Project Summary. I will focus on understanding energy release in solar flares magnetic reconnection, which refers
to magnetic field lines changing connectivities and thus bringing the
magnetic field configuration to a lower energy state. The major approach will be analyzing satellite observations (such as SDO observations) of a solar flare. From the analysis, we will derive how much magnetic flux is
reconnected, a key parameter in reconnection physics, and we will also
investigate the temporal and spatial distribution of reconnection events
during a flare, and use that information to infer how much energy
is released during reconnection to heat flare loops, which then radiates
prominently at EUV and X-ray temperatures.
For my introductory idl project I was given a UV image of a solar flare and a magnetogram of the flare region together with their coordinate information, which includes the physical size of a pixel, the field of view, and the centre coordinates of the images.
I then tried to figure out how to co-align these two images(doing math by hand). In IDL I tvscl the UV image and then contour the co-aligned magnetogram on top of the UV image.
The two images to begin with were:
Using the number of arcsecs per pixal I scaled the magnetogram to have the same number or arcsecs per pixal as the UV image.
I then used the relative distances of the two images from the sun's centre, in arcsecs, to coallign them. However, this did not quite work correctly as I used negatives in my code to translate them as they did not completely overlap which produced a slight error.
To fix this I cut of the section of the contour magnetogram not overlapping the UV image and produced this better co-allignment:
With the images coaligned I then went on to find pixels that are located in positive and negative magnetic field,respectively. Then Sum up all the brightness (counts) in positive region and those in negative region.
I summed the counts for more than 10%, or 30%, or 50% of the maximum counts in the image.
The code I used for this was:
new=congrid(smap.data, 101*1.98564/0.5 , 101*1.98654/0.5)
ndata=data(0: -129.1648+400, 19.76: 19.76+400)
newnew=new(129 :400, 0 :400)
ndata=data(0: -129.1648+400, 19.76: 19.76+400)
contour,newnew, pos=[0, 19.76, 271.83552, 420.76], /noerase, xst=1, yst=1, /dev, lev=[-100,100]
for k=0,2 do begin $
s2=where(newnew lt 0 and data gt ((((2*k)+1)/10.)*max(data))) & $
s1=where(newnew gt 0 and data gt ((((2*k)+1)/10.)*max(data))) & $
print, total(data(s2)), total(data(s1)) & $
The results I obtained were:
And in each case you can see that the total counts in the two kernels are not equal.
I have been told I will be using a more advanced program that follows a similar idea to this in my actual research project so it was good to get a feel for how it worked.
Date: 06 June
Today I began work on my actual project.
To begin, I am looking at a flare from March 7 2011 which was an M1.7 classification . I used data of images already co-aligned with each other and which are also already 'congrided' to a smaller size.
I will be using UV observations to identify pixels that are brightned during the flare, and use this information to derive reconnection flux and reconnection rate.
I summed data to derive the uv light curve (scaled by factor of 1e13 in graph). The UV lightcurve gives the number read from the SDO (Solar Dynamics Observatory) satellite CCDs (which is related to photons of a certain waveband received by the satellite detector). This is plotted in blue.
I then compared this with GOES soft X-rays plots which shows the radiation flux at two wavebands at 1AU from the sun. (In W/m^2)
I have plotted both of these against time in hours (UT).
From this we can see the correlation, which supports the principle that magnetic flux is conserved along the reconnecting field line, so that energy released from reconnection is transported down the fieldlines to the lower atmosphere and gives off radiation signatures.
The 'GOES'data also allows me to plot the tempr (in MK) for the time period of the flare.
Date: 07 June
I also made a movie of 325 images to show the flare.
I have now used the program which produces the total flux reconnection and reconnection rates for the positive magnetic field and then for the negative. It uses a system of cuts in which it takes values greater than some cut*background.This is to identify which pixals are flaring pixals. I then plotted the total reconnection flux (in Maxwells) against time (in minutes) shown in the first graph for both positive (in white) and negative (in colour) magnetic field.
Below I have also plotted the reconnection rates (positive and negative) in Maxwell per second
Neither rates of flux or the totals of flux are equal for positive and negative but appear to follow same trends.
I then tried to scale all the graphs onto one:
bright blue - UV light curve - scaled by 1e10
white and red - GOES radiation - scaled by 1e22 and 1e23 respectively
turquoise -reconnection rate for neg B
light green - reconnection rate for pos B
Here you can see correlation, though the GOES curves which measure radiation have maximum slightly later. My mentor Jiong was initially concerned with the dip in the rate of reconnection, that it may have been artificial, but now seeing the comparison with the GOES curves and UV lightcurve which both also show a dip at the same time it is more likely that it is a real feature and we are seeing two stages of reconnection/energy release in the flare.
Date: 13-15 June
I have spent the last few days going over a paper, called 'Highly Efficient modeling of dynamic coronal loops' by Klimchuk and Patsourakos (2008), that will be relevent to my project. The paper descibes a 0D model called EBTEL, (Enthalpy based thermal evolution of loops). This model describes the evolution of the average temperature, pressure and density along a strand in a coronal loop. Where each loop is not treated as one object but as hundred of smaller strands to be modelled individually. As this is a 0D model and uses averages (Has 1 value of temp, pressure and density at any given time in simulation) it is able to calculate the strands evolution quickly while still showing good agreement with more complicated 1D models such as Cargill, but takes 4 orders of magnitude less time.
I have also been using the output from the previous programs I was using to look at pixels that are recognized as flaring pixels in the UV images. We assume that these pixels reflect where the flare lower atmosphere is heated, and therefore are where coronal loops are rooted.
To begin with I have been looking at the pixels classified as flaring pixals for cut 6 (the lowest cut) so where they are more than 6*background. I have found 1611 brightened pixals.
I can also show a movie of the pixels that are brightened with time for this cut: