kopia lustrzana https://github.com/micropython/micropython
140 wiersze
4.3 KiB
Python
140 wiersze
4.3 KiB
Python
# evolve the RGEs of the standard model from electroweak scale up
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# by dpgeorge
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import math
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class RungeKutta(object):
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def __init__(self, functions, initConditions, t0, dh, save=True):
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self.Trajectory, self.save = [[t0] + initConditions], save
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self.functions = [lambda *args: 1.0] + list(functions)
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self.N, self.dh = len(self.functions), dh
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self.coeff = [1.0 / 6.0, 2.0 / 6.0, 2.0 / 6.0, 1.0 / 6.0]
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self.InArgCoeff = [0.0, 0.5, 0.5, 1.0]
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def iterate(self):
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step = self.Trajectory[-1][:]
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istep, iac = step[:], self.InArgCoeff
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k, ktmp = self.N * [0.0], self.N * [0.0]
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for ic, c in enumerate(self.coeff):
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for if_, f in enumerate(self.functions):
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arguments = [(x + k[i] * iac[ic]) for i, x in enumerate(istep)]
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try:
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feval = f(*arguments)
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except OverflowError:
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return False
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if abs(feval) > 1e2: # stop integrating
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return False
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ktmp[if_] = self.dh * feval
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k = ktmp[:]
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step = [s + c * k[ik] for ik, s in enumerate(step)]
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if self.save:
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self.Trajectory += [step]
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else:
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self.Trajectory = [step]
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return True
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def solve(self, finishtime):
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while self.Trajectory[-1][0] < finishtime:
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if not self.iterate():
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break
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def solveNSteps(self, nSteps):
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for i in range(nSteps):
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if not self.iterate():
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break
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def series(self):
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return zip(*self.Trajectory)
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# 1-loop RGES for the main parameters of the SM
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# couplings are: g1, g2, g3 of U(1), SU(2), SU(3); yt (top Yukawa), lambda (Higgs quartic)
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# see arxiv.org/abs/0812.4950, eqs 10-15
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sysSM = (
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lambda *a: 41.0 / 96.0 / math.pi**2 * a[1] ** 3, # g1
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lambda *a: -19.0 / 96.0 / math.pi**2 * a[2] ** 3, # g2
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lambda *a: -42.0 / 96.0 / math.pi**2 * a[3] ** 3, # g3
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lambda *a: 1.0
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/ 16.0
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/ math.pi**2
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* (
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9.0 / 2.0 * a[4] ** 3
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- 8.0 * a[3] ** 2 * a[4]
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- 9.0 / 4.0 * a[2] ** 2 * a[4]
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- 17.0 / 12.0 * a[1] ** 2 * a[4]
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), # yt
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lambda *a: 1.0
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/ 16.0
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/ math.pi**2
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* (
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24.0 * a[5] ** 2
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+ 12.0 * a[4] ** 2 * a[5]
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- 9.0 * a[5] * (a[2] ** 2 + 1.0 / 3.0 * a[1] ** 2)
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- 6.0 * a[4] ** 4
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+ 9.0 / 8.0 * a[2] ** 4
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+ 3.0 / 8.0 * a[1] ** 4
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+ 3.0 / 4.0 * a[2] ** 2 * a[1] ** 2
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), # lambda
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)
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def drange(start, stop, step):
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r = start
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while r < stop:
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yield r
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r += step
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def phaseDiagram(system, trajStart, trajPlot, h=0.1, tend=1.0, range=1.0):
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tstart = 0.0
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for i in drange(0, range, 0.1 * range):
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for j in drange(0, range, 0.1 * range):
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rk = RungeKutta(system, trajStart(i, j), tstart, h)
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rk.solve(tend)
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# draw the line
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for tr in rk.Trajectory:
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x, y = trajPlot(tr)
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print(x, y)
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print()
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# draw the arrow
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continue
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l = (len(rk.Trajectory) - 1) / 3
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if l > 0 and 2 * l < len(rk.Trajectory):
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p1 = rk.Trajectory[l]
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p2 = rk.Trajectory[2 * l]
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x1, y1 = trajPlot(p1)
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x2, y2 = trajPlot(p2)
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dx = -0.5 * (y2 - y1) # orthogonal to line
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dy = 0.5 * (x2 - x1) # orthogonal to line
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# l = math.sqrt(dx*dx + dy*dy)
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# if abs(l) > 1e-3:
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# l = 0.1 / l
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# dx *= l
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# dy *= l
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print(x1 + dx, y1 + dy)
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print(x2, y2)
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print(x1 - dx, y1 - dy)
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print()
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def singleTraj(system, trajStart, h=0.02, tend=1.0):
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tstart = 0.0
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# compute the trajectory
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rk = RungeKutta(system, trajStart, tstart, h)
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rk.solve(tend)
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# print out trajectory
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for i in range(len(rk.Trajectory)):
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tr = rk.Trajectory[i]
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print(" ".join(["{:.4f}".format(t) for t in tr]))
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# phaseDiagram(sysSM, (lambda i, j: [0.354, 0.654, 1.278, 0.8 + 0.2 * i, 0.1 + 0.1 * j]), (lambda a: (a[4], a[5])), h=0.1, tend=math.log(10**17))
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# initial conditions at M_Z
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singleTraj(sysSM, [0.354, 0.654, 1.278, 0.983, 0.131], h=0.5, tend=math.log(10**17)) # true values
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