{ "cells": [ { "cell_type": "markdown", "id": "1e388f26", "metadata": {}, "source": [ "# $^{241}\\text{Am}$ $\\alpha\\text{-}\\gamma$ Coincidence Example" ] }, { "cell_type": "code", "execution_count": 1, "id": "3defe307", "metadata": {}, "outputs": [], "source": [ "import sauce\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.stats import norm" ] }, { "cell_type": "markdown", "id": "88afa784", "metadata": {}, "source": [ "This setup includes two detectors: a silicon surface barrier (SSB) detector and cerium bromide (CeBr). In *sauce* we would define two detector instances to hold the hits for each detector." ] }, { "cell_type": "code", "execution_count": 2, "id": "3e404341", "metadata": {}, "outputs": [], "source": [ "run_data = sauce.Run(\"./241Am_p_a.parquet\")\n", "ssb = sauce.Detector(\"ssb\").find_hits(run_data, module=2, channel=0)\n", "cebr = sauce.Detector(\"cebr\").find_hits(run_data, module=2, channel=1)" ] }, { "cell_type": "markdown", "id": "afc746af", "metadata": {}, "source": [ "We first focus on the SSB analysis. We need to determine the singles count rate, $N_{\\alpha}$. In order to do this we need to clean up the SSB spectrum and find the total number of counts. We need to see the data to do this step, since our spectrum is not energy calibrated." ] }, { "cell_type": "code", "execution_count": 3, "id": "b7ba4613", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sauce.utils.step(*ssb.hist(0, 32000, 32000))\n", "plt.yscale(\"log\")" ] }, { "cell_type": "markdown", "id": "daf55e72", "metadata": {}, "source": [ "We can see that the main peak is at around channel 3000. The high energy events are overflow events, while the peak around channel 3000 is due to pulse pileup. We zoom in to finalize the gate." ] }, { "cell_type": "code", "execution_count": 4, "id": "8fd1574a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sauce.utils.step(*ssb.hist(0, 32000, 32000))\n", "plt.xlim(0, 3100)\n", "plt.yscale(\"log\")" ] }, { "cell_type": "markdown", "id": "40e841b0", "metadata": {}, "source": [ "It looks like 3100 is a reasonable upper limit. In *sauce* we apply 1D cuts using the *apply_cut* method. Note that this is a destructive operation!" ] }, { "cell_type": "code", "execution_count": 5, "id": "cfbc47e9", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ssb.apply_cut((0, 3100))\n", "sauce.utils.step(*ssb.hist(0, 32000, 32000))\n", "plt.yscale(\"log\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "09df5780", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1719419\n" ] } ], "source": [ "c_ssb = ssb.counts()\n", "print(c_ssb)" ] }, { "cell_type": "markdown", "id": "851003f4", "metadata": {}, "source": [ "We now have the counts for the SSB, we still need the CeBr counts and the coincident counts." ] }, { "cell_type": "code", "execution_count": 7, "id": "2bf22b79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 5000.0)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sauce.utils.step(*cebr.hist(0, 32000, 32000))\n", "plt.yscale(\"log\")\n", "plt.xlim(0, 5000)" ] }, { "cell_type": "markdown", "id": "8efa1881", "metadata": {}, "source": [ "We are interseted in the 60 keV peak, which is around channel 1500. In order to get the correct counts we need an estimate of the background as well apply a gate. We will do this by selecting two regions, one around the peak region of the 60 keV line and the other around channel 2000 for a side band estimate of the background." ] }, { "cell_type": "code", "execution_count": 8, "id": "5c59c28d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "164766.0\n" ] } ], "source": [ "peak_region = [1200, 1600]\n", "bkg_region = [2000, 2100]\n", "peak_len_cebr = peak_region[1] - peak_region[0] \n", "bkg_len_cebr = bkg_region[1] - bkg_region[0]\n", "\n", "bkg_cebr = (\n", " cebr.copy().apply_cut(bkg_region).counts()\n", ") # copy the data to avoid destructive operation.\n", "bkg_per_bin = bkg_cebr / bkg_len_cebr\n", "bkg_total = bkg_per_bin * peak_len_cebr\n", "\n", "# now apply the cut to the peak region\n", "cebr.apply_cut(peak_region)\n", "c_cebr = cebr.counts() - bkg_total\n", "print(c_cebr)" ] }, { "cell_type": "markdown", "id": "a77a757a", "metadata": {}, "source": [ "Now we need to event build to get the coincident counts. We will use the SSB as the reference detector and have a $\\pm 1000$ ns build window. " ] }, { "cell_type": "code", "execution_count": 9, "id": "d8819811", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eb = sauce.EventBuilder()\n", "eb.add_timestamps(ssb)\n", "eb.create_build_windows(-1000, 1000)" ] }, { "cell_type": "markdown", "id": "560575a9", "metadata": {}, "source": [ "Now that we have the events, we still need to filter the hits in each detector. *sauce* provides the *Coincidence* class for this purpose. " ] }, { "cell_type": "code", "execution_count": 10, "id": "6d93e7f3", "metadata": {}, "outputs": [], "source": [ "coin = sauce.Coincident(eb)\n", "ssb_cebr = coin[\n", " ssb, cebr\n", "] # this creates a new Detector that will have the coincident events from both detectors." ] }, { "cell_type": "markdown", "id": "8b0515a0", "metadata": {}, "source": [ "We also need to do background subtraction for the coincidence spectrum, which we can do by using a side band estimate from the timing spectrum." ] }, { "cell_type": "code", "execution_count": 11, "id": "f0582487", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, '$\\\\Delta t_{ssb - cebr}$ (ns)')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# time difference between the detectors.\n", "ssb_cebr[\"dt\"] = ssb_cebr[\"evt_ts_ssb\"] - ssb_cebr[\"evt_ts_cebr\"]\n", "sauce.utils.step(*ssb_cebr.hist(-1000, 1000, 1000, \"dt\"))\n", "plt.xlabel(r\"$\\Delta t_{ssb - cebr}$ (ns)\")" ] }, { "cell_type": "markdown", "id": "43d817d1", "metadata": {}, "source": [ "We can see the evidence of the finite lifetime of the 60 keV state. We use 250 to 1000 ns for the background and a peak region of -500 to 250 ns." ] }, { "cell_type": "code", "execution_count": 12, "id": "2068a864", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3333.0\n" ] } ], "source": [ "peak_region = [-500, 250]\n", "bkg_region = [250, 1000]\n", "bkg_len_coin = bkg_region[1] - bkg_region[0]\n", "peak_len_coin = peak_region[1] - peak_region[0]\n", "\n", "bkg_coin = ssb_cebr.copy().apply_cut(bkg_region, \"dt\").counts()\n", "bkg_per_bin = bkg_coin / bkg_len_coin\n", "bkg_total = bkg_per_bin * peak_len_coin\n", "\n", "# now apply the cut to the peak region\n", "ssb_cebr.apply_cut(peak_region, \"dt\")\n", "c_coin = ssb_cebr.counts() - bkg_total\n", "print(c_coin)" ] }, { "cell_type": "markdown", "id": "be108bb2", "metadata": {}, "source": [ "Finally, $N_{\\alpha}$, $N_{\\gamma}$, and $N_{\\alpha\\text{-}\\gamma}$ are rates, which means we need the total run time. This should be closely approximated by the first and last hit in the run." ] }, { "cell_type": "code", "execution_count": 13, "id": "7f5e3168", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1789.1253802425347\n" ] } ], "source": [ "dt = (run_data.data[\"evt_ts\"][-1] - run_data.data[\"evt_ts\"][0]) / 1e9 # convert to seconds\n", "print(dt)" ] }, { "cell_type": "code", "execution_count": 14, "id": "feba4a31", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The activity is 1.284 uCi\n" ] } ], "source": [ "activity = ((c_cebr * c_ssb) / c_coin) / dt / 37000 # convert to uCi\n", "print(rf\"The activity is {activity:.3f} uCi\")" ] }, { "cell_type": "markdown", "id": "8c66e0f7", "metadata": {}, "source": [ "Statistical errors will be estimated assuming Possion statistics for all quantities. \"_s\" on a variable will indicate that is is a set of samples." ] }, { "cell_type": "code", "execution_count": 15, "id": "22199b54", "metadata": {}, "outputs": [], "source": [ "def draw_samples(val, samples=1000000):\n", " return norm.rvs(loc=val, scale=np.sqrt(val), size=samples)" ] }, { "cell_type": "code", "execution_count": 16, "id": "df08bc5e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The activity is 1.284 +/- 0.023 uCi\n" ] } ], "source": [ "\n", "c_ssb_s = draw_samples(c_ssb)\n", "bkg_cebr_s = peak_len_cebr * draw_samples(bkg_cebr)/bkg_len_cebr\n", "c_cebr_s = draw_samples(cebr.counts()) - bkg_cebr_s\n", "bkg_coin_s = peak_len_coin * draw_samples(bkg_coin)/bkg_len_coin\n", "c_coin_s = draw_samples(ssb_cebr.counts()) - bkg_coin_s\n", "\n", "\n", "activity_s = ((c_cebr_s * c_ssb_s) / c_coin_s) / dt / 37000 # convert to uCi\n", "print(rf\"The activity is {activity_s.mean():.3f} +/- {activity_s.std():.3f} uCi\")" ] }, { "cell_type": "markdown", "id": "7543e552", "metadata": {}, "source": [ "The samples look fairly Gaussian, so we are done." ] }, { "cell_type": "code", "execution_count": 17, "id": "421e7f64", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(activity_s, bins=100)" ] } ], "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": "3.9.15" }, "name": "241Am_example.ipynb" }, "nbformat": 4, "nbformat_minor": 5 }