The European Space Agency's astronomical observatory known as INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory) has been responsible for numerous significant scientific discoveries over the past few decades. Since 2002, it has been in an extremely elliptical orbit around Earth, passing through the Van Allen belts, which contain high-energy ionized particles that have the potential to harm the spacecraft's onboard equipment. Thus, predicting the entry and exit times of its radiation belts is an essential component of INTEGRAL's mission planning and operation. Using a variety of machine learning techniques, we evaluate the potential of a novel, compact data representation. Gradient-boosted trees with quantile loss are found to be the most effective approach in the experimental validation. With uncertainty adjusted to the 95th percentile, our method allows INTEGRAL to carry out two additional hours of scientific measurements per orbit. INTEGRAL is shielded from harm and sees an increase in its scientific return as a result of this strategy. It is simple to apply it to other spacecraft with similar orbits and easily extend it.
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Astrophysics & Aerospace Technology received 114 citations as per Google Scholar report
