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Polynomial hybrid Monte Carlo algorithm for lattice QCD with an odd number of flavors

Authors :
JLQCD, Collaboration
Aoki, Sinya
Burkhalter, Rudolf
Fukugita, Masataka
Hashimoto, Shoji
Ishikawa, Kenichi
Ishizuka, Naruhito
Iwasaki, Yoichi
Kanaya, Kazuyuki
Kaneko, Takashi
Kuramashi, Yoshinobu
Okawa, Masanori
Onogi, Tetsuya
Tominaga, Shin-ichi
Tsutsui, Naoto
Ukawa, Akira
Yamada, Norikazu
Yoshie, Tomoteru
JLQCD, Collaboration
Aoki, Sinya
Burkhalter, Rudolf
Fukugita, Masataka
Hashimoto, Shoji
Ishikawa, Kenichi
Ishizuka, Naruhito
Iwasaki, Yoichi
Kanaya, Kazuyuki
Kaneko, Takashi
Kuramashi, Yoshinobu
Okawa, Masanori
Onogi, Tetsuya
Tominaga, Shin-ichi
Tsutsui, Naoto
Ukawa, Akira
Yamada, Norikazu
Yoshie, Tomoteru

Abstract

We present a polynomial hybrid Monte Carlo (PHMC) algorithm for lattice QCD with odd numbers of flavors of O(a)-improved Wilson quark action. The algorithm makes use of the non-Hermitian Chebyshev polynomial to approximate the inverse square root of the fermion matrix required for an odd number of flavors. The systematic error from the polynomial approximation is removed by a noisy Metropolis test for which a new method is developed. Investigating the property of our PHMC algorithm in the Nf=2 QCD case, we find that it is as efficient as the conventional HMC algorithm for a moderately large lattice size (163×48) with intermediate quark masses (mPS/mV∼0.7–0.8). We test our odd-flavor algorithm through extensive simulations of two-flavor QCD treated as an Nf=1+1 system, and comparing the results with those of the established algorithms for Nf=2 QCD. These tests establish that our PHMC algorithm works on a moderately large lattice size with intermediate quark masses (163×48,mPS/mV∼0.7–0.8). Finally we experiment with the (2+1)-flavor QCD simulation on small lattices (43×8 and 83×16), and confirm the agreement of our results with those obtained with the R algorithm and extrapolated to a zero molecular dynamics step size.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1262555764
Document Type :
Electronic Resource