Shorlisted images on the Astronomy Photographer of the Year 2021 competition!
Update 16th Sep 2021: my image Another Cloudy Day on Jupiter: A Surface Map Based on 8 Filters Data was selected as the winner of the Annie Maunder category jointly with Leonardo DiMaggio's "Celestial Fracture".
I am more than delighted to announce that not one, but two of the images I submitted to the Astronomy Photographer of the Year 2021 competition have been shortlisted under the Annie Maunder Prize for Image Innovation category, first introduced in the 2020 edition. In this category the objective is to achieve an innovative image taking advantage of publicly accessible data captured by professional telescopes, being able to combine different sources (NASA, ESA, ESO, etc.).
Shortlisted images will be exhibited in the Royal Observatory Museum in Greenwich and that they will appear in the astrophotography book that the Museum publishes each year. Oh my, I don't believe it yet!! Thanks so much to Royal Museums Greenwich for hosting the competition and to Insight Investment for supporting astronomy! And of course, to NASA, who provided the data for these images! They wouldn't have been possible without the effort of the people who built those space telescopes, wrote the observing proposals, maintain the archives, financed the projects, those who invented the algorithms or wrote the software used to process them. So, for me, this category of the competition emphasizes collaboration, even among people who never met, but share a common interest.
Multiband Whirlpool Galaxy: From Infrared to X-Rays
The first image, titled Multiband Whirlpool Galaxy: From Infrared to X-Rays, presents a different view of M51, an interacting pair of galaxies: the grand design spiral galaxy NGC 5194 or "Whirlpool", and its distorted, smaller companion, NGC 5195, located in the constellation Canes Venatici, about 31 million light-years away. Discovered in 1773, it's the first object that later would be classified as a spiral galaxy. It's one of the most widely observed astronomical objects as it is well within the reach of amateur telescopes and has been widely studied by professional astronomers. 1
From the data captured by professional telescopes of the same astronomical object, in this case the Hubble, Spitzer and Chandra space telescopes 2, the approach I have followed in this "experiment" has been to create a graphical, "photo-like" visualization by using data science techniques.
Interdisciplinary field that applies statistics and artificial intelligence on datasets for analysis and visualization, as well as the generation of predictive or explanatory models.
Light can be broken down into different bands: some of these bands correspond to colors that we are capable of perceiving, while others are invisible to the human eye, such as infrared, ultraviolet and X-rays. Scientific instruments observe in a subset of these bands: by grouping these observations, we get an input image for the visualization process. Each pixel of this image represents a small part of the object by assigning the luminosity measurements in the different bands that have been observed.
In this case, by having scientific data corresponding to 6 bands or "colors", visible or invisible, each pixel has 6 dimensions. Our vision consists of cells that are sensitive to the colors red, green and blue, which mixed in different proportions, produce the perception of different colors. Therefore, to be able to visualize an image with pixels in 6 dimensions, we need to "project" them to only 3, those of red, green and blue. As an example from our daily lives, this projection operation is conceptually similar to the one we perform when we have a three-dimensional object in mind and make a (two-dimensional) drawing to represent it.
Usually we manually merge the input bands to build the final tri-color image. Here, however, I've applied a technique widely known in data science and commonly used for the visualization of datasets, known as Principal Component Analysis (PCA), to perform this projection or dimensionality. reduction. Continuing with the previous example, this technique would try to find the orientation of the object that makes the drawing as representative as possible, based on certain mathematical criteria.
The use of this technique was motivated by identifying that the first component that it produces, the most important in terms of quantity of information, can be interpreted as a kind of "summary" of all the bands, therefore representative of the general luminosity of the object, what in image processing we call a luminance channel. Using additional components we can add color or chrominance information. From the luminance and chrominance, we produce the final image, in the usual channels, red, green and blue.
As in any visualization process, the objective is to interpret the original data by using shape, texture, intensity and color 3 to represent the maximum possible information contained in it, but here I also attempted to create a result with photographic, artistic content 4 5, wishing that this different view of M51 turned out to be novel not only for the general public, but also for those who are already familiar with this object, well known among amateur and professional astronomers.
Another Cloudy Day on Jupiter: A Surface Map Based on 8 Filters Data
The second image, Another Cloudy Day on Jupiter: A Surface Map Based on 8 Filters Data, is a map of Jupiter's surface clouds constructed from multiple Hubble Space Telescope observations made in 2019 under the OPAL program 6, taking advantage of the planet's rotation to be able to cover its entire extension, on different bands.
In this case, the same technique and approach as for the M51 image has been applied, with the exception that in this case up to a total of 8 bands have been used, available in the space telescope public archive.
Who is Annie Maunder?
"This prize is named after Annie Maunder, an astronomer who worked at the Royal Observatory at the turn of the 20th century. She was an avid astrophotographer who overcame adversity to pursue her passion for astronomy. She even published some of the first popular astronomy books, featuring some of her ground-breaking images of space." 7.
I highly recommend reading the article on the biography of Annie Maunder, written by Dr. Louise Devoy, Senior Curator at the Royal Greenwich Observatory.
Data credit: Chandra: NASA/CXC/Wesleyan Univ./R.Kilgard, et al; HST: NASA/STScI/S. V. W. Beckwith (obs 10452); Spitzer: NASA/Spitzer Science Center/IRSA. Telescopes, instruments and filters: Chandra ACIS (X-rays); HST ACS: F435W (B), F555W (V), F658N (H-alpha), F814W (I); Spitzer IRAC4 (8 microns) ↩
Data credit: "This work used data acquired from the NASA/ESA HST Space Telescope, associated with OPAL program (PI: Simon, GO13937), and archived by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. All maps are available at http://dx.doi.org/10.17909/T9G593". Instrument: Hubble Space Telescope WFC3/UVIS. Filters: FQ889, F631N, F502N, F395N, F467M, F658N, F275W, F343N ↩