As an astrophysics postdoc at Arizona State University’s School of Earth and Space Exploration, in the Low-Frequency Cosmology (LoCo) lab, I develop the statistical, numerical, instrumental and simulation infrastructure required to detect the Cosmic Dawn and Epoch of Reionization (CD/EoR) using the 21cm hydrogen line.
I am an avid Bayesian, and most of my work centres around developing Bayesian statistical models to tie observations to theory.
I’m also a strong advocate of good open-source practices, especially in Python. This includes documentation, continuous integration and testing, code coverage, robust package management, semantic versioning, clean code formatting and more. I have taught these things more formally at numerous boot camps and workshops.
Along with several smaller collaborative projects, I actively work on three large projects, EDGES, HERA and 21cmFAST.
EDGES
The Experiment to Detect the Global EoR Signal is a focused experiment that aims to detect the “global” signal from neutral hydrogen during the CD/EoR. It measures a single frequency spectrum averaged over the whole sky. This traces the total temperature of the intergalactic medium (IGM) as it evolves throughout cosmic time. The three distinct antennas deployed by EDGES cover redshifts between 6 — 27, or from 7.7 — 13.3 billion years ago.
EDGES is the first 21cm experiment to report evidence for the first stars. The surprising amplitude and shape of the signal has generated substantial interest, with explanations in terms of exotic dark matter properties, and the first generation of black holes.
My focus is on statistical validation of the result, building sophisticated Bayesian forward-models of the instrument to understand how confident we should be in the detection. I’m also completely re-writing the analysis code to be open-source and reproducible.
Want more? See my EDGES page.
HERA
The Hydrogen Epoch of Reionization Array (HERA) is an experiment to detect spatial fluctuations of the hydrogen signature during CD/EoR, potentially even building a 3D ‘map’ of the early Universe!
Doing so is extremely difficult — the early Universe is distant and faint, and plenty of other stuff (‘foregrounds’) get in the way. The amount of data being taken is also incredibly large, making it a challenge on both scientific and technical levels.
I primarily work on Validation, and have helped to build the most realistic end-to-end simulation of a full observation of an interferometer (including all the warts the instrument puts into the data), to ensure that our data processing itself doesn’t contaminate the results. I’ve also developed analytic models to help validate our simulators.
Want More? See my HERA page.
21cmFAST and 21cmMC
How do we explore the effect of different astrophysical parameters on the globally averaged temperature, or the power spectrum? To do this requires simulating the Universe throughout cosmic history, given the input parameters. There are several ways to do this, and several codes that implement these ways. The fastest such code (mostly because it makes approximations) is 21cmFAST.
Since 2017, I have been heavily involved in bringing this code up to modern coding standards, and making it easier both to use and develop/modify. We also have a driver that will run thousands of simulations in a way that efficiently samples the parameter space, to create Bayesian posteriors, called 21cmMC. I’ve been simultaneously developing this code to generalize it and enable more realistic instrumental effects to be “painted” onto the theoretical predictions, in order to give us more realistic and sophisticated ideas about how well we can infer the parameters.
Other Projects
My PhD thesis “Next-generation tools for next-generation surveys” presented several tools (including one for calculating halo mass functions, along with a web-application for doing it more easily, as well as a more extensive halo model framework). These focused on dark matter statistics and large-scale structure in the Universe. I’ve also helped apply this to Hydrogen Intensity Mapping.
I contributed a mathematical proof to a paper in which we developed a general method for mitigating Eddington Bias in density-function estimates. I also developed a statistical model for power spectrum covariance of extragalactic point-source foregrounds under the context of angular clustering.