Bulletin of the American Physical Society
Joint Fall 2017 Meeting of the Texas Section of the APS, Texas Section of the AAPT, and Zone 13 of the Society of Physics Students
Volume 62, Number 16
Friday–Saturday, October 20–21, 2017; The University of Texas at Dallas, Richardson, Texas
Session K1: Astro III |
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Chair: Jennifer Marshall, Texas A&M University Room: DGAC 1.102A |
Saturday, October 21, 2017 10:30AM - 10:54AM |
K1.00001: Gravitational waves, LIGO, and Virgo Invited Speaker: Benjamin Owen Advanced LIGO detected gravitational waves with its first observing run and now has wrapped up its second run, in which it was joined by Advanced Virgo. I summarize the current status of ongoing data analysis. [Preview Abstract] |
Saturday, October 21, 2017 10:54AM - 11:06AM |
K1.00002: Explaining LIGO’s observations via isolated binary evolution with natal kicks Michael Kesden, Daniel Wysocki, Davide Gerosa, Richard O’Shaughnessy, Krzysztof Belczynski, Wojciech Gladysz, Emanuele Berti, Daniel Holz We compare binary evolution models with different assumptions about black-hole natal kicks to the first gravitational-wave observations performed by the LIGO detectors. Our comparisons attempt to reconcile merger rate, masses, spins, and spin-orbit misalignments of all of current observations with state-of-the-art formation scenarios of binary black holes formed in isolation. We estimate that black holes should receive natal kicks at birth of the order of $\sigma \simeq 200 (50)$ km/s if tidal processes do (not) realign stellar spins. Our estimate is driven by two simple factors. The natal kick dispersion $\sigma$ is bounded from above because large kicks disrupt too many binaries (reducing the merger rate below the observed value). Conversely, the natal kick distribution is bounded from below because modest kicks are needed to produce a range of spin-orbit misalignments. A distribution of misalignments increases our models’ compatibility with LIGO’s observations, if all BHs are likely to have natal spins. [Preview Abstract] |
Saturday, October 21, 2017 11:06AM - 11:18AM |
K1.00003: Cosmological distance measurement of 12 low $z$ IIP Supernovae using Expanding Photosphere Method Govinda Dhungana, Robert Kehoe Supernova (SN) cosmology has matured remarkably over the past decade. While SNe of Type Ia has been the most strongly studied, more recently, interest is growing to use Type II SNe as standardizable candles. SN II physics is better understood and several studies have presented strong correlations in the photometric and spectroscopic observables that establish these as equally viable cosmological distance indicators. It is particularly important to build multiple analysis frameworks to use these events now as their discoveries sky with the advent of deeper surveys. We present an analysis on distance measurement of 12 nearby SNe IIP ($z<0.06$) observed by ROTSE-III telescopes, using the Expanding Photosphere Method (EPM). The EPM is a geometrical method that relies on the approximation that the SN explosion is isotropically symmetric, and the photosphere behaves as a diluted blackbody. We derive the distance of 12 nearby events and show that our measurement is consistent with the standard $\Lambda CDM$ framework. By establishing an emperical model of temperature evolution and using alike for velocity evolution, this study demonstrates that this approach yields competitive measurements as other methods, even when the data is limited to one or few epochs. [Preview Abstract] |
Saturday, October 21, 2017 11:18AM - 11:30AM |
K1.00004: Photometric Redshift Estimation of Astrophysical Objects Using Machine Learning Nathan Steinle Modern astronomical surveys produce copious amounts of data, so that effective tools to handle these datasets are increasingly valuable. To this end, machine learning techniques are finding their way into astronomy and astrophysics. For instance, well established classification schemes are improved by implementing machine learning methodology to separate stars from quasars, and other galaxies. Here, a Random Forest (RF) regression model is trained on the Sloan Digital Sky Survey DR12 to learn the relationships between the photometric magnitudes and the spectroscopic redshift. Then that trained RF is used to estimate photometric redshifts of objects for other surveys which provide appropriate photometry but no spectroscopy. The functionality and reliability of the photometric redshift predictor is demonstrated, and the value of this tool is explored in the context of future surveys. [Preview Abstract] |
Saturday, October 21, 2017 11:30AM - 11:42AM |
K1.00005: Identifying Satellites of the LMC Peter Chi Recent surveys, such as the Dark Energy Survey (DES), have discovered a multitude of dwarf galaxies around the Milky Way, many in close proximity to the Large Magellanic Cloud (LMC). We investigate the possibility that a fraction of these dwarf galaxies are satellites of the LMC using high resolution cosmological dark matter simulations. A set of dark matter subhalos capable of hosting the LMC are identified from the simulation, from which a sample of possibly bound sub subhalos are extracted and studied. We then compare the distribution of simulated subhalos to the observed satellites and calculate probabilities of whether they are satellites of the LMC. [Preview Abstract] |
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