Atmospheric emission and absorption =================================== .. autoclass:: qmostetc.Atmosphere :members: :undoc-members: Default sky model ----------------- The sky model is based on the ESO sky model, which interpolates the spectra individually for * scattered moonlight (dependent on moon phase and airmass), * atmospheric emission lines and airglow (dependent on airmass), * zodacial light emission and scatteres starlight (dependent on airmass) Additionally, the atmospheric transmission is computed. Data format ........... All data are stored in the file ``qmostetc/data/skymodel.fits``, which is a FITS file with three tables: **MAIN_SPECTRUM** High-res sky model with standard parameters. Columns: * **wavelength**: Wavelength (0.01 nm steps) [nm] * **trans**: Zenith transmission spectrum * **flux_moon**: Moon light flux for grey light [ph / (arcsec² m² nm s)] * **flux_ext**: Flux of scattered starlight and zodacial light [ph / (arcsec² m² nm s)] * **flux_air**: Flux of atmospheric emission lines and air glow continuum [ph / (arcsec² m² nm s)] The emission fluxes are without the transmission spectrum **MOON_SUN_SEP** Moon flux parameters depending on the moon-sun separation. Columns: * **wavelength**: Wavelength (10 nm steps) [nm] * **moon_sun_sep**: Moon-Sun separation [deg] * **coeff**: Coefficient for the moon flux **AIRMASS_PROP** Wavelength dependent coefficients for selected airmasses. These numbers are to be multiplied to the spectra. Columns: * **wavelength**: Wavelength (10 nm steps) [nm] * **airmass**: Airmass * **moon**: Coefficient for the moon flux * **ext**: Coefficient for the flux of scattered starlight and zodacial light * **air**: coefficient for the flux of atmospheric emission lines and air glow continuum The data file can be recreated with the **tools/get_skymodel** program. Scattered moonlight ................... The scattered moonlight is based on a gray moonlight spectrum with airmass 1.05 stored in the **flux_moon** column of the **MAIN_SPECTRUM** table. The angular moon-target separation is 45°, the moon altitude 45°. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable main_tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='MAIN_SPECTRUM') plt.plot(main_tbl['wavelength'], main_tbl['flux_moon'], label='airmass: 1.05, moon: gray') plt.ylim(0, None) plt.xlim(350., 970.) plt.title("Scattered moonlight emission") plt.xlabel(f"Wavelength [{main_tbl['wavelength'].unit}]") plt.ylabel(f"Emission [{main_tbl['flux_moon'].unit}]") plt.legend() plt.show() This spectrum is multiplied with an interpolated wavelength dependent factor from the **coeff** column in the **MOON_SUN_SEP** table for the given moon-sun separation angle **moon_sun_sep**: .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='MOON_SUN_SEP') for sep in sorted(set(tbl['moon_sun_sep']), reverse=True): tbl0 = tbl[tbl['moon_sun_sep'] == sep] br = {35*u.deg: 'dark', 90*u.deg: 'grey', 145*u.deg: 'bright', 175*u.deg: 'superbright'} if sep in br: label = f"{br[sep]} ({sep.value:.0f}°)" color = None else: label = None color = 'lightgrey' plt.plot(tbl0['wavelength'], tbl0['coeff'], label=label, color=color) plt.ylim(0, None) plt.xlim(350., 970.) plt.title("Moon brightness dependent factor") plt.xlabel(f"Wavelength [{tbl['wavelength'].unit}]") plt.ylabel(f"Factor") plt.legend() plt.show() Second the spectrum is multiplied by an airmass and wavelength dependent factor taken from the **moon** column of the **AIRMASS_PROP** table: .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='AIRMASS_PROP') for airmass in sorted(set(tbl['airmass']), reverse=True): tbl0 = tbl[tbl['airmass'] == airmass] plt.plot(tbl0['wavelength'], tbl0['moon'], label=f'airmass: {airmass}') plt.title("Airmass dependent factor") plt.ylim(0.7, 3.9) plt.xlim(350., 970.) plt.xlabel(f"Wavelength [{tbl['wavelength'].unit}]") plt.ylabel(f"Factor") plt.legend() plt.show() And finally, the spectrum is multiplied by the atmospheric absorption spectrum for the given airmass. Atmospheric light emission .......................... The atmospheric light emission (atmospheric emission lines and airglow) is based on the spectrum with airmass 1.05 stored in the **flux_air** column of the **MAIN_SPECTRUM** table. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable main_tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='MAIN_SPECTRUM') plt.plot(main_tbl['wavelength'], main_tbl['flux_air'], label='airmass: 1.05') plt.xlim(350., 970.) plt.title("Atmospheric light emission") plt.yscale('log') plt.xlabel(f"Wavelength [{main_tbl['wavelength'].unit}]") plt.ylabel(f"Emission [{main_tbl['flux_air'].unit}]") plt.legend() plt.show() The spectrum is multiplied by an airmass and wavelength dependent factor taken from the **air** column of the **AIRMASS_PROP** table: .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='AIRMASS_PROP') for airmass in sorted(set(tbl['airmass']), reverse=True): tbl0 = tbl[tbl['airmass'] == airmass] plt.plot(tbl0['wavelength'], tbl0['air'], label=f'airmass: {airmass}') plt.xlim(350., 970.) plt.ylim(None, 2.8) plt.title("Airmass dependent factor") plt.xlabel(f"Wavelength [{tbl['wavelength'].unit}]") plt.ylabel(f"Factor") plt.legend() plt.show() And finally, the spectrum is multiplied by the atmospheric absorption spectrum for the given airmass. Zodacial light emission ....................... The zodacial light emission and scatteres starlight spectrum is based on the spectrum with airmass 1.05 stored in the **flux_ext** column of the **MAIN_SPECTRUM** table. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable main_tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='MAIN_SPECTRUM') plt.plot(main_tbl['wavelength'], main_tbl['flux_ext'], label='airmass: 1.05') plt.xlim(350., 970.) plt.ylim(0., None) plt.title("Zodacial light emission") plt.xlabel(f"Wavelength [{main_tbl['wavelength'].unit}]") plt.ylabel(f"Emission [{main_tbl['flux_ext'].unit}]") plt.legend() plt.show() The spectrum is multiplied by an airmass and wavelength dependent factor taken from the **ext** column of the **AIRMASS_PROP** table: .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='AIRMASS_PROP') for airmass in sorted(set(tbl['airmass']), reverse=True): tbl0 = tbl[tbl['airmass'] == airmass] plt.plot(tbl0['wavelength'], tbl0['ext'], label=f'airmass: {airmass}') plt.ylim(0.9, 2.4) plt.xlim(350., 970.) plt.title("Airmass dependent factor") plt.xlabel(f"Wavelength [{tbl['wavelength'].unit}]") plt.ylabel(f"Factor") plt.legend() plt.show() And finally, the spectrum is multiplied by the atmospheric absorption spectrum for the given airmass. Atmospheric light transmission .............................. The atmospheric light transmission is based on the spectrum with airmass 1.05 stored in the **trans** column of the **MAIN_SPECTRUM** table. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable main_tbl = QTable.read('qmostetc/data/skymodel.fits', hdu='MAIN_SPECTRUM') plt.plot(main_tbl['wavelength'], main_tbl['trans'], label='airmass: 1.05') plt.xlim(350., 970.) plt.ylim(0., None) plt.title("Atmospheric light transmission") plt.xlabel(f"Wavelength [{main_tbl['wavelength'].unit}]") plt.ylabel(f"Transmission") plt.legend() plt.show() The wavelength dependent transmission at a specific airmass :math:`a` is then taken by the formula :math:`t(\lambda) = t_0(\lambda)^{a - 1.05}`. Twilight sky model ------------------ The twilight sky model is taken from data generated with the `rubin_sim`_ package. To save space and computation time, only one hi-res spectrum is stored. An additional low-res coefficient grid provides the dependencies on sun altitude and airmass. Note that all values for sun altitudes above -12° were extrapolated by rubin_sim, so they should be taken with care. .. _rubin_sim: https://github.com/lsst/rubin_sim The twilight data are stored in the file ``qmostetc/data/twilightmodel.fits``, which is a FITS file with two tables: **MAIN_SPECTRUM** High-res sky model with standard parameters. Columns: * **wavelength**: Wavelength (0.2 nm steps) [nm] * **twilight**: Twilight zenith flux for sun altitude -12° The non-twilight components were subtracted from the spectrum. **SUNALT_PROP** Wavelength dependent coefficients for selected airmasses and sun altitudes. These numbers are relative magnitudes to be applied to the spectra. Columns: * **wavelength**: Wavelength (10 nm steps) [nm] * **airmass**: Airmass (1…2.8 in steps of 0.1) * **sunAlt**: Sun altitude [deg] (-19°…-1° in steps of 1°) * **twilight**: Magnitude to be applied to the spectral flux [mag] The values spawn a rectangular grid. If the rubin_sim Python package is installed, the data file can be recreated with the **tools/get_twilight** program. Twilight base spectrum ...................... The twilight base spectrum is based on the spectrum with airmass 1.0 and sun altitude -12° stored in the **twilight** column of the **MAIN_SPECTRUM** table. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable main_tbl = QTable.read('qmostetc/data/twilightmodel.fits', hdu='MAIN_SPECTRUM') plt.plot(main_tbl['wavelength'], main_tbl['twilight'], label='airmass: 1, sun altitude: -12°') plt.xlim(350., 970.) plt.ylim(0., None) plt.title("Twilight spectrum") plt.xlabel(f"Wavelength [{main_tbl['wavelength'].unit}]") plt.ylabel(f"Emission [{main_tbl['twilight'].unit}]") plt.legend() plt.show() Twilight magnitude coefficients ............................... The coefficients scale the base spectrum dependent on airmass and sun altitude. They are stored as magnitudes in the **twilight** column of the **SUNALT_PROP** table. .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/twilightmodel.fits', hdu='SUNALT_PROP') tbl = tbl[tbl['airmass'] == 1.0] for alt in sorted(set(tbl['sunAlt'])): atbl = tbl[tbl['sunAlt'] == alt] if round(alt.value) in (-2, -6, -12, -18): plt.plot(atbl['wavelength'], atbl['twilight'], label=f'sun altitude: {alt:.0f}') else: plt.plot(atbl['wavelength'], atbl['twilight'], color='lightgrey') plt.xlim(350., 970.) plt.title("Twilight coefficient, airmass: 1") plt.xlabel(f"Wavelength [{atbl['wavelength'].unit}]") plt.ylabel(f"Coefficient [{atbl['twilight'].unit}]") plt.legend(loc='right') plt.show() .. plot:: import matplotlib.pyplot as plt import astropy.units as u from astropy.table import QTable tbl = QTable.read('qmostetc/data/twilightmodel.fits', hdu='SUNALT_PROP') tbl = tbl[np.abs(tbl['sunAlt']+12*u.deg) < 0.1*u.deg] for airmass in sorted(set(tbl['airmass']))[::2]: atbl = tbl[tbl['airmass'] == airmass] plt.plot(atbl['wavelength'], atbl['twilight'], label=f'airmass: {airmass:.1f}') plt.xlim(350., 970.) plt.title("Twilight coefficient, sun altitude: -12°") plt.xlabel(f"Wavelength [{atbl['wavelength'].unit}]") plt.ylabel(f"Coefficient [{atbl['twilight'].unit}]") plt.legend(loc='right') plt.show()