论文标题
LSTMCPIPE库
The lstMCpipe library
论文作者
论文摘要
Cherenkov望远镜阵列（CTA）是下一代地面伽马射线天文学观测站，它将通过一个数量级提高当前一代仪器的敏感性。 LST-1是在加那利岛La Palma岛上建造的第一个望远镜原型，并且已经采用了几年的数据。 Like all imaging atmospheric Cherenkov telescopes (IACTs), the LST-1 works by capturing the light produced by the Cherenkov process when high-energy particles enter the atmosphere.对摄像机的记录快照的分析允许区分伽马光子和哈子，并重建所选光子的物理参数。为了构建歧视和重建的模型，以及估计望远镜响应（通过模拟大气阵雨和望远镜的光学和电子设备），必须进行广泛的蒙特卡洛模拟。这些训练有素的模型后来用于分析来自实际观察结果的数据。 LSTMCPIPE是一个开源Python软件包，该软件包开发了用于在计算设施上安排MC文件分析的不同阶段。当前，该库处于生产状态，在Slurm群集中安排完整管道。它通过添加抽象级别来大大简化分析工作流程，从而允许用户使用简单的配置文件启动整个管道。此外，LST合作的成员可以通过项目存储库中的拉动请求进行调整参数来进行新的分析，从而允许其他人合作者的仔细审查以及对制作的中心管理，从而减少人类错误并优化计算资源的使用。
The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray astronomy observatory that will improve the sensitivity of current generation instruments by one order of magnitude. The LST-1 is the first telescope prototype built on-site on the Canary Island of La Palma and has been taking data for a few years already. Like all imaging atmospheric Cherenkov telescopes (IACTs), the LST-1 works by capturing the light produced by the Cherenkov process when high-energy particles enter the atmosphere. The analysis of the recorded snapshot of the camera allows to discriminate between gamma photons and hadrons, and to reconstruct the physical parameters of the selected photons. To build the models for the discrimination and reconstruction, as well as to estimate the telescope response (by simulating the atmospheric showers and the telescope optics and electronics), extensive Monte Carlo simulations have to be performed. These trained models are later used to analyse data from real observations. lstMCpipe is an open source python package developed to orchestrate the different stages of the analysis of the MC files on a computing facility. Currently, the library is in production status, scheduling the full pipeline in a SLURM cluster. It greatly simplifies the analysis workflow by adding a level of abstraction, allowing users to start the entire pipeline using a simple configuration file. Moreover, members of the LST collaboration can ask for a new analysis to be produced with their tuned parameters through a pull request in the project repository, allowing careful review by others collaborators and a central management of the productions, thus reducing human errors and optimising the usage of the computing resources.
