Searching for Extraterrestrial Technological Artefacts (SETA)
As we humans have left evidence of space exploration in other worlds, we can, therefore, try to find artefacts in our solar system possibly being left by another civilisation.
The following is an excerpt from my upcoming book “A Story of More Than 5000 Worlds: An Insight Into The Possibility of Life Beyond Earth”.
Defined by the prominent Dr. Jill Tarter, technosignatures are the “evidence of some technology that modifies its environment in ways that are detectable”.
In the search of extraterrestrial intelligence, researchers traditionally look for signals originated far away in the stars. However, another significant source of technosignatures could be the detection of technological artefacts.
In all the missions that reached the Moon, starting with NASA’s Apollo XI in 1969, the crews performed a number of experiments (yes, despite all the crazy conspiracy theories out there, humans did land on the Moon in 1969) and left a number of technological artefacts behind. The list, which is not by any means comprehensive, includes seismometers, cameras, lunar module ascent stages, and Laser Ranging RetroReflectors (LRRR). These last ones still work and have been vital for measuring accurately the distance between the Moon and the Earth. Apollos XV, XVI, and XVII left lunar rovers.
But the USA is not the only country that have left things behind on Earth’s natural satellite. In 2023, the entire world celebrated the achievement of the Indian Space Research Organization (ISRO), India’s national space agency. ISRO launched the spacecraft Chandrayaan-3 aboard of the LVM3-M4 heavy lift launch vehicle on July 14, 2023, from Satish Dhawan Space Centre Second Launch Pad in Sriharikota, Andhra Pradesh, India.
Chandrayaan-3 successfully entered Moon’s orbit on August 5, 2023; its payload included the lunar lander named Vikram, and a lunar rover named Pragyan.
On the 23 August, the lunar lander touched down near the lunar south pole. This magnificent achievement not only made India the fourth country until then (being China and Russia the other two) in successfully landing on the Moon, but also the first to do so near the lunar south pole. Those artefacts and instruments will join their USA counterparts as a technological artefact left by humankind.
Then, less than six months later, on January 19, the Japan Aerospace Exploration Agency (JAXA) successfully landed on the Moon, making Japan the 5th country to land a spacecraft. Japan’s Smart Lander for Investigating Moon (SLIM), nicknamed The Moon Sniper, used a technology known as pinpoint landing. This technology allowed the spacecraft to land within a high precision zone of just 100 meters wide. SLIM was carrying two small autonomous rovers — lunar excursion vehicles, LEV-1 and LEV-2, which were released just before landing.
These Indian and Japanese landers, rovers, and instruments will join their USA counterparts as technological artefacts left by humankind in the Moon.
Humans have done the same in Mars, as several rovers, probes, and even a small helicopter, among other things, have also been left behind. If a hypothetical alien crew mission in the future comes to perform some exploration in our solar system, long after we are gone (see The Great Filter — are we alone?), these artefacts will serve as evidence that we once existed. It is only logical to think that potential civilizations out there have also left behind scattered over their home solar system, or even ours, evidence of their eagerness to explore.
Finding artefacts in our solar system possibly being left by another civilization is a worthwhile endeavour. SETI researchers describe this technosignature search as an enterprise that is definitively something worth to pursue. In terms of the observing capability, we can search for them right now. The cost is cheap, as we can rely on existing instrumentation and resources. Ancillary benefits are also high as we could learn about the planetary surface processes in the potential body where we find such artefact. The amount of information we could extract from such finding is enormous, however, who knows if we would be able to decipher the language or understand the tools that such artefacts contain.
Using Artificial Intelligence to Find ET
In an interesting project [1], researchers used data-driven machine learning techniques with the aim of finding the landing module of the Apollo 15 in the surface of the Moon. The study is a proof of concept that demonstrates that such technique can be applied to other objects of the solar system, and the Moon itself, to potentially find technological artefacts left behind by a hypothetical extraterrestrial civilization.
The Moon was used as test bed for this proof of concept due to the wealth of satellite data available and the fact that numerous exploration missions have left non-natural relics on the surface. With the aim of training the algorithms, the researchers used images from parts of the Moon’s surface where no human-made artefacts were present. The algorithm was then told that such images were non-anomalous (normal) samples. On the contrary, parts of the Moon’s surface with human-made artefacts, such as the lunar lander modules left from the Apollo 15 and 17 missions, were categorised as anomalous samples.
Three experiments were conducted, in which the algorithm was fed with a large number of images, the majority of which did not contain any artefact.
For instance, one of the experiments utilised a dataset of 8000 images around the Apollo 15 mission landing site, including ten images displaying artefacts left by the mission’s crew. At the end of the exercise, the computer, trained using unsupervised learning techniques, successfully identified the landing module of the Apollo 15 mission.
Works like these serve as excellent demonstrators of the potential of such technologies and how we can employ them in the search for this type of technosignatures elsewhere in the solar system.
[1] Lesnikowski, A., Bickel, V.T. and Angerhausen, D., 2020. Unsupervised distribution learning for lunar surface anomaly detection. arXiv preprint arXiv:2001.04634.
