Date/Time: Wed Dec 11 2024 at 14:00<br/><br/>Location: Auditorium/Remote<br/><br/>Speaker: Vinicius Mikuni (NERSC)<br/><br/>Title: Accelerating Discovery in High Energy Physics using AI (Early Career Talks)<br/><br/>Abstract: The past decade was marked by an exponential increase in the availability of experimental data in high energy physics, leading to unprecedented precision in the description of particle interactions. However, indirect evidence for new physics processes, such as the existence of dark matter, motivates the development of new methodologies to scrutinize the data in the search for new scientific discoveries. In this talk, I will introduce different applications of how artificial intelligence (AI) has been transformative in the way to analyse data from collider experiments. These include the development of fast simulation routines, high-dimensional deconvolution algorithms, and alternative ways to search new particle interactions. I will discuss future directions for each of these areas and potential synergies with other fields in the physical sciences.
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Remote access:
https://ubc.zoom.us/j/62977946035?pwd=5VT5r5y3LYZwupU8RpUIa5rOPxC0rn.1
Meeting ID: 629 7794 6035
Passcode: 724459
<br/><br/>Particle/Sci-Tech Seminar
Coffee and cookies available 15min before. BYO mug/cup<br/><br/>______________________________<br/><br/>Detailed information available can be found at <a href='https://www.triumf.ca/research-program/lectures-conferences/upcoming-seminars-lectures'>https://www.triumf.ca/research-program/lectures-conferences/upcoming-seminars-lectures</a> <br/><br/>Date/Time: Mon Dec 09 2024 at 10:00<br/><br/>Location: Auditorium/Remote<br/><br/>Speaker: Sascha Diefenbacher (UC Berkeley)<br/><br/>Title: Generative Models for Fast (Calorimeter) Simulation<br/><br/>Abstract: Simulation in High Energy Physics (HEP) places a heavy burden on the available computing resources and is expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and for future Higgs factories, motivating a concerted effort to develop computationally efficient solutions. Methods based on generative machine learning methods hold promise to alleviate the computational strain produced by simulation while providing the physical accuracy required of a surrogate simulator.
In this talk, an overview of a growing body of work focused on simulating showers in highly granular calorimeters will be reported, which is making significant steps towards realistic fast simulation tools based on deep generative models. Progress on the simulation of both electromagnetic and hadronic showers will be presented, with a focus on the high degree of physical fidelity and computational performance achieved. Additional steps taken to address the challenges faced when broadening the scope of these simulators, such as those posed by multi-parameter conditioning, will also be discussed.
Coffee available 15min before. BYO mug/cup
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Remote access:
https://ubc.zoom.us/j/65414015498?pwd=rgMlCYJAu9VbwWykoceH8zvNb9VW9t.1
Meeting ID: 654 1401 5498
Passcode: 773071<br/><br/>GAPS Early Career Seminar
Coffee available 15min before. BYO mug/cup<br/><br/>______________________________<br/><br/>Detailed information available can be found at <a href='https://www.triumf.ca/research-program/lectures-conferences/upcoming-seminars-lectures'>https://www.triumf.ca/research-program/lectures-conferences/upcoming-seminars-lectures</a> <br/><br/>