Abel Lawrence Peirson
alpv95 at stanford dot edu
[Main | Blog]

I am a Ph.D student in Physics at Stanford University, where I research high energy astrophysics, astrostatistics and applications of machine learning as a NASA FINESST fellow and Stanford data science scholar.

I am interested in scientific applications of machine learning and statistics that incorporate domain knowledge.

One of my ongoing projects has been increasing the science output of NASA's flagship X-ray polarimetry mission IXPE using statistical learning and computer vision techniques. I am equally interested in the science made possible by these improvements, for example, how the geometry of relativistic jets affects their polarized emission, and statistical techniques to properly interpret observations. See my research highlights below for an overview.

Previously, I received my bachelors and masters (MPhys) in Physics from the University of Oxford, Christ Church, where I specialised in theoretical and particle physics.

• 06/20/22 Starting an internship at Google Brain!
• 02/10/22 Invited talk at Max-Planck-Institut für Radioastronomie blazar seminar.
• 12/13/21 Talk and poster at Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021). [Paper][Poster]
• 12/09/21 Invited to NASA IXPE telescope launch (via Space X Falcon 9) at Cape Canaveral.
• 10/31/21 Contributed a chapter to the Springer Nature Handbook of X-ray and Gamma-ray Astrophysics. [02/22]
• 10/30/21 Made official science collaborator for NASA's IXPE telescope mission.
• 00/13/21 Awarded Stanford Data Science Scholarship and joined the program. [Profile]
• 09/23/21 Invited Naval Research Laboratory Astrophysics colloquium in Washington D.C. [Slides]
• 06/20/21 Quantitative research internship at G-Research in London (job offer).
• 06/01/21 Invited Talk at the IXPE Third Science Collaboration Meeting on optimal signal extraction for IXPE.
• 05/26/21 Talk at RoboPol conference Looking at the polarized Universe: past, present and future. [Recording]
• 09/20/19 Selected as Bay Area Wonderfest science envoy. [Profile]
• 09/14/19 Awarded NASA Future Investigator of NASA Earth and Space Sciences (FINESST) grant.
• 08/24/19 President of the Stanford Judo Club 2019-20.
• 11/12/18 Panel discussion at the Pratt institute NY on the future of AI in jewellery design.
• 08/29/18 Interviewed on episode 68 of the NVIDIA AI podcast. [Recording]
• 06/15/18 My work featured in Techcrunch: Dank learning system autogenerates memes.

• G-Research, Quantitative research intern
  June - September 2021
  Forecasting capital markets and identifying trading strategies. [Webpage]
• Stanford PHYS113: Computational Physics, Teaching Assistant and Lecturer
  January - April 2021
  Prepared and delivered lectures on optimization and statistical inference. [Slides 1][Slides 2]
• Stanford PHYS100: Observational Astrophysics, Teaching Assistant
  April - June 2019
  Optical telescopes, practical observation and data analysis.

• MulensModel: Fast gravitational microlensing simulation/fitting package.
  [Code][Paper][Docs]
• SSCpol: Polarized relativistic jet simulation in C with Python wrapper.
  [Code][Paper]
• Dank Learning: 'Show and Tell' image captioning for meme generation.
  [Code][Paper][Webpage]

Presently, I am incorporating convex optimization and nested sampling into searches for rare blazar gravitational lensing events, further improving IXPE's spatial and energy resolutions, and preparing for the scientific discoveries imminent with IXPE's launch in December. Some representative publications are listed below.

Setting the state of the art signal recovery in X-ray Polarimetry. The code from this and my previous works will be used as part of IXPE's official data analysis pipeline.

We model the uncertainties on heteroskedastic photoelectron track angle predictions using deep ensembles of CNNs and derive the appropriate maximum likelihood estimator for polarization parameter reconstruction. Our work will reduce telescope exposure times by up to 40% for a given signal-to-noise ratio.

We fit a complex binary lens magnification model to multiwavelength blazar lightcurves using Bayesian nested sampling (MultiNest). To speed up the algorithm, I used convex optimization to marginalize over the (constrained) linear parameters in real time, reducing the number of free parameters to be sampled. Our empirical results tentatively suggest that the blazar could be lensed.

We show our polarization estimators using deep ensemble quantified uncertainties are optimize the signal-to-noise ratio for realistic observations. Using a CNN based classifier we can exclude, with high confidence, events converting outside of the main detector volume. We apply our new technique to a selection of astrophysical spectra, including complex extreme examples.

PKS 1413+135 is one of the most peculiar blazars known. We provide observational evidence suggesting that the blazar could be gravitationally microlensed.

Our first work using deep ensembles and heteroskedastic uncertainty quantification to improve IXPE's sensitivity by more than 30%.

In order to measure polarization using a gas pixel detector, a distribution of photoelectron angles must be extracted from noisy images of their individual tracks on a hexagonal grid. We use a ResNet-18 architecture to predict the initial photoelectron angle and its uncertainty for each track image, as well as the absorption point and energy. By combining predictions from an ensemble of networks in a weighted maximum likelihood analysis, we estimate the polarization fraction and EVPA of the X-ray source.

Exploring the expected synchrotron self-Compton polarization from blazar jets. A continuation of our multizone geometric jet model.

For low-synchrotron peaked blazars, the X-ray emission will be dominated by synchrotron self-Compton. Understanding what polarization levels IXPE is likely to see in this case is important for distinguishing between hadronic and leptonic blazar emission models. Importantly we find that a rise in synchrotron polarization fraction at high energies is guaranteed by basic relativity considerations.

A study in the detectability of X-ray polarization in blazars for IXPE.

Using the models developed in the adjacent relativistic jet papers and optical polarization observations by RoboPol, we are able to make predictions of the average expected X-ray polarization fraction. This work helped the IXPE team choose appropriate first year observing targets.

A white paper for the upcoming Decadal survey introducing XPP, a second generation X-ray polarimeter that will cover 0.2 - 60keV energies (IXPE covers 1-10keV).

Hopefully we will demonstrate the potential of imaging X-ray polarimeters with IXPE.

Using simple helical geometry and relativistic aberration effects to explain blazar polarization angle rotations.

Our follow up paper for the synchrotron self-Compton emission is above.

An image captioning system that can take any image and turn it into a first generation meme. Uses pre trained Inception CNN followed by an LSTM with temperature selection for language generation. Trained on a dataset of over 300,000 image caption pairs I scraped from memegenerator.net.

What started as a fun class project ended up being featured on Techcrunch and AINews. Inspired, I worked together with Dylan Freedman to create the free iOS app Dank Learning so anyone can use the 'AI' to generate memes. I discuss the work in the NVIDIA AI podcast, and its implications for the future of art and jewellery at the Pratt Institute.

We bought a 1982 HP7470a plotter printer and we make it plot starfields around exoplanets. Check out our gallery of all the exoplanets we know of (we can plot other astronomical objects too... The image on the left is M51: the whirlpool galaxy).

We made a lightweight implementation in javascript of one of our favourite games: Achtung, die Kurve!. We have also made a 3d version in Unity: Achtung, 3die Kurve!, not available online yet. I hope to build an RL agent in collaboration with Taylor Howell that solves Achtung, die Kurve!

Inspired by ✩