16 research outputs found
Poisson-FOCuS: An efficient online method for detecting count bursts with application to gamma ray burst detection
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
Searching for long faint astronomical high energy transients: a data driven approach
HERMES (High Energy Rapid Modular Ensemble of Satellites) pathfinder is an
in-orbit demonstration consisting of a constellation of six 3U nano-satellites
hosting simple but innovative detectors for the monitoring of cosmic
high-energy transients. The main objective of HERMES Pathfinder is to prove
that accurate position of high-energy cosmic transients can be obtained using
miniaturized hardware. The transient position is obtained by studying the delay
time of arrival of the signal to different detectors hosted by nano-satellites
on low Earth orbits. To this purpose, the goal is to achive an overall accuracy
of a fraction of a micro-second. In this context, we need to develop novel
tools to fully exploit the future scientific data output of HERMES Pathfinder.
In this paper, we introduce a new framework to assess the background count rate
of a space-born, high energy detector; a key step towards the identification of
faint astrophysical transients. We employ a Neural Network (NN) to estimate the
background lightcurves on different timescales. Subsequently, we employ a fast
change-point and anomaly detection technique to isolate observation segments
where statistically significant excesses in the observed count rate relative to
the background estimate exist. We test the new software on archival data from
the NASA Fermi Gamma-ray Burst Monitor (GBM), which has a collecting area and
background level of the same order of magnitude to those of HERMES Pathfinder.
The NN performances are discussed and analyzed over period of both high and low
solar activity. We were able to confirm events in the Fermi/GBM catalog and
found events, not present in Fermi/GBM database, that could be attributed to
Solar Flares, Terrestrial Gamma-ray Flashes, Gamma-Ray Bursts, Galactic X-ray
flash. Seven of these are selected and analyzed further, providing an estimate
of localisation and a tentative classification
Anomaly Detection in SIGMA data
The Poisson Functional Online Cumulative Sum (Poisson-FOCuS) method is a method for solving the likelihood ratio test of Poisson(λ) null against Poisson(μλ) alternative where μ>1, i.e. searching for an increase in count. This can be thought of as equivalent to testing all possible anomaly start points τ<T at each timestep T, giving a computationally efficient way to analyse count anomalies that occur over intervals of time. We run the Poisson-FOCuS method on SIGMA data, with an additional adjustment to remove anomaly tail traces, and report the results
Poisson-FOCuS on SIGMA data
The Poisson Functional Online Cumulative Sum (Poisson-FOCuS) method is a method for solving the likelihood ratio test of Poisson(λ) null against Poisson(μλ) alternative where μ>1, i.e. searching for an increase in count. This can be thought of as equivalent to testing all possible anomaly start points τ<T at each timestep T, giving a computationally efficient way to analyse count anomalies that occur over intervals of time. We run the Poisson-FOCuS method on SIGMA data, with an additional adjustment to remove anomaly tail traces, and report the results
A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models
Online changepoint detection algorithms that are based on (generalised) likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time T, it involves considering O(T) possible locations for the change. Recently, the FOCuS algorithm has been introduced for detecting changes in mean in Gaussian data that decreases the per-iteration cost to . This is possible by using pruning ideas, which reduce the set of changepoint locations that need to be considered at time T to approximately . We show that if one wishes to perform the likelihood ratio test for a different one-parameter exponential family model, then exactly the same pruning rule can be used, and again one need only consider approximately locations at iteration T. Furthermore, we show how we can adaptively perform the maximisation step of the algorithm so that we need only maximise the test statistic over a small subset of these possible locations. Empirical results show that the resulting online algorithm, which can detect changes under a wide range of models, has a constant-per-iteration cost on average
Gamma-Ray Burst Detection with Poisson-FOCuS and Other Trigger Algorithms
We describe how a novel online change-point detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder constellation. Poisson-FOCuS enables testing for gamma-ray burst onset at all intervals in a count time series, across all timescales and offsets, in real time and at a fraction of the computational cost of conventional strategies. We validate an implementation with automatic background assessment through exponential smoothing, using archival data from Fermi-GBM. Through simulations of lightcurves modeled after real short and long gamma-ray bursts, we demonstrate that the same implementation has higher detection power than algorithms designed to emulate the logic of Fermi-GBM and Compton-BATSE, reaching the performance of a brute-force benchmark with oracle information on the true background rate, when not hindered by automatic background assessment. Finally, using simulated data with different lengths and means, we show that Poisson-FOCuS can analyze data twice as fast as a similarly implemented benchmark emulator for the historic Fermi-GBM on-board trigger algorithms
Gamma-Ray Burst Detection with Poisson-FOCuS and Other Trigger Algorithms
We describe how a novel online change-point detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder constellation. Poisson-FOCuS enables testing for gamma-ray burst onset at all intervals in a count time series, across all timescales and offsets, in real time and at a fraction of the computational cost of conventional strategies. We validate an implementation with automatic background assessment through exponential smoothing, using archival data from Fermi-GBM. Through simulations of lightcurves modeled after real short and long gamma-ray bursts, we demonstrate that the same implementation has higher detection power than algorithms designed to emulate the logic of Fermi-GBM and Compton-BATSE, reaching the performance of a brute-force benchmark with oracle information on the true background rate, when not hindered by automatic background assessment. Finally, using simulated data with different lengths and means, we show that Poisson-FOCuS can analyze data twice as fast as a similarly implemented benchmark emulator for the historic Fermi-GBM on-board trigger algorithms