{{Short description|Algorithm used for image processing}} [[File:Black hole - Messier 87 crop max res.jpg|alt=|thumb|right|First combined image reconstruction of the event horizon of a black hole (M87*) captured by the Event Horizon Telescope.<ref name="NYT-20190410">{{cite news |last=Overbye |first=Dennis |authorlink=Dennis Overbye |title=Black Hole Picture Revealed for the First Time – Astronomers at last have captured an image of the darkest entities in the cosmos |url=https://www.nytimes.com/2019/04/10/science/black-hole-picture.html?comments#permid=31473598 |date=April 10, 2019 |work=The New York Times |accessdate=April 10, 2019 }}</ref>]]

'''CHIRP''' ('''Continuous High-resolution Image Reconstruction using Patch priors''') is a Bayesian algorithm used to perform a deconvolution on images created in radio astronomy. The acronym was coined by lead author Katherine L. Bouman in 2016.<ref name=IEEE-CV/><ref name=":0">{{Cite web|url=https://news.mit.edu/2016/method-image-black-holes-0606|title=A method to image black holes|last=MIT News Office|first=Larry Hardesty|date=June 6, 2016|website=MIT News}}</ref>

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.<ref>{{Cite news|url=https://techcrunch.com/2019/04/10/the-creation-of-the-algorithm-that-made-the-first-black-hole-image-possible-was-led-by-mit-grad-student-katie-bouman/|title=The creation of the algorithm that made the first black hole image possible was led by MIT grad student Katie Bouman|last=Shu|first=Catherine|date=April 11, 2019|work=TechChrunch|access-date=April 12, 2019}}</ref><ref>{{Cite news|url=https://www.standard.co.uk/news/world/mit-grad-student-katie-bouman-algorithm-black-hole-photo-a4114946.html|title=Grad student Katie Bouman created the algorithm that led to the first-ever black hole photo|last=Brewis|first=Harriet|date=April 12, 2019|work=Evening Standard|access-date=April 13, 2019}}</ref> It was first presented publicly by Bouman at the IEEE Computer Vision and Pattern Recognition conference in June 2016.<ref name=IEEE-CV>{{Cite book|arxiv=1512.01413|bibcode=2016cvpr.conf..913B|doi=10.1109/CVPR.2016.105|chapter-url=https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Bouman_Computational_Imaging_for_CVPR_2016_paper.pdf |chapter=Computational Imaging for VLBI Image Reconstruction |title=2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |date=2016 |last1=Bouman |first1=Katherine L. |last2=Johnson |first2=Michael D. |last3=Zoran |first3=Daniel |last4=Fish |first4=Vincent L. |last5=Doeleman |first5=Sheperd S. |last6=Freeman |first6=William T. |pages=913–922 |isbn=978-1-4673-8851-1 }}</ref>

== Development == 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. CHIRP was not used to produce the image,<ref>{{Cite journal|title=First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole. Appendix A: Regularizer Definitions.|journal=The Astrophysical Journal|volume=875|pages=L4|last=The Event Horizon Telescope Collaboration|doi=10.3847/2041-8213/ab0e85| arxiv=1906.11241|year=2019|issue=1 |bibcode=2019ApJ...875L...4E |hdl=10150/633752|s2cid=146068771 |hdl-access=free |doi-access=free }}</ref> but was an algebraic solution for the extraction of information from radio signals producing data by an array of radio telescopes scattered around the globe.<ref name=":0" /><ref>{{Cite news|url=https://phys.org/news/2019-04-scientist-superstar-katie-bouman-algorithm.html|title=Scientist superstar Katie Bouman designed algorithm for black hole image|last=Science X|first=News Network|date=April 11, 2019|work=PHYS.ORG|access-date=April 13, 2019}}</ref> Stable sources (that don't change over short periods of time) can also gain signal by integrating the change at each location with the rotation of the earth.<ref name=":0"/>{{rp|915}} Because the radio telescopes used in the project produce vast amounts of data, which contain gaps, the CHIRP algorithm is one of the ways to fill the gaps in the collected data.<ref>{{Cite news|url=https://indianexpress.com/article/technology/science/the-image-of-a-black-hole-event-horizon-algorithm-katie-bouman-mit-5669801/|title=This MIT grad student's work helped make the black hole event horizon image a reality|last=Tech Desk|first=New Delhi|date=April 22, 2019|work=Express tech, The Indian Press|access-date=April 13, 2019}}</ref><ref>{{Cite web|url=http://www.cs.mun.ca/~wlodek/technews/technews-2016/tn-CHIRP-algorithm-16-06-06.html|title=A Method to Image Black Holes|last=ACM (Association for Computing Machinery)|first=TechNews|date=June 6, 2016|website=ACM News Service|access-date=April 13, 2019|archive-date=April 13, 2019|archive-url=https://web.archive.org/web/20190413102434/http://www.cs.mun.ca/~wlodek/technews/technews-2016/tn-CHIRP-algorithm-16-06-06.html|url-status=dead}}</ref>

== Evaluation== 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.<ref>{{Cite journal |date=July 2017|title=Observing—and Imaging—Active Galactic Nuclei with the Event Horizon Telescope|journal=Galaxies|volume=4|issue=4|pages=54 and 64|doi=10.3390/galaxies4040054|doi-access=free|arxiv=1607.03034 |last1=Fish |first1=Vincent |last2=Akiyama |first2=Kazunori |last3=Bouman |first3=Katherine |last4=Chael |first4=Andrew |last5=Johnson |first5=Michael |last6=Doeleman |first6=Sheperd |last7=Blackburn |first7=Lindy |last8=Wardle |first8=John |last9=Freeman |first9=William |author10=the Event Horizon Telescope Collaboration }}</ref>

== See also== * {{section link|Van Cittert–Zernike theorem|Aperture synthesis}}

== References == {{reflist}}

Category:Radio astronomy