{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "adc5eb9b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyerrors as pe" ] }, { "cell_type": "code", "execution_count": 2, "id": "a921a326", "metadata": {}, "outputs": [], "source": [ "plt.style.use('./base_style.mplstyle')\n", "plt.rc('text', usetex=True)" ] }, { "cell_type": "markdown", "id": "dd0fac2c", "metadata": {}, "source": [ "A correlator can not only contain a list of Obs, but also a list of matrices of obs. \n", "This is useful, if there are multiple sources and sinks used. In our example, the sources have a different Gaussian smearing applied. \n", "\n", "We will load such a correlator." ] }, { "cell_type": "code", "execution_count": 3, "id": "c49f7c4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data has been written using pyerrors 2.0.0+dev.\n", "Format version 0.1\n", "Written by jan on 2022-01-27 10:49:51 +0100 on host endwings, Linux-5.13.0-27-generic-x86_64-with-glibc2.10\n" ] } ], "source": [ "matrix_V1V1 = pe.input.json.load_json(\"./data/matrix_correlator_V1V1\")" ] }, { "cell_type": "markdown", "id": "a56dca5f", "metadata": {}, "source": [ "We can print the content at timeslice 0. As we can see, it is a matrix of Obs. " ] }, { "cell_type": "code", "execution_count": 4, "id": "dd28fe5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[Obs[0.9521372299563664] Obs[0.012396221358945024]\n", " Obs[0.005964522290444636] Obs[0.00271868582032491]]\n", " [Obs[0.012406080421737313] Obs[0.004389377599144552]\n", " Obs[0.0026720900942348203] Obs[0.0014322792739559524]]\n", " [Obs[0.00597528112202714] Obs[0.0026722311560493962]\n", " Obs[0.0017410144722897988] Obs[0.0009900129922259138]]\n", " [Obs[0.002728787039125839] Obs[0.001432511366233049]\n", " Obs[0.0009901084333529782] Obs[0.0005964594911910594]]]\n" ] } ], "source": [ "print(matrix_V1V1.content[0])" ] }, { "cell_type": "markdown", "id": "b965e080", "metadata": {}, "source": [ "Let us try to get the effective mass. " ] }, { "cell_type": "code", "execution_count": 5, "id": "bd58d1f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Something is wrong\n" ] } ], "source": [ "try:\n", " matrix_V1V1.m_eff() #This does not work! \n", "except:\n", " print(\"Something is wrong\")" ] }, { "cell_type": "markdown", "id": "044503dc", "metadata": {}, "source": [ "Many methods we could use for regular correlators do not work with matrix-correlators. \n", "In order to get the effective mass, we need to convert to a regular correlator first. \n", "\n", "One way to do it, is to pick an element out of the matrix:" ] }, { "cell_type": "code", "execution_count": 6, "id": "90e54c2f", "metadata": {}, "outputs": [], "source": [ "single_smearing = matrix_V1V1.item(0,0)" ] }, { "cell_type": "markdown", "id": "5c25a23c", "metadata": {}, "source": [ "**Corr.index(i,j)** picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice. \n", "But there is a more usefull way to retrieve a single value per timeslice. \n", "We might want a linear combination of different sources and sinks. \n", "We can formalize this as\n", "\n", "$$C_{\\textrm{projected}}(t)=v_1^T \\underline{C}(t) v_2$$\n", "\n", "If we choose the vectors to be $v_1=v_2=(0,1,0,0)$, we should get the same correlator as in the cell above. \n", "\n", "Thinking about it this way is usefull in the Context of the generalized eigenvalue problem (GEVP), used to find the source-sink combination, which best describes a certain energy eigenstate.\n", "A good introduction is found in https://arxiv.org/abs/0902.1265." ] }, { "cell_type": "code", "execution_count": 7, "id": "1d0e8a06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ -0.71920537 162.3903938 -492.21321233 714.06804297]\n" ] } ], "source": [ "vec = matrix_V1V1.GEVP(t0=3,ts=6,state=0)\n", "assert len(vec) == matrix_V1V1.N\n", "print(vec)" ] }, { "cell_type": "markdown", "id": "0d7aeb89", "metadata": {}, "source": [ "As we see, the eigenvector is of length **matrix_V1V1.N** and contains regular floats. \n", "We can use it to project the correlator. " ] }, { "cell_type": "code", "execution_count": 8, "id": "fb0b7b57", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "matrix_V1V1.projected(vec).m_eff().show(comp=single_smearing.m_eff(), auto_gamma=True)" ] }, { "cell_type": "markdown", "id": "f26cdaf2", "metadata": {}, "source": [ "There is a lot going on in this line of code. \n", "We start with our matrix correlator and we project it, using the vector we got from the GEVP routine. \n", "\n", "This gives us a new correlator with one Obs per timeslice. We then calculate its effective mass and plot it. \n", "We tell the **Corr.show** method to show another correlator as a comparison. \n", "\n", "We can see, that the projected correlator (*blue*) converges to a mass plateau much quicker than the single smearing level." ] }, { "cell_type": "markdown", "id": "06c366ed", "metadata": {}, "source": [ "## Example Analysis\n", "\n", "We can use what we learned so far to perform an actually usefull analysis. \n", "The correlator **matrix_V1V1** we looked at corresponds to vector-charmonium. \n", "\n", "We might be interested in the mass of the $J/\\Psi$ state." ] }, { "cell_type": "code", "execution_count": 12, "id": "def98bc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--- The mass was calculated to be 3079.8(7.9) MeV ---\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We do not just have V1V1, but also the two other spacial directions. We can average over them for better statistics. \n", "matrix_V2V2= pe.input.json.load_json(\"./data/matrix_correlator_V2V2\", verbose=False)\n", "matrix_V3V3= pe.input.json.load_json(\"./data/matrix_correlator_V3V3\", verbose=False)\n", "matrix_VnVn=(matrix_V1V1 + matrix_V2V2 + matrix_V3V3) / 3. \n", "\n", "#We then solve the GEVP to get eigenvectors corresponding to the ground state. \n", "\n", "vec_ground = matrix_VnVn.GEVP(t0=3, ts=6, state=0)\n", "\n", "#Now we project the matrix-correlators to get new correlators belonging to the ground state.\n", "\n", "corr_ground = matrix_VnVn.projected(vec_ground)\n", "\n", "# We get the effective mass using the periodic cosh method. \n", "m_eff_Jpsi = corr_ground.m_eff(variant=\"cosh\")\n", "\n", "# We can now pick a plateau range and get a single value for the mass. \n", "m_eff_Jpsi.gamma_method()\n", "m_Jpsi = m_eff_Jpsi.plateau([5, 24],method=\"avg\")\n", "m_Jpsi.gamma_method()\n", "# We can now visually compare our plateau value to the data\n", "\n", "m_eff_Jpsi.show([10, 25], plateau=m_Jpsi)\n", "\n", "# Since the lattice spacing is known, we can multiply with hbar*c/a to see it in physical units \n", "\n", "m_Jpsi = m_Jpsi * 197 / 0.0653\n", "\n", "#As a last step we call the gamma method to get the error \n", "\n", "m_Jpsi.gamma_method()\n", "\n", "print(\"--- The mass was calculated to be\" , m_Jpsi, \"MeV ---\")" ] }, { "cell_type": "code", "execution_count": null, "id": "c55c6066", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }