Hugh McDougall Astro
@hughmcd-astro.bsky.social
Hi, I’m Hugh! I'm an astronomy PhD student at the University of Queensland. I work in Bayesian stats, JAX + NumPyro, doingreverberation mapping of active galactic nuclei with OzDES
Website: https://hughmcdougall.github.io
ORCiD: 0009-0008-5846-1543
🔭
Website: https://hughmcdougall.github.io
ORCiD: 0009-0008-5846-1543
🔭
Actual slide from a public outreach talk last week
May 25, 2025 at 8:19 AM
Actual slide from a public outreach talk last week
No AI image will ever match the charm of a poorly photoshopped stock image with the watermark still in it
May 25, 2025 at 8:14 AM
No AI image will ever match the charm of a poorly photoshopped stock image with the watermark still in it
I'm adding new features every week and would love to hear from AGN scientists who would like to see new tools added. Reach out by email or BlueSky, or check my website out at hughmcdougall.github.io.
Hugh McDougall - Home
hughmcdougall.github.io
May 16, 2025 at 10:12 AM
I'm adding new features every week and would love to hear from AGN scientists who would like to see new tools added. Reach out by email or BlueSky, or check my website out at hughmcdougall.github.io.
LITMUS is built for measuring lags in industrial scale RM surveys like OzDES, but its extendable modelling means you can adapt it to _any_ delay estimation problem (Quasar lensing delay, accretion disk RM, even BLR transfer function recovery). For you traditionalists: it also has nested sampling!
May 16, 2025 at 10:12 AM
LITMUS is built for measuring lags in industrial scale RM surveys like OzDES, but its extendable modelling means you can adapt it to _any_ delay estimation problem (Quasar lensing delay, accretion disk RM, even BLR transfer function recovery). For you traditionalists: it also has nested sampling!
LITMUS manages this by using JAX's autodiff for a new approach the Bayesian fitting- using the Laplace Approximation (or SVI) to Slice the posterior into Gaussians. This is a new technique for RM, and gives us all the good Bayesian stuff while fitting lags in a fraction of the time of older tools
May 16, 2025 at 10:12 AM
LITMUS manages this by using JAX's autodiff for a new approach the Bayesian fitting- using the Laplace Approximation (or SVI) to Slice the posterior into Gaussians. This is a new technique for RM, and gives us all the good Bayesian stuff while fitting lags in a fraction of the time of older tools
LITMUS offers AGN scientists a suite of new stats tools to sort the signal from the noise, including the established Gaussian Process model for AGN _and_ a flexible framework that lets you write your own more advanced physics model if you need!
May 16, 2025 at 10:12 AM
LITMUS offers AGN scientists a suite of new stats tools to sort the signal from the noise, including the established Gaussian Process model for AGN _and_ a flexible framework that lets you write your own more advanced physics model if you need!
If you've never heard of RM before - it's a trick where we use time series data to infer the geometry of Quasars. Like the gap in lighting and thunder telling us how far away a storm is, we can listen for the way light "echoes" around the AGN as a ruler to map their size.
May 16, 2025 at 10:12 AM
If you've never heard of RM before - it's a trick where we use time series data to infer the geometry of Quasars. Like the gap in lighting and thunder telling us how far away a storm is, we can listen for the way light "echoes" around the AGN as a ruler to map their size.
Not only does it massively improve on earlier MCMC based RM tools tendency to over-report half yearly lags (180 days, 540 days etc), it also, for the first time in RM, offers Bayesian model evidences for proper model comparison! Check out the docs example at hughmcdougall.github.io/litmus/examp...
May 16, 2025 at 10:12 AM
Not only does it massively improve on earlier MCMC based RM tools tendency to over-report half yearly lags (180 days, 540 days etc), it also, for the first time in RM, offers Bayesian model evidences for proper model comparison! Check out the docs example at hughmcdougall.github.io/litmus/examp...
LITMUS is a new tool purpose built to solve the “aliasing” problem that has plagued reverberation mapping in high redshift surveys like OzDES and SDSS – using a new approach to the Bayesian fitting to properly handle the window function and to principled false positive rejection!
May 16, 2025 at 10:12 AM
LITMUS is a new tool purpose built to solve the “aliasing” problem that has plagued reverberation mapping in high redshift surveys like OzDES and SDSS – using a new approach to the Bayesian fitting to properly handle the window function and to principled false positive rejection!