Every wondered how we know that supermassive black holes are so supermassive? Well one trick is to watch how light echoes around them, using "reverberations" to map how big they are. In my latest paper I close out the Australian Dark Energy Survey (OzDES) to measure the masses of hundreds of SMBH.

Importance Sampling (recycling posterior samples) has a reputation as crude brute force, but this underappreciated method can be a surprisingly powerful tool in many practical cases. Find my new article about the importance of importance sampling here: 🔭
hughmcdougall.github.io/blog/03_stat...

Call me old fashioned but sometimes my python IDE questions my grammar choices and frankly I think it should mind its own business

Everytime I read LVK my brain tells me it stands for Ligo Virgo Kirgo

🔭

🔭 Excited to be starting my 3 month visiting research placement at @monashastro.bsky.social working on some numerical specifics in GW population fitting!

🔭 It's called bluesky because of Rayleigh Scattering.

🔭
"I don't know why so many grad students have imposter syndrome. Where does the fear of failure come from?"

Meanwhile, in the arXiv submission form:

Watch the solar plasma curve along magnetic field lines like iron filings near a bar magnet. #stellarastro 🧪🔭

This work was funded in part by NSF.

You might have heard of the cool chaotic patterns you get from the logistic map, including the islands of stability that appear at certain values of 'r'.

Here's a fun animation of a different recursion relation that bounds the output as we change the itteration gain, wrapped around a polar axis.

Wild that Adobe's forced AI features means that you can open a program called "pdf reader", and then be inflicted with a popup asking if you'd like to not read a pdf

🔭 You've got two sets of data: can you combine them, or are the in tension? How do you know?

Answer: the Bayesian suspiciousness statistic! This blog (+examples) shows how you can consistently tell if two data sets are in tension with a full Bayes approach!

hughmcdougall.github.io/blog/03_stat...

🔭If you work in stats you've probably heard of nested sampling: MCMCs fancy cousin. Enough people have asked me how it works that I've written an article (including examples!) explaining:
1. What NS is
2. Why we need it over MCMC
3. How to know if it's working
hughmcdougall.github.io/blog/03_stat...

Extremely excited to announce the first paper / project of my PhD: LITMUS – a new Bayesian framework for reverberation mapping lag recovery! The paper is on arXiv at arxiv.org/abs/2505.09832, or you can find the code at hughmcdougall.github.io/litmus/ and the docs at hughmcdougall.github.io/litmus/

For my first post on bluesky, I present to the world an astronomer friendly guide to taking your first steps with #NumPyro:
hughmcdougall.github.io/blog/02_nump...