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BEGIN:VEVENT
SUMMARY:What Can We Learn with ML in Particle Physics\, and How?
DTSTART;VALUE=DATE-TIME:20191107T064500Z
DTEND;VALUE=DATE-TIME:20191107T070500Z
DTSTAMP;VALUE=DATE-TIME:20260725T224836Z
UID:indico-contribution-2-5@sshep.snu.ac.kr
DESCRIPTION:Speakers: Sunghoon Jung (Seoul National University)\nML has a 
 deep potential to extend our ability to understand Nature\, beyond common 
 knowledge. As the very first steps to realize it\, we use ML to seek for a
 nswers to one of the not-well-solved problems in particle physics. Our foc
 us is not only to improve the solution\, but to develop ways to figure out
  what the network has learned.\n\nhttps://sshep.snu.ac.kr/event/107/contri
 butions/5/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Searching for Axions and new concepts in fundamental physics with 
 Machine Learning
DTSTART;VALUE=DATE-TIME:20191107T070500Z
DTEND;VALUE=DATE-TIME:20191107T073500Z
DTSTAMP;VALUE=DATE-TIME:20260725T224836Z
UID:indico-contribution-2-6@sshep.snu.ac.kr
DESCRIPTION:Speakers: Sven Krippendorf (Ludwig Maximilian University of Mu
 nich)\nIn this talk\, I will review how ultralight axion-like particles ca
 n be constrained using X-ray observations of bright localized sources (AGN
 s\, Quasars) in and behind galaxy clusters. To find axion-like particles i
 n these settings corresponds to finding their characteristic pattern (quas
 i-sinusoidal oscillations) in noisy data. Using ML-techniques we are able 
 to improve the search sensitivity for these particles and are able to set 
 stronger bounds compared to previous methods. In the second part of the ta
 lk\, I shall highlight avenues where ML can be used to accelerate our unde
 rstanding of fundamental physics (string theory) and potentially vice vers
 a.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/6/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Recent Developments of Deep Learning in Experimental High Energy P
 hysics
DTSTART;VALUE=DATE-TIME:20191107T075500Z
DTEND;VALUE=DATE-TIME:20191107T081500Z
DTSTAMP;VALUE=DATE-TIME:20260725T224836Z
UID:indico-contribution-2-10@sshep.snu.ac.kr
DESCRIPTION:Speakers: Hwidong Yoo (Seoul National University)\n최근들
 어 딥러닝 기술은 기초 과학 분야에도 광범위하게 연구
 되고 있다. 이를 통해 얻어진 지식 및 기술이 차세대 연
 구 시스템 및 환경을 변화시키고 결과를 획기적으로 향
 상시키기 위해 다양하게 적용될 것으로 기대되고 있다. 
 현재 고에너지 물리 실험분야에서 활발하게 연구되고 
 있는 딥러닝 관련 연구에 대해 소개하고 향후 기대 효과
 에 대해 논의 한다.\n\nhttps://sshep.snu.ac.kr/event/107/contributio
 ns/10/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Learning QCD Jet Flavors in search for Invisible Higgs Decays
DTSTART;VALUE=DATE-TIME:20191107T073500Z
DTEND;VALUE=DATE-TIME:20191107T075500Z
DTSTAMP;VALUE=DATE-TIME:20260725T224836Z
UID:indico-contribution-2-11@sshep.snu.ac.kr
DESCRIPTION:Speakers: Hyung Do Kim (Seoul National University)\nhttps://ss
 hep.snu.ac.kr/event/107/contributions/11/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/11/
END:VEVENT
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