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  • metrica_Halpha
    Research news
    A Hα metric for identifying dormant black holes in X-ray transients
    Dormant black holes in X-ray transients can be identified by the presence of broad Hα emission lines from quiescent accretion discs. Unfortunately, short-period cataclysmic variables can also produce broad Hα lines, especially when viewed at high inclinations, and are thus a major source of contamination. Here we compare the full width at half maximum (FWHM) and equivalent width (EW) of the Hα line in a sample of 20 quiescent black hole transients and 354 cataclysmic variables (305 from SDSS I to IV) with secure orbital periods (Porb) and find that: (1) FWHM and EW values decrease with Porb
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  • Official poster of the 2nd UNDARK Workshop, “Astrophysical Probes of Dark Sectors Using Gamma-Ray Observations”, held from 28 September to 2 October 2026.
    UNDARK brings together researchers to explore the frontiers of the dark UniversE
    UNDARK continues its programme of scientific activities with a new workshop bringing together researchers to discuss some of the key questions at the frontiers of cosmology, astrophysics, and particle physics. The meeting provides a space for scientific exchange and discussion around the fundamental challenges involved in understanding the dark Universe, from the nature of dark matter and dark energy to their implications for our current picture of the cosmos. The opening day featured contributions from Alicia and Daniela, whose presentations introduced different perspectives and research
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  • Participants in the EDUCADO Training School on Astro–AI and Machine Learning
    Press release
    Three European Doctoral Networks Join Forces in Ghent for the "Third EDUCADO Training School on Astro–AI and Machine Learning"
    From 2–6 March 2026, Ghent University will host a landmark event at the intersection of astrophysics and artificial intelligence. Jointly organized by the EDUCADO , MWGaiaDN , and alongside a third partnering TALES , all MSCA Doctoral Networks. The EDUCADO Training School on Astro–AI and Machine Learning will bring together leading experts and early-career researchers to tackle the data challenges of modern science. The five-day programme offers a blend of expert-led lectures and hands-on training sessions. The curriculum focuses on high-impact fields, including artificial intelligence
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