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SUMMARY:Simulation\, Data Analysis and Machine Learning in Particle Physic
 s
DTSTART:20260601T080000Z
DTEND:20260602T150000Z
DTSTAMP:20260601T151100Z
UID:indico-event-17091@indico.global
DESCRIPTION:Speakers: Artemiy Belousov\n\nOverview\nThis training series i
 ntroduces modern workflows used in particle and nuclear physics\, combinin
 g Monte Carlo simulations\, detector data analysis\, and machine learning 
 techniques. The workshops are based on analysis pipelines used in the CBM 
 experiment and reflect real research workflows for studying heavy-ion coll
 isions.The workshops are designed as a consecutive training track.\nWorksh
 op 1 introduces simulation and data analysis workflows\, which provide the
  foundation for Workshop 2\, where machine learning methods are applied to
  scientific datasets.\nLocation: Takes place on Campus Riedberg of Goethe 
 University Frankfurt in Biozentrum room N260/313. Detailed travel instruct
 ions will be provided to participants in advance.\nEligibility: Exclusive 
 to members of academic institutions. The organizers reserve the right to r
 evoke non-eligible registrations!\nRegistration Fee:  None (exclusive to 
 academic institution members)\nOrganization: Organized by the NHR-SW.\nDa
 ily Timings:  10:00–17:00\nLanguage:  English\nWorkshop Structure• W
 orkshop 1: Monte Carlo Simulation and Data Analysis with ROOT• Workshop 
 2: Introduction to Neural Networks for Scientific Data Analysis\nWorkshop 
 DependencyWorkshop 1 provides the necessary understanding of simulation da
 ta\, detector reconstruction\, and physics observables. The datasets and a
 nalysis concepts introduced in Workshop 1 will be used as input for machin
 e learning applications in Workshop 2.Participation in Workshop 1 is stron
 gly recommended before attending Workshop 2.\nWorkshop 1\nMonte Carlo Simu
 lation and Data Analysis with ROOT\nDescriptionThis workshop introduces si
 mulation and analysis workflows used in particle and nuclear physics\, wit
 h examples inspired by the CBM experiment. Participants will learn how Mon
 te Carlo simulations generate collision events using transport models such
  as PHSD and how reconstruction algorithms\, including the Kalman Filter P
 article Finder (KFPF)\, are used to extract physics observables.The course
  focuses on practical data processing and analysis using the CERN ROOT fra
 mework\, including visualization and statistical analysis of simulated and
  reconstructed event data.\nLearning GoalsParticipants will learn how to:
 • Understand Monte Carlo simulation workflows• Work with reconstructed
  detector data• Use ROOT for data visualization and analysis• Extract 
 physics observables from collision events\nWorkshop 2\nIntroduction to Neu
 ral Networks for Scientific Data Analysis\nDescriptionThis workshop introd
 uces the fundamental concepts of neural networks with a focus on convoluti
 onal neural networks (CNNs). Examples are based on modern data analysis wo
 rkflows from the CBM experiment\, where CNNs are used to classify collisio
 n events and identify signatures of quark–gluon plasma formation.Partici
 pants will learn how machine learning models can be trained using simulate
 d datasets generated with transport models such as PHSD and how neural net
 works can be applied to realistic detector data.\nLearning GoalsParticipan
 ts will learn how to:• Understand neural network architectures• Prepar
 e scientific datasets for machine learning• Train and validate CNN model
 s• Apply ML methods to detector and simulation data\nTwo-Day Training Pr
 ogramDay 1 — Simulation and ROOT AnalysisMorning• Introduction to heav
 y-ion collision simulations• PHSD transport model overview• Monte Carl
 o event generation• Detector reconstruction and KFPF workflowAfternoon
 • Introduction to CERN ROOT• Event data structure and storage• Data 
 visualization and statistical analysis• Hands-on ROOT exercises\nDay 2 
 — Neural Networks and Event ClassificationMorning• Introduction to mac
 hine learning in particle physics• Fundamentals of neural networks• Co
 nvolutional Neural Networks (CNNs)• Dataset preparation and feature engi
 neeringAfternoon• CNN training and validation• Event classification ex
 amples from CBM• Performance evaluation and interpretation• Hands-on M
 L exercises\n\nhttps://indico.global/event/17091/
LOCATION:N260/3.13
URL:https://indico.global/event/17091/
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