arXiv subjects

Lucas Hoefs

Publications and source records attributed to Lucas Hoefs.

2 recordsLinked to original sources

arXiv2024

Deep Learning VLBI Image Reconstruction with Closure Invariants

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure invariants is directly obtainable from measured visibilities, the inverse transformation from closure invariants to the source intensity distribution is not established. In this work, we demonstrate a deep learning approach, Deep learning Image Reconstruction with Closure Terms (DIReCT), to directly reconstruct the image from closure invariants. Trained on both well-defined mathematical shapes (two-dimensional gaussians, disks, ellipses, $m$-rings) and natural images (CIFAR-10), the results from our specially designed model are insensitive to station-based corruptions and thermal noise. The median fidelity score between the reconstruction and the blurred ground truth achieved is $\gtrsim 0.9$ even for untrained morphologies, where a unit score denotes perfect reconstruction. In our validation tests, DIReCT's results are comparable to other state-of-the-art deconvolution and regularised maximum-likelihood image reconstruction algorithms, with the advantage that DIReCT does not require hand-tuned hyperparameters for each individual prediction. This independent approach shows promising results and offers a calibration-independent constraint on source morphology, ultimately complementing and improving the reliability of sparse VLBI imaging results.

astro-ph.IM↗

arXiv2023

Interferometric Image Reconstruction using Closure Invariants and Machine Learning

Interferometric closure invariants encode calibration-independent details of an object's morphology. Excepting simple cases, a direct backward transformation from closure invariants to morphologies is not well established. We demonstrate using simple Machine Learning models that closure invariants can aid in morphological classification and parameter estimation. We consider six phenomenologically parametrised morphologies: point-like, uniform circular disc, crescent, dual disc, crescent with elliptical accretion disc, and crescent with double jet lobes. Using logistic regression (LR), multi-layer perceptron (MLP), and random forest models on closure invariants obtained from a sparsely covered aperture, we find that all methods except LR can classify morphologies with $\gtrsim$80% accuracy, which improves with greater aperture coverage. Separately from the classification problem, given an independently confirmed class, we estimate parameters of uniform circular disc, crescent, and dual disc morphologies using simple MLP models, and parametrically reconstruct images. The estimated parameters and images correspond well with inputs, but the accuracy worsens when degeneracies between parameters are present. This independent approach to interferometric imaging under challenging observing conditions such as that faced by the Event Horizon Telescope and Very Long Baseline Interferometry in general can complement other methods in robustly constraining an object's morphology.

astro-ph.IM↗