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Neural Inference of Gaussian Processes for Time Series Data of Quasars
Egor Danilov, Aleksandra Ćiprijanović, Brian Nord
The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is…
DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation
Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic +2
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quanti…
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Aleksandra Ćiprijanović, Ashia Lewis, Kevin Pedro +4
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discove…
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović +4
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as th…
A robust estimator of mutual information for deep learning interpretability
Davide Piras, Hiranya V. Peiris, Andrew Pontzen +3
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI f…
Noise2Astro: Astronomical Image Denoising With Self-Supervised NeuralNetworks
Yunchong Zhang, Brian Nord, Amanda Pagul +1
In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the w…