Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Poisson-FOCuS
T2 - An Efficient Online Method for Detecting Count Bursts with Application to Gamma Ray Burst Detection
AU - Ward, K.
AU - Dilillo, G.
AU - Eckley, I.
AU - Fearnhead, P.
PY - 2023/9/6
Y1 - 2023/9/6
N2 - Gamma ray bursts are flashes of light from distant, new-born black holes. CubeSats that monitor high-energy 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 CubeSats, 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 method, which extends the recently proposed FOCuS approach for online change detection to Poisson data. Our method 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 monitor archive. Supplementary materials for this article are available online.
AB - Gamma ray bursts are flashes of light from distant, new-born black holes. CubeSats that monitor high-energy 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 CubeSats, 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 method, which extends the recently proposed FOCuS approach for online change detection to Poisson data. Our method 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 monitor archive. Supplementary materials for this article are available online.
KW - Anomaly detection
KW - Functional pruning
KW - Gamma ray bursts
KW - Page-CUSUM
KW - Streaming data
U2 - 10.1080/01621459.2023.2235059
DO - 10.1080/01621459.2023.2235059
M3 - Journal article
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
ER -