Constraints on a hadronic model for unidentified off-plane galactic gamma-ray sources

Recently the H.E.S.S. collaboration announced the detection of an unidentified gamma-ray source with an off-set from the galactic plane of 3.5 degrees: HESS J1507-622. If the distance of the object is larger than about one kpc it would be physically located outside the galactic disk. The density profile of the ISM perpendicular to the galactic plane, which acts as target material for hadronic gamma-ray production, drops quite fast with increasing distance. This fact places distance dependent constraints on the energetics and properties of off-plane gamma-ray sources like HESS J1507-622 if a hadronic origin of the gamma-ray emission is assumed. For the case of this source it is found that there seems to be no simple way to link this object to the remnant of a stellar explosions.Comment: 11 pages, 4 figures, accepted for publication in AdSp

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: Fast Scattering/Crown and Low-frequency Blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that Fast Scattering/Crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that 27% of all transient noise at LIGO Livingston belongs to the Fast Scattering class, while 8% belongs to the Low-frequency Blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets