Guo Chin Liu on Behave of TGWG Group Members

Guo Chin Liu on Behave of TGWG Group Members

Data Management and data analysis in Taiwan Guo Chin Liu on behave of TGWG Group Members http://taipeigravitationalwavegroup.weebly.com/members.html ‧ Faculty: C. Chen(TKU), T. Chiu(NTU, NTNU), S. Haino (AS), C. Lin (NCHC), F. Lin (NTNU), G. Liu(TKU) ‧ Post Doc.: D. Chiou, Y. Inoue, S. Ko ‧ Student: B. Chen, Y. Chu, W. Hsu, M. Lin, Y. Pu, J. Peng, C. Yang, U. Zaman, Y. Zheng Supported by NCTS 2-year seed group grant Plans for the Group ‧ Short term: Data analysis −21 10 - C. Lin(NCHC), G. Liu(TKU), students(NTNU) −22 10 - focus on CBC Hz] √ −23 ‧ Long term: physics with gravitational wave 10 Strain [1/ −24 initial Virgo - BBH of Brans-Dicke model (F. Lin, D. 10 initial LIGO Adv LIGO KAGRA (VRSE) Chiou) Adv Virgo LIGO III −25 Einstein Telescope (ET−D) 10 0 1 2 3 10 10 10 10 - Dark stars(C. Chen) Frequency [Hz] Adhikari et al. 2014 - Numerical relativity or PN to generate Waveform - physics in KAGRA’s sensitive window Mini Summer School http://taipeigravitationalwavegroup.weebly.com/mini-school.html ‧ Date: July 13-15 ‧ Invited speakers: H. Tagoshi, L. Baiotti, S. Kuroyanagi, T. Li, Y. Itoh Data Management ‧ Mirror data storage in ASGC ‧ possibility to mirror (Proc.) data from AS to NCHC? Computing Power ‧ NCHC(National Center for High-performance Computing) - 25 K cores (shared) - 100kNTD/year for 100 cores ‧ Prototype: two servers (12 cores with 400GB MEM; 4cores with 64GB MEM and two GPUs), one workstation (6 cores, 64GB MEM and K80 GPU) ‧ Production: server with 400 cores and 200 GPUs Pipeline Construction based on matched filtering, with tools provided by Kagali or LALsuite ‧ MCMC: C. Lin ‧ Construct waveform catalogs: G. Liu + NTNU - GPU, model reduction technique GPU(graphics processing unit ) ‧ FFTW, LAPACK included in CUDA ‧ experience: gomoku game (Hance and Guozhang) ‧ set up a training schedule in near future Model Reduction Techniques Singular Value Decomposition/ Principal Component Analysis ‧ decompose the waveform into a set of orthogonal basis vectors. ‧ reduce the computation costs ‧ applied on - stellar core-collapse wave form(PCA: Heng. 2009) - CBC templates (SVD: Cannon et al 2010) Model Reduction Techniques Reduced basis: ‧ construction of a reduced basis catalog(Field. et al 2011, Caudill et al. 2014 ) ‧ searching the points in parameter space by greedy sweep algorithm ‧ seek an N dimensional linear space to accurately represent the space for considered sources Welcome to Join Us.

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