Keynote talk + discussion session
I will present the neural network architect "spender", which is specifically designed for galaxy spectra at variable redshifts. Trained in 500k SDSS or DESI spectra, it is capable of automatically detecting highly meaningful outliers as well as making predictions of the physical state of the galaxies, thus serving as a summary for simulation-based inference approaches. Recently, my group has...
Score Based Diffusion models have emerged as a powerful tool for high dimensional Bayesian analysis. Here we present work done at the Université de Montréal to analyze strong gravitational lenses. As a first step, we sample robust posteriors over high resolution point spread function models to represent the image distortions in the Hubble Space Telescope. We then perform source reconstruction...
Galaxy surveys such as LSST require robust deblending methods to separate overlapping sources in crowded fields, a challenging inverse problem due to PSF convolution, noise, and source mixing. In this talk, I present a Bayesian framework that leverages diffusion models to learn a prior on galaxy light profiles directly from blended observations. Building on a recent expectation-maximization...
Variational autoencoders (VAEs) are powerful tools for inferring object properties, particularly well-suited for processing imaging data due to their architectural design. In this work, we use multi-modal VAEs to generate probability distributions for various galaxy properties using multi-band photometric observations. The VAE is trained on synthetic photometric and spectral datasets to infer...
Cold dark matter, the standard cosmological model, faces several challenges on small scales that self-interacting dark matter may help resolve. Traditional methods to constrain the nature of dark matter often rely on summary statistics, which discard much of the available information, or require complex and computationally expensive lensing models. Machine learning (ML) has gained traction in...
The revolutionary methods of Machine Learning (ML) support most data science analyses today in many ways. An often neglected question remains on the interpretability of used models, and clarity on how the information inside our data is used. This work presents a physics-guided method combined with the architecture of Deep Learning, to provide both the reliability and explainability of...
In this talk the discrepancies include the long-standing difference in the Hubble constant ( H_0 ), as well as variations between Planck data and weak lensing measurements regarding the matter-energy density ( \Omega_m ) and the amplitude ( \sigma_8 ) (or the redshift space distortion ( f \sigma_8 )) of cosmic structures will be explored. These inconsistencies suggest the possibility...
