Measuring spectral indices
- Consider pre-defined spectral indices to measure the depth of water, methane, ammonia, and silicates absorption features in mid-infrared spectra of ultracool objects.
- Define your own spectral indices to measure the strength of other spectral features of interest at any wavelength.
[1]:
import seda
import os
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter, AutoMinorLocator
from astropy.io import ascii
SEDA v0.5.6.dev3 package imported
Pre-defined spectral indices
Read the spectra
As an example, for silicate and water indices let’s read the Spitzer IRS spectrum for the L6 dwarf 2MASS J21481628+4003593 published in Looper et al. (2008) and reprocessed by Suárez & Metchev (2022).
This spectrum has a strong silicate absorption at 9.3 microns and prominent water absorption at 6.25 microns.
[2]:
# path to the seda package
path_seda = os.path.dirname(os.path.dirname(seda.__file__))
IRS = ascii.read(path_seda+'/docs/notebooks/data/2148+4003_IRS_spectrum.dat')
wl_2148 = IRS['wl(um)'] # in um
flux_2148 = IRS['flux(Jy)'] # in Jy
eflux_2148 = IRS['eflux(Jy)'] # in Jy
Plot the spectrum:
[3]:
fig, ax = plt.subplots()
plt.plot(wl_2148, flux_2148, label='Fluxes')
plt.plot(wl_2148, eflux_2148, label='Flux errors')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.grid(True, which='both', color='gainsboro', linewidth=0.5, alpha=0.5)
ax.legend()
plt.xlabel(r'$\lambda\ (\mu$m)', size=12)
plt.ylabel(r'$F_\nu$ (Jy)', size=12)
plt.title('Spitzer/IRS spectrum for 2MASS J21481628+4003593')
plt.show()
Silicate index
Measure the silicate index, show the results, and save the figure.
Look at the input parameters here or using the following command directly in your notebook:
help(seda.spectral_indices.silicate_index)
[4]:
out_silicate_index = seda.spectral_indices.silicate_index(wl_2148, flux_2148, eflux_2148,
plot=True, plot_save=True)
Extract relevant parameters from the output dictionary
[5]:
silicate_index = out_silicate_index['silicate_index'] # silicate index
esilicate_index = out_silicate_index['esilicate_index'] # silicate index error
print(f'silicate index = {round(silicate_index,2)}+-{round(esilicate_index,2)}')
silicate index = 1.44+-0.01
Water index
Measure the water index, show the results, and save the figure.
Look at the input parameters here.
[6]:
out_water_index = seda.spectral_indices.water_index(wl=wl_2148, flux=flux_2148, eflux=eflux_2148,
plot=True, plot_save=True)
Extract relevant parameters from the output dictionary
[7]:
water_index = out_water_index['water_index'] # water index
ewater_index = out_water_index['ewater_index'] # water index uncertainty
print(f'water index = {round(water_index,2)}+-{round(ewater_index,2)}')
water index = 1.08+-0.01
For methane and ammonia indices:
Read Spitzer IRS spectrum for the T8 dwarf 2MASS J04151954-0935066 published in Saumon et al. (2007) and reprocessed by Suárez & Metchev (2022).
This spectrum has strong absorptions by methane at 7.65 microns and ammonia at 6.25 microns.
[8]:
IRS = ascii.read(path_seda+'/docs/notebooks/data/0415-0935_IRS_spectrum.dat')
wl_0415 = IRS['wl(um)'] # in um
flux_0415 = IRS['flux(Jy)'] # in Jy
eflux_0415 = IRS['eflux(Jy)'] # in Jy
Plot the spectrum:
[9]:
fig, ax = plt.subplots()
plt.plot(wl_0415, flux_0415, label='Fluxes')
plt.plot(wl_0415, eflux_0415, label='Flux errors')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.grid(True, which='both', color='gainsboro', linewidth=0.5, alpha=0.5)
ax.legend()
plt.xlabel(r'$\lambda\ (\mu$m)', size=12)
plt.ylabel(r'$F_\nu$ (Jy)', size=12)
plt.title('Spitzer/IRS spectrum for 2MASS J04151954-0935066 ')
plt.show()
Methane index
Measure the methane index, show the results, and save the figure.
Look at the input parameters here.
[10]:
out_methane_index = seda.spectral_indices.methane_index(wl_0415, flux_0415, eflux_0415,
plot=True, plot_save=True)
Extract relevant parameters from the output dictionary
[11]:
methane_index = out_methane_index['methane_index'] # methane index
emethane_index = out_methane_index['emethane_index'] # methane index uncertainty
print(f'methane index = {round(methane_index,2)}+-{round(emethane_index,2)}')
methane index = 3.93+-0.29
Ammonia index
Measure the ammonia index, show the results, and save the figure.
Look at the input parameters here.
[12]:
out_ammonia_index = seda.spectral_indices.ammonia_index(wl_0415, flux_0415, eflux_0415,
plot=True, plot_save=True)
Plot the spectrum
User-defined index
Measure a user-defined index for CO:math:`_2`
Read the spectrum exbihiting the feature of interest.
As an example, let’s read the AKARI/IRC spectrum for the T8 dwarf 2MASS J04151954-0935066 in Sorahana & Yamamura (2012).
This spectrum has strong CH\(_4\) at 3.3 microns, CO at 4.6 microns, and CO\(_2\) at 4.2 microns.
Read the spectrum
Read the AKARI spectrum:
[13]:
AKARI = ascii.read(path_seda+'/docs/notebooks/data/0415-0935_AKARI_spectrum.dat')
wl_AKARI = AKARI['wl(um)'] # um
flux_AKARI = AKARI['flux(Jy)'] # Jy
eflux_AKARI = AKARI['eflux(Jy)'] # Jy
[14]:
fig, ax = plt.subplots()
plt.plot(wl_AKARI, flux_AKARI, label='Fluxes')
plt.plot(wl_AKARI, eflux_AKARI, label='Flux errors')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.grid(True, which='both', color='gainsboro', linewidth=0.5, alpha=0.5)
ax.legend()
plt.xlabel(r'$\lambda\ (\mu$m)', size=12)
plt.ylabel(r'$F_\nu$ (Jy)', size=12)
plt.title('AKARI spectrum for 2MASS J04151954-0935066')
plt.show()
Index for the CO2 absorption feature
Define the index parameters and measure the index.
Look at the input parameters here.
Index considering one continuum region
[15]:
# wavelength at the center of the feature
feature_wl = 4.29 # um
# wavelength window to obtain the mean flux at the feature
feature_window = 0.05 # um
# wavelength at the continuum region
continuum_wl = 4.15 # um
# wavelength window to obtain the mean flux at the continuum
continuum_window = 0.05 # um
# set a name for the index
index_name = 'CO2'
# measure the user-defined index
out_user_index = seda.spectral_indices.user_index(wl=wl_AKARI, flux=flux_AKARI, eflux=eflux_AKARI,
feature_wl=feature_wl, feature_window=feature_window,
continuum_wl=continuum_wl, continuum_window=continuum_window,
index_name=index_name,
plot=True, plot_save=True)
Index considering two continuum regions
[16]:
# wavelength at the center of the feature
feature_wl = 4.29 # um
# wavelength window to obtain the mean flux at the feature
feature_window = 0.05 # um
# wavelength at the short-wavelength continuum region
continuum_wl1 = 4.15 # um
# wavelength window to obtain the mean flux at the short-wavelength continuum
continuum_window1 = 0.05 # um
# wavelength at the long-wavelength continuum region
continuum_wl2 = 4.40 # um
# wavelength window to obtain the mean flux at the long-wavelength continuum
continuum_window2 = 0.05 # um
# set a name for the index
index_name = 'CO2'
# indicate the curve fit to the continuum regions
continuum_fit = 'exponential'
# indicate the option to estimate the continuum flux error
continuum_error = 'empirical'
# measure the user-defined index
out_user_index = seda.spectral_indices.user_index(wl=wl_AKARI, flux=flux_AKARI, eflux=eflux_AKARI,
feature_wl=feature_wl, feature_window=feature_window,
continuum_wl1=continuum_wl1,
continuum_window1=continuum_window1,
continuum_wl2=continuum_wl2,
continuum_window2=continuum_window2,
index_name=index_name, continuum_fit=continuum_fit,
continuum_error=continuum_error,plot=True, plot_save=True)
