3 \usepackage[intlimits]{amsmath}
9 \title{Solving the sign problem of scalar, two-flavored electrodynamics
10 for finite chemical potential and exploring its full phase-diagram}
12 \ShortTitle{Solving the sign problem of scalar electrodynamics at final chemical potential}
14 \author{\speaker{Ydalia Delgado}
15 \\Institut f\"ur Physik, Karl-Franzens Universit\"at, Graz, Austria
16 \\E-mail: \email{ydalia.delgado-mercado@uni-graz.at}}
18 \author{Christof Gattringer
19 \\Institut f\"ur Physik, Karl-Franzens Universit\"at, Graz, Austria
20 \\E-mail: \email{christof.gattringer@uni-graz.at}}
22 \author{\speaker{Alexander Schmidt}
23 \\Institut f\"ur Physik, Karl-Franzens Universit\"at, Graz, Austria
24 \\E-mail: \email{alexander.schmidt@uni-graz.at}}
28 We explore two-flavored scalar electrodynamics on the lattice, which has a complex phase problem
29 at finite chemical potential. By rewriting the action in terms of dual variables
30 this complex phase problem can be solved exactly. The dual variables are links and plaquettes, subject to non-trivial
31 constraints, which have to be respected by the Monte Carlo algorithm.
32 Therefore, for the simulation we use a local update and the surface worm algorithm (SWA).
33 The SWA is a generalization of the Prokof'ev Svistunov
34 worm algorithm concept to simulate the dual representation of abelian Gauge-Higgs models on a lattice.
35 We also assess the performance of the SWA and compare it with a local update in the dual representation.
36 Finally, we determine the full phase diagram of the model.
39 \FullConference{XXIX International Symposium on Lattice Field Theory \\
40 July 29 $-$ August 03 2013\\
48 At finite chemical potential $\mu$ the fermion determinant becomes complex
49 and cannot be interpreted as a probability weight in the Monte Carlo simulation.
50 This complex phase problem has slowed down considerably the exploration of QCD
51 at finite density using Lattice QCDl. Although many efforts have been put into
52 solving the complex phase problem of QCD (see e.g. \cite{reviews}), the final goal
53 has not been achieved yet.
55 For some models or QCD in limiting cases, it is possible to deal with the complex phase
56 problem (e.g. \cite{solve-sign-problem}). Among the different techniques, we use the dual representation,
57 which has been shown to be a very powerful method that can be solve the solve the complex
58 phase problem without making any approximation of the partition sum, i.e. it is an exact method \cite{dual}.
59 In this proceedings we present another example where the dual representation can be applied succesfully. We consider a compact
60 U(1) gauge field coupled with two complex scalar fields with opposite charge. We explore the full phase diagram
61 as a function of the gauge coupling, the mass parameter and the chemical potential, which has not yet been studied in detail.
62 At finite density we present some preliminary results.
64 After mapping the degrees of freedom of the system to its dual variables, the terms of the
65 partition sum are positive and real and usual Monte Carlo techniques can be applied. However,
66 the dual variables, links and plaquettes for this model, are subject to non-trivial constraints.
67 Therefore one has to choose a proper algorithm in order to sample the system efficiently. In our case, we have
68 used two different Monte Carlo algorithms: A local update (LMA) \cite{z3} and an extension \cite{swa} of the
69 Prokof'ev Svistunov worm algorithm \cite{worm}. Here we present
70 some technical comparison of both algorithms in addition to the physics of the model.
73 \section{Two-flavored scalar electrodynamics}
75 \noindent ?????????????
78 \section{Monte Carlo simulation}
81 Because the dual variables are subject to non-trivial constraints, they cannot be updated randomly.
82 The most straight forward way to update the system is to change complete allowed objects. In order to
83 increase the acceptance rate we use the smallest possible structures. This algorithm is called local update
84 (LMA) and was used in \cite{z3,swa,prl}. Other possibility is to use an extension of the worm
85 algorithm \cite{worm}, the so called surface worm algorithm \cite{swa}. For this model we use both algorithms and
86 assess their performance.
88 First, we start describing the LMA. It consists of the following updates:
91 \item A sweep for each unconstrained variable $\overline{l}$ and $\overline{k}$
92 rising or lowering their occupation number by one unit.
95 \item ``Plaquette update'':
96 It consists of increasing or decreasing a plaquette occupation number
98 the link fluxes (either $l_{x,\sigma}$ or $k_{x,\sigma}$) at the edges of $p_{x,\nu\rho}$ by $\pm 1$ as
99 illustrated in Fig.~\ref{plaquette}. The change of $p_{x, \nu \rho}$
100 by $\pm 1$ is indicated by the signs $+$ or $-$, while the flux variables $l$($k$) are denoted by the red(blue) lines
101 and we use a dashed line to indicate a decrease by $-1$ and a full line for an increase by $+1$.
104 \item ``Winding loop update'':
105 It consists of increasing or decreasing the occupation number of both link variables $l$ and $k$ by
106 one unit along a winding loop in any of the 4 directions. This update is very important because the winding loops
107 in time direction are the only objects that couple to the chemical potential.
110 \item ``Cube update'': The plaquettes of 3-cubes
111 of our 4d lattice are changed according to one of the two patterns illustrated in
113 Although the plaquette and winding loop update are enough to satisfy ergodicity,
114 the cube update helps for decorrelation in the region of
115 parameters where the system is dominated by closed surfaces, i.e., the link
116 acceptance rate is small.
119 A full sweep consists of updating all links, plaquettes, 3-cubes and winding loops on the lattice,
120 offering one of the changes mentioned above and accepting them with the Metropolis
121 probability computed from the local weight factors.
125 \includegraphics[width=\textwidth,clip]{pics/plaquettes}
128 \caption{Plaquette update: A plaquette occupation number is changed by $+1$ or
129 $-1$ and the links $l$ (red) or $k$ (blue) of the plaquette are changed simultaneously. The
130 full line indicates an increase by +1 and a dashed line a decrease by $-1$.
131 The directions $1 \le \nu_1 < \nu_2 \le 4$
132 indicate the plane of the plaquette.} \label{plaquette}
138 \includegraphics[width=0.7\textwidth,clip]{pics/cubes}
141 \caption{Cube update: Here we show the changes in the plaquette occupation numbers.
142 The edges of the 3-cube are parallel to
143 the directions $1 \leq \nu_1 < \nu_2 < \nu_3 \leq 4$.} \label{cube}
148 Instead of the plaquette and cube updates we can use the worm algorithm.
149 Here we will shortly describe the SWA (see \cite{swa} for a detailed description)
150 for the variable $l$ (red).
151 The algorithm for the other type of link variable works in exactly the same way.
153 The SWA is constructed by breaking up the smallest update, i.e., the plaquette update
154 into smaller building blocks called ``segments''
155 (examples are shown in Fig.~\ref{segments}) used to build larger surfaces
156 on which the flux and plaquette variables are changed.
157 In the SWA the constraints are temporarily violated at a link
158 $L_V$, the head of the worm, and the two sites at its endpoints.
159 The admissible configurations are produced using 3 steps:
161 \item The worm starts by changing the flux by $\pm 1$ at a randomly chosen link (step 1 in Fig.~\ref{worm}).
162 \item The first link becomes the head of the worm $L_V$.
163 The defect at $L_V$ is then propagated through the lattice by
164 attaching segments, which are chosen in such a way that the constraints are always
165 obeyed (step 2 in Fig.~\ref{worm}).
166 \item The defect is propagated through the lattice until the worm decides to
167 end with the insertion of another unit of link flux at $L_V$ (step 3 in Fig.~\ref{worm}).
170 A full sweep consists of $V_4$ worms using the SWA plus a sweep of the unconstraint
171 variables $\overline{l}$ and $\overline{k}$,
172 and a sweep of winding loops (as explained in the LMA).
176 \includegraphics[width=\textwidth,clip]{pics/segments}
179 \caption{Examples of positive (lhs.) and negative segments (rhs.)
180 in the $\nu_1$-$\nu_2$-plane ($\nu_1 < \nu_2$).
181 The plaquette occupation numbers are changed as indicated by the signs.
182 The full (dashed) links are changed by $+1$ ($-1$). The empty link shows
183 where the segment is attached to the worm and the dotted link is the new position of the link
184 $L_V$ where the constraints are violated.} \label{segments}
190 \includegraphics[width=\textwidth,clip]{pics/worm}
193 \caption{Illustration of the worm algorithm. See text for an explanation.} \label{worm}
198 \section{Algorithm Assessment}
201 For the assessment of both algorithms we used two different models, the U(1) gauge-Higgs model but couple
202 only to one scalar field (see \cite{swa}) and the model presented in this proceedings. In both cases we
203 analyzed the bulk observables (and their fluctuations):
204 $U_P$ which is the derivative wrt. $\beta$ and $|\phi|^2$ (derivative wrt.
205 $\kappa$). First we checked the correctness of the SWA comparing the results for different
206 lattices sizes and parameters. Examples for the one flavor model are shown in \cite{swa}.
207 Fig.~\ref{obs} shows two observables for the two flavor case.
208 $\langle |\phi|^2 \rangle$ (lhs.) and its susceptibility (rhs.) as a function of $\mu$
209 for point ``f'' (see phase diagram) on a lattice of size $12^3 \times 60$.
210 We observe very good agreement among the different algorithms.
214 \includegraphics[width=\textwidth,clip]{pics/f}
215 \includegraphics[width=\textwidth,clip]{pics/f}
218 \caption{Observables $\langle |\phi|^2 \rangle$ (lhs.) and $\chi_\phi$ (rhs.)
219 as a function of $\mu$ for point f on a $12^3 \times 60$ lattice size.
220 We compare results from the SWA (circles) and the LMA (crosses).} \label{obs}
225 In order to obtain a measure of the computational effort, we compared the normalized
226 autocorrelation time $\overline{\tau}$ as defined in \cite{swa} of the SWA and LMA for
227 the one flavored model for different volumes and parameters. We concluded that,
228 the SWA outperforms the local update near a phase transition and if
229 the acceptance rate of the constrained link variable is not very low (eg. lhs. of Fig.~\ref{auto}).
230 On the other hand, when the constrained links have a very low acceptance rate
231 the worm algorithm has difficulties to efficiently sample the
232 system because it modifies the link occupation number in every move, while the LMA has a sweep with only
233 closed surfaces. The plot on the rhs. of Fig.~\ref{auto} shows how $\overline{\tau}$ for
234 $U_P$ is larger for the SWA than for the LMA. But this can be overcome by offering
235 a sweep of cube updates.
239 \includegraphics[width=\textwidth,clip]{pics/u2}
242 \caption{Normalized autocorrelation times $\overline{\tau}$ for 2 different set
243 of parameters. Left: parameters close to a first order phase transition.
244 Right: low acceptance rate of the variable $l$. Both simulations correspond
245 to a $16^4$ lattice. Data taken from \cite{swa}.} \label{auto}
255 \section*{Acknowledgments}
258 We thank Hans Gerd Evertz
259 for numerous discussions that helped to shape this project and for
260 providing us with the software to compute the autocorrelation times.
261 This work was supported by the Austrian Science Fund,
262 FWF, DK {\it Hadrons in Vacuum, Nuclei, and Stars} (FWF DK W1203-N16)
263 and by the Research Executive Agency (REA) of the European Union
264 under Grant Agreement number PITN-GA-2009-238353 (ITN STRONGnet).
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