Here are my first author publications. For a list of all co-authored publications follow this link.
Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images
We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF characterization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly…
caskade: building Pythonic scientific simulators
Scientific simulators and pipelines form the core of many research projects. Writing high-quality, modular code allows for efficiently scaling a project, but this can be challenging in a research context. Research project goals and solutions to those goals are constantly in flux, requiring many refactoring rounds to meet these changes. The result can be a…
Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations
Summary: Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are…
AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images
Abstract: We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automatic differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PyTorch. Everything: AstroPhot can fit models for sky, stars, galaxies, PSFs, and more in a principled χ2 forward optimization, recovering Bayesian posterior information and…
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Connor Stone 