htps://ethantreg.github.io/Ethan TregidgaWebsite for Ethan Tregidga's research in astronomy and machine learning 2026-04-15T23:35:26+02:00 Ethan Tregidga htps://ethantreg.github.io/ Jekyll © 2026 Ethan Tregidga /assets/img/favicons/favicon.ico /assets/img/favicons/favicon-96x96.png Variational Autoencoder with Normalizing Flow for X-Ray Spectral Fitting2026-01-12T00:00:00+01:00 2026-01-12T00:00:00+01:00 htps://ethantreg.github.io/posts/variational-autoencoder-with-normalizing-flow-for-x-ray-spectral-fitting/ Ethan Tregidga Abstract Black hole X-ray binaries (BHBs) can be studied with spectral fitting to provide physical constraints on accretion in extreme gravitational environments. Traditional methods of spectral fitting such as Markov Chain Monte Carlo (MCMC) face limitations due to computational times. We introduce a probabilistic model, utilizing a variational autoencoder with a normalizing flow, trained to ... Measuring the Dark Matter Self-Interaction Cross-Section with Deep Compact Clustering for Robust Machine Learning Inference2025-11-12T00:00:00+01:00 2025-11-12T00:00:00+01:00 htps://ethantreg.github.io/posts/interpretable-dark-matter-clustering/ Ethan Tregidga Abstract We have developed a machine learning algorithm capable of detecting ‘out-of-domain data’ for trustworthy cosmological inference. By using data from two separate suites of cosmological simulations, we show that our algorithm is able to determine whether ‘observed’ data is consistent with its training domain, returning confidence estimates as well as accurate parameter estimations. We a... DARKSKIES: A Suite of Super-Sampled Zoom-In Simulations of Galaxy Clusters with Self-Interacting Dark Matter2025-09-24T00:00:00+02:00 2025-09-24T00:00:00+02:00 htps://ethantreg.github.io/posts/super-sampled-sidm/ Ethan Tregidga Abstract We present the ‘DARKSKIES’ suite of one hundred, zoom-in hydrodynamic simulations of massive ($M_{200}>5\times10^{14}M_\odot$) galaxy clusters with self-interacting dark matter (SIDM). We super-sampled the simulations such that $m_{\rm DM}/m_{\rm gas}\sim0.1$, enabling us to simulate a dark matter particle mass of $m=0.68\times10^8M_\odot$ an order of magnitude faster, whilst explo... Impact of Line-of-Sight Structure on Weak Lensing Observables of Galaxy Clusters2025-09-16T00:00:00+02:00 2025-09-16T00:00:00+02:00 htps://ethantreg.github.io/posts/Impact-of-line-of-sight-structure-on-weak-lensing-observables-of-galaxy-clusters/ Ethan Tregidga Abstract Weak gravitational lensing observations of galaxy clusters are sensitive to all the mass that is present along the line of sight (LoS). Thus, the systematic and additional statistical uncertainties due to intervening structures must be taken into consideration. In this work, we quantify the impact of these structures on the recovery of mass density profile parameters using 967 cluster... Rapid Spectral Parameter Prediction for Black Hole X-Ray Binaries Using Physicalised Autoencoders2023-10-26T00:00:00+02:00 2026-02-19T00:07:21+01:00 htps://ethantreg.github.io/posts/autoencoder-spectral-fitting/ Ethan Tregidga Abstract Black hole X-ray binaries (BHBs) offer insights into extreme gravitational environments and the testing of general relativity. The X-ray spectrum collected by NICER offers valuable information on the properties and behaviour of BHBs through spectral fitting. However, traditional spectral fitting methods are slow and scale poorly with model complexity. This paper presents a new semi-su...