This notebook shows effective temperatures in the quiet Sun observed by 3PAMIS and EIS. Link to Figure 7.

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In [1]:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
from matplotlib import transforms
import astropy.constants as const
import pandas as pd
import cmcrameri.cm as cmcm
from matplotlib import rcParams
rcParams['text.usetex'] = True
rcParams['text.latex.preamble'] = r'\usepackage[T1]{fontenc} \usepackage{amsmath} \usepackage{color}'
rcParams['font.family'] = 'serif'
rcParams['axes.linewidth'] = 2
rcParams['xtick.major.width'] = 1.2
rcParams['xtick.major.size'] = 10
rcParams['xtick.minor.width'] = 1.2
rcParams['xtick.minor.size'] = 6
rcParams['ytick.major.width'] = 1.2
rcParams['ytick.major.size'] = 8
rcParams['ytick.minor.width'] = 1.2
rcParams['ytick.minor.size'] = 6 

In [2]:

df1 = pd.read_excel("../../sav/EIS/EQSPY/atlas30_fitres.xlsx",sheet_name="r1")
df1["Z2A"] = df1["Z"]/df1["A"]
df1["ion"] = df1["ion"].str.strip()
df1["charge_state"] = df1["charge_state"].str.strip()
df1["fwhm_true_err"] = df1["fwhm_fit"]/(df1["fwhm_true"]*1e-3)*df1["fwhm_err"]*1e3

In [3]:

df1

Out[3]:

	ion	charge_state	Z	A	wvl_fit	wvl_chianti	fwhm_fit	fwhm_err	fwhm_true	Z2A	fwhm_true_err
0	Fe	x	9	55.8500	184.5127	184.537	0.07390	0.00140	25.9	0.161146	3.994595
1	Fe	viii	7	55.8500	185.1589	185.213	0.07620	0.00350	31.9	0.125336	8.360502
2	Fe	xi	10	55.8500	188.1929	188.216	0.07440	0.00100	27.1	0.179051	2.745387
3	Fe	xii	11	55.8500	193.4862	193.509	0.07570	0.00110	30.7	0.196956	2.712378
4	Fe	ix	8	55.8500	197.8343	197.854	0.07790	0.00170	35.7	0.143241	3.709524
5	Fe	xiii	12	55.8500	202.0236	202.044	0.07484	0.00055	28.4	0.214861	1.449366
6	Si	x	9	28.0855	258.3772	258.374	0.08490	0.00220	49.1	0.320450	3.804073
7	S	x	9	32.0600	264.2337	264.230	0.08600	0.00210	51.0	0.280724	3.541176
8	Fe	xiv	13	55.8500	264.7868	264.788	0.08200	0.00180	43.9	0.232766	3.362187
9	Fe	xv	14	55.8500	284.1600	284.163	0.08050	0.00280	41.1	0.250671	5.484185

In [4]:

c = const.c.cgs.value
amu = const.u.cgs.value
k_B = const.k_B.cgs.value
hplanck = const.h.cgs.value

In [5]:

line_name = df1["ion"] + r" \textsc{"+ df1["charge_state"] + r"}"

In [6]:

Teff_1 = df1["A"]*amu/(8*np.log(2)*k_B)*(c/(df1["wvl_chianti"]*1e-8))**2*(df1["fwhm_true"]*1e-11)**2
Teff_err_1 = 2*df1["A"]*amu/(8*np.log(2)*k_B)*(c/(df1["wvl_chianti"]*1e-8))**2*(df1["fwhm_true"]*1e-11)*df1["fwhm_true_err"]*1e-11

In [7]:

df_ion = df1[["ion","charge_state","Z","A","Z2A"]]
df_ion = df_ion.drop_duplicates()
ion_name = r"\textbf{" + df_ion["ion"] + r" \textsc{"+ df_ion["charge_state"] + r"}}"

In [8]:

df2 = pd.read_excel("../../sav/EIS/EQSPY/atlas30_fitres.xlsx",sheet_name="r2")
df2["Z2A"] = df2["Z"]/df2["A"]
df2["ion"] = df2["ion"].str.strip()
df2["charge_state"] = df2["charge_state"].str.strip()
df2["fwhm_true_err"] = df2["fwhm_fit"]/(df2["fwhm_true"]*1e-3)*df2["fwhm_err"]*1e3

In [9]:

df2

Out[9]:

	ion	charge_state	Z	A	wvl_fit	wvl_chianti	fwhm_fit	fwhm_err	fwhm_true	Z2A	fwhm_true_err
0	Fe	x	9	55.8500	184.5120	184.537	0.07380	0.00310	25.5	0.161146	8.971765
1	Fe	viii	7	55.8500	185.1910	185.213	0.07670	0.00680	33.0	0.125336	15.804848
2	Fe	xi	10	55.8500	188.1926	188.216	0.07670	0.00110	33.0	0.179051	2.556667
3	Fe	xii	11	55.8500	193.4862	193.509	0.07570	0.00120	30.6	0.196956	2.968627
4	Fe	ix	8	55.8500	197.8320	197.854	0.07600	0.00340	31.2	0.143241	8.282051
5	Fe	xiii	12	55.8500	202.0242	202.044	0.07543	0.00075	29.9	0.214861	1.892057
6	Si	x	9	28.0855	258.3800	258.374	0.08430	0.00320	48.0	0.320450	5.620000
7	S	x	9	32.0600	264.2310	264.230	0.08540	0.00340	50.0	0.280724	5.807200
8	Fe	xiv	13	55.8500	264.7860	264.788	0.08570	0.00380	50.5	0.232766	6.448713
9	Fe	xv	14	55.8500	284.1580	284.163	0.07730	0.00440	34.4	0.250671	9.887209

In [10]:

Teff_2 = df2["A"]*amu/(8*np.log(2)*k_B)*(c/(df2["wvl_chianti"]*1e-8))**2*(df2["fwhm_true"]*1e-11)**2
Teff_err_2 = 2*df2["A"]*amu/(8*np.log(2)*k_B)*(c/(df2["wvl_chianti"]*1e-8))**2*(df2["fwhm_true"]*1e-11)*df2["fwhm_true_err"]*1e-11

In [11]:

PaMIS_veff = np.array([28.54, 31.96])
PaMIS_veff_err = np.array([1.09, 0.97])
PaMIS_Teff = 55.85*amu/2/k_B*(PaMIS_veff*1e5)**2
PaMIS_Teff_err = 2*55.85*amu/2/k_B*(PaMIS_veff*1e5)*(PaMIS_veff_err*1e5)
PaMIS_Z2A = np.array([9,13])/np.array([55.85,55.85])

Figure 7¶

back to top

In [12]:

fig, ax = plt.subplots(figsize=(8,6),constrained_layout=True)

ax.errorbar(df1["Z2A"].values, Teff_1.values/1e6, Teff_err_1.values/1e6, color="#CC543A",ls="none",marker="o",
            markersize=15,capsize=5,lw=2.5,label=r"\textbf{EIS [1.035,1.06]}",zorder=15,markeredgecolor='white',
            markeredgewidth=2.5,alpha=0.9,capthick=2.5)

ax.errorbar(df2["Z2A"].values, Teff_2.values/1e6, Teff_err_2.values/1e6, color="#F9BF45",ls="none",marker="s",
            markersize=15,capsize=5,lw=2.5,label=r"\textbf{EIS [1.06,1.1]}",zorder=14,markeredgecolor='white',
            markeredgewidth=2.5,alpha=0.9,capthick=2.5)

ax.errorbar(PaMIS_Z2A, PaMIS_Teff/1e6, PaMIS_Teff_err/1e6, color="#0089A7",ls="none",marker="D",
            markersize=15,capsize=5,lw=2.5,label=r"\textbf{3PAMIS}",zorder=16,markeredgecolor='white',
            markeredgewidth=2,alpha=0.9,capthick=2.5)

ax_ion = ax.secondary_xaxis(location="top")
ax_ion.set_xlim(ax.get_xlim())
ax_ion.set_xticks(df_ion["Z2A"].values)
ax_ion.set_xticklabels(ion_name,rotation=45,ha="left")
ax_ion.tick_params(labelsize=18)

ax.legend(loc="upper right",fontsize=18,frameon=False) 
ax.set_ylim(bottom=0,top=6)

ax.tick_params(labelsize=18,direction="in",which="both",right=True,top=False)
ax.set_xlabel(r"$\boldsymbol{\mathrm{Charge\ to\ Mass\ Ratio}\ Z/A}$",fontsize=18)
ax.set_ylabel(r"$\boldsymbol{T_{\rm eff}}$ \textbf{[MK]}",fontsize=18)
ax.grid("on")

plt.savefig(fname="../../figs/ms/Teff_Atlas30.pdf",dpi=300,format="pdf",bbox_inches="tight")