SHEEP is a new artificial intelligence software, developed by researchers at Instituto de Astrofísica e Ciências do Espaço, in Portugal, to help classify astronomical sources. 

Classifying celestial objects is a long-standing problem. With sources at nearly unimaginable distances, sometimes it’s difficult for researchers to distinguish, for example, between stars, galaxies, quasars, or supernovae. 

Instituto de Astrofísica e Ciências do Espaço’s (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classical problem by creating SHEEP, a machine learning algorithm, which determines the nature of astronomical sources. 

The first author of the article now published in the journal Astronomy & Astrophysics, Pedro Cunha, a Ph.D. student at IA and in the Dep. of Physics and Astronomy of the Science Faculty of the University of Porto, thinks: “This work was born as a side project from my MSc thesis. It combined the lessons learned during that time into a unique project.”. Andrew Humphrey, Pedro Cunha’s MSc advisor and now Ph.D. co-advisor pointed out that: “It was very cool to get such an interesting result, especially from a master’s thesis!”

SHEEP is a supervised machine learning pipeline that estimates photometric redshifts and uses this information when subsequently classifying the sources as a galaxy, quasar, or star. “The photometric information is the easiest to obtain and thus is very important to provide a first analysis about the nature of the observed sources.”, says Pedro Cunha.

A novel step in our pipeline is that before performing the classification, SHEEP first estimates photometric redshifts, which are then placed into the data set as an additional feature for classification model training.

The team found that including the redshift and the coordinates of the objects allows the AI to understand them within a 3D map of the Universe, and use that together with color information, to make better estimations of source properties. For example, the AI learned that there is a higher chance of finding stars closer to the Milky Way plane than at the Galactic Poles. Andrew Humphrey (IA & University of Porto) added: “When we allowed the AI to have a 3D view of the Universe, this really improved its ability to make accurate decisions about what each celestial object was.”

Wide-area surveys, both ground- and space-based, like the Sloan Digital Sky Survey (SDSS), have yielded high volumes of data, revolutionizing the field of astronomy. Future surveys, carried out by the likes of the Vera C. Rubin Observatory, the Dark Energy Spectroscopic Instrument (DESI), the Euclid (ESA) space mission or the James Webb Space Telescope (NASA/ESA) will continue to give us more detailed imaging. However, analyzing all the data using traditional analysis methods can be time-consuming due to the extremely high volume of data. AI or machine learning will be crucial for analyzing and making the best scientific use of this new data.

This work is part of the team’s effort toward exploiting the expected deluge of data to come from those surveys, by developing artificial intelligence systems that efficiently classify and characterize billions of sources.

Imaging and spectroscopic surveys are one of the main resources for understanding the visible content of the Universe. The data from these surveys enable statistical studies of stars, quasars, and galaxies, and the discovery of more peculiar objects.

For the Principal Investigator of the research group “The assembly history of galaxies resolved in space and time” at IA, Polychronis Papaderos: “The development of advanced Machine Learning algorithms, such as SHEEP, is an integral component of IA’s coherent strategy toward scientific exploitation of unprecedentedly large sets of photometric data for billions of galaxies with ESA’s Euclid space mission, scheduled for launch in 2023.”

Euclid will provide detailed cartography of the Universe and shed light on the nature of the enigmatic dark matter and dark energy. The IA coordinates the Portuguese participation in Euclid and leads or co-leads, within the Euclid consortium, several work packages in the field of Cosmology and Extragalactic Astronomy.

The climate crisis can especially be felt in the Arctic: in the High North, the average air temperature has risen by more than two degrees Celsius over the past 50 years – far more than anywhere else. And this trend will only continue. If ambitious greenhouse-gas reduction measures (Emissions Scenario RCP 2.6) are taken, the further warming of the Arctic through the end of the century could be limited to just below two degrees. According to supercomputer model-based forecasts, if the emissions remain high (Scenario RCP 8.5), we could see a dramatic rise in the average summer temperatures in the Arctic – by up to 14 degrees Celsius over today’s norm by 2100. 

“For the Arctic Ocean and the sea ice, the current and future warming will have serious consequences,” says Prof Ulrike Herzschuh, Head of the Polar Terrestrial Environmental Systems Division at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) in Germany. “But the environment on land will also change drastically. The broad expanses of tundra in Siberia and North America will be massively reduced, as the treeline, which is already slowly changing, rapidly advances northward shortly. In the worst-case scenario, there will be virtually no tundra left by the middle of the millennium. In the course of our study, we simulated this process for the tundra in northeast Russia. The central question that concerned us was: which emissions path does humanity have to follow to preserve the tundra as a refuge for flora and fauna, as well its role for the cultures of indigenous peoples and their traditional ties to the environment?”

The tundra is home to a unique community of plants, roughly five percent of which are endemic, i.e., can only be found in the Arctic. Typical species include the mountain avens, Arctic poppy, and prostrate shrubs like willows and birches, all of which have adapted to the harsh local conditions: brief summers and long, arduous winters. It also offers a home for rare species like reindeer, lemmings, and insects like the Arctic bumblebee.

For their simulation, Ulrike Herzschuh and AWI modeler Dr Stefan Kruse employed the AWI vegetation model LAVESI. “What sets LAVESI apart is that it allows us to display the entire treeline at the level of individual trees,” Kruse explains. “The model portrays the entire lifecycle of Siberian larches in the transition zone to the tundra – from seed production and distribution, to germination, to fully grown trees. In this way, we can very realistically depict the advancing treeline in a warming climate.”

The findings speak for themselves: the larch forests could spread northward at a rate of up to 30 kilometers per decade. The tundra expenses, which can’t shift to colder regions due to the adjacent Arctic Ocean, would increasingly dwindle. Since trees aren’t mobile and each one’s seeds can only reach a limited distribution radius, initially the vegetation would significantly lag behind the warming, but then catch up to it again. In the majority of scenarios, by mid-millennium less than six percent of today’s tundra would remain; saving roughly 30 percent would only be possible with the aid of ambitious greenhouse-gas reduction measures. Otherwise, Siberia’s once 4,000-kilometre-long, unbroken tundra belt would shrink to two patches, 2,500 kilometers apart, on the Taimyr Peninsula to the west and Chukotka Peninsula to the east. Interestingly, even if the atmosphere cooled again in the course of the millennium, the forests would not completely release the former tundra areas.

“At this point, it’s a matter of life and death for the Siberian tundra,” says Eva Klebelsberg, Project Manager Protected Areas and Climate Change / Russian Arctic at the WWF Germany, about the study. “Larger areas can only be saved with very ambitious climate protection targets. And even then, in the best case, there will ultimately be two discrete refuges, with smaller flora and fauna populations that are highly vulnerable to disrupting influences. That’s why we must intensify and expand protective measures and protected areas in these regions, to preserve refuges for the tundra’s unparalleled biodiversity,” adds Klebelsberg, who, in collaboration with the Alfred Wegener Institute, is an advocate for the establishment of protected areas. “After all, one thing is clear: if we continue with business as usual, this ecosystem will gradually disappear.”