论文标题
Sora：恒星固化的减少和分析
SORA: Stellar Occultation Reduction and Analysis
论文作者
论文摘要
恒星固化技术在确定宣传体的大小，形状，天体等方面具有竞争精度，可与航天器的原位观察值相当。随着LSST预期的已知太阳系对象数量的增加，高度精确的星体目录（例如Gaia）和胚层的改善，在每个观察过程中，使用较高数量的和弦将变得越来越普遍。在大数据时代的背景下，我们开发了Sora，这是一个开源Python库Sora，以有效地减少和分析恒星掩盖数据。它包括从预测此类事件到确定太阳系物体大小，形状和位置的例程。
The stellar occultation technique provides competitive accuracy in determining the sizes, shapes, astrometry, etc., of the occulting body, comparable to in-situ observations by spacecraft. With the increase in the number of known Solar System objects expected from the LSST, the highly precise astrometric catalogues, such as Gaia, and the improvement of ephemerides, occultations observations will become more common with a higher number of chords in each observation. In the context of the Big Data era, we developed SORA, an open-source python library to reduce and analyse stellar occultation data efficiently. It includes routines from predicting such events up to the determination of Solar System bodies' sizes, shapes, and positions.
