Solar Spectroscopy
with Hinode/EIS and IRIS
Magnus Woods
With thanks to David Long
4th Solarnet Workshop, Mullard Space Science Laboratory
UCL-Mullard Space Science Laboratory
magnus.woods.15@ucl.ac.uk
Why we make observations
•
•
•
Observations are measurements
• Fit existing models/theories/interpretations
• Provoke new models/theories/interpretations
Models/theories/simulations no use in isolation
Need to be able to understand what we’re seeing
• Account for errors/uncertainties
• Determine densities/temperatures
• Different processes produce different signatures using different
emission lines
Remote sensing
•
•
•
We obtain information about the object by looking at it
Study the different types of radiation emitted by the Sun and associated phenomena
But..
• Earths atmosphere only transmits radiation in radio & visible bands (with distortion)
• Need to go to space to observe sub-mm, UV, IR, X-rays & γ-rays
Ground-based observing
•
Pros:
• Easy to do
• Low cost
• Can get lots of data at very high
cadence & resolution relatively easily
Observers: Michiel van Noort and Luc
Rouppe van der Voort, Oslo.
Images taken using the Swedish Solar
Telescope, courtesy of The Royal Swedish
Academy of Sciences & The Institute for
Solar Physics
Ground-based observing
•
Cons:
•
Weather-permitting
•
~8 hours observing/day
•
Atmospheric effects
•
Limited spectral coverage (radio,
visible, near IR, near UV)
•
Telescope owners/observers tend
to keep the data (starting to
change)
•
No consensus on data
management/storage
•
Other assorted artefacts..
Kanzelhöhe Observatory
Balloon & Rocket flights
•
•
Pros:
•
Above (most of) the atmosphere
•
Wider wavelength range available
•
Relatively cheap
•
Good for testing new equipment/instruments
Cons:
•
Very short duration
•
~Minutes for rockets, ~Days/weeks
for Balloons
•
Data recovery can be tricky
•
Weather dependent
•
No responsiveness to solar conditions
•
Size/mass constraints
Space-based observing
•
•
Pros:
•
Can observe all parts of the spectrum
•
Can get ~24/7 coverage
•
No atmospheric effects
•
No weather effects
Cons:
•
Expensive!
•
$10,000/lb (~$4536/kg)
•
Size & mass constraints
•
Cannot be fixed once launched (with the exception of Hubble..)
•
Limited data downlink (with the exception of SDO)
•
Rigorous testing & quality control
Spectrograms
•
Separate radiation out by wavelength
• Spatial (x,y) and spectral (λ) information in one image
Overlappograms!
•
Spreading wavelengths out means spatial & spectral information gets confused
•
Detectors still 2-D
•
How to get x, y, λ into one image..
Spectrometers
•
•
•
•
Take a 1-D slice of the image
Disperse that in the direction perpendicular to the slit
Remove confusion in direction of dispersion
But, it can be difficult to identify where we’re observing…
•
Operated by JAXA, in collaboration with UK(MSSL) and United States
•
Launched in 2006.
•
Three Science instruments:
SOT - Solar Optical Telescope
XRT - X-ray Telescope
EIS - Extreme Ultraviolet Imaging Spectrometer
Extreme ultraviolet Imaging Spectrometer (Hinode/EIS)
Extreme ultraviolet Imaging Spectrometer (Hinode/EIS)
Extreme ultraviolet Imaging Spectrometer (Hinode/EIS)
•
•
•
•
1” & 2” slits or 40” & 266” slots, y ~ 512”
λ ~ 170 - 210Å & 250 - 290Å
1” per pixel (spatial)
~22mÅ per pixel (spectral)
Creating an image
Temporal variation
•
Can see temporal variation of corona at different temperatures
EIS Spectrum
What else can we do?
•
Fit the intensity in each pixel using a Gaussian
•
•
Assumes thermal (Maxwellian) distribution
of particle velocities along the line-of-sight
•
A = peak intensity
•
λ0 = rest wavelength
•
σ = line width
ξ = thermal width
•
•
Mechanical motion not associated with
Brownian motion
May be combination of wave or nonMaxwellian motion
What else can we do?
•
•
•
If two different electron transitions for same ion have
common lower level
• May be able to estimate density
Take ratio of line intensities & compare to theoretical values
Need to choose ratio to match density range!
Young et al., 2007
Doppler velocity
•
•
•
•
Most of the emission here
comes from the strong
emission line (red)
Also a weaker component
of the same line Dopplershifter to shorter
wavelengths
Can work out the difference
between the two fits &
derive the Doppler velocity
of the line
Repeat this for each pixel
Doppler velocity
•
•
Doppler velocity very useful for getting three-dimensional plasma variation
• Waves, flows, filament eruptions etc.
Harra et al. (2011)
Non-Thermal velocity
• The width of a spectral line
can be described as:
Observed Width = Thermal Width + Instrumental Width
…..........................+ Non-thermal Width
• Non-thermal velocities (line
width) can possibly indicate
activity in the plasma, in the
absence of a significant
intensity increase.
1.5×104
1.0×104
5.0×103
0
192.25
192.30
192.35
192.40
Wavelenth (Angstrom)’
192.45
192.50
Where to get the data?
•
•
Search for EIS data on MSSL/EIS website:
http://solarb.mssl.ucl.ac.uk/SolarB/Solar-B.jsp
Want to use the data archive search tool:
Set dates/times for your search
When you’re happy, submit!
Selecting and downloading data
•
After submitting your query, a list of all available data in your time range will
be given.
Use quicklook thumbnails to check
which lines are in observation
Selecting and downloading data
•
After submitting your query, a list of all available data in your time range will
be given.
Use quicklook thumbnails to check
which lines are in observation
Selecting and downloading data
•
After submitting your query, a list of all available data in your time range will
be given.
Use quicklook thumbnails to check
which lines are in observation
Right click to download
So you have the data, now what?
•
The data you have downloaded is level-0. This is not science ready!
•
We need to calibrate it!
•
•
In SSW we will make use of the eis_prep routine:
Flags saturated, warm/hot and dusty pixels
Dark Current and Comic Ray correction
IDL> eis_prep, filename, /quiet, /retain, /default, /save
•
Can run multiple L0 files through this
•
Outputs are calibrated L1 files and corresponding error files.
•
We can work with these!
X-FILES
•
We can quickly check our data and access the header information using
IDL> xfiles
X-FILES
•
Control search dates
We can quickly check our data and access the header information using
IDL> xfiles
Set file type
Set desired directory
Choose desired file from list
X-FILES
•
Control search dates
We can quickly check our data and access the header information using
IDL> xfiles
Set file type
Set desired directory
Choose desired file from list
X-FILES
•
We can quickly check our data and access the header information using
IDL> xfiles
Fitting(1)
•
We first need to load in the data in the desired wavelength window:
IDL> windata=eis_getwindata(‘Path_to_file/your_file.fits’, line, /refill)
•
•
Line is the wavelength of the desired spectral window. Use xfiles to show
available windows.
Sometimes the fit windows can be too large and contain multiple lines. To
select the part of the to fit, we can use:
IDL> eis_wvl_select, windata, wvl_select
Fitting(1)
•
We first need to load in the data in the desired wavelength window:
IDL> windata=eis_getwindata(‘Path_to_file/your_file.fits’, line, /refill)
•
•
Line is the wavelength of the desired spectral window. Use xfiles to show
available windows.
Sometimes the fit windows can be too large and contain multiple lines. To
select the part of the to fit, we can use:
IDL> eis_wvl_select, windata, wvl_select
Fitting(2)
•
We now have the windata and wvl_select structures.
•
We will fit the data using the routine:
IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select
•
•
This gives us the fitdata structure, containing the results of the gaussian
fitting.
We can view the results of the fitting using:
IDL>eis_fit_viewer, windata, fitdata
Fitting(2)
•
•
We now have the windata and wvl_select structures.
REMEMBER: Always be careful of how you have defined
your reference wavelength!
We will fit the data using the routine:
IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select
•
•
This gives us the fitdata structure, containing the results of the gaussian
fitting.
We can view the results of the fitting using:
IDL>eis_fit_viewer, windata, fitdata
Corrections
As with any instrument, EIS data is subject to several instrumental
effects that must be corrected for. These are:
•
Grating tilt: Small misalignment of CCD and grating axes. offset < 1 pixel
•
Slit tilt: Slits are perpendicular to the CCD dispersion axes.
•
Orbital variation: Line position is shifted over spacecraft orbit due to thermal
•
changes
Detector offset: Offset of ~15-20 pixels is present in the Y-direction between the
SW and LW detectors
Advanced activities
•
•
For most cases we want to extract the data displayed by the fit viewer and
carry out further analysis upon it.
To extract the fits for intensity, Doppler velocity and line-widths use:
IDL>i_map=eis_get_fitdata(fitdata, /int, /map, /quiet)
IDL>v_map=eis_get_fitdata(fitdata, /vel, /map, /quiet)
IDL>w_map=eis_get_fitdata(fitdata, /wid, /map, /quiet)
•
If non thermal velocities are wanted, use the routine:
vnt=eis_width2velocity(‘Fe’,’XII’, line, linewidth_data, ti_max=t)
•
IMPORTANT: This calculation must be done per pixel eg. in a loop!
Advanced Activites (2)
•
Take care when calculating Vnt for Fe XII 195Å. There is an instrumental
line that need to be accounted for using:
inst_wid=eis_slit_width(ypix, slit_ind=slit_ind)
•
•
Here, slit_ind is a parameter that denotes which slit the observation uses.
It has value 0 for 1’’ slit and 2 for 2’’ slit.
Then to calculate Vnt:
vnt=eis_width2velocity(‘Fe’, ‘XII’, line, linewidth_data, instr_fwhm=inst_wid+0.0026,
therm_fwhm=therm_fwhm, thermal=thermal, ti_max=ti_max)
EIS Practical
1.
Using the described method, prep the l0 eis data you have been given.
2.
Using the methods described, load in and fit the file during the flare cclass flare. Use the Fe XII 192Å line.
3.
Using the same methods load in and fit the file containing the pre-flare
data. Again use the Fe XII 192Å line.
EIS Practical: Results
EIS Practical: Results
Interface Region Imaging Spectrometer (IRIS)
•
•
•
Mostly measuring optically thick lines in chromosphere and transition region
Mg II h & k, Si IV, C III, O I, Fe XII
λ ~ 1331.56Å - 1358.40Å & 1390.00Å - 1406.79Å (FUV), 2782.56Å - 2833.89Å (NUV)
De Pontieu et al. (2014)
IRIS observations
•
~0.3” (FUV), 0.4” (NUV) per pixel (spatial)
•
~26Å (FUV) - 53Å (NUV) per pixel (spectral)
•
Closest to AIA 304Å in observations
IRIS observations
Accessing IRIS data
•
•
•
You can search for IRIS data using a handy tool created by LMSAL:
http://www.lmsal.com/heksearch/
This can also search for corresponding Hinode data. Very good when
looking for joint observations!
Once downloaded, quicklook files using:
IDL> iris_xfiles
Accessing IRIS data
•
•
•
You can search for IRIS data using a handy tool created by LMSAL:
http://www.lmsal.com/heksearch/
This can also search for corresponding Hinode data. Very good when
looking for joint observations!
Once downloaded, quicklook
using:
Set tickfiles
box to
display
desired Hinode data
IDL> iris_xfiles
Need to click search after
changing any parameters
Accessing IRIS data
•
•
•
You can search for IRIS data using a handy tool created by LMSAL:
http://www.lmsal.com/heksearch/
This can also search for corresponding Hinode data. Very good when
looking for joint observations!
Once downloaded, quicklook files using:
IDL> iris_xfiles
Data analysis
•
•
IRIS data is downloaded as level 2 data, pre-calibrated
This means we don’t have to run it through a prepping routine: It’s already
science ready!
•
Can carry out fitting using two possible routes:
•
Route 1: Very similar to the method we used for EIS analysis
•
Route 2: Object orientated method, which is the recommended method of
the IRIS team.
Route 1
•
•
Written by Peter Young, these methods follow the syntax of and in some
cases use the same code as the EIS Routines
Load data:
IDL>windata=iris_getwindata(file,line)
BEWARE! This method has a known bug that should be fixed, but you need to be sure you
are using the most up to date version!!!
•
Fitting the data:
IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select
Route 2 (IRIS approved)
•
This method is described fully in ITN26 (https://iris.lmsal.com/itn26/quickstart.html)
•
It is actually the method underpinning Route 1.
IDL> filename = ‘my_iris_file.fits’
IDL> d = iris_obj(filename)
•
Then to view the available spectral lines:
IDL> d ->show_lines
•
To then select and load in the data use:
IDL> wave = d -> getlam(4)
IDL> data = d -> getvar(4, /load)
Route 2 (IRIS approved)
•
This method is described fully in ITN26 (https://iris.lmsal.com/itn26/quickstart.html)
•
It is actually the method underpinning Route 1.
IDL> filename = ‘my_iris_file.fits’
IDL> d = iris_obj(filename)
•
Then to view the available spectral lines:
IDL> d ->show_lines
•
To then select and load in the data use:
IDL> wave = d -> getlam(4)
IDL> data = d -> getvar(4, /load)
Further Analysis
•
•
•
With data now loaded, analysis of IRIS data can be carried out.
The IRIS software tree contains several routines that can be used in the
further analysis of the data.
These codes can be found in ITN26’s ‘Useful codes’ section:
https://iris.lmsal.com/itn26/codes.html#codes-lev2
IRIS Practical
•
•
Using the method described in Route 2, load the Si IV 1402Å and plot it
Using Route 1, again load the Si IV data. Then using the routines
described, fit the data and view the results.