Hi, Dave here, and welcome to my site! I’m a graduate physics student and researcher at the University of Central Florida. As a member of the Eikenberry Research Group, I focus on using multi-messenger observations to precisely determine cosmological parameters. I also work with the Exoplanet Measurement Group, where I simulate data for the James Webb Space Telescope and act as system administrator for the group’s computing cluster.
PhD in Physics (ongoing), 2021
University of Central Florida
BSc in Physics, Astronomy Concentration, 2021
University of Central Florida
BSc in Math, 2021
University of Central Florida
We present the open-source Bayesian Atmospheric Radiative Transfer (BART) retrieval package, which produces estimates and uncertainties for an atmosphere’s thermal profile and chemical abundances from observations. Several BART components are also stand-alone packages, including the parallel Multi-Core Markov chain Monte Carlo (MC3), which implements several Bayesian samplers; a line-by-line radiative-transfer model, transit; a code that calculates Thermochemical Equilibrium Abundances, TEA; and a test suite for verifying radiative-transfer and retrieval codes, BARTTest. The codes are in Python and C. BART and TEA are under a Reproducible Research (RR) license, which requires reviewed-paper authors to publish a compendium of all inputs, codes, and outputs supporting the paper’s scientific claims. BART and TEA produce the compendium’s content. Otherwise, these codes are under permissive open-source terms, as are MC3 and BARTTest, for any purpose. This paper presents an overview of the code, BARTTest, and an application to eclipse data for exoplanet HD 189733 b. Appendices address RR methodology for accelerating science, a reporting checklist for retrieval papers, the spectral resolution required for synthetic tests, and a derivation of the effective sample size required to estimate any Bayesian posterior distribution to a given precision, which determines how many iterations to run. Paper II, by Cubillos et al., presents the underlying radiative-transfer scheme and an application to transit data for exoplanet HAT-P-11b. Paper III, by Blecic et al., discusses the initialization and post-processing routines, with an application to eclipse data for exoplanet WASP-43b. We invite the community to use and improve BART and its components at http://GitHub.com/ExOSPORTS/BART/.
Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843–0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90–180x faster per chain than BART on that CPU.
The James Webb Space Telescope (JWST) is a next-generation space telescope that will be capable of making transformative observations of planetary transits. As its launch date grows ever closer, it becomes imperative that astronomers have access to accurate simulations of JWST observations in order to best plan observations and devise data analysis pipelines. Unfortunately, available simulation tools do not provide the most accurate or realistic simulations, including noise and systematic errors. In this thesis, I present an open-source time-domain simulator of planetary transits that is capable of accurately modeling these effects in observations made by JWST.
Research with the Eikenberry Research Group. Topics include:
Sports Data Analysis. Effects of alternative soccer point systems on league rankings and Pareto distribution models of soccer team manager rankings. Relevant experience gained:
Created a web based curriculum for introductory physics. Topics included:
Research with the Exoplanets Measurement Group. Topics include:
Assisted Professors in Physics 2 classrooms in a role that was identical to a traditional teaching assistant.
I learned/gained:
Classify LIGO Data Using a Convolutional Neural Net and Scaleograms
An open-source simulator of exoplanet transits