Abel Lawrence Peirson

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.

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

I have spent time at CERN working on beam parameter measurement software for the CLIC test facility's electron-positron collider and at the University of Science and Technology of China working on plasma confinement in the KMAX axisymmetric tandem mirror machine. At Stanford I have recently worked on visual unsupervised learning in the NeuroAILab and on developing a 2D path integration model in the fly brain with the Druckmann Lab. I currently focus on astrophysics, characterising the polarization behaviour of astrophysical jets and improving X-ray polarization sensitivity using machine learning for the NASA IXPE telescope.

Outside of Physics, I designed an AI that generates memes: Dank Learning. 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 jewellery (yes this is real) at the Pratt Institute. A current fun side project I am excited about is making exoplanet inspired art.

Besides my research, I enjoy engaging in scientfic outreach. I have recently become a Wonderfest Science Envoy; I look forward to improving my public speaking and communicating science to public audiences through the program. I am a science pen pal, an undergraduate student mentor and a leading member of the Stanford Astronomical Society. I also enjoy competing for the Stanford Judo Club and the Stanford Triathlon Club.

• I am interviewed on Episode 68 of the NVIDIA AI podcast.
• I am invited to give a talk at the Pratt institute NY on the future of AI in jewellery design.
• Dank Learning & Information Gerrymandering
• Techcrunch -- Dank learning system autogenerates memes
• Dank Learning & Information Gerrymandering

I'm interested in how the geometry of relativistic jets affects their polarized emission, and how we can improve high energy polarimetry using statistical learning and computer vision techniques. I am currently incorporating convex optimization into searches for rare blazar lensing events, further improving IXPE's spatial and energy resolutions, and taking Dank Learning to the next stage.

Using Deep ensembles and parametric density estimation with uncertainty to improve IXPE's sensitivity by more than 30%.

In order to measure polarization using a GPD, a distribution of photoelectron angles must be extracted from noisy images of their individual tracks on a hexagonal grid. We use a convolutional neural network 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. This work sets the new state of the art in high energy polarimetry.

Exploring the expected synchrotron self-Compton polarization from blazar jets. A continuation of our multizone 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. We find that our multizone model recovers simple predictions for SSC polarization, but describes new dependencies on jet viewing geometry. 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 will help 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!

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.

Follow up paper for the synchrotron self-Compton emission coming soon.

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.

Characterising the transverse phase space of the CTF3 beam before and after beam recombination with the Delay Loop.

I'm indebted to Dylan Freedman for introducing me to the very American concept of a side project -- as such many of my projects are collaborations with him. For fun and more...

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!. I have to admit, this one was mainly D (I still smash him at the game though); one of my main contributions was including a poisson distribution for generating threadable gaps in the curves. We have also made a 3d version in Unity: Achtung, 3die Kurve!, not available online yet.

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