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Full-Text Articles in Physical Processes
Measuring Cosmic Expansion From Early To Late Times Using Quasars, Galaxies, & Local Supernovae, Rajeev Vaisakh
Measuring Cosmic Expansion From Early To Late Times Using Quasars, Galaxies, & Local Supernovae, Rajeev Vaisakh
Physics Theses and Dissertations
This dissertation investigates the evolution of cosmic expansion across a wide redshift ($z$) range by combining measurements from quasars \& galaxies in the distant universe $(z > 0.8)$ with those obtained from stripped-core collapse supernovae observations from the nearby universe $(z < 0.1)$. Using the DESI spectroscopic survey, we analyze large-scale clustering from quasars over $0.8 < z < 2.1$ and emission line galaxies over $0.8 < z < 1.6$, contributing to DESI DR2 BAO and full-shape measurements of cosmic expansion and structure growth. Combined with BBN in flat $\Lambda$CDM, DESI DR2 BAO gives $H_0 = 68.51 \pm 0.58$ km s$^{-1}$ Mpc$^{-1}$, while combinations of DESI BAO with CMB and supernova data show a preference for evolving dark energy, with representative constraints such as $w_0 = -0.752 \pm 0.057$ and $w_a = -0.86^{+0.23}_{-0.20}$ for DESI+CMB+DESY5. Using the ROTSE-III Supernova Survey and the expanding photosphere method (EPM), we measure distances to a sample of six supernovae for which the thermal phase can be clearly identified. This analysis focuses on testing whether reliable EPM distances can be obtained for this population. The resulting distances will eventually be compared with independent estimates from the literature. Establishing consistency with these external measurements represents an important step toward applying this methodology to larger samples, with the eventual goal of using ROTSE supernova distances to provide an independent measurement of the local Hubble constant.
Automated Spectroscopic Detection And Mapping Using Alma And Machine Learningtechniques, Steven Cocke, Andrew Wilkins, Josephine Mcdaniel, John Santerre, Conor Nixon
Automated Spectroscopic Detection And Mapping Using Alma And Machine Learningtechniques, Steven Cocke, Andrew Wilkins, Josephine Mcdaniel, John Santerre, Conor Nixon
SMU Data Science Review
In this paper we present a methodology for automating theclassification of spectrally resolved observations of multiple emissionlines with the Atacama Large Millimeter/submillimeter Array (ALMA).Molecules in planetary atmospheres emit or absorb different wavelengthsof light thereby providing a unique signature for each species. ALMAdata were taken from interferometric observations of Titan made be-tween UT 2012 July 03 23:22:14 and 2012 July 04 01:06:18 as part ofALMA project 2011.0.00319.S. We first employed a greedy set cover algorithm to identify the most probable molecules that would reproducethe set of frequencies with respective flux greater than 3σaway from themean. We then selected a subset of …