Physics case for quarkonium studies at the Electron Ion Collider
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Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.
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X-ray emission from the massive stellar binary system, Eta Carinae, drops strongly around periastron passage; the event is called the X-ray minimum. We launched a focused observing campaign in early 2009 to understand the mechanism of causing the X-ray minimum. During the campaign, hard X-ray emission (<10 keV) from Eta Carinae declined as in the previous minimum, though it recovered a month earlier. Extremely hard X-ray emission between 15-25 keV, closely monitored for the first time with the Suzaku HXD/PIN, decreased similarly to the hard X-rays, but it reached minimum only after hard X-ray emission from the star had already began to recover. This indicates that the X-ray minimum is produced by two composite mechanisms: the thick primary wind first obscured the hard, 2−10 keV thermal X-ray emission from the wind-wind collision (WWC) plasma; the WWC activity then decays as the two stars reach periastron.