Gamma-ray bursts are flashes of light from distant exploding stars. Cube satellites that monitor photons across different energy bands are used to detect these bursts. There is a need for computationally efficient algorithms, able to run using the limited computational resource onboard a cube satellite, that can detect when gamma-ray bursts occur. Current algorithms are based on monitoring photon counts across a grid of different sizes of time window. We propose a new algorithm, which extends the recently developed FOCuS algorithm for online change detection to Poisson data. Our algorithm is mathematically equivalent to searching over all possible window sizes, but at half the computational cost of the current grid-based methods. We demonstrate the additional power of our approach using simulations and data drawn from the Fermi gamma-ray burst catalogue.
翻译:伽玛射线暴是远方爆炸恒星的光亮闪光。 用于监测不同能源带光子的立方体卫星用来检测这些暴发。 需要计算高效的算法,能够使用立方体卫星上有限的计算资源运行,能够在伽马射线暴发生时进行检测。 目前的算法基于对不同大小的时间窗口网格的光子计的监测。 我们提出一个新的算法,将最近开发的FOCuS算法的在线变化检测扩展至 Poisson 数据。 我们的算法在数学上相当于搜索所有可能的窗口大小,但相当于目前基于网格方法计算成本的一半。 我们用模拟和从Fermi伽马射线暴目录中提取的数据来展示我们方法的额外力量。