<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Publications on Connor Stone</title><link>https://connorjstone.com/publications/</link><description>Recent content in Publications on Connor Stone</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 16 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://connorjstone.com/publications/index.xml" rel="self" type="application/rss+xml"/><item><title>💻 PTED: A multi-dimensional two-sample test for scientific inference and generative machine learning</title><link>https://connorjstone.com/publications/pted/</link><pubDate>Wed, 16 Sep 2026 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/pted/</guid><description>PTED is a multi-dimensional two-sample test. The test applies in high dimensions, on learned feature representations, at large or small or imbalanced sample sizes, and to any data type on which a distance can be defined.</description></item><item><title>📄 Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images</title><link>https://connorjstone.com/publications/pixellated-psf-sampling/</link><pubDate>Wed, 26 Nov 2025 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/pixellated-psf-sampling/</guid><description>A framework for fully probabilistic, pixel-level inference of the point spread function directly from astronomical images, enabling principled uncertainty quantification in downstream photometric analyses.</description></item><item><title>💻 caskade: Building Pythonic Scientific Simulators</title><link>https://connorjstone.com/publications/caskade/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/caskade/</guid><description>caskade is a Python library for constructing modular, differentiable scientific simulators with automatic parameter tracking and Bayesian inference integration.</description></item><item><title>💻 Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations</title><link>https://connorjstone.com/publications/caustics/</link><pubDate>Sat, 22 Jun 2024 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/caustics/</guid><description>Caustics is a GPU-accelerated Python package for strong gravitational lensing forward modelling, enabling fast simulation and inference for large-scale lensing surveys.</description></item><item><title>💻 AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images</title><link>https://connorjstone.com/publications/astrophot/</link><pubDate>Thu, 03 Aug 2023 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/astrophot/</guid><description>AstroPhot is a Python package for simultaneous multi-band, multi-object photometric fitting of astronomical images using automatic differentiation and GPU acceleration.</description></item><item><title>📄 PROBES-I: A Compendium of Deep Rotation Curves and Matched Multiband Photometry</title><link>https://connorjstone.com/publications/probes-i/</link><pubDate>Tue, 20 Sep 2022 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/probes-i/</guid><description>PROBES-I presents a homogenised catalogue of 3163 deep optical rotation curves with matched multiband photometry for late-type galaxies, enabling precision tests of galaxy structure and dark matter models.</description></item><item><title>💻 AutoProf: An Automated Non-Parametric Light Profile Pipeline for Modern Galaxy Surveys</title><link>https://connorjstone.com/publications/autoprof/</link><pubDate>Mon, 28 Jun 2021 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/autoprof/</guid><description>AutoProf is an automated pipeline for extracting non-parametric surface brightness profiles from galaxy images, designed to scale to the large datasets of modern wide-field surveys.</description></item><item><title>📄 The Intrinsic Scatter of Galaxy Scaling Relations</title><link>https://connorjstone.com/publications/intrinsic-scatter-scaling/</link><pubDate>Wed, 14 Apr 2021 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/intrinsic-scatter-scaling/</guid><description>A Bayesian hierarchical analysis of the intrinsic scatter in late-type galaxy scaling relations, separating measurement error from genuine physical scatter and constraining galaxy formation models.</description></item><item><title>📄 The Intrinsic Scatter of the Radial Acceleration Relation</title><link>https://connorjstone.com/publications/intrinsic-scatter-rar/</link><pubDate>Fri, 16 Aug 2019 00:00:00 +0000</pubDate><guid>https://connorjstone.com/publications/intrinsic-scatter-rar/</guid><description>A statistical analysis of the intrinsic scatter in the Radial Acceleration Relation (RAR), placing tight constraints on the universality of the baryonic-to-total acceleration connection in late-type galaxies.</description></item></channel></rss>