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Match s1 only (#75)
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* match s1 only

* put match back

* example notebook
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@FaroutYLq
FaroutYLq authored Dec 20, 2023
1 parent 85c8b69 commit 42fc818
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{
"cells": [
{
"cell_type": "markdown",
"id": "8efad73c-6dd9-46bc-bf55-d58dc54dc63c",
"metadata": {},
"source": [
"This notebook is a basic example for applying salting method to study ambience interference."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "232ecce7-d687-4eab-ab7e-561f285899cc",
"metadata": {},
"outputs": [],
"source": [
"import saltax\n",
"import straxen\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"\n",
"straxen.print_versions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e86c4c17-a60d-46c0-8aea-560558ba18b3",
"metadata": {},
"outputs": [],
"source": [
"rn_runs = ['022510','022735','022723','022717']#,'022685'] bad run for match?\n",
"bk_runs = ['026577','028031','031147','027705','025579','030079','029139','028047']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3e8dae8-ec60-4a2b-bb70-4298ee1080d4",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"st_salt = saltax.contexts.sxenonnt(runid=21952,\n",
" saltax_mode='salt',\n",
" output_folder='/project/lgrandi/yuanlq/salt',\n",
" faxconf_version=\"sr0_v4\",\n",
" generator_name='flat',\n",
" recoil=8,\n",
" mode='all')\n",
"st_simu = saltax.contexts.sxenonnt(runid=21952,\n",
" saltax_mode='simu',\n",
" output_folder='/project/lgrandi/yuanlq/salt',\n",
" faxconf_version=\"sr0_v4\",\n",
" generator_name='flat',\n",
" recoil=8,\n",
" mode='all')\n",
"st_data = saltax.contexts.sxenonnt(runid=21952,\n",
" saltax_mode='data',\n",
" output_folder='/project/lgrandi/yuanlq/salt',\n",
" faxconf_version=\"sr0_v4\",\n",
" generator_name='flat',\n",
" recoil=8,\n",
" mode='all')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c64b4064-041d-4e90-a0f3-df39bfbaa701",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"for i, run in enumerate(rn_runs):\n",
" events_simu_i = st_simu.get_array(run, 'event_info')\n",
" events_salt_i = st_salt.get_array(run, 'event_info')\n",
" events_data_i = st_data.get_array(run, 'event_info')\n",
" truth_i = st_simu.get_array(run, 'truth')\n",
" match_i = st_simu.get_array(run, 'match_acceptance_extended')\n",
" events_salt_matched_to_simu_i, \\\n",
" events_simu_matched_to_salt_i = saltax.match(truth_i[(truth_i['z']<-13)&(truth_i['z']>-145)&\n",
" (np.sqrt(truth_i['x']**2 + truth_i['y']**2)<63)], \n",
" match_i[(truth_i['z']<-13)&(truth_i['z']>-145)&\n",
" (np.sqrt(truth_i['x']**2 + truth_i['y']**2)<63)],\n",
" events_simu_i[(events_simu_i['z']<-13)&(events_simu_i['z']>-145)], \n",
" events_salt_i[(events_salt_i['z']<-13)&(events_salt_i['z']>-145)])\n",
" if i==0:\n",
" events_simu = events_simu_i\n",
" events_salt = events_salt_i\n",
" events_data = events_data_i\n",
" truth = truth_i\n",
" match = match_i\n",
" events_salt_matched_to_simu = events_salt_matched_to_simu_i\n",
" events_simu_matched_to_salt = events_simu_matched_to_salt_i\n",
" else:\n",
" events_simu = np.concatenate((events_simu,events_simu_i))\n",
" events_salt = np.concatenate((events_salt,events_salt_i))\n",
" events_data = np.concatenate((events_data,events_data_i))\n",
" truth = np.concatenate((truth,truth_i))\n",
" match = np.concatenate((match,match_i))\n",
" events_salt_matched_to_simu = np.concatenate((events_salt_matched_to_simu,\n",
" events_salt_matched_to_simu_i))\n",
" events_simu_matched_to_salt = np.concatenate((events_simu_matched_to_salt,\n",
" events_simu_matched_to_salt_i))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c344665-7059-4404-af19-4436e2ad64ee",
"metadata": {},
"outputs": [],
"source": [
"truth_s1 = []\n",
"truth_s2 = []\n",
"for i in range(np.max(truth_i['event_number'])):\n",
" selected_truth = truth_i[truth_i['event_number']==i]\n",
" if len(selected_truth) == 2 and len(np.unique(selected_truth['type']))==2:\n",
" if np.sqrt(selected_truth['x']**2 + selected_truth['y']**2)[0] <= 63:\n",
" truth_s1.append(selected_truth[selected_truth['type']==1][0]['raw_area_trigger'])\n",
" truth_s2.append(selected_truth[selected_truth['type']==2][0]['raw_area_trigger'])\n",
"\n",
"plt.scatter(truth_s1, truth_s2, s=1)\n",
"plt.xlim(0,150)\n",
"plt.ylim(0,6000)\n",
"plt.xlabel('True S1 [PE]')\n",
"plt.ylabel('True S2 [PE]')\n",
"plt.title('WFSim Truth ER Band')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f203e22-2502-4ea4-bd83-e9153853a043",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"for i, run in enumerate(bk_runs):\n",
" events_simu_i = st_simu.get_array(run, 'event_info')\n",
" events_salt_i = st_salt.get_array(run, 'event_info')\n",
" events_data_i = st_data.get_array(run, 'event_info')\n",
" truth_i = st_simu.get_array(run, 'truth')\n",
" match_i = st_simu.get_array(run, 'match_acceptance_extended')\n",
" events_salt_matched_to_simu_i, \\\n",
" events_simu_matched_to_salt_i = saltax.match(truth_i[(truth_i['z']<-13)&(truth_i['z']>-145)], \n",
" match_i[(truth_i['z']<-13)&(truth_i['z']>-145)],\n",
" events_simu_i[(events_simu_i['z']<-13)&(events_simu_i['z']>-145)], \n",
" events_salt_i[(events_salt_i['z']<-13)&(events_salt_i['z']>-145)])\n",
" if i == 0:\n",
" events_simub = events_simu_i\n",
" events_saltb = events_salt_i\n",
" events_datab = events_data_i\n",
" truthb = truth_i\n",
" matchb = match_i\n",
" events_salt_matched_to_simub = events_salt_matched_to_simu_i\n",
" events_simu_matched_to_saltb = events_simu_matched_to_salt_i\n",
" else:\n",
" events_simub = np.concatenate((events_simub,events_simu_i))\n",
" events_saltb = np.concatenate((events_saltb,events_salt_i))\n",
" events_datab = np.concatenate((events_datab,events_data_i))\n",
" truthb = np.concatenate((truthb,truth_i))\n",
" matchb = np.concatenate((matchb,match_i))\n",
" events_salt_matched_to_simub = np.concatenate((events_salt_matched_to_simub,\n",
" events_salt_matched_to_simu_i))\n",
" events_simu_matched_to_saltb = np.concatenate((events_simu_matched_to_saltb,\n",
" events_simu_matched_to_salt_i))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5903dc56-6cb1-4b25-9dfc-8e884ea79c81",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=150)\n",
"plt.scatter(events_simu['cs1'],events_simu['cs2'],s=1, label='Salted')\n",
"plt.scatter(events_data['cs1'],events_data['cs2'],s=1, label='Data')\n",
"plt.legend()\n",
"plt.xlim(0,100)\n",
"plt.ylim(0,6000)\n",
"plt.xlabel('CS1 [PE]')\n",
"plt.ylabel('CS2 [PE]')\n",
"plt.title('Salted Rn220 into SR0 Rn220 Calibration')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7891b6cc-2fd7-4e21-b9dd-a1aca03308f1",
"metadata": {},
"outputs": [],
"source": [
"n_bins = 31\n",
"cs1_bins = np.linspace(0,100,n_bins)\n",
"salt_med = []\n",
"simu_med = []\n",
"salt_1sig_u = []\n",
"simu_1sig_u = []\n",
"salt_1sig_l = []\n",
"simu_1sig_l = []\n",
"salt_2sig_u = []\n",
"simu_2sig_u = []\n",
"salt_2sig_l = []\n",
"simu_2sig_l = []\n",
"\n",
"for i in range(n_bins-1):\n",
" selected_salt = events_salt_matched_to_simu[(events_salt_matched_to_simu['cs1']>=cs1_bins[i])*\n",
" (events_salt_matched_to_simu['cs1']<cs1_bins[i+1])]\n",
" salt_med.append(np.median(selected_salt['cs2'][selected_salt['cs2']>0]))\n",
" salt_1sig_l.append(np.percentile(selected_salt['cs2'][selected_salt['cs2']>0], 16.5))\n",
" salt_1sig_u.append(np.percentile(selected_salt['cs2'][selected_salt['cs2']>0], 83.5))\n",
" salt_2sig_l.append(np.percentile(selected_salt['cs2'][selected_salt['cs2']>0], 2.5))\n",
" salt_2sig_u.append(np.percentile(selected_salt['cs2'][selected_salt['cs2']>0], 97.5))\n",
" \n",
" selected_simu = events_simu_matched_to_salt[(events_simu_matched_to_salt['cs1']>=cs1_bins[i])*\n",
" (events_simu_matched_to_salt['cs1']<cs1_bins[i+1])]\n",
" simu_med.append(np.median(selected_simu['cs2'][selected_simu['cs2']>0]))\n",
" simu_1sig_l.append(np.percentile(selected_simu['cs2'][selected_simu['cs2']>0], 16.5))\n",
" simu_1sig_u.append(np.percentile(selected_simu['cs2'][selected_simu['cs2']>0], 83.5))\n",
" simu_2sig_l.append(np.percentile(selected_simu['cs2'][selected_simu['cs2']>0], 2.5))\n",
" simu_2sig_u.append(np.percentile(selected_simu['cs2'][selected_simu['cs2']>0], 97.5))\n",
"\n",
"cs1_coord = np.linspace(0,100,n_bins-1) + 100/2/(n_bins-1)\n",
"\n",
"plt.figure(dpi=150)\n",
"plt.scatter(events_salt_matched_to_simu['cs1'],events_salt_matched_to_simu['cs2'],s=0.5, label='Salted', alpha=0.1)\n",
"plt.scatter(events_simu_matched_to_salt['cs1'],events_simu_matched_to_salt['cs2'],s=0.5, label='Simulated', alpha=0.1)\n",
"plt.plot(cs1_coord, salt_med, color='b', label='Salt Median')\n",
"plt.plot(cs1_coord, simu_med, color='r', label='Simu Median')\n",
"plt.plot(cs1_coord, salt_1sig_l, color='b', label='Salt 1sig',linestyle='dashed')\n",
"plt.plot(cs1_coord, salt_1sig_u, color='b', linestyle='dashed')\n",
"plt.plot(cs1_coord, salt_2sig_l, color='b', label='Salt 2sig',linestyle='dashed', alpha=0.5)\n",
"plt.plot(cs1_coord, salt_2sig_u, color='b', linestyle='dashed', alpha=0.5)\n",
"plt.plot(cs1_coord, simu_1sig_l, color='r', label='Simu 1sig',linestyle='dashed')\n",
"plt.plot(cs1_coord, simu_1sig_u, color='r', linestyle='dashed')\n",
"plt.plot(cs1_coord, simu_2sig_l, color='r', label='Simu 2sig',linestyle='dashed', alpha=0.5)\n",
"plt.plot(cs1_coord, simu_2sig_u, color='r', linestyle='dashed', alpha=0.5)\n",
"\n",
"\n",
"plt.legend()\n",
"plt.xlim(0,100)\n",
"plt.ylim(0,6000)\n",
"plt.xlabel('CS1 [PE]')\n",
"plt.ylabel('CS2 [PE]')\n",
"plt.title('Ambience Interference in SR0 Rn220')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9637ca13-f562-4c0a-9aec-dd7cdd252115",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=150)\n",
"plt.scatter(events_simu_matched_to_salt['cs1'],events_salt_matched_to_simu['cs1'], s=1, label='Rn220')\n",
"plt.scatter(events_simu_matched_to_saltb['cs1'],events_salt_matched_to_simub['cs1'], s=1, label='BKG')\n",
"plt.legend()\n",
"plt.xlim(0,150)\n",
"plt.ylim(0,150)\n",
"plt.title('Salted Rn220 into SR0 Rn220/BKG')\n",
"plt.xlabel('Simu CS1 [PE]')\n",
"plt.ylabel('Salt CS1 [PE]')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "313dd015-e521-4064-8b4a-54bd5e4f07b5",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=150)\n",
"plt.scatter(events_simu_matched_to_salt['cs2'],events_salt_matched_to_simu['cs2'], s=1, label='Rn220')\n",
"plt.scatter(events_simu_matched_to_saltb['cs2'],events_salt_matched_to_simub['cs2'], s=1, label='BKG')\n",
"plt.legend()\n",
"plt.xlim(0,6000)\n",
"plt.ylim(0,6000)\n",
"plt.title('Salted Rn220 into SR0 Rn220/BKG')\n",
"plt.xlabel('Simu CS2 [PE]')\n",
"plt.ylabel('Salt CS2 [PE]')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13e9f630-14f7-4d0b-a056-e804f99130ab",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=150)\n",
"plt.hist(events_salt_matched_to_simu['cs1'] - events_simu_matched_to_salt['cs1'], \n",
" bins=np.linspace(-200,200,50), alpha=0.5, label='Rn220', density=True)\n",
"plt.hist(events_salt_matched_to_simub['cs1'] - events_simu_matched_to_saltb['cs1'], \n",
" bins=np.linspace(-200,200,50), alpha=0.5, label='BKG', density=True)\n",
"plt.legend()\n",
"plt.title('Salted Rn220 into SR0 Rn220/BKG')\n",
"plt.xlabel('Ambience Contribution in CS1 [PE]')\n",
"plt.yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc505f30-a053-40f2-af51-bc6434da8afb",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=150)\n",
"plt.hist(events_salt_matched_to_simu['cs2'] - events_simu_matched_to_salt['cs2'], \n",
" bins=np.linspace(-200,200,50), alpha=0.5, label='Rn220', density=True)\n",
"plt.hist(events_salt_matched_to_simub['cs2'] - events_simu_matched_to_saltb['cs2'], \n",
" bins=np.linspace(-200,200,50), alpha=0.5, label='BKG', density=True)\n",
"plt.title('Salted Rn220 into SR0 Rn220/BKG')\n",
"plt.legend()\n",
"plt.xlabel('Ambience Contribution in CS2 [PE]')\n",
"plt.yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c91e880-28e0-4776-85d8-6ab6f5f88e3b",
"metadata": {},
"outputs": [],
"source": [
"inds_big_change_in_s2 = np.where((events_salt_matched_to_simu['cs2'] - events_simu_matched_to_salt['cs2']>100)&\n",
" (events_salt_matched_to_simu['cs2'] - events_simu_matched_to_salt['cs2']<200))[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f26b7651-b235-4d7e-9c10-b0a7fb81766b",
"metadata": {},
"outputs": [],
"source": [
"saltax.plot_wf(ind=inds_big_change_in_s2[7], st_salt=st_salt, st_simu=st_simu, st_data=st_data, \n",
" runid='022685', matched_simu=events_simu_matched_to_salt, \n",
" event_ext_window_ns=2.4e6, \n",
" s1_ext_window_samples=25, \n",
" s2_ext_window_samples=100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d8b48e1-116f-48d6-bb14-7cfba27ad2a8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:anaconda-XENONnT_development]",
"language": "python",
"name": "conda-env-anaconda-XENONnT_development-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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