On December 13, according to the information of the Chinese Academy of Sciences, the Purple Mountain Observatory carried out preliminary research around the Closeby Habitable Exoplanet Survey (CHES) of the space exploration mission, and built a Python-based platform, including planetary orbit parameter inversion, satellite observation field of view, observation strategy and scheme, etc.
It is reported that the relevant research results were published in Research in Astronomy and Astrophysics (RAA) as the cover**.
The Nearby Habitable Planetary Survey was proposed by a scientific research team led by the Purple Mountain Observatory. The program will launch a 1A 2-meter-aperture space telescope detects terrestrial planets in the habitable zone around 100 neighboring Sun-like stars (about 32 light-years from the solar system) by means of micro-arcsecond relative astrometric measurements. The plan is a "Chinese solution" with an original technical route. The research will answer scientific questions such as "Is the Earth unique" and "How did planets become the cradle of life".
With the development of astronomy, high-precision astrometric technology and the establishment of spatiotemporal datums are particularly important for basic astronomical research. After many definitions, discussions and unification of issues such as time, space, coordinate system, and Earth's attitude, the International Astronomical Union has issued a number of resolutions to coordinate and solve related problems, providing key algorithms for the parameters required by astronomers in basic astronomical research, and establishing and maintaining the FORTRAN and C versions of SOFA software.
Based on the SOFA (The Standards of Fundamental Astronomy Service) software released by the International Astronomical Union, the team has launched a set of authoritative algorithms, called pymsofa, that can be applied to the Python platform and is consistent with the resolutions of the International Astronomical Union, and added this program to the CHES** platform. pymsofa fills the gap of SOFA software in Python language, and provides researchers with a direct, convenient and efficient application of SOFA algorithm in Python. pymSOFA provides three ways to implement SOFA algorithms in Python, including CTYPES, CFFI and SOFA algorithms based on SOFA algorithms written in Python language. All three methods can implement the full functionality of all 247 functions in SOFA. The current version is based on the latest version released by SOFA on October 11, 2023.