Identifying stars in a STEREO/SECCHI COR2 coronagraph image

Since the field of view in SECCHI COR2 images can span 2 to 15 solar radii, we often observe stars in these image data. In this example, we will use the Astroquery package to query the VizieR star catalog for stars observed by the Gaia satellite within the SECCHI COR2 field of view. Then we will use the coordinates framework in SunPy and AstroPy to transform the coordinates returned by VizieR into SECCHI COR2 image coordinates. As a bonus, we’ll also identify Mars.”

This requires the installation of the astroquery package, which can be installed on top of the existing sunpy conda environment: conda install -c astropy astroquery and an active internet connection.

import hvpy
import matplotlib.pyplot as plt
from astroquery.vizier import Vizier

import astropy.units as u
from astropy.coordinates import Distance, SkyCoord
from astropy.time import Time

import sunpy.map
from sunpy.coordinates import get_body_heliographic_stonyhurst
from sunpy.time import parse_time

Let’s download a STEREO-A SECCHI COR2 image from Helioviewer.org which provide pre-processed images and load it into a Map.

cor2_file = hvpy.save_file(hvpy.getJP2Image(parse_time('2014/05/15 07:54').datetime, hvpy.DataSource.COR2_A.value), "COR2.JPEG2000")
cor2_map = sunpy.map.Map(cor2_file)

To efficiently search the star field, we need to know what stars are near the Sun as observed by STEREO. We need the vector that points from STEREO to the Sun. The location of STEREO in HCRS provides the Sun-to-STEREO vector.

sun_to_stereo = cor2_map.observer_coordinate.transform_to('hcrs')

INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. [sunpy.map.mapbase]

We next reflect the vector to get our search vector which points from STEREO to the Sun.

stereo_to_sun = SkyCoord(-sun_to_stereo.spherical, obstime=sun_to_stereo.obstime, frame='hcrs')

Let’s look up bright stars using the Vizier search capability provided by astroquery. We will search the GAIA2 star catalog for stars with magnitude brighter than 7.

vv = Vizier(columns=['**'], row_limit=-1, column_filters={'Gmag': '<7'}, timeout=1200)
vv.ROW_LIMIT = -1
result = vv.query_region(stereo_to_sun, radius=4 * u.deg, catalog='I/345/gaia2')

Let’s see how many stars we’ve found.

print(len(result[0]))

23

Now we load all stars into an array coordinate. The reference epoch for the star positions is J2015.5,so we update these positions to the date of the COR2 observation using astropy.coordinates.SkyCoord.apply_space_motion().

tbl_crds = SkyCoord(ra=result[0]['RA_ICRS'],
                    dec=result[0]['DE_ICRS'],
                    distance=Distance(parallax=u.Quantity(result[0]['Plx'])),
                    pm_ra_cosdec=result[0]['pmRA'],
                    pm_dec=result[0]['pmDE'],
                    radial_velocity=result[0]['RV'],
                    frame='icrs',
                    obstime=Time(result[0]['Epoch'], format='jyear'))
tbl_crds = tbl_crds.apply_space_motion(new_obstime=cor2_map.date)

One of the bright features is actually Mars, so let’s also get that coordinate.

mars = get_body_heliographic_stonyhurst('mars', cor2_map.date, observer=cor2_map.observer_coordinate)

INFO: Apparent body location accounts for 1269.95 seconds of light travel time [sunpy.coordinates.ephemeris]

Let’s plot the results. The coordinates will be transformed automatically when plotted using plot_coord().

fig = plt.figure()
ax = fig.add_subplot(projection=cor2_map)

# Let's tweak the axis to show in degrees instead of arcsec
lon, lat = ax.coords
lon.set_major_formatter('d.dd')
lat.set_major_formatter('d.dd')

cor2_map.plot(axes=ax, vmin=0, vmax=600)
cor2_map.draw_limb(axes=ax)

# Plot the position of Mars
ax.plot_coord(mars, 's', color='white', fillstyle='none', markersize=12, label='Mars')
# Plot all of the stars
ax.plot_coord(tbl_crds, 'o', color='white', fillstyle='none')
ax.legend()

plt.show()

INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. [sunpy.map.mapbase]
INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. [sunpy.map.mapbase]