared to 21-cm data of the same region from the Tianlai Array, a new 21-cm survey. I am also working with data from the Arecibo Legacy Fast ALFA (ALFALFA) survey, a different 21-cm survey, which can be correlated with optical data from the Sloan Digital Sky Survey. This will give us a sense of how the optical survey of the NCC will compare to the Tianlai survey. This work was supported by the National Science Foundation's REU program in Astrophysics through NSF award AST-1852136. [Preview Abstract] |
Sunday, January 24, 2021 12:40PM - 12:50PM |
U07.00005: Studying the Effects of Overlapping Objects in Dark Energy Katarzyna Krzyzanska Observing the clustering of galaxies allows us to calculate the cosmological parameters necessary for understanding dark energy. However, as the density of observed objects increases, multiple galaxies can appear blended and be observed as one galaxy. This affects the galaxy bias (b) and matter-energy density ($\Omega_M$). To see whether incorrectly inferring the galaxy count is significant, we compare the correlation functions in simulated data for “true” and “observed” data sets with 1-to-1 and multiple-to-1 correspondences, respectively. For each data set, we create two correlation functions: one measured directly from the galaxies’ positions and one model derived from their power spectrum. By minimizing the residual between the functions, we compute the ideal values for b and $\Omega_M$ across the possible redshifts that position the galaxies in 3D space. This minimization is done with a Markov chain Monte Carlo (MCMC) estimate that finds one value of $\Omega_M$ and ten values for b corresponding to the ten redshift bins ranging from z = 0.2 to z = 1.2. We find that neither b nor $\Omega_M$ is particularly affected by the inclusion of blended galaxies. The data suggest that the fluctuations found are a result of noise or limitations on the modeling. [Preview Abstract] |
Sunday, January 24, 2021 12:50PM - 1:00PM |
U07.00006: Properties of Filamentary Structures in A Simulated Molecular Cloud and How They Evolve Over Time Gina Chen, Stella Offner, Hope Chen Star formation occurs in molecular clouds, which contain networks of filamentary structures. The properties of these filamentary structures are not yet well understood. In this project, we measure the physical properties of filaments within a simulated molecular cloud and determine how they correlate with star formation and how they change over time. We use the Computational Ridge Identification with SCMS for Python (CRISPy) package to identify density ridges. From the ridges, we obtain filamentary spines, which we cut into segments about 0.25pc long. A profile of the filament is generated for each segment using RadFil, a package for building filament radial density profiles. We fit the profiles with Gaussian and Plummer functions, as well as a two component Gaussian function. We show that the majority of segments are best fit by a two-component function. In the simulation, regions of high density are converted into sink particles, which represent stars or star systems. The majority of filaments within 0.05pc of a sink particle are also best fit by a two-component function. The narrower of the two Gaussians has a width of about 0.08pc, consistent with recent observational results. This was consistent through multiple timesteps of the simulation, ranging over about 900,000 years. [Preview Abstract] |
Sunday, January 24, 2021 1:00PM - 1:10PM |
U07.00007: Neutron Stars from the Early Universe Rabia Husain, Volker Bromm We investigated Population III neutron star remnants to assess the feasibility of detecting one in our Milky Way. First, we used the Press-Schechter formalism to calculate the number of dark matter minihalos, regions where Population III stars form, incorporated into the Milky Way. Then, we determined the amount of star forming gas available per minihalo to find the number of neutron star remnants per minihalo. From this, we discerned that there are about 20,000 Population III neutron star remnants in the Milky Way. Next, we sought to distinguish them from those of other stars. Since they are more massive, it stands to reason that they are also brighter. We calculated a timescale for binary capture, finding that a Population III neutron star will acquire a companion every million years. Due to the bright emission from binaries, we should be able to detect these sources. We are constructing a luminosity function to show the number of neutron stars at a particular luminosity as a function of Eddington luminosity. At the high-luminosity end of this plot, we expect to find the signature of Population III remnants. From this, we can constrain the properties of the first stars, thus guiding direct searches with telescopes, such as the James Webb Space Telescope. [Preview Abstract] |
Sunday, January 24, 2021 1:10PM - 1:20PM |
U07.00008: Stellar Nucleosynthesis Viridiana Marquez I've been working the last few months in Stellar Nucleosynthesis, more specific in the proton-proton chain decay, which is considered the most important nuclear process given in the Sun because is the beginning of a series of nuclear reactions and also it takes the longest period of the star's life. Basically I used the four principal nuclear reactions that occur in the Sun where the product of one reaction is the starting material of the next reaction, leading from Hydrogen to Helium. Using some approximations in order to obtain a simpler model we got only three reactions.From these reactions we can get the differential equations that describe the variation of the number of nuclei with time. Using the basic model for the velocities of the reactions, in which we are no taking account the temperature, just the concentration of nuclei, we get this system of ODE's \begin{eqnarray} \frac{d[H]}{dt}&=&k_{-1}[D]-k_1[H]^2+k_{-2}[^3He]-k_2[D][H]\nonumber\\ &+&k_3[^3He]^2-k_{-3}[^4He][H]^2\\ \frac{d[D]}{dt}&=&k_1[H]^2-k_{-1}[D]+k_{-2}[^3He]-k_2[D][H]\\ \frac{d[^3He]}{dt}&=&k_2[D][H]-k_{-2}[^3He]+k_{-3}[^4He][H]^2-k_3[^3He]^2\\ \frac{d[^4He]}{dt}&=&k_3[^3He]^2-k_{-3}[^4He][H]^2 \end{eqnarray} That we solved it doing these ODE's dimensionless and using the numerical Euler's method. [Preview Abstract] |
Sunday, January 24, 2021 1:20PM - 1:30PM |
U07.00009: Deep Learning Anomaly Detection Afra Ashraf, Jonathan Fraine, Jennifer Medina, Heather Olszewski WFC3/IR data has shown a range of known anomalies that are consistently occurring and have known corrections using pipeline processing. The Quicklook project is a data management software for quick access to and inspection of Hubble Space Telescope Wide Field Camera 3 data. One of the features of the projects is anomaly detection, which allows Quicklook team members to visually inspect new observations and flag them for anomalies. We introduce a method for creating a deep learning algorithm to complement the existing Quicklook software by automatically detecting known and unknown WFC3 image anomalies, thus improving detection accuracy and reducing time spent on manual image inspection. [Preview Abstract] |
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