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arXiv 1902.08378astro-ph.IMastro-ph.GA
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现实约束下大天差区瞬变观测的最优光学搜索策略

Optimal Optical Search Strategy for Finding Transient in Large Sky Error Region Under Realistic Constraints

Javed Rana, Shreya Anand, Sukanta Bose

摘要:

为了识别快速衰落的引力波(GW)源的光学瞬态对应物,需要一种有效的跟踪策略。由于大多数地面光学天文台的目标是后续GW源有一个小视场(FOV)的望远镜相比,GW的天空误差区域,我们专注于搜索策略,涉及到GW补丁划分成瓷砖的相同面积的望远镜FOV战略图像的整个补丁。我们对Rana等人(2016)中概述的最佳望远镜调度算法进行了改进,通过结合平铺和星系目标搜索策略,并将源气团和望远镜回转的影响以及设置约束因素纳入调度算法,以增加识别GW对应物的机会。我们提出了两种单独的算法:气团加权算法,匈牙利算法的一种特定解决方案,可最大化获取的概率,同时最小化图像气团,以及回转优化算法,可最小化图像之间覆盖的整体回转角,以获得给定的概率Rana等人(2016)中的最佳望远镜调度算法。以GROWTH-印度望远镜的观测站为例,我们生成了100张天空图来测试我们的算法的性能。我们的研究结果表明,回转优化可以减少100度的观测时间表中的累积回转角,节省几分钟的观测时间,而不损失瓦片和概率。此外,我们证明,相比贪婪算法,空气质量加权算法可以获得高达20%以上的概率和30平方米。°的更多的面积覆盖所有大小和配置的天空图。

英文摘要:

In order to identify the rapidly-fading, optical transient counterparts of gravitational wave (GW) sources, an efficient follow-up strategy is required. Since most ground-based optical observatories aimed at following-up GW sources have a telescope with a small field-of-view (FOV) as compared to the GW sky error region, we focus on a search strategy that involves dividing the GW patch into tiles of the same area as the telescope FOV to strategically image the entire patch. We present an improvement over the optimal telescope-scheduling algorithm outlined in Rana et al. (2016), by combining the tiling and galaxy-targeted search strategies, and factoring the effects of the source airmass and telescope slew, along with setting constraints, into the scheduling algorithm in order to increase the chances of identifying the GW counterpart. We propose two separate algorithms: the airmass-weighted algorithm, a specific solution to the Hungarian algorithm that maximizes probability acquired, while minimizing the image airmass, and the slew-optimization algorithm that minimizes the overall slew angle covered between images for the given probability acquired by the optimal telescope-scheduling algorithm in Rana et al. (2016). Using the observatory site of the GROWTH-India telescope as an example, we generate 100s of skymaps to test the performance of our algorithms. Our results indicate that slew-optimization can reduce the cumulative slew angle in the observing schedule by 100s of degrees, saving several of minutes of observing time without the loss of tiles and probability. Further, we demonstrate that as compared to the greedy algorithm, the airmass-weighted algorithm can acquire up to 20 % more probability and 30 sq. deg. more in areal coverage for skymaps of all sizes and configurations.

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