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Guillaume Payeur

guillaume.payeur@mail.mcgill.ca
Doctorat - UdeM
Superviseur⋅e principal⋅e
Laurence Perreault-Levasseur
Sujets de recherche
Apprentissage profond
Astrophysique
Cosmologie
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Google Scholar
GitHub

Publications

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
Guillaume Payeur
Laurence Perreault-Levasseur
Gabriel Missael Barco
Yashar Hezaveh
Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground … (voir plus)galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise observations. In this regime, high-dimensional representations of both the source and the foreground mass distribution are necessary to model the data down to the noise level. This inference problem has been challenging for both traditional and machine learning-based methods because of its high dimensionality and its non-linearity in the foreground mass distribution. We present a method to generate joint posterior samples of the source galaxy and foreground mass distribution as pixelated images conditioned on observations. The method combines diffusion-based generative modeling and recurrent inference machines. It can model realistic gravitational lensing simulations with background and foreground galaxies drawn from cosmological hydrodynamical simulations down to the noise level.
2026-05-29
AI4Physics @ International Conference on Machine Learning (publié)
openreview.net
openreview.net
Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Guillaume Payeur
Étienne Artigaud
Laurence Perreault Levasseur
René Doyon
We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using H… (voir plus)xRG detectors in low-flux regimes. It improves on the drawbacks of the conventional algorithms to construct 2D images from multiple readouts by using the readout scheme of the detectors to reduce the impact of correlated readout noise. We train a convolutional recurrent neural network on simulated astrophysical scenes added to laboratory darks to estimate the flux on each pixel of science images. This method achieves a reduction of the noise on constructed science images when compared to standard flux-measurement schemes (correlated double sampling, up-the-ramp sampling), which results in a reduction of the error on the spectrum extracted from these science images. Over simulated data cubes created in a low signal-to-noise ratio regime where this method could have the largest impact, we find that the error on our constructed science images falls faster than a
2022-05-24
Astronomical Journal (publié)
doi.org
arxiv.org