{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PMT Pulse analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jelle, updated May 2019\n", "\n", "updated Feb 2022 by Joran" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standard python setup:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "\n", "# This just ensures some comments in dataframes below display nicely\n", "import pandas as pd\n", "\n", "pd.options.display.max_colwidth = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After this standard setup, we can load straxen:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cutax is not installed\n" ] }, { "data": { "text/html": [ "
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moduleversionpathgit
0python3.10.0/home/angevaare/miniconda3/envs/dev_strax/bin/pythonNone
1strax1.1.7/home/angevaare/software/dev_strax/strax/straxbranch:master | db14f80
2straxen1.2.8/home/angevaare/software/dev_strax/straxen/straxenbranch:master | 024602e
\n", "
" ], "text/plain": [ " module version path \\\n", "0 python 3.10.0 /home/angevaare/miniconda3/envs/dev_strax/bin/python \n", "1 strax 1.1.7 /home/angevaare/software/dev_strax/strax/strax \n", "2 straxen 1.2.8 /home/angevaare/software/dev_strax/straxen/straxen \n", "\n", " git \n", "0 None \n", "1 branch:master | db14f80 \n", "2 branch:master | 024602e " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import strax\n", "import straxen\n", "\n", "st = straxen.contexts.xenonnt_online()\n", "straxen.print_versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Raw records" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's select a run for which we have strax data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d01228bb51e144fbaf896189dce7d62f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Checking data availability: 0%| | 0/2 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Field nameData typeComment
0timeint64Start time since unix epoch [ns]
1lengthint32Length of the interval in samples
2dtint16Width of one sample [ns]
3channelint16Channel/PMT number
4pulse_lengthint32Length of pulse to which the record belongs (without zero-padding)
5record_iint16Fragment number in the pulse
6baselineint16Baseline determined by the digitizer (if this is supported)
7data('<i2', (110,))Waveform data in raw ADC counts
\n", "" ], "text/plain": [ " Field name Data type \\\n", "0 time int64 \n", "1 length int32 \n", "2 dt int16 \n", "3 channel int16 \n", "4 pulse_length int32 \n", "5 record_i int16 \n", "6 baseline int16 \n", "7 data ('" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_record(sample_r):\n", " plt.plot(sample_r[\"data\"][: sample_r[\"length\"]], drawstyle=\"steps-mid\")\n", " plt.xlabel(\"Sample number\")\n", " plt.ylabel(\"Amplitude (ADCc)\")\n", "\n", "\n", "sample_r = rr[0]\n", "plot_record(sample_r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, this record contains a single PE pulse, straight as it comes off the DAQ. No operations have been done on it; we did not even subtract the baseline and flip the pulse. \n", "\n", "Each record is 110 samples long, but this digitizer pulse was only 102 samples long:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "102" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_r[\"pulse_length\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and thus the pulse has been zero-padded:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([15991, 15991, 15987, 15991, 15993, 15993, 15993, 15992, 15988,\n", " 15990, 15990, 15992, 15992, 15992, 15992, 15993, 15992, 15995,\n", " 15991, 15993, 15991, 15992, 15989, 15993, 15991, 15991, 15994,\n", " 15992, 15990, 15989, 15990, 15991, 15989, 15990, 15988, 15989,\n", " 15991, 15993, 15992, 15994, 15992, 15994, 15991, 15994, 15996,\n", " 15994, 15994, 15993, 15993, 15977, 15975, 15987, 15988, 15988,\n", " 15986, 15989, 15990, 15992, 15989, 15992, 15992, 15991, 15992,\n", " 15993, 15992, 15994, 15995, 15992, 15990, 15995, 15993, 15989,\n", " 15991, 15993, 15991, 15992, 15991, 15994, 15992, 15994, 15995,\n", " 15992, 15990, 15996, 15994, 15994, 15993, 15994, 15991, 15992,\n", " 15991, 15992, 15990, 15991, 15990, 15990, 15989, 15994, 15995,\n", " 15994, 15995, 15991, 0, 0, 0, 0, 0, 0,\n", " 0, 0], dtype=int16)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_r[\"data\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take care of the baseline and zero padding:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Convert from raw_records to the records datatype, which has more fields.\n", "# Without this we cannot baseline.\n", "rr = strax.raw_to_records(rr)\n", "\n", "# Subtract baseline and flip channel\n", "strax.baseline(rr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now things look more reasonable:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_record(rr[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pulse shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's focus on channel 100:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "r = rr[rr[\"channel\"] == 100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the distribution of amplitudes in each sample. This is very roughly the mean pulse shape:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Amplitude (ADCc)')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ns = np.arange(len(r[0][\"data\"]))\n", "\n", "plt.plot(ns, r[\"data\"].mean(axis=0), drawstyle=\"steps-mid\")\n", "plt.fill_between(\n", " ns,\n", " np.percentile(r[\"data\"], 25, axis=0),\n", " np.percentile(r[\"data\"], 75, axis=0),\n", " step=\"mid\",\n", " alpha=0.3,\n", " linewidth=0,\n", ")\n", "plt.xlabel(\"Sample in record\")\n", "plt.ylabel(\"Amplitude (ADCc)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can clearly see we're dealing with single PEs here, and also see the infamous long PE pulse tail. For a serious pulse shape study you should of course first normalize the pulses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gain calibration" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'008334'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's integrate between sample 40 and 70, to get the mean single-PE area. We have to add a small correction for the baseline, see [here](https://github.com/AxFoundation/strax/issues/2) for details. Usually this is done inside strax and you don't have to worry about it." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "left, right = 40, 70\n", "areas = r[\"data\"][:, left:right].sum(axis=1)\n", "\n", "# Small correction for baseline, see strax issue #2\n", "areas += ((r[\"baseline\"] % 1) * (right - left)).round().astype(np.int64)\n", "\n", "# Hack to get ADC -> PE conversion factors. We'll make this better someday...\n", "to_pe = st.get_single_plugin(run_id, \"peaklets\").to_pe\n", "\n", "plt.hist(areas, bins=100)\n", "plt.xlabel(\"Area (ADCc)\")\n", "plt.ylabel(\"Counts\")\n", "plt.axvline(1 / to_pe[100], color=\"r\", linestyle=\"--\")\n", "plt.axvline(np.median(areas), color=\"purple\", linestyle=\"--\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purple line is an extremely bad gain estimate (the median area) of pulses, which nonetheless comes close for this PMT. It should be obvious this is a bad method: it takes no account of 2PE hits or the hitfinder efficiency at all.\n", "\n", "The red line indicates where the 1 PE area should be according to the XENON1T gain calibration. Looks like the gain is, at least approximately, correct!\n", "\n", "Let's do the same for all PMTs:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 2%|▏ | 12/494 [00:00<00:16, 29.33it/s]/home/angevaare/miniconda3/envs/dev_strax/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/home/angevaare/miniconda3/envs/dev_strax/lib/python3.10/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n", "100%|██████████| 494/494 [00:18<00:00, 27.23it/s]\n" ] } ], "source": [ "areas = rr[\"data\"][:, 40:70].sum(axis=1)\n", "channels = rr[\"channel\"]\n", "\n", "gain_ests = np.array(\n", " [np.median(areas[channels == pmt]) for pmt in tqdm(np.arange(straxen.n_tpc_pmts))]\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/jobs/17887550/ipykernel_29675/2656599034.py:1: RuntimeWarning: divide by zero encountered in true_divide\n", " plt.scatter(1/to_pe, gain_ests, s=2)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(1 / to_pe, gain_ests, s=2)\n", "plt.plot([0, 250], [0, 250], c=\"k\", alpha=0.2)\n", "plt.xlabel(\"XENONnT gain calibration (ADCc)\")\n", "plt.ylabel(\"Median short pulse area (ADCc)\")\n", "plt.title(\"Pulse areas in ADCc for each PMT\")\n", "plt.xlim(left=0)\n", "plt.ylim(bottom=0)\n", "plt.gca().set_aspect(\"equal\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, even this extremely basic method (median area of short pulses in 30sec of background data) gives a somewhat plausible gain estimate for most PMTs." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }