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Register a new userA Fast Algorithm for Finding Point Sources in the Fermi Data Stream: FermiFAST
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We present a new and efficient algorithm for finding point sources in the photon event data stream from the Fermi Gamma-Ray Space Telescope, FermiFAST. The key advantage of FermiFAST is that it constructs a catalogue of potential sources very fast by arranging the photon data in a hierarchical data structure. Using this structure FermiFAST rapidly finds the photons that could have originated from a potential gamma-ray source. It calculates a likehihood ratio for the contribution of the potential source using the angular distribution of the photons within the region of interest. It can find within a few minutes the most significant half of the Fermi Third Point Source catalogue (3FGL) with nearly 80% purity from the four years of data used to construct the catalogue. If a higher purity sample is desirable, one can achieve a sample that includes the most significant third of the Fermi 3FGL with only five percent of the sources unassociated with Fermi sources. Outside the galaxy plane, all but eight of the 580 FermiFAST detections are associated with 3FGL sources. And of these eight, six yield significant detections of greater than five sigma when a further binned likelihood analysis is performed. This software allows for rapid exploration of the Fermi data, simulation of the source detection to calculate the selection function of various sources and the errors in the obtained parameters of the sources detected.
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An algorithm is presented, that provides a fast and robust reconstruction of neutrino induced upward-going muons and a discrimination of these events from downward-going atmospheric muon background in data collected by the ANTARES neutrino telescope. The algorithm consists of a hit merging and hit selection procedure followed by fitting steps for a track hypothesis and a point-like light source. It is particularly well-suited for real time applications such as online monitoring and fast triggering of optical follow-up observations for multi-messenger studies. The performance of the algorithm is evaluated with Monte Carlo simulations and various distributions are compared with that obtained in ANTARES data.
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Finding the common subsequences of $L$ multiple strings has many applications in the area of bioinformatics, computational linguistics, and information retrieval. A well-known result states that finding a Longest Common Subsequence (LCS) for $L$ strings is NP-hard, e.g., the computational complexity is exponential in $L$. In this paper, we develop a randomized algorithm, referred to as {em Random-MCS}, for finding a random instance of Maximal Common Subsequence ($MCS$) of multiple strings. A common subsequence is {em maximal} if inserting any character into the subsequence no longer yields a common subsequence. A special case of MCS is LCS where the length is the longest. We show the complexity of our algorithm is linear in $L$, and therefore is suitable for large $L$. Furthermore, we study the occurrence probability for a single instance of MCS and demonstrate via both theoretical and experimental studies that the longest subsequence from multiple runs of {em Random-MCS} often yields a solution to $LCS$.
Previous analyses of point sources in the gamma-ray range were done either below 30 MeV or above 100 MeV. Below 30 MeV, the imaging Compton telescope (COMPTEL) onboard NASAs Compton Gamma-Ray Observatory detected 26 steady sources in the energy range from 0.75 to 30 MeV. At high energy, the Fermi Large Area Telescope (LAT) has detected more than three thousand sources between 100 MeV and 300 GeV. Since the Fermi LAT detects gamma rays also below 100 MeV, we apply a point source detection algorithm in the energy range between 30 MeV and 100 MeV. In the analysis we use PGWave, which is a background independent tool based on a wavelet transform.
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