DeepAI AI Chat
Log In Sign Up

VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks

by   Joshua Yao-Yu Lin, et al.

The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for the EHT is how well key parameters, such as trapped magnetic flux and the associated disk models, can be extracted from present and future EHT VLBI data products. The process of modeling visibilities and analyzing them is complicated by the fact that the data are sparsely sampled in the Fourier domain while most of the theory/simulation is constructed in the image domain. Here we propose a data-driven approach to analyze complex visibilities and closure quantities for radio interferometric data with neural networks. Using mock interferometric data, we show that our neural networks are able to infer the accretion state as either high magnetic flux (MAD) or low magnetic flux (SANE), suggesting that it is possible to perform parameter extraction directly in the visibility domain without image reconstruction. We have applied VLBInet to real M87 EHT data taken on four different days in 2017 (April 5, 6, 10, 11), and our neural networks give a score prediction 0.52, 0.4, 0.43, 0.76 for each day, with an average score 0.53, which shows no significant indication for the data to lean toward either the MAD or SANE state.


page 2

page 5


Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning

We attempt to estimate the spatial distribution of heterogeneous physica...

Can Un-trained Neural Networks Compete with Trained Neural Networks at Image Reconstruction?

Convolutional Neural Networks (CNNs) are highly effective for image reco...

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

Unrolled neural networks have enabled state-of-the-art reconstruction pe...

Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering

To increase the flexibility and scalability of deep neural networks for ...

A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction

In radio astronomy, signals from radio telescopes are transformed into i...