KIAS HEP-PH Journal Club meeting

Friday 29 Mar 2024, 11:00 → 13:00 Asia/Seoul

=============== journal club meeting ===============

Time: Friday 11:00-12:00 (KST)

Place: Room 1423

 

- Talk Information

 

Talk1

 

Speaker: Dr. Kayoung Ban (welcome talk)

Title: Collider phenomenology using machine learning

Abstract: In the first part of my talk, I will talk about DeeLeMa algorithm. We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. DeeLeMa is constructed based on the kinematic constraints and symmetry of the event topologies. We show that DeeLeMa can robustly estimate mass distribution even in the presence of combinatorial uncertainties and detector smearing effects. The approach is flexible and can be applied to various event topologies by leveraging the relevant kinematic symmetries. This work opens up exciting opportunities for the analysis of high-energy particle collision data, and we believe that DeeLeMa has the potential to become a valuable tool for the high-energy physics community.

And the second part of my talk, I will talk about multi-modal network. In collider experiments, an event is characterized by two distinct yet mutually complementary features: the `global features' and the `local features'. Kinematic information such as the event topology of a hard process, masses, and spins of particles comprises global features spanning the entire phase space. This global feature can be inferred from reconstructed objects. In contrast, representations of particles in gauge groups, such as Quantum Chromodynamics (QCD), offer localized features revealing the dynamics of an underlying theory. These local features, particularly observed in the patterns of radiation as raw data in various detector components, complement the global kinematic features. In this letter, we propose a simple but effective neural network architecture that seamlessly integrates information from both kinematics and QCD to enhance the signal sensitivity at colliders.

 

 

 

Talk 2

 

Speaker: 

Title: 

References: 
 

===========lunch polling===============

Lunch menu will be pork cutlets.

Please participate in the polling for the options below before 18:00 this Thursday.

Menu and vote: https://forms.gle/GhgDp5a8kCTWnyQEA

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KIAS official Youtube channel:
https://www.youtube.com/channel/UCDHm-rkJk6lEApBOkj9q-ZQ

KIAS HEP-ph Youtube channel:
https://www.youtube.com/channel/UCVXTeRaZ8oFPEN40IMvgeww

You can find KIAS HEP-ph calendar:
https://indico.kias.re.kr/category/0/calendar