After inserting more stellar activity data into the algorithm, the researchers discovered the algorithm successfully identified a simulated exoplanet approximately 2.2 times the size of the Earth while orbiting the same distance as the Earth from our Sun
How can machine learning help astronomers find Earth-like exoplanets? This is what a recently accepted study to Astronomy & Astrophysics hopes to address as a team of international researchers investigated how a novel neural network-based algorithm could be used to detect Earth-like exoplanets using data from the radial velocity (RV) detection method. This study holds the potential to help astronomers develop more efficient methods in detecting Earth-like exoplanets, which are traditionally difficult to identify within RV data due to intense stellar activity from the host star.
For the study, the researchers applied their algorithm to three stars to ascertain its ability to identify exoplanets within the stellar activity data: our Sun, Alpha Centauri B (HD 128621), and Tau ceti (HD 10700), with Alpha Centauri B being located approximately 4.3 light-years from Earth and Tau ceti being located approximately 12 light-years from Earth. After inserting simulated planetary signals within the algorithm, the researchers found their algorithm successfully identified simulated exoplanets with potential orbital periods ranging between 10 to 550 days for our Sun, 10 to 300 days for Alpha Centauri B, and 10 to 350 days for Tau ceti. It’s important to note that Alpha Centauri B currently has had several potential exoplanets detections non confirmed while Tau ceti currently has eight exoplanets listed as unconfirmed ”within its system
The study of extrasolar planets is a relatively new field of research. Although the first evidence of the existence of this type of body was uncovered in 1917 (Landau 2017), it was not until the 1990s that the first confirmed detections were made. In 1992, by analysing variations in the period of the pulses received from the radio millisecond pulsar PSR1257+12, Wolszczan & Frail (1992)concluded that at least two Earth-mass planets are in orbit around the pulsar. Three years later, Mayor & Queloz (1995) discovered the first exoplanet orbiting a solar-type star, 51 Peg b, by measuring variations in the line-of-sight (radial) velocity of the host star induced by the unseen companion. In the years that followed this first detection, the radial velocity (RV) technique allowed a large number of planet candidates and information-rich systems to be unveiled, some with masses as small as a few times that of Earth
With billions of galaxies in the Universe and billions of stars in those galaxies, finding an Earth-like planet orbiting a Sun-like star makes finding a needle in a haystack sound easy. However, astronomers, using a machine learning technique called hierarchical Bayesian modeling, to better understand the likelihood of potential Earths orbiting stars that are similar to the Sun. The model may help astronomers find rocky planets that – if our own planet can be used as a guide – could support life. The scientists used a catalog of data from NASA’s Kepler mission and the second data release for Gaia, the European Space Agency’s one billion star survey mission
New artificial Intelligence-based tools can help finding habitable planets
Utilising an Artificial Intelligence-based algorithm, Indian Astronomers have devised a new approach for identifying potentially habitable planets with a high probability.
Since time immemorial, humanity has been looking at the cosmos and believing that other inhabited worlds are out there. Current estimates are that the number of planets in our Galaxy alone run into billions, possibly a number greater than the number of stars itself. The question that naturally arises is whether there are other life-harboring planets and if there is a way to predict which exoplanet can potentially harbour life?
In the present work, astronomers from the Indian Institute of Astrophysics, an autonomous institute of the Department of Science & Technology, Govt. Of India, along with astronomers from BITS Pilani, Goa campus have devised a new approach — an anomaly detection method — by which they can identify potentially habitable ones with a high probability. The method is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points. The study is published in the journal, Monthly Notices of the Royal Astronomical Society (MNRAS).
According to the study, there are 60 potentially habitable planets out of about 5000 confirmed, and nearly 8000 candidate planets proposed. The assessment is based on their close similarity to Earth. These planets can be viewed as candidates for anomalous instances in a huge pool of `non-habitable’ exoplanets.
“Earth being the only habitable planet among thousands of planets is defined as an anomaly. We explored whether similar ‘anomaly candidates can be found using novel anomaly detection methods,’ said Dr. Snehanshu Saha of BITS Pilani K K Birla Goa Campus and Dr. Margarita Safonova of Indian Institute of Astrophysics
The IIA team explains that the fulcrum of the idea that postulates (potentially) habitable exoplanets as anomalies pivots around the well-known anomaly detection problem in predictive maintenance of industrial systems. Anomaly detection technique suitable for industrial systems applies equally well for habitable planet detection since in both cases, the anomaly detector is dealing with “imbalanced” data, where the anomalies (number of habitable exoplanets or anomalous behavior of industrial components) are outliers. These are far less in number compared to the normal data.
However, with the large number of discovered exoplanets, finding those rare anomalous instances by characterizing them in terms of planetary parameters, types, populations, and, ultimately, the habitability potential requires the knowledge of multiple planetary parameters from observations. This, in turn, demands hours of expensive telescope time. It is a tedious job to scan thousands of planets manually and to identify planets potentially similar to Earth. Artificial Intelligence (AI) can be utilized effectively to find habitable planets.
Researchers, under the supervision of Prof. Snehanshu Saha of BITS Pilani Goa Campus and Dr. Margarita Safonova of Indian Institute of Astrophysics (IIA), Bengaluru, have thus developed a novel Artificial Intelligence-based algorithm to detect anomalies and extended it to an unsupervised clustering algorithm to use it to identify the probably habitable exoplanets from the exoplanet datasets. The research team also included Prof. Santonu Sarkar, Jyotirmoy Sarkar — a doctoral student, Kartik Bhatia — an undergraduate student, all from BITS Pilani Goa Campus.
The AI-based method, named Multi-Stage Memetic Binary Tree Anomaly Identifier (MSMBTAI), is based on a novel multi-stage memetic algorithm (MSMA). MSMA uses the generic notion of a meme, which is an idea or knowledge that gets transferred from one person to another by imitation. A meme indicates cross-cultural evolution in posterity and, therefore, can induce new learning mechanisms as generations pass. The algorithm can act as a quick screening tool for evaluating habitability perspectives from observed properties.
The study identified a few planets which exhibit similar anomalous characteristics as Earth via the proposed technique, which shows reasonably good results, in agreement with what astronomers believe. Interestingly, this method resulted in similar results in terms of anomalous candidate detection when it did not use surface temperature as a feature, compared to when it actually did. This will make future analysis of exoplanets much easier.
What is a deep learning algorithm?
Deep learning algorithms are neural networks that are modeled after the human brain. For example, a human brain contains millions of interconnected neurons that work together to learn and process information
What is deep learning with an example?
Deep learning algorithms
Once a deep learning algorithm has been trained, it can be used to make predictions on new data. For example, a deep learning algorithm that has been trained to recognize images of dogs can be used to identify dogs in new images
What are the 4 algorithms in machine learning?
There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement
Which AI uses deep learning?
Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them. In a similar way, deep learning algorithms can automatically translate between languages
What is the algorithm for detection of exoplanets?
ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data. The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion
What technology is used to find planets?
It’s a technique known as “transit spectroscopy,” when light from a star travels through the atmosphere of an orbiting planet and reaches our telescopes – in space or on the ground – and tells about where it’s been
How do we detect Earth like planets?
Thus, astronomers now can detect solid surfaces, like Earth’s, and to probe the atmospheres of these exoplanets. Using telescopes and observatories, astronomers can detect an exoplanet by looking for a small dip in light, called a “transit,” shining from a star as the planet orbits in front of it.
The radial-velocity method for detecting exoplanets relies on the fact that a star does not remain completely stationary when it is orbited by a planet. The star moves, ever so slightly, in a small circle or ellipse, responding to the gravitational tug of its smaller companion
What is the radial-velocity method of detection?
Doppler spectroscopy (also known as the radial-velocity method, or colloquially, the wobble method) is an indirect method for finding extrasolar planets and brown dwarfs from radial-velocity measurements via observation of Doppler shifts in the spectrum of the planet’s parent star.
Our Solar System formed around 4600 million years ago. We know this from the study of meteorites and radioactivity. It all began with a cloud of gas and dust. A nearby supernova explosion probably perturbed the calm cloud, which then started to contract due to gravity, forming a flat, rotating disk with most of the material concentrated in the center: the protosun. Later, gravity pulled the rest of the material into clumps and rounded some of them, forming the planets and dwarf planets. The leftovers resulted in comets, asteroids, and meteoroids.
But what are Exoplanets?
Exoplanets are planets beyond our own solar system. Thousands have been discovered in the past two decades, mostly with NASA’s Kepler Space Telescope.
These exoplanets come in a huge variety of sizes and orbits. Some are gigantic planets hugging close to their parent stars; others are icy, some rocky. NASA and other agencies are looking for a special kind of planet: one that’s the same size as Earth, orbiting a sun-like star in the habitable zone.
The habitable zone is the area around a star where it is not too hot and not too cold for liquid water to exist on the surface of surrounding planets. Imagine if Earth was where Pluto is. The Sun would be barely visible (about the size of a pea) and Earth’s ocean and much of its atmosphere would freeze.
Why even search for exoplanets?
There are about 100,000,000,000 stars in our Galaxy, the Milky Way. How many exoplanets — planets outside of the Solar System — do we expect to exist? Why are some stars surrounded by planets? How diverse are planetary systems? Does this diversity tell us something about the process of planet formation? These are some of the many questions that motivate the study of exoplanets. Some exoplanets may have the necessary physical conditions (amount and quality of light from the star, temperature, atmospheric composition) for the existence of complex organic chemistry and perhaps for the development of Life (which may be quite different from Life on Earth).
How do scientists know what planets look like?
The Hubble and James Webb space telescopes have the needed resolution and have been able to make images of planets around stars other than our Sun. With this technique we can also learn about the planets’ orbital periods and the distances to their stars.
What is earth 2.0
Earth 2.0 is a planet similar enough to Earth to enable the existence of life as we know it. It would be the right temperature for liquid water, and it would orbit a star with a steady supply of light. Ideally, it would be close enough that we could imagine going there or at least sending a probe to explore it
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