A&A 492, L13-L16 (2008)
DOI: 10.1051/0004-6361:200810911
LETTER TO THE EDITOR
M. Bazarghan1,2 - H. Safari2,3,4 - D. E. Innes3 - E. Karami5 - S. K. Solanki3
1 - IUCAA, Post Bag 4, Ganeshkhind, Pune 411 007, India
2 - Institute for Advanced Studies in Basic Sciences, Zanjan,
Iran
3 - Max-Planck Institut für Sonnensystemforschung,
37191 Katlenburg-Lindau, Germany
4 - Department of Physics, Zanjan University, Zanjan, Iran
5 -
Department of Electronics Science, University of Pune, Pune 411007, India
Received 4 September 2008 / Accepted 22 October 2008
Abstract
Context. Nanoflares are small impulsive bursts of energy that blend with and possibly make up much of the solar background emission. Determining their frequency and energy input is central to understanding the heating of the solar corona. One method is to extrapolate the energy frequency distribution of larger individually observed flares to lower energies. Only if the power law exponent is greater than 2 is it considered possible that nanoflares contribute significantly to the energy input.
Aims. Time sequences of ultraviolet line radiances observed in the corona of an active region are modelled with the aim of determining the power law exponent of the nanoflare energy distribution.
Methods. A simple nanoflare model based on three key parameters (the flare rate, the flare duration, and the power law exponent of the flare energy frequency distribution) is used to simulate emission line radiances from the ions Fe
,
Ca
,
and Si III, observed by SUMER in the corona of an active region as it rotates around the east limb of the Sun. Light curve pattern recognition by an Artificial Neural Network (ANN) scheme is used to determine the values.
Results. The power law exponents,
,
2.8, and 2.6 are obtained for Fe
,
Ca
,
and Si III respectively.
Conclusions. The light curve simulations imply a power law exponent greater than the critical value of 2 for all ion species. This implies that if the energy of flare-like events is extrapolated to low energies, nanoflares could provide a significant contribution to the heating of active region coronae.
Key words: Sun: activity - Sun: flares - Sun: UV radiation
The approach taken to estimate their contribution has been
to extrapolate the energy frequency distribution of detectable flare-like events.
The energy frequency distribution of larger flares tends to follow a power law
distribution
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(1) |
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Figure 1: EIT 195 Å images of the observed active region at two times, showing the position of the SUMER slit, indicated by the vertical line. |
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Here we take an alternative approach and model ultraviolet (UV) radiances observed by the Solar Ultraviolet Measurements of Emitted Radiation (SUMER; Wilhelm et al. 1997,1995) in an active region corona, assuming that the radiance fluctuations and the nearly constant ``background'' emission are caused by small-scale stochastic flaring (Pauluhn & Solanki 2007,2004). The model has been applied successfully to UV radiance fluctuations in the quiet Sun (Pauluhn & Solanki 2007). The method compares light curves generated assuming random flaring with a power law frequency distribution to the light curves of an observed emission line. It has the advantage that it takes into account without bias weak, blended micro- and nanoflares that produce a nearly continuous background.
Here we apply this technique to off-limb time series recorded by SUMER.
The three lines modelled,
Fe
1118.07 (6.3 MK), Ca
1133.76 (2.2 MK) and
Si III
1113.23 (0.06 MK), cover two decades
of formation temperature from the lower transition region
to the hotter gas in the corona.
The analysis described here uses Artificial Neural Networks (ANNs) to
find the optimum match to the three parameters of the model.
The main advantage of this
method over previous analyses
based on the radiance distribution function (Pauluhn & Solanki 2007; Safari et al. 2007) is that
we are able to obtain quantitative values for all parameters,
including .
Another advantage of the ANN method is that it concentrates on the
number and shape of the emission peaks
along the light curves with little weight on the low radiance pixels,
which was a problem with the
Safari et al. (2007) analysis.
The observed active region (AR 1967) is
shown in Fig. 1. This is the region and data set
discussed in
Wang et al. (2006).
The SUMER
slit was placed, as shown,
at a fixed position above the limb.
Observations with a cadence of 90 s
in six spectral lines,
Fe
1118.07 (6.3 MK), Ca
1098.48 and
555.38 (3.5 MK), Ca
1133.76 (2.2 MK),
Ne
558.62 (0.3 MK)
and Si III
1113.23 (0.06 MK) were transmitted, for periods of
12.6 h followed by
a full spectrum scan (800-1600 Å) of 3.4 h.
A typical time sequence in any one line consists of
500 exposures.
The three strongest lines, Fe
,
Ca
,
and Si III, are analysed here.
Images of their radiance along the slit
are shown in Fig. 2 for a typical 12.6 h period.
Distinct events can be seen in Fe
,
but only the very strongest make
an impression on the bright active region Ca
emission when
they cool (Innes & Wang 2004).
Si III is seen
close to the limb and appears to be generated by small
surge-like ejections.
Our results are based on three such time series, taken over the days
16-18 September 2000.
The emission along each row was very noisy at several positions. To
improve the signal-to-noise but at the same time not to lose individual
structures, the light curves were obtained by first averaging
SUMER data over five spatial pixels (5
)
along the slit.
Only the light curves with all 500 data points above a chosen threshold
were selected for analysis. We did not want to base the
threshold on an absolute intensity because this would have biased the
input data against low background. So for the
Fe
and Ca
,
the threshold was set such that the ratio of the Ne
to
Fe
intensity was less than 0.5. This ensures that only light curves
from the central part of the active region were taken.
Most of the Si III emission was concentrated near
the limb to the south (Fig. 2). The Si III selection was based on the
local scatter in the second moment of the line, the line width.
If the standard deviation of the line width was greater than 1.0 over a local
space-time block, then the central data point and associated
light curve were excluded from the analysis.
This resulted in 35 test light curves for both Fe
and Ca
and 11 test curves
for Si III.
Before being fed to the neural network, all light curves were
normalized to their maximum.
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Figure 2: Time series of line radiance along the SUMER spectrometer slit for the period 16 Sep. 19:00 UT to 17 Sep. 7:36 UT. The distance in pixels along the SUMER slit is shown on the vertical axis. |
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The emission in the active region corona is assumed to be caused by many
random flares with flare radiances following a
power law frequency distribution.
Flares with a power law frequency distribution, ,
in
radiance are assumed to erupt with a frequency, pf, and have a flare duration
,
where
is the rise time and
the decay time. We assume
.
The other free parameter in the model is the ratio of the maximum to minimum
flare energy which is set to
.
For a large number of
independent random flares, the distribution of normalized radiances,
where I is the radiance, is
lognormal with shape parameter
(Pauluhn & Solanki 2007):
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Figure 3: Distribution functions of SUMER data in the active region corona (solid lines) and best fit lognormal functions (dashed lines). The radiances are normalized to their median and their distributions to the number of data points. |
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Light curves for the stochastic flare model are shown for
and
,
and two combinations
of
in Fig. 4.
The light curves are visibly different, although they all
have shape parameter
.
The effect of
on the light curve is seen
in the ratio of strong to weak flares. The left-hand light curves have more
large flares
because they have a smaller exponent. Picking up these pattern changes is
the strength of the ANN method.
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Figure 4:
Light curves for flare models run with different ![]() ![]() ![]() |
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We applied
the ANN method to probe the unknown parameters (power law exponent, ,
duration time,
,
and flare rate, pf) of
the three lines. ANNs have become a popular tool in almost
every field of science. In recent years, ANNs have been widely
used in astronomy for applications such as star/galaxy
discrimination, (Andreon et al. 2000; Cortiglioni et al. 2001), morphological
classification of galaxies, (Ball et al. 2004; Storrie-Lombardi et al. 1992), and
spectral classification of stars (Bazarghan & Gupta 2008; Bazarghan 2008; von Hippel et al. 1994).
We employ probabilistic neural networks (PNNs Specht 1988,1990). The PNN learns to approximate the probability density function of the training samples. It uses a supervised training set to develop distribution functions within a pattern layer. These functions in the recall mode are used to estimate the likelihood of an input feature vector being part of a learned category or class.
An example of a PNN is shown in Fig. 5. This network has four layers. The network contains an input layer which has as many elements as there are separable parameters needed to describe the objects to be classified. It has a pattern layer, which organizes the training set such that each input vector is represented by an individual processing element. The third layer is the summation layer, which has as many processing elements as there are classes to be recognized. Each element in this layer combines via processing elements with the pattern layer which relates to the same class and prepares that category for output. Finally, there is the output layer that corresponds to the summation unit with the maximum output.
For the identification of SUMER light curves, the input vector,
X = (x1, x2, ..., xn ), is the light curve with 500 data points
(n=500).
The network is first trained to classify light curves corresponding
to all the possible combinations of ,
,
and pf.
For this we synthetically generate light curves
with the nanoflare code described in Pauluhn & Solanki (2007).
We generate one light curve for each
combination of the parameters:
When a SUMER light curve of an unknown classification is fed to the network, the summation layer of the network computes the probability functions Sk of each class. Finally at the output layer we have C, the value with the highest probability.
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Figure 5: Schematic of a typical probabilistic neural network. |
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In the present work, PNN is used
as a tool to extract the three flare model parameters required to
reproduce the SUMER light curves. All 35 Fe
and Ca
,
and
11 Si III SUMER light curves from
the three days of observations were fed individually into the neural
network and the parameters were obtained for each light curve separately.
The final PNN outputs are shown in Table 1.
The bold numbers are the statistically maximum occurrence for each parameter.
For example for Fe
,
is found in more than 70% of the light
curves. The minimum and maximum values, given on the left and right, indicate
the scatter in the light curve parameters.
Table 1: The SUMER spectral lines and the parameter values given by PNN.
In each line there is 20% scatter in ,
and 50% scatter
in
.
The range of pf values for Fe
and Si III is much broader,
suggesting that events producing emission in these temperature ranges do not have the
same rate everywhere but are
seen in irregular bursts. We also note that the value of
is
roughly the same for both Fe
and Si III, as
suggested by their shape parameter (Fig. 3).
The Ca
light curves are all matched with a high
value of pf, consistent with the idea that the 1 MK active
region corona requires almost continuous flaring.
The four times higher rate for Ca
than Fe
suggests that most of the
Ca
emission is produced by heating events below the Fe
formation
temperature (6.6 MK).
Example light curves obtained using these parameters are compared with the observed
ones in Fig. 6. Both the Si III and Ca
simulations
look remarkably similar to their observed light curves. The background radiance of
the Fe
light curve is about a factor of 2 too low. The Fe
light curves had
a pf ranging from 0.7 to 0.1, so we suspect that in this case
the pf value is slightly too low. Also for Fe
,
the ratio
deduced from the data is smaller than
the fixed value 0.5 used here.
This may influence the accuracy of the method.
The sensitivity of the PNN output depends on the training set. During the training session, the network must see all possible patterns that it is supposed to classify in the testing session. With 500 simulated light curves in the training set, PNN was not able to converge for several of the SUMER light curves. When we increased the number of simulated light curves to 6930, we were able to obtain unique parameters for all observed light curves.
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Figure 6: Samples of the radiance time series: left panel: SUMER data, and right panel: simulation data obtained with the parameters given in Table 1. |
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The concept that the solar corona may be heated by numerous, randomly distributed, small flare-like events called nanoflares is considered by comparing simulated and observed emission line light curves. The difference between this and previous methods is the fully automated modelling of the light curve structure. There is no human decision required for background/event cut-off levels or best fit parameters.
The result is power law flare energy frequency exponents greater than 2.5 for
all three emission lines considered, Si III, Ca
and Fe
.
This is consistent with the corona being heated
mainly by nanoflares, and
demonstrates the importance of nanoflare ``background'' emission in determining the
power law exponents.
The parameter with highest uncertainty or largest scatter is the flare rate,
especially for the lines formed at transition region and hot flare temperatures.
Coronal plasma at these temperatures is produced sporadically and
is associated with more specific coronal and chromospheric loop structures than the
general active region corona, so the scatter is to be expected.
The next step will be to determine the actual flare energies producing the nanoflare emission. This is a much more complicated exercise because the modelled light curves are observed in the corona which may be heated by events occurring lower in the atmosphere (Aschwanden 2008), so that it requires a model for the energy transfer to the observation position.
Acknowledgements
H. Safari acknowledges the warm hospitality and financial support during his research visit to the solar group, MPS.