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@simonres
Created October 6, 2015 12:01
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Higgs in THDM-III of SARAH"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os, sys, inspect\n",
"import commands"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### THDM-III"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"20\n",
"30\n",
"40\n",
"50\n"
]
}
],
"source": [
"from hep import *\n",
"if 1==1:\n",
" xd=pd.DataFrame()\n",
" h=hep()\n",
" h.MODEL='THDMIII'\n",
" t=THDM()\n",
" LHA=h.buildSLHA(['modsel','sminputs','minpar','sphenoinput','EPSUIN','EPSDIN','EPSEIN'])\n",
" LHA=t.init_lha_blocks(LHA) #ininit_model(LHA)\n",
" \n",
" t.m_h0=1.25E+02;t.m_H0=2088.3;t.m_A0=2100.26;t.m_Hp=2090.99\n",
" \n",
" t.tanb=1.\n",
" t.sab=0.98 #np.sin(np.arctan(t.tanb)-np.arccos(cosa))\n",
" t.lambdas[6]=0.;t.lambdas[7]=0.;t.m12_2=0.#;t.sab=-0.31\n",
"\n",
" \n",
" #if perturbativity:\n",
" ins=pd.Series({'m_A0':t.m_A0})\n",
" ii=0\n",
" for t.m_h0 in np.linspace(80,2000.):\n",
" #for t.m_H0 in np.linspace(t.m_h0+10,2000.):\n",
" ii=ii+1\n",
" if ii%10==0: print ii\n",
" #convert into general THDM basis\n",
" perturbativity=t.phys_to_gen()\n",
" #generate LHA input file\n",
" for i in range(1,8):\n",
" LHA.blocks['MINPAR'][i]='%.8E # Lambda%dInput' %(t.lambdas[i],i) \n",
" LHA.blocks['MINPAR'][9]='%.8E # M12input' %t.m12_2\n",
" LHA.blocks['MINPAR'][10]='%.8E # 2.8TanBeta' %t.tanb\n",
" SPC=h.runSPheno(LHA)\n",
" #reset breanchings\n",
" for p in [h.pdg.h0,h.pdg.H0]:\n",
" h.Gamma[p]=0\n",
" h.Br[p]={}\n",
" h.Br[p][h.pdg.g,h.pdg.g]=0\n",
"\n",
" decays=h.branchings(SPC.decays) # -> h.Gamma[pdg], h.Br[pdg][pdg1,pdg2,...] \n",
" \n",
" od=pd.Series({'m_h0':SPC.blocks['MASS'][h.pdg.h0],'m_H0':SPC.blocks['MASS'][h.pdg.H0],\\\n",
" 'Gamma_h0_to_g_g':h.Br[h.pdg.h0][h.pdg.g,h.pdg.g]*h.Gamma[h.pdg.h0],\\\n",
" 'Gamma_H0_to_g_g':h.Br[h.pdg.H0][h.pdg.g,h.pdg.g]*h.Gamma[h.pdg.H0],\\\n",
" 'pert.':perturbativity})\n",
" xs=ins.append(od)\n",
" xd=xd.append(xs.to_dict(),ignore_index=True)\n",
"\n",
" xd.to_csv('thdm.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f5c0ee300d0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NoNyUw/Vz+UzZcYfbboPrr4fHH4cePZKuSEQkflocssBWrAhzS6ZOhaefhs6d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\n/wdISp1SNo8liwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c1068a950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(xd.m_h0,xd.Gamma_h0_to_g_g)\n",
"plt.xlim(9E1,2E3)\n",
"plt.ylim(1E-4,3E-1)\n",
"plt.xlabel(r'$m_h$ (GeV)',size=20)\n",
"plt.ylabel(r'$\\Gamma(h\\to gg)$ (GeV)',size=20) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"20\n",
"30\n",
"40\n",
"50\n"
]
}
],
"source": [
"from hep import *\n",
"if 1==1:\n",
" xd=pd.DataFrame()\n",
" h=hep()\n",
" h.MODEL='SM'\n",
" s=SM()\n",
" LHA=h.buildSLHA(['modsel','sminputs','minpar','sphenoinput'])\n",
" LHA=s.init_lha_blocks(LHA) #ininit_model(LHA)\n",
" \n",
" #if perturbativity:\n",
" ins=pd.Series()\n",
" ii=0\n",
" for s.m_h0 in np.linspace(80,2000.):\n",
" #for t.m_H0 in np.linspace(t.m_h0+10,2000.):\n",
" ii=ii+1\n",
" if ii%10==0: print ii\n",
" #convert into general THDM basis\n",
" perturbativity=s.phys_to_gen()\n",
" #generate LHA input file\n",
" LHA.blocks['MINPAR'][1]='%.8E # LambdaIn' %s.lambda_sm \n",
" SPC=h.runSPheno(LHA)\n",
" #reset breanchings\n",
" for p in [h.pdg.h0]:\n",
" h.Gamma[p]=0\n",
" h.Br[p]={}\n",
" h.Br[p][h.pdg.g,h.pdg.g]=0\n",
"\n",
" decays=h.branchings(SPC.decays) # -> h.Gamma[pdg], h.Br[pdg][pdg1,pdg2,...] \n",
" \n",
" od=pd.Series({'m_h0':SPC.blocks['MASS'][h.pdg.h0],\\\n",
" 'Gamma_h0_to_g_g':h.Br[h.pdg.h0][h.pdg.g,h.pdg.g]*h.Gamma[h.pdg.h0],\\\n",
" 'pert.':perturbativity})\n",
" xs=ins.append(od)\n",
" xd=xd.append(xs.to_dict(),ignore_index=True)\n",
"\n",
" xd.to_csv('thdm.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f5c16371a10>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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cQjZ6NBx8MBxzTGjhKHBEJJcksl31x+4+M2OVSErc4e674Z57wtppxx0XdUUi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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c16485950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(xd.m_h0,xd.Gamma_h0_to_g_g)\n",
"plt.xlim(9E1,1E3)\n",
"plt.ylim(1E-4,1E-1)\n",
"plt.xlabel(r'$m_h$ (GeV)',size=20)\n",
"plt.ylabel(r'$\\Gamma(h\\to gg)$ (GeV)',size=20) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Official plot from Higgs Working Group (refence here)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qm+kjGSJ1wDoRRKI9MDHC98RLYKbXcnNhzBg78+bPf7bu0JUq+R2ViMih1GXa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\nIiIiIiIiIiIiIiIiImX2/3y9O/rBSU88AAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='hggoff.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross section approximation"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(90, 1000)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ISOUox0KC1DGzvkAfYDPgL+4+NuEmiYjEwsy6AoOBdsBz7v6X9e6vnk7xmNkW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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c16247210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(xd.m_h0,np.pi*xd.Gamma_h0_to_g_g/(8.*xd.m_h0**3)*h.convert['pb/(1/GeV^2)'])\n",
"plt.xlabel(r'$m_{h^0}$ (GeV)',size=20)\n",
"plt.ylabel(r'$\\sigma(gg \\to gg)$ (pb)',size=20)\n",
"plt.xlim(90,1000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LCAFCwDgIyOjZRA0l9UMxFHqm2LN++GMkVo6lLTKt5vFGKw7VsDQj6XASx/jl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\nLhjWCFen3HDD7T/6kTxc7U4Ch2BZaJerihvzW74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X/aQ1nY5AN1VXb/rb34b/4hc+Kd9u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aSni/2kGUl7cZ1JLOm0NB+GNlMiN9Gzbu8dA0Kp29q8B0LWGDzVAYuvcAOrkIYmqLxCOpAaaITm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\nED3TGSAECAFCgBAgBAyHwP8fa2PI4syb2dYAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='smhiggsxs.png')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(90, 2000)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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z4bjjYFUE65hkGiiXAkPM7PRqXl+X4fVFRCSLzj03DCk+44ywn0omMt0C+FXg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\nQoEiIiKRUKCIiEgkFCgiIhKJ/wf9zzImObTzJgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c485bf490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(xd.m_h0,np.pi*xd.Gamma_h0_to_g_g/(8.*xd.m_h0**3)*h.convert['pb/(1/GeV^2)'])\n",
"plt.xlabel(r'$m_{h^0}$ (GeV)',size=20)\n",
"plt.ylabel(r'$\\sigma(gg \\to gg)$ (pb)',size=20)\n",
"plt.xlim(90,2000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Diego A. Result"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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R7issTaifcRnGNOkcLjAM3aBNeNK0t2qCfYOoy2PHKw8jrHH62t3O6LC7KRcK2PserW/X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\nZXJzaW9uAFBERi0xLjQgHEc6eAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {
"image/png": {
"width": 400
}
},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='SM-xsec-vs-mH-top-loop.png',width=400)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### THDM-III result for H0"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"20\n",
"30\n",
"40\n",
"50\n"
]
}
],
"source": [
"from hep import *\n",
"if 1==1:\n",
" xd=pd.DataFrame()\n",
" h=hep()\n",
" h.MODEL='THDMIII'\n",
" t=THDM()\n",
" LHA=h.buildSLHA(['modsel','sminputs','minpar','sphenoinput','EPSUIN','EPSDIN','EPSEIN'])\n",
" LHA=t.init_lha_blocks(LHA) #ininit_model(LHA)\n",
" \n",
" t.m_h0=1.25E+02;t.m_H0=2088.3;t.m_A0=2100.26;t.m_Hp=2090.99\n",
" \n",
" t.tanb=1.\n",
" t.sab=0.98 #np.sin(np.arctan(t.tanb)-np.arccos(cosa))\n",
" t.lambdas[6]=0.;t.lambdas[7]=0.;t.m12_2=0.#;t.sab=-0.31\n",
"\n",
" \n",
" #if perturbativity:\n",
" ins=pd.Series({'m_A0':t.m_A0})\n",
" ii=0\n",
" #for t.m_h0 in np.linspace(80,2000.):\n",
" for t.m_H0 in np.linspace(t.m_h0+10,2000.):\n",
" ii=ii+1\n",
" if ii%10==0: print ii\n",
" #convert into general THDM basis\n",
" perturbativity=t.phys_to_gen()\n",
" #generate LHA input file\n",
" for i in range(1,8):\n",
" LHA.blocks['MINPAR'][i]='%.8E # Lambda%dInput' %(t.lambdas[i],i) \n",
" LHA.blocks['MINPAR'][9]='%.8E # M12input' %t.m12_2\n",
" LHA.blocks['MINPAR'][10]='%.8E # 2.8TanBeta' %t.tanb\n",
" SPC=h.runSPheno(LHA)\n",
" #reset breanchings\n",
" for p in [h.pdg.h0,h.pdg.H0]:\n",
" h.Gamma[p]=0\n",
" h.Br[p]={}\n",
" h.Br[p][h.pdg.g,h.pdg.g]=0\n",
"\n",
" decays=h.branchings(SPC.decays) # -> h.Gamma[pdg], h.Br[pdg][pdg1,pdg2,...] \n",
" \n",
" od=pd.Series({'m_h0':SPC.blocks['MASS'][h.pdg.h0],'m_H0':SPC.blocks['MASS'][h.pdg.H0],\\\n",
" 'Gamma_h0_to_g_g':h.Br[h.pdg.h0][h.pdg.g,h.pdg.g]*h.Gamma[h.pdg.h0],\\\n",
" 'Gamma_H0_to_g_g':h.Br[h.pdg.H0][h.pdg.g,h.pdg.g]*h.Gamma[h.pdg.H0],\\\n",
" 'pert.':perturbativity})\n",
" xs=ins.append(od)\n",
" xd=xd.append(xs.to_dict(),ignore_index=True)\n",
"\n",
" xd.to_csv('thdm.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f5c0e6fb3d0>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PW9Mz9JcsCYtQnnsunHZa0tWISKWIe4b+LYSJkiPMrOc6LtoD+CfQGB1TDHNY/XkyXQmt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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c0e82f090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(xd.m_H0,xd.Gamma_H0_to_g_g)\n",
"plt.xlim(200,2E3)\n",
"plt.xlabel(r'$m_H^0$ (GeV)',size=20)\n",
"plt.ylabel(r'$\\Gamma(H^0\\to gg)$ (GeV)',size=20) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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