BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Jan Kieseler (European Organization for Nuclear Research (CERN)) DTSTART:20220303T170000Z DTEND:20220303T180000Z DTSTAMP:20260423T003236Z UID:MPML/66 DESCRIPTION:Title: Th e MODE project\nby Jan Kieseler (European Organization for Nuclear Res earch (CERN)) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nThe effective design of instruments that rely on t he interaction of radiation with matter for their operation is a complex t ask. Furthermore\, the underlying physics processes are intrinsically stoc hastic in nature and open a vast space of possible choices for the physica l characteristics of the instrument. While even large scale detectors such as e.g. at the LHC are built using surrogates for the ultimate physics ob jective\, the MODE Collaboration (an acronym for Machine-learning Optimize d Design of Experiments) aims at developing tools also based on deep learn ing techniques to achieve end-to-end optimization of the design of instrum ents via a fully differentiable pipeline capable of exploring the Pareto-o ptimal frontier of the utility function for future particle collider exper iments and related detectors. The construction of such a differentiable mo del requires inclusion of information-extraction procedures\, including da ta collection\, detector response\, pattern recognition\, and other existi ng constraints such as cost. This talk will give an introduction to the go als of the newly founded MODE collaboration and highlight some of the alre ady existing ingredients.\n LOCATION:https://researchseminars.org/talk/MPML/66/ END:VEVENT END:VCALENDAR