CHIRP (algorithm)

[2][3] The development of CHIRP involved a large team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory, the Center for Astrophysics | Harvard & Smithsonian and the MIT Haystack Observatory, including Bill Freeman and Sheperd Doeleman.

[4][5] It was first presented publicly by Bouman at the IEEE Computer Vision and Pattern Recognition conference in June 2016.

[2] The CHIRP algorithm was developed to process data collected by the very-long-baseline Event Horizon Telescope, the international collaboration that in 2019 captured the black hole image of M87* for the first time.

[8][9] For reconstruction of such images which have sparse frequency measurements the CHIRP algorithm tends to outperform CLEAN, BSMEM (BiSpectrum Maximum Entropy Method), and SQUEEZE, especially for datasets with lower signal-to-noise ratios and for reconstructing images of extended sources.

While the BSMEM and SQUEEZE algorithms may perform better with hand-tuned parameters, tests show CHIRP can do better with less user expertise.

First combined image reconstruction of the event horizon of a black hole ( M87* ) captured by the Event Horizon Telescope . [ 1 ]