IRSOL data taken during the Parker Solar Probe 4th Perihelion Campaign
In this data collection, we report context spectropolarimetric observations for the January 2020 Parker Solar Probe 4th Perihelion campaign carried out in the frequency range around 4227 Å (Ca I line), using the Zurich Imaging Polarimeter (ZIMPOL) at the Istituto Ricerche Solari Locarno (IRSOL) in Switzerland.
The radiation in the Ca I line core is sensitive to the thermodynamic properties of the mid chromosphere (about 1000 km) while the line wings provide information on deeper atmospheric layers. The neighboring lines (almost all iron lines) are photospheric.
In these measurements, some structures with magnetic activity can be observed in the considered resonance line as well in the neighboring lines. The presence of a longitudinal magnetic field is revealed by the typical Stokes V/I signatures produced by the Zeeman effect. At the solar limb, the Ca I 4227 Å line shows the scattering polarization signal with the largest amplitude in the visible spectral range. This scattering polarization Q/I profile shows a triplet-peak structure, typical of strong resonance lines, with a sharp peak in the line core and broad lobes in the wings. The spatial variation of the line-core Q/I peak, is mainly induced by the presence of magnetic field through the Hanle effect (see Stenflo 1982; Trujillo Bueno 2001; Landi Degl’Innocenti & Landolfi 2004). Also the spatial variation in the amplitude of the wing linear polarization signals is mainly of magnetic origin, but it is due to a different physical process, i.e. the magneto-optical effects (MO) (e.g. Alsina Ballester et al. 2018, Capozzi et al. 2020 https://arxiv.org/abs/2006.13653 ). MO effects induce a rotation of the plane of linear polarization as the radiation propagates in the solar atmosphere in the presence of an external longitudinal magnetic field.
Below we list our available data (Tx, Ty coordinates in arcsec are based on the magnetic footpoint predictions of https://whpi.hao.ucar.edu/whpi_campaign-cr2226.php).
Video tutorial:
A video tutorial about these data has been presented at the WHPI “Show and Tell” days occurred on 14th-15th September, 2020. To see the tutorial please follow this link.
Credit:
These data are provided under the Creative Commons License BY.
Please use following credit notice:
“IRSOL data taken during the Parker Solar Probe 4th Perihelion Campaign”
http://www.irsol.usi.ch/data-archive/4th-perihelion-campaign — CC BY IRSOL —
Setup
Location: IRSOL. Polarimeter: ZIMPOL3. Using the photo-elastic modulator (PEM), a glass plate was used to compensate for telescope linear polarization offset. Spectrograph was tuned to be centered around the Ca I 4227 Å line. See also https://www.irsol.usi.ch/research/instrumentation/
Flatfield measurements (taken regularly) and calibration data are obtained at disk center. Darks are automatically taken after each science measurement. These are used to reduce the reported observations.
Slit jaw camera context images are taken regularly (and manually) in H-alpha, with a plate scale of about 0.41 arcsec/pixel (will be provided, together with explications on how to use them, under request ).
Measurements
Date: 2020-01-23 (data to be provided under request)
Two observations per target
(HMI) Tx=-447.68 , Ty=856.31
(GONG) Tx=-687.81 , Ty=846.80
Dates: 2020-01-26 and 2020-01-27 (data to be provided under request)
Observation of the sunspot
Date: 2020-01-31
(GONG) Tx=-190.39, Ty=777.66
(GONG) Tx=522, Ty=-722, three scans (we reduced the scan with the best seeing conditions, which is provided below, whereas the other two will be provided under request).
Reduced observation
2020-01-31 between UTC16:30 and UTC17:42
Measurement sequence: 3×60 images per linear Stokes parameter, integration time 1 sec per image, due to the PEM properties, Q and U can not be measured simultaneously, the seeing was good.
Targets: close to the estimation based on GONG data at Tx=522, Ty=-722, the slit is oriented parallel to the limb-tangent at latitude 215° (in the S-W quadrant). A small region of about 12 arcsec was observed with 5 scan steps.
Data reduction steps:
- Dark current subtraction.
- Polarimetric calibration.
- Application of a flatfield to intensity image.
- Subtraction of the mean given by a disk center measurement.
- V to Q and U cross-talk corrected by a Poincare rotation (7.5 degrees and -5 degrees), where the axis which is not measured by the PEM is kept fixed. The cross-talk is produced by the folding mirrors M3 and M4 that are located before the calibration unit.
- Wavelength calibration with the Kitt Peak Atlas data. The spatial scale was estimated to be about 1.4 arcsec/pixel.
- Correction of the solar curvature (if necessary), which aligns the maximum intensity with zero stokes U.
Data may still suffer from:
- Temporally persistent fringes in Stokes I, since the flatfield and the science data are taken at different times and an image derotator is put between modulator and camera.
- Cross-talk between Q and U (probably very small).
- Unknown absolute zero polarization level. It is only estimated at reduction step 4.
- Wavelength calibration does not account for Doppler shifts by solar rotation.
- U is not measured simultaneously with I, Q and V.
Measurements for download
limb distance 88 arcsec: https://irsol.staging8.usi.ch/storage/app/media/4227_m14_final.dat
limb distance 85 arcsec: https://irsol.staging8.usi.ch/storage/app/media/4227_m15_final.dat
limb distance 82 arcsec: https://irsol.staging8.usi.ch/storage/app/media/4227_m16_final.dat
limb distance 79 arcsec: https://irsol.staging8.usi.ch/storage/app/media/4227_m17_final.dat
limb distance 76 arcsec: https://irsol.staging8.usi.ch/storage/app/media/4227_m18_final.dat
Data structure
The data is saved as IDL save files, where the “info” keyword contains the header information (with data reduction steps applied, spatial and spectral scales, etc.). The Stokes (I, Q, U, V) spectrograph images are stored in “si,sq,su,sv”. To read the data with python instead of IDL, use
from scipy.io import readsav
import numpy as np
s = readsav(file_name, verbose=False,python_dict=True)
info = s[‘info’]
z=np.array( tuple(s.values()) )
si=z[0]
sq=z[1]
su=z[2]
sv=z[3]
