A student-built satellite from the University of Alberta that will capture images of active wildfires has made it into orbit after a successful launch last week. 

The satellite Ex-Alta 2, a miniature satellite about the size of a loaf of bread and weighing about two kilograms, launched from NASA’s Kennedy Space Centre aboard the Falcon 9 SpaceX Dragon cargo spacecraft on March 14.

“The moment it launched there was a pin-drop silence,” Thomas Ganley, lead manager on the AlbertaSat’s project, told CBC’s Edmonton AM.

The atmosphere was celebratory and he and his teammates were there to watch the countless years of their hard work blast off into space as part of a resupply mission to the International Space Station. 

“Everyone was in awe and just jaw dropped looking at the amazing marvel happening in front of us.”

The satellite, known as a cubesat, is a small, light and affordable device that will burn upon re-entry, meaning it doesn’t leave behind space debris. Each mission could take up to a year to complete. 

AlbertaSat builds cubesats composed of three units.

Ex-Alta 2 includes a multispectral camera, called an Iris, to take the images they need.

“We’re going to be studying active wildfires post-burn, the effect on vegetation to hopefully enable wildfire scientists to make some conclusions that will help us mitigate wildfires in the future,” Ganley said. 

“It’s quite impressive the amount of technology that you can pack into there and the really valuable science that you can still do with such a small size,” he said. 

Real space mission opportunity for students

Students from various degrees at the university have been working on the Ex-Alta 2 project for six years now. In 2017, they launched Ex-Alta 1. 

Ex-Alta 1 was designed to study space weather and carried instruments that measured the electron density of the ionosphere, magnetic signatures and radiation of the spacecraft. 

Both satellites are part of the Canadian Space Agency’s Canadian CubeSat Project and the Northern Space Program for Innovative Research and Integrated Training (Northern SPIRIT), which aim to give students the opportunity to experience a real space mission. 

The project is made up of a collaboration between three post-secondary institutions to create a nanosatellite design. 

AlbertaSat worked with Yukon University and Aurora Research Institute in the Northwest Territories to build three cubesats. 

“It really sets you up for leadership in the industry,” said Nikhil Velagapudi, a third-year chemical engineering student. 

“Having that leadership and management skills from an early age in the student group sector really helps us, it sets us up for success in the workforce.”

AlbertaSat plans on partnering up with the Canadian Space Agency to develop a satellite that will monitor snow and ice in the country’s northern region.

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Using ESO’s Very Large Telescope (VLT), two teams of astronomers have observed the aftermath of the collision between NASA’s Double Asteroid Redirection Test (DART) spacecraft and the asteroid Dimorphos. The controlled impact was a test of planetary defense, but also gave astronomers a unique opportunity to learn more about the asteroid’s composition from the expelled material.

On September 26, 2022, the DART spacecraft collided with the asteroid Dimorphos in a controlled test of our asteroid deflection capabilities. The impact took place 11 million kilometers away from Earth, close enough to be observed in detail with many telescopes. All four 8.2-meter telescopes of ESO’s VLT in Chile observed the aftermath of the impact, and the first results of these VLT observations have now been published in two papers.

“Asteroids are some of the most basic relics of what all the planets and moons in our solar system were created from,” says Brian Murphy, a Ph.D. student at the University of Edinburgh in the UK and co-author of one of the studies. “Studying the cloud of material ejected after DART’s impact can therefore tell us about how our solar system formed.”

“Impacts between asteroids happen naturally, but you never know it in advance,” continues Cyrielle Opitom, an astronomer also at the University of Edinburgh and lead author of one of the articles. “DART is a really great opportunity to study a controlled impact, almost as in a laboratory.”

Opitom and her team followed the evolution of the cloud of debris for a month with the Multi Unit Spectroscopic Explorer (MUSE) instrument at ESO’s VLT. They found that the ejected cloud was bluer than the asteroid itself was before the impact, indicating that the cloud could be made of very fine particles. In the hours and days that followed the impact other structures developed: clumps, spirals and a long tail pushed away by the sun’s radiation. The spirals and tail were redder than the initial cloud, and so could be made of larger particles.

MUSE allowed Opitom’s team to break up the light from the cloud into a rainbow-like pattern and look for the chemical fingerprints of different gases. In particular, they searched for oxygen and water coming from ice exposed by the impact. But they found nothing.

“Asteroids are not expected to contain significant amounts of ice, so detecting any trace of water would have been a real surprise,” explains Opitom. They also looked for traces of the propellant of the DART spacecraft, but found none. “We knew it was a long shot,” she says, “as the amount of gas that would be left in the tanks from the propulsion system would not be huge. Furthermore, some of it would have traveled too far to detect it with MUSE by the time we started observing.”

Another team, led by Stefano Bagnulo, an astronomer at the Armagh Observatory and Planetarium in the UK, studied how the DART impact altered the surface of the asteroid.

“When we observe the objects in our solar system, we are looking at the sunlight that is scattered by their surface or by their atmosphere, which becomes partially polarized,” explains Bagnulo. This means that light waves oscillate along a preferred direction rather than randomly. “Tracking how the polarization changes with the orientation of the asteroid relative to us and the sun reveals the structure and composition of its surface.”

Bagnulo and his colleagues used the FOcal Reducer/low dispersion Spectrograph 2 (FORS2) instrument at the VLT to monitor the asteroid, and found that the level of polarization suddenly dropped after the impact. At the same time, the overall brightness of the system increased. One possible explanation is that the impact exposed more pristine material from the interior of the asteroid.

“Maybe the material excavated by the impact was intrinsically brighter and less polarizing than the material on the surface, because it was never exposed to solar wind and solar radiation,” says Bagnulo.

Another possibility is that the impact destroyed particles on the surface, thus ejecting much smaller ones into the cloud of debris. “We know that under certain circumstances, smaller fragments are more efficient at reflecting light and less efficient at polarizing it,” explains Zuri Gray, a Ph.D. student also at the Armagh Observatory and Planetarium.

The studies by the teams led by Bagnulo and Opitom show the potential of the VLT when its different instruments work together. In fact, in addition to MUSE and FORS2, the aftermath of the impact was observed with two other VLT instruments, and analysis of these data is ongoing.

“This research took advantage of a unique opportunity when NASA impacted an asteroid,” concludes Opitom, “so it cannot be repeated by any future facility. This makes the data obtained with the VLT around the time of impact extremely precious when it comes to better understanding the nature of asteroids.”

The research highlighted in the first part of this article was presented in the paper “Morphology and spectral properties of the DART impact ejecta with VLT/MUSE,” which appears in Astronomy & Astrophysics. The second part of this article refers to the paper “Optical spectropolarimetry of binary asteroid Didymos-Dimorphos before and after the DART impact” in Astrophysical Journal Letters.

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An international team of researchers has found that Artificial Intelligence (AI) can help identify hidden patterns within geographical data that could indicate life on Mars.  

As there are only a few opportunities to collect samples from Mars in the search for life beyond Earth, it is crucial that these missions target locations that have the best chance of harbouring extra-terrestrial life. The new study, led by an international team of over 50 researchers, ensures that this can be supported by using Artificial Intelligence and Machine Learning methods. This technology can be used to identify hidden patterns within geographical data that could indicate the presence of life on Mars. 

The work, ‘Orbit-to-Ground Framework to Decode and Predict Biosignature Patterns in Terrestrial Analogues,’ has been published in Nature Astronomy.  

The resulting model was capable of locating biosignatures that have the potential to indicate life on Mars 

The first part of the study, led by Dr Kimberley Warren-Rhodes at the SETI Institute, was an ecological survey of a 3 km² area in the Salar de Pajonales basin, at the boundary of the Chilean Atacama Desert and Altiplano in South America. This was used to map the distribution of photosynthetic microorganisms. Gene sequencing and infrared spectroscopy were also used to reveal distinct markers of life, called ‘biosignatures.’ Aerial images were then combined with this data to train a Machine Learning model to predict which macro- and microhabitat types would be associated with biosignatures that could indicate life on Mars and other areas. 

The resulting model could locate and detect biosignatures up to 87.5% of the time on data on which it was not trained. This decreased the search area required to find a positive result by up to 97%. In the future, life on Mars could be detected through the identification of the areas most likely to contain signs of life. These can then be extensively searched by rovers. 

Dr Freddie Kalaitzis from the University of Oxford’s Department of Computer Science led the application of Machine Learning methods to microhabitat data. He said: “This work demonstrates an AI-guided protocol for searching for life on a Mars-like terrestrial analogue on Earth. This protocol is the first of its kind trained on actual field data, and its application can, in principle, generalise to other extreme life-harbouring environments. Our next steps will be to test this method further on Earth with the aim that it will eventually aid our exploration for biosignatures elsewhere in the solar system, such as Mars, Titan, and Europa.” 

On Earth, one of the most similar analogues to Mars is the Pajonales, a four-million-year-old lakebed. This area is considered to be inhospitable to most forms of life. Comparable to the evaporitic basins of Mars, the high altitude (3,541 m) basin experiences exceptionally strong levels of ultraviolet radiation, hypersalinity, and low temperatures. 

Water availability is likely to be the key factor determining the position of biological hotspots 

The researchers collected over 7,700 images and 1,150 samples and tested for the presence of photosynthetic microbes living within the salt domes, rocks, and alabaster crystals that make up the basin’s surface. Here, biosignature markers, such as carotenoid and chlorophyll pigments, could be seen as orange-pink and green layers respectively. 

Ground sampling data and 3D topographical mapping were combined with the drone images to classify regions into four macrohabitats (metre to kilometre scales) and six microhabitats (centimetre scale). The team found that the microbial organisms across the study site were clustered in distinct regions, despite the Pajonales having a near-uniform mineral composition.  

Follow-up experiments showed that rather than environmental variables, like nutrient or light availability, determining the position of, biological hotspots water availability is the most likely factor.  

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The combined dataset was used to train convolutional neural networks to predict which macro- and microhabitats were most strongly associated with biosignatures.  

“For both the aerial images and ground-based centimetre-scale data, the model demonstrated high predictive capability for the presence of geological materials strongly likely to contain biosignatures,” said Dr Kalaitzis.  

“The results aligned well with ground-truth data, with the distribution of biosignatures being strongly associated with hydrological features.” 

The model will be used to map other harsh ecosystems  

Now, the researchers aim to test the model’s ability to predict the location of similar yet different natural systems in the Pajonales basin, such as ancient stromatolite fossils. The model will also be used to map other harsh ecosystems, including hot springs and permafrost soils. The data from these studies will inform and test hypotheses on the mechanisms that living organisms use to survive in extreme environments. 

“Our study has once again demonstrated the power of Machine Learning methods to accelerate scientific discovery through its ability to analyse immense volumes of different data and identify patterns that would be indiscernible to a human being,” Dr Kalaitzis added.  

“Ultimately, we hope the approach will facilitate the compilation of a databank of biosignature probability and habitability algorithms, roadmaps, and models that can serve as a guide for exploration of life on Mars.” 

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