A multi-scale spectral pipeline using deep learning filament detection automatically identifies 91 oscillatory events in two weeks of 2014 GONG data, recovering known events and finding new ones with periods 20-126 min.
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abstract
The Lomb-Scargle periodogram is a well-known algorithm for detecting and characterizing periodic signals in unevenly-sampled data. This paper presents a conceptual introduction to the Lomb-Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb-Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation.
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AGILE detection of transient {\gamma}-ray emission from the region of the supergiant fast X-ray transient source IGR J17354-3255
AGILE archival data reveal gamma-ray flares from a source matching IGR J17354-3255, with orbital phase correlation supporting physical association and high-energy emission from SFXTs.
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Multiwavelength Characterization of a New Magnetic Cataclysmic Variable 2CXO J050740.7-091337
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A Search for High Frequency Oscillations in TESS Cycle 7
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