Identifying and Optimizing the Observation Strategy to Detect NEOs (Near-Earth Objects) Using Python

Near-Earth Objects (NEOs) are any little celestial bodies that have orbits with closer proximity to that with Earth. A celestial body can be called a NEO if its closest distance to the Sun, i.e. Perihelion is at least 1.3 Astronomical Units (AU), where 1 AU is approximately 150 million kilometers (Km). If a NEO crosses Earth’s orbit at a distance of 140 m across, it can be called as a Potentially Hazardous Object (PHO). Consequently, a certain sub-set of NEOs advance our Earth quite nearly, and may cross our Earth’s orbit penetrating the atmosphere to the exterior of the Earth, or even collide with it to create craters if they impact a landmass or tsunamis if they strike on the oceans. Continuous sky observations over the years have already recognized and classified over 30,000 and more NEOs over a period of two centuries. Recent NEO detection models propose that there are still hundred thousand of NEOs yet more to be discovered. The study focuses on tracing these objects, classifying them under categories and visualizing the detected NEOs to build an observation strategy for our telescopes to detect these objects in a day sky using Python programming.