An expert in statistical and mathematics from Institute of Cosmos Sciences at the University of Barcelona, Dr. Fergus Simpson constructed a statistical model to predict the dry land in the exoplanets. Based on his model, he finds the habitable exoplanets are lacked the dry land to sustain its inhabitants.
Dr. Simpson constructed his model using Bayesian probability to predict the division of land and water in on the habitable planets. He published his research in the Monthly Notice of the Royal Astronomical Society. In his model, he concludes that 90 percent of the surface in the most habitable planets are covered in ocean.
In order to sustain the life of its inhabitants in the exoplanets, the balance between dry land and water is required. There are two quantities that substantial for the planet's inhabitants based on the division between dry land and water. The first one is the volume of water the exoplanet retains over time, and the second one is its capacity to store the water in the oceanic basins.
Dr. Simpson uses the Earth as the base for his statistical model of the exoplanet according to Phys.org. Dr. Simpson holds the anthropic principle for his model, which consider the observation of universe should fit and compatible with the laws of nature and parameter in the universe of the observers. Based on the anthropic principle, Earth and its 70 percent of water covered the surface is the delicate balance between ocean and dry land, which makes it capable of sustaining life.
"Our understanding of the development of life may be far from complete," Dr. Simpson said. "But it is not so dire that we must adhere to the conventional approximation that all habitable planets have an equal chance of hosting intelligent life."
In order to test his model, Dr. Simpson also considered feedback mechanism, including deep water cycle, erosion and deposition process to determine whether the habitable exoplanets are capable of sustaining life. Watch the NASA report on Feb. 22 regarding the findings of habitable exoplanets below:
