Hoki：通过Python访问BPASS
Hoki: Making BPASS accessible through Python
摘要：
我们现在知道，大量的恒星诞生在多个系统中。此外，超过70%的大质量恒星存在于近距离双星系统中，这意味着它们在一生中都会相互作用。这对它们的演化以及瞬变（如超新星）和它们产生的潜在引力波祖先有很强的影响。因此，为了理解和正确解释恒星星族的天文观测，我们必须使用能够解释双星效应的理论模型。这是二进制人口和光谱合成代码（BPASS）的情况，它已经成为该领域的主食超过10年。与大多数其他理论模型的情况一样，BPASS的数据产品很大，变化多样且复杂。因此，使用这些数据需要一定程度的专门知识，而可能掌握关键观测数据的更广泛群体无法立即获得这种知识。Hoki的目标是通过提供一套工具来弥合观察和理论之间的差距，使BPASS数据易于访问并促进分析。Python的使用是故意的，因为它是天文学中无处不在的语言。这使得BPASS结果可以在大多数天文学家的现有工作流程中自然使用。
英文摘要：
We now know that a large number of stars are born in multiple systems. Additionally, more than 70% of massive stars are found in close binary systems, meaning that they will interact over the course of their lifetime. This has strong implications for their evolution as well as the transients (e.g supernovae) and the potential gravitational wave progenitors they produce. Therefore, in order to understand and correctly interpret astronomical observations of stellar populations, we must use theoretical models able to account for the effects of binary stars. This is the case of the Binary Population and Spectral Synthesis code (BPASS), which has been a staple of the field for over 10 years. As is the case for most other theoretical models, the data products of BPASS are large, varied and complex. As a result, their use requires a level of expertise that is not immediately accessible to a wider community that may hold key observational data. The goal of hoki is to bridge the gap between observation and theory, by providing a set of tools to make BPASS data easily accessible and facilitate analysis. The use of Python is deliberate as it is a ubiquitous language within Astronomy. This allows BPASS results to be used naturally within the pre-existing workflow of most astronomers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
