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Center of Mass to Lab frame conversion
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# A small notebook detailing the conversion of an angular distribution from one frame to another" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline \n", | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"def boost(theta,beta):\n", | |
" return np.arccos((np.cos(theta)+beta)/(1+beta*np.cos(theta)))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Load an angular distribution that I found laying around." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"theta_cm,dsigdomega = np.asarray([[0.00000 , 1.905E+01], [0.20000 , 1.874E+01], [0.40000 , 1.785E+01], [0.60000 , 1.646E+01], [0.80000 , 1.470E+01], [1.00000 , 1.270E+01], [1.20000 , 1.062E+01], [1.40000 , 8.615E+00], [1.60000 , 6.800E+00], [1.80000 , 5.259E+00], [2.00000 , 4.042E+00], [2.20000 , 3.157E+00], [2.40000 , 2.584E+00], [2.60000 , 2.278E+00], [2.80000 , 2.183E+00], [3.00000 , 2.235E+00], [3.20000 , 2.375E+00], [3.40000 , 2.549E+00], [3.60000 , 2.715E+00], [3.80000 , 2.845E+00], [4.00000 , 2.919E+00], [4.20000 , 2.929E+00], [4.40000 , 2.876E+00], [4.60000 , 2.765E+00], [4.80000 , 2.608E+00], [5.00000 , 2.415E+00], [5.20000 , 2.199E+00], [5.40000 , 1.973E+00], [5.60000 , 1.746E+00], [5.80000 , 1.527E+00], [6.00000 , 1.323E+00], [6.20000 , 1.137E+00], [6.40000 , 9.726E-01], [6.60000 , 8.297E-01], [6.80000 , 7.081E-01], [7.00000 , 6.063E-01], [7.20000 , 5.223E-01], [7.40000 , 4.538E-01], [7.60000 , 3.982E-01], [7.80000 , 3.532E-01], [8.00000 , 3.166E-01], [8.20000 , 2.862E-01], [8.40000 , 2.606E-01], [8.60000 , 2.384E-01], [8.80000 , 2.184E-01], [9.00000 , 2.001E-01], [9.20000 , 1.828E-01], [9.40000 , 1.663E-01], [9.60000 , 1.504E-01], [9.80000 , 1.352E-01], [10.00000 , 1.207E-01], [10.20000 , 1.070E-01], [10.40000 , 9.406E-02], [10.60000 , 8.212E-02], [10.80000 , 7.119E-02], [11.00000 , 6.132E-02], [11.20000 , 5.252E-02], [11.40000 , 4.477E-02], [11.60000 , 3.804E-02], [11.80000 , 3.226E-02], [12.00000 , 2.735E-02]]).T" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# CM frame" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The theoretical calculation is reported in the center of mass frame" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 31, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(0, 7)" | |
] | |
}, | |
"execution_count": 31, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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aDEpC98UXX/C73/2Op556irPPPpvx48dz4403YmZhR0uoQ4cOsWnTJrKysvIf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\nrkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f7dbc0604d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig,axes = plt.subplots()\n", | |
"axes.plot(theta_cm,dsigdomega,color='black')\n", | |
"axes.set_xlabel(r'$\\theta_{cm}$ (deg.)')\n", | |
"axes.set_ylabel(r'$d\\sigma/d\\Omega$ (mb/sr)')\n", | |
"axes.set_xlim(0,7)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Lab frame" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We must convert it to the lab frame to compare with data (and to apply resolution effects, for example)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(0, 7)" | |
] | |
}, | |
"execution_count": 32, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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ww56Tk+OAX3HFFf7uu+/W2/F37drl06dP9wkTJniXLl0c8GbNmnm/fv389ttv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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f7dbbe59a90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"theta_lab = boost(theta_cm*np.pi/180.,0.429) # here I use a beta of 0.429\n", | |
"\n", | |
"fig,axes = plt.subplots()\n", | |
"axes.plot(theta_lab*180./np.pi,dsigdomega,color='black')\n", | |
"axes.set_xlabel(r'$\\theta_{lab}$ (deg.)')\n", | |
"axes.set_ylabel(r'$d\\sigma/d\\Omega$ (mb/sr)')\n", | |
"axes.set_xlim(0,7)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"And just a sanity check that we get the original CM distribution back when going the opposite direction" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 35, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(0, 7)" | |
] | |
}, | |
"execution_count": 35, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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Zt94adBwRkQajMtdw+wDnAN8HWgDTgX8AX7h7hceoPMKxE4BtQL/IGM1ltzUH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\nAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f7dbbdb2b50>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"theta_cm2 = boost(theta_lab,-0.429) # here I use a beta of -0.429 because we are doing the reverse boost back into the moving frame\n", | |
"\n", | |
"fig,axes = plt.subplots()\n", | |
"axes.plot(theta_cm2*180./np.pi,dsigdomega,color='black',label='boosted')\n", | |
"axes.plot(theta_cm,dsigdomega,color='red',label='original')\n", | |
"axes.set_xlabel(r'$\\theta_{cm}$ (deg.)')\n", | |
"axes.set_ylabel(r'$d\\sigma/d\\Omega$ (mb/sr)')\n", | |
"axes.legend(loc='center left',bbox_to_anchor=(1,0.5))\n", | |
"axes.set_xlim(0,7)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"They are identical, yay (:" | |
] | |
}, | |
{ | |
"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": "2.7.13" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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