未来全天空gamma-ray观测Cubesat机群定时定位的仿真框架
The simulation framework of the timing-based localization for future all-sky gamma-ray observations with a fleet of Cubesats
摘要：
利用伽玛射线光子到达时间不同的三角测量原理，利用Cubesat卫星群对伽玛射线进行定时定位，是未来全天空伽玛射线观测的一种独特而有效的解决方案，也是识别引力波源电磁对应源的关键。目前，匈牙利和日本正在合作促进用于测量和定位瞬变的立方体卫星飞行任务，其基本概念是在低地球轨道上放置九个立方体卫星星座。已经开发了用于估计定位能力的仿真框架，包括轨道参数、用于估计伽马射线光子的预期观测轮廓的算法、寻找交叉关联函数的峰值以及用于找到最佳拟合位置及其不确定性的统计方法。结果表明，CAMELOT任务概念可以实现度尺度的定位不确定性，用于明亮的短伽马射线暴，这可以被未来的大视场地面望远镜所覆盖。利用机器学习方法的新方法也进行了研究，使未来的大规模星座的过程自动化。训练的神经网络与人工短脉冲模板生成的10个$^6$模拟光变曲线成功地预测了真实光变曲线的时间延迟，并实现了与全自动程序的互相关算法相当的性能。
英文摘要：
The timing-based localization, which utilize the triangulation principle with the different arrival time of gamma-ray photons, with a fleet of Cubesats is a unique and powerful solution for the future all-sky gamma-ray observation, which is a key for identification of the electromagnetic counterpart of the gravitational wave sources. The Cubesats Applied for MEasuring and Localising Transients (CAMELOT) mission is now being promoted by the Hungarian and Japanese collaboration with a basic concept of the nine Cubesats constellations in low earth orbit. The simulation framework for estimation of the localization capability has been developed including orbital parameters, an algorithm to estimate the expected observed profile of gamma-ray photons, finding the peak of the cross-correlation function, and a statistical method to find a best-fit position and its uncertainty. It is revealed that a degree-scale localization uncertainty can be achieved by the CAMELOT mission concept for bright short gamma-ray bursts, which could be covered by future large field of view ground-based telescopes. The new approach utilizing machine-learning approach is also investigated to make the procedure automated for the future large scale constellations. The trained neural network with 10$^6$ simulated light curves generated by the artificial short burst templates successfully predicts the time-delay of the real light curve and achieves a comparable performance to the cross-correlation algorithm with full automated procedures.
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。
