Dark Matter Research Cluster based on Computational Science 2017 KIAS-IBS Joint Workshop on Particle Physics and Cosmology High1 resort, Jeongseon, Korea Dark Matter Research Cluster based on Computational Science 2017. 2. 5 ~ 10 Kihyeon Cho(KISTI)

Contents Introduction What we needs for Dark Matter Research Dark Matter Research Big Data Platform Summary

Introduction

Project Title Dark Matter Research Cluster Leader Kihyeon Cho Host Institute KISTI Partner Institutes KASI and 15 Institutes Fund 50,000,000 won/year * 2years Period 2015.9.23~2017.9.22 Sponsor NST (National Council of Science and Technology)

Universities (Students) Dark Matter Research From Collider to Astronomy Dark Matter Components Standard Model Evolution of Universe KASI ⇒ Astronomical Data KISTI (GSDC) ⇒ Collider Data Origin of Matter Standard Model Universities (Students)

Goal To make synergies for searching dark matter in experiments and theories To make a national project in the field of dark matter Prof. K.Y. Choi

Meetings Seminar and Meeting: once per month Dark Matter Research Cluster Kickoff workshop KISTI, October 27, 2015, 2:00-6:00 PM 2016 Dark Matter Cluster Workshop KISTI, April 22-23, 2016 Yangyang, July 20~21, 2016 2016 Joint workshop on France-Korea and France-Japan Particle Physics Workshop KIAS, May 18~20, 2016 ~100 persons from France, Japan, China, Korea

Outreach Home pages Advertisement (panel, conference) http://cluster.nst.re.kr https://hep.kisti.re.kr/indico ⇒ Dark Matter Portal Advertisement (panel, conference) Dark Matter Research White Paper SCOPUS Journal(KPS) Special Edition 2016. 8.31

What we need for Dark Matter Research

Scientific discovery drivers Data Sensors, Instruments, DB, Internet, Storage Computational Science Theory-Experiment-Simulation Machine Learning AI, Statistics, Data Mining, Algorithms, Deep Learning

Dark Matter Search Data Direct Collider Indirect Prof. J.C. Park KISTI (GSDC)⇒Collider Data KASI ⇒ Astronomical Data Prof. J.C. Park

Astronomical Data Up to ~500 PB

Collider Data

Prof. Y. S. Song

Theory-Experiment-Simulation => computational Science

From Theory Rapid update & sharing of information Key point summary DB site by specialists Regular meeting, Network Feedback between experimentalists and theorists ⇒ Through a (new) center Rapid & Easy comparing check of theoretical model Development of new numerical package Computing power and Person power ⇒ new jobs Cross check with astrophysics (theoretical model) N- body simulation Discussion with Dark Energy survey group Prof. J.C. Park

From Experiment Person Power: Networking Dedicated team/scientists for DM search for HW/SW Networking Networking for both Korea experimentalists in local and international institutions in DM search experiments (in Indirect, Direct and Colliders) Regular workshop including international workshop Enhancement communication between experimental and theoretical community ⇒ brain-storming to new ideas Computing resources for big data : HPC (High-Performance Computing) for MC Storage for experimental data and theoretical model Prof. H.D. Yoo & Y. Kwon

From simulations To comply with different architecture (GPU, MIC and etc.) To draw community interests for collateral effort

Dark Matter Research Big Data Platform - Creative Alliance Project(CAP)

Research on Dark Matter Issue To combine astronomical data and HEP data based on Named Data Networking(NDN) system to make processing/analysis more efficient Solution Based on deep learning, we process and analyze convergence data using big data platform R&D for Intelligence Information to open outputs ⇒Dark Matter Research Big Data Platform

Named Data Networking(NDN) System GSDC Data Production Accelerator (CERN,KEK Fermilab) Astronomy (DESI,LSST) KASI Data Center Supercomputer Theory Intelligence Information (Efficiency) Dark Matter Research Big Data Platform Researcher Deep Learning Research Data Processing (timeliness) KISTI Institute Experiment/ Observatory NDN Router 4 NDN router1 NDN router 2 NDN router 3 IP-less NDN system Named Data Networking(NDN) System Computational Science (reliability) Deep Learning Simulation Data

Current Status of Data

Requirement of Data E. Dobson

From theory-driven approach to data-driven approach => Named Data Networking system E. Dobson

Deep Learning VS Theory Experiment Dark Matter Physics Model Data Physics Simulation (MadGraph/MadDM) Compression Event Simulation (PHYTHIA) Reconstruction Simulation Detector Simulation (Delph/PGS/Geant4) Analysis Tools (PAW/ROOT) Result

Deep Learning in HEP (Current) Higgs Boson Detection (Nature Comm. 2014) Higgs Decay (NIPS 2015, PRL 2015 Dark Knowledge, Dark Matter (JMLR C&P 2015) Tracking in Run 4 Imaging Processing

T. Golling

Dark Knowledge Problem In HEP Very deep and complex modes are computationally expensive. Bad for time/CPU sensitive applications In HEP Possible integration of Kaggle Challenge winners Reproduce output of massive ensemble with simple model Trigger, real time tracking, etc Can push models directly to hardware

T. Golling

Example

Summary

Summary(1/2) Proposing Dark Matter Research Big Data Platform Proposed Organization Dark Matter Research Big Data Platform Collaboration IBS-CAPP IBS-CUP IBS-CTPU Domestic International KIAS, GNUE, SNUST, SSU , Yonsei U , CNU, CAU, etc. KNU , GSNU, Korea U, SNU, Yonsei U, JBNU CAU, CNU, etc. Accelerator Big Data (GDDC) Simulation Big Data Astronomical Big Data Fermilab CERN KEK DESI, LSST Big Data Provider Big Data User Dark Matter Theory KASI KISTI Dark Matter Experiment NDN system

To prove mystery of Dark Matter Summary(2/2) To prove mystery of Dark Matter Astronomical Data Theory Accelerator Data Dark Matter Research Big Data Platform

Thank you.

Core Value Core Value Requirement Status Current Status Convergence SW ･Based on deep learning, convergence data processing and analysis ･Data transfer using NDN ･ Separated data processing ･ IP based data transfer Convergence Research ･Theory-Experiment-Simulation using convergence data ･ Theory, Experiment, Simulation respectively using separated data Efficiency ･ Big data platform/ ･ NDN based data transfer (Time and cost reduction) ･ Each computer