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X-WR-CALDESC:Events for Department of Theoretical Physics
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DTSTART:20230326T010000
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DTSTART;TZID=Europe/Ljubljana:20230615T140000
DTEND;TZID=Europe/Ljubljana:20230615T150000
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SUMMARY:Biophysics Journal Club: Veronika Bukina\, "Mexican jumping beans exhibit diffusive motion"
DESCRIPTION:American physicists investigated the statistical properties of the movements of Mexican jumping beans (the so-called boxes of Sebastiania pavoniana\, which set the larvae of the leaflet growing in them in motion) in the absence of a temperature gradient. It turned out that in this case the motion is well described by the random walk model. The authors showed that such a strategy is more successful in finding the shadow than blind directional movement.\nhttps://indico.ijs.si/event/1643/
URL:https://web-f1.ijs.si/event/veronika-bukina-tba/
LOCATION:Seminar room of physics (106) (IJS)
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DTSTART;TZID=Europe/Ljubljana:20230615T140000
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DTSTAMP:20260426T044754
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LAST-MODIFIED:20230615T125117Z
UID:9073-1686837600-1686841200@web-f1.ijs.si
SUMMARY:Santiago Tanco: Exploring unsupervised top tagging using Bayesian inference
DESCRIPTION:Identifying hadronically decaying top-quark jets in a sample of jets is an important task in many LHC searches. Although there are outstanding supervised algorithms\, their construction and expected performance rely on Monte Carlo simulations. After introducing the general framework of Bayesian inference over mixture models\, I will present some results on the development of two simple unsupervised top tagging algorithms based on these techniques. \nhttps://indico.ijs.si/event/1691/
URL:https://web-f1.ijs.si/event/santiago-tanco-exploring-unsupervised-top-tagging-using-bayesian-inference/
LOCATION:https://zoom.us/j/3601731049?pwd=bVNQRjUxU2ExZ0cveWcxYXNUUGdjZz09 (F1 tea room)
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