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Physics
Beyond the Standard Models
What is new with Koide sum rules?
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[QUOTE="arivero, post: 6503850, member: 81"] I ran some extra ideas and... well, the only positive thing is that the charged lepton tuple is always in the first positions. For reference, let me include here the python algo to produce gaussian error statistics for each tuple. The code generates a sample of 1000 random masses distributed gaussian and then it averages the result: [CODE]e=dict() for line in p.split("\n")[1:-1]: d=line[32:].split() e[d[-2]+d[-1]]=[float(d[1]),float(d[2])] import numpy as np def rmass(mass,errors): scale = - errors[1]/errors[0] base = np.random.normal(mass, errors[0], 1000) if scale==1: calc = base else: calc = np.where(base < mass, base*scale, base) return np.where(calc < 0, 0, calc) def rkoide(triplet,signs): if signs[0]=="0": a=0 mb,mc = map(m.get,triplet) eb,ec = map(e.get,triplet) b,c = rmass(mb,eb), rmass(mc,ec) sign = 0 else: ma,mb,mc = map(m.get,triplet) ea,eb,ec = map(e.get,triplet) a,b,c = rmass(ma,ea), rmass(mb,eb), rmass(mc,ec) sign = +1 if signs[0]=="+" else -1 koide=(a+b+c)/np.square(sign*np.sqrt(a)+np.sqrt(b)+np.sqrt(c)) return np.mean(koide), np.std(koide ) #, np.std(koide, ddof=1) %%time for x in result: k,std=rkoide(x[1],x[3]) x[0]=max(abs(k+std-2/3),abs(max(k-std,0)-2/3)) #print(f'{"|".join(x[1]):<40}',"\t{:.8f} +- {:.8f}".format(abs(k-2/3),std))[/CODE] [/QUOTE]
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Beyond the Standard Models
What is new with Koide sum rules?
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