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Merge pull request #102 from icecube/gen-sim
Gen2 geometry and effvol extension
2 parents cc31010 + 32a1ad8 commit ad52ef6

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‎.github/workflows/coverage.yml‎

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- name: Install ASTERIA
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run: |
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pip install ".[dev]"
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- name: Set ASTERIA environment
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echo "ASTERIA=${{ github.workspace }}" >> "$GITHUB_ENV"
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- name: Run tests with coverage
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pytest --cov

‎.github/workflows/tests.yml‎

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- name: Install ASTERIA
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pip install ".[dev]"
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- name: Set ASTERIA environment
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run: |
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echo "ASTERIA=${{ github.workspace }}" >> "$GITHUB_ENV"
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if: runner.os != 'Windows'
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- name: Set ASTERIA environment for Windows
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run: |
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echo "ASTERIA=${{ github.workspace }}" | Out-File -FilePath $env:GITHUB_ENV -Append
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if: runner.os == 'Windows'
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- name: Run unit tests with pytest
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pytest

‎docs/scratch/sim_tester.ipynb‎

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‎docs/source/nb/detector_hits.ipynb‎

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‎docs/source/nb/detector_hits_Gen2.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "115a4e1c-1ab6-40f9-ba0d-1be45e0bbd4d",
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"metadata": {},
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"source": [
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"# Detector Hits in IceCube-Gen2\n",
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"\n",
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"Demonstrate detector hits in the IC86 + Gen2 configuration of IceCube.\n",
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"\n",
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"This includes both mDOMs and WLS modules."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e2eb397f-92c2-40d2-932f-5816b85cf46b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from astropy import units as u\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from snewpy.neutrino import Flavor\n",
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"\n",
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"from asteria.simulation import Simulation\n",
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"from asteria import set_rcparams\n",
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"from asteria import interactions\n",
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"\n",
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"from importlib.resources import files\n",
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"\n",
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"set_rcparams(verbose=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7af47266-c61c-45a7-b572-b865a1092864",
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"metadata": {},
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"source": [
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"## Configure an mDOM Gen2 Simulation\n",
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"\n",
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"Set up a progenitor 10 kpc from Earth and generate simulated hits.\n",
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"\n",
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"To simulate the Gen2 detector, add the argument `detector_scope='Gen2'` when initializing the simulation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1dff7931-b55f-4563-ab3b-12cf5e78e2bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = {'name': 'Nakazato_2013',\n",
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" 'param':{\n",
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" 'progenitor_mass': 13 * u.Msun,\n",
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" 'revival_time': 300 * u.ms,\n",
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" 'metallicity': 0.004,\n",
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" 'eos': 'shen'}\n",
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" }\n",
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"\n",
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"sim = Simulation(model=model,\n",
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" distance=10 * u.kpc, \n",
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" Emin=0*u.MeV, Emax=100*u.MeV, dE=1*u.MeV,\n",
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" tmin=-1*u.s, tmax=1*u.s, dt=1*u.ms,\n",
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" detector_scope='Gen2')\n",
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"sim.run()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3052237c-8160-44b7-ac0e-bb2c4e986ee2",
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"metadata": {},
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"source": [
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"## Plot the Energy Deposit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8252ebd0-52b8-4504-915e-3f36189f5419",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"\n",
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"for flavor in sim.flavors:\n",
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" ax.plot(sim.time, sim.E_per_V[flavor], label=flavor.to_tex())\n",
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"ax.legend()\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel='energy deposit [Mev m$^{-3}$])',\n",
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" xlim=(-0.15, 0.75));"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fe116039-a071-467a-8bd6-06aa2a812468",
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"metadata": {},
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"source": [
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"## Plot Detector Response\n",
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"\n",
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"### Expected Signal from Each Subdetector\n",
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"\n",
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"Set a time resolution `dt`. Using the `sim.detector_signal()` function we can read out the detector signal for each subdetector class. Separately plot hits from the main IceCube strings and DeepCore."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d90d09cf-5ecc-475c-bbc5-178c95e4e2c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"dt = 2 * u.ms\n",
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"t, sim_i3 = sim.detector_signal(subdetector='i3', dt=dt)\n",
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"t, sim_dc = sim.detector_signal(subdetector='dc', dt=dt)\n",
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"t, sim_md = sim.detector_signal(subdetector='md', dt=dt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52d3c595-17e0-4df0-9746-af9d47e77a35",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"ax.plot(t, sim_i3, label='IceCube DOM')\n",
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"ax.plot(t, sim_dc, label='HQE DOM (DeepCore)')\n",
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"ax.plot(t, sim_md, label='mDOM (Gen2)')\n",
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"ax.legend(fontsize=14)\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel=f'detector hits',\n",
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" xlim=(-0.15, 0.75),\n",
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" ylim=(0,10000));"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5854b46f-9889-48fd-bdef-d69678b5c147",
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"metadata": {},
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"source": [
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"### Generated Hits from Signal Only"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c29ae3a0-b978-4ada-8580-a0a167d89dba",
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"metadata": {},
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"outputs": [],
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"source": [
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"t, hits_i3 = sim.detector_hits(subdetector='i3', dt=dt)\n",
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"t, hits_dc = sim.detector_hits(subdetector='dc', dt=dt)\n",
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"t, hits_md = sim.detector_signal(subdetector='md', dt=dt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb6a1409-3da3-4071-ab48-e9a8d5bfdfef",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"ax.plot(t, hits_i3, label='IceCube DOM')\n",
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"ax.plot(t, hits_dc, label='HQE DOM (DeepCore)')\n",
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"ax.plot(t, hits_md, label='mDOM (Gen2)')\n",
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"\n",
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"ax.legend(fontsize=14)\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel=f'detector hits',\n",
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" xlim=(-0.15, 0.75),\n",
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" ylim=(0,10000));"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f3f7030-fb90-4a89-b98f-d0d0e9787446",
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"metadata": {},
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"source": [
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"### Generated Hits from Signal + Background\n",
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"\n",
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"Separately compute the background hits and signal from each subdetector and add them."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2f39f58-504e-42ca-8f0f-8a72f7dd8eb2",
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"metadata": {},
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"outputs": [],
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"source": [
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"bkg_i3 = sim.detector.i3_bg(dt, size=hits_i3.size)\n",
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"bkg_dc = sim.detector.dc_bg(dt, size=hits_dc.size)\n",
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"bkg_md = sim.detector.md_bg(dt, size=hits_md.size)\n",
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"\n",
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"bkg = bkg_i3 + bkg_dc + bkg_md\n",
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"hits = hits_i3 + hits_dc + hits_md"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f4df03ef-e5f6-40a4-a2cd-1f0c94c51d46",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"ax.plot(t, hits_i3 + bkg_i3, label='IceCube DOM')\n",
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"ax.plot(t, hits_dc + bkg_dc, label='HQE DOM (DeepCore)')\n",
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"ax.plot(t, hits_md + bkg_md, label='mDOM (Gen2)')\n",
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"ax.plot(t, hits + bkg, label='Total hits')\n",
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"\n",
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"ax.legend(loc='center right', fontsize=14)\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel=f'detector hits',\n",
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" xlim=(-0.15, 0.75),\n",
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" ylim=(0,65000)\n",
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");"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e03521b3-ec42-4bd9-87ce-d289bd8a1591",
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"metadata": {},
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"source": [
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"## Add WLS Modules\n",
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"\n",
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"In the Gen2 detector scope, using the `add_wls=True` argument will add wavelength-shifting modules to the Gen2 detector response.\n",
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"\n",
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"The hits can be accessed as the `ws` subdetector."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7cebd2b2-7533-4d1f-a074-d5299a2f00df",
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"metadata": {},
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"outputs": [],
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"source": [
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"sim_ws = Simulation(model=model,\n",
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" distance=10 * u.kpc, \n",
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" Emin=0*u.MeV, Emax=100*u.MeV, dE=1*u.MeV,\n",
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" tmin=-1*u.s, tmax=1*u.s, dt=1*u.ms,\n",
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" detector_scope='Gen2',\n",
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" add_wls=True)\n",
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"sim_ws.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2c8de85b-f01a-41f1-99cd-7cd79a296ad0",
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"metadata": {},
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"outputs": [],
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"source": [
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"t, hits_i3 = sim_ws.detector_hits(subdetector='i3', dt=dt)\n",
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"t, hits_dc = sim_ws.detector_hits(subdetector='dc', dt=dt)\n",
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"t, hits_md = sim_ws.detector_signal(subdetector='md', dt=dt)\n",
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"t, hits_ws = sim_ws.detector_signal(subdetector='ws', dt=dt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00ac3f74-787d-4098-bd3b-24f2d394eb39",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"ax.plot(t, hits_i3, label='IceCube DOM')\n",
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"ax.plot(t, hits_dc, label='HQE DOM (DeepCore)')\n",
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"ax.plot(t, hits_md, label='mDOM (Gen2)')\n",
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"ax.plot(t, hits_ws, label='mDOM+WLS (Gen2)')\n",
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"\n",
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"ax.legend(fontsize=14)\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel=f'detector hits',\n",
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" xlim=(-0.15, 0.75),\n",
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" ylim=(0,10000));"
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]
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},
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{
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"cell_type": "markdown",
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"id": "286dd290-371f-4672-92d5-0a54eb7fb77b",
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"metadata": {},
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"source": [
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"### Plot the Sum of Signal + Background"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c2d27df-8173-45c6-84ad-5c63214a681c",
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"metadata": {},
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"outputs": [],
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"source": [
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"bkg_i3 = sim.detector.i3_bg(dt, size=hits_i3.size)\n",
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"bkg_dc = sim.detector.dc_bg(dt, size=hits_dc.size)\n",
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"bkg_md = sim.detector.md_bg(dt, size=hits_md.size)\n",
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"bkg_ws = sim.detector.ws_bg(dt, size=hits_ws.size)\n",
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"\n",
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"bkg = bkg_i3 + bkg_dc + bkg_md + bkg_ws\n",
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"hits = hits_i3 + hits_dc + hits_md + hits_ws"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8a6fe116-4718-46b8-8991-a2d6cb0db220",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
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"ax.plot(t, hits_i3 + bkg_i3, label='IceCube DOM')\n",
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"ax.plot(t, hits_dc + bkg_dc, label='HQE DOM (DeepCore)')\n",
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"ax.plot(t, hits_md + bkg_md, label='mDOM (Gen2)')\n",
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"ax.plot(t, hits_ws + bkg_ws, label='mDOM+WLS (Gen2)')\n",
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"ax.plot(t, hits + bkg, label='Total hits')\n",
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"\n",
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"ax.legend(loc='center', ncol=2, fontsize=14)\n",
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"ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
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" ylabel=f'detector hits',\n",
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" xlim=(-0.15, 0.75),\n",
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" ylim=(0,75000)\n",
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");"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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