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Инфоурок / Астрономия / Статьи / SOLAR IMAGE PROCESSING AND ANALYSIS Solar Cycle 23 in Coronal Bright Points

SOLAR IMAGE PROCESSING AND ANALYSIS Solar Cycle 23 in Coronal Bright Points

  • Астрономия

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Solar Phys (2010) 262: 321–335

DOI 10.1007/s11207-010-9524-5


Solar Cycle 23 in Coronal Bright Points

Isroil Sattarov · Alexei A. Pevtsov · Nina V. Karachik ·

Chori T. Sherdanov · A.M. Tillaboev

Received: 13 May 2009 / Accepted: 23 January 2010 / Published online: 13 February 2010

© Springer Science+Business Media B.V. 2010

Abstract We describe an automatic routine to identify coronal bright points (CBPs) and

apply this routine to SOHO/EIT observations taken in the 195 Å spectral range during solar

cycle 23. We examine the total number of CBPs and its change in the course of this solar

cycle. Unlike some other recent studies, we do find a modest 30% decrease in the number

of CBPs associated with maximum of sunspot activity. Using the maximum brightness of

CBPs as a criterion, we separate them on two categories: dim CBPs, associated with areas

of a quiet Sun, and bright CBPs, associated with an active Sun. We find that the number of

dim coronal bright points decreases at the maximum of sunspot cycle, while the number of

bright CBPs increases. The latitudinal distributions suggest that dim CBPs are distributed

uniformly over the solar disk. Active Sun CBPs exhibit a well-defined two-hump latitudinal

profile suggestive of enhanced production of this type of CBPs in sunspot activity belts. Finally,

we investigate the relative role of two mechanisms in cycle variations of CBP number,

and conclude that a change in fraction of solar surface occupied by the quiet Sun’s magnetic

field is the primary cause, with the visibility effect playing a secondary role.

Keywords Instrumentation and data management Solar corona, structures Solar cycle

Solar Image Processing and Analysis

Guest Editors: J. Ireland and C.A. Young

I. Sattarov A.M. Tillaboev

Tashkent State Pedagogical University, 103 Yusuf Khos Khojib Street, Tashkent 700100, Uzbekistan

I. Sattarov

e-mail: isattar@astrin.uzsci.net

A.A. Pevtsov (_) N.V. Karachik

National Solar Observatory, PO Box 62, Sunspot, NM 88349, USA

e-mail: apevtsov@nso.edu

N.V. Karachik

e-mail: nkarachi@nso.edu

C.T. Sherdanov

Astronomical Institute AS of Uzbekistan, 33 Astronomical str., Tashkent 100052, Uzbekistan

e-mail: chori@astrin.uzsci.net

1. Introduction

Historically, the term X-ray bright points (XBPs) was used to describe compact coronal

brightenings observed in X-rays. Here, we adopt a more general term, coronal bright points

(CBPs), as first suggested by Webb et al. (1993). CBPs were discovered on first X-ray images

of the Sun taken from sounding rockets. Later, their properties were extensively studied

using observations from the Skylab and the sounding rocket experiments. These early studies

had indicated several major properties of these coronal features: i) the existence of two

types of CBPs (Golub, Krieger, and Vaiana, 1975), ii) their association with cancelling magnetic

bipoles (Harvey-Angle, 1993), iii) a cycle variation of the number of CBPs (Davis,

Golub, and Krieger, 1977; Golub, Davis, and Krieger, 1979) and iv) enhancement of the

CBP number near sunspot activity complexes (Golub, Krieger, and Vaiana, 1975).

Using a limited data set of daily full disk magnetograms,Webb et al. (1993) andHarvey-

Angle (1993) studied magnetic counterparts of CBPs. They found that a majority of these

coronal structures were associated with canceling (converging and disappearing) magnetic

bipoles. About 30% of the CBPs was found to be related to ephemeral active regions.

Golub, Krieger, and Vaiana (1975) studied latitudinal and longitudinal distribution of Xray

bright points during a single solar rotation and concluded that the latitudinal distribution

of CBPs has two components: one represents uniform distribution of CBPs over solar surface,

and the other characterizes CBPs formed in sunspot activity belts. Statistical study of

X-ray bright points (Sattarov et al., 2002) using Yohkoh soft X-ray telescope data (SXT,

Tsuneta et al., 1991) showed a clear presence of the Spörer’s butterfly distribution in XBPs

similar to solar active regions. Observations with the Extreme ultraviolet Imaging Telescope

(EIT) on board of the Solar and Heliospheric Observatory (SOHO) also showed enhancement

of CBPs in sunspot activity belts (McIntosh and Gurman, 2005). In respect to the

longitudinal distribution, Golub, Krieger, and Vaiana (1975) noted that the CBP number is

enhanced at longitudes near sunspot complexes of activity. This was later confirmed by Sattarov

et al. (2005a), who had described enhancement in the CBP number near the location

of sunspot activity complexes.

Davis, Golub, and Krieger (1977) had found that the number of CBPs varies inversely

with sunspot cycle. Following this discovery, Golub, Davis, and Krieger (1979) had suggested

the existence of a secondary cycle of magnetic activity running in opposite phase

to the sunspot cycle. However, later, Hara and Nakakubo-Morimoto (2000) found that the

variation in the number of CBPs might be explained by the visibility effect (see, also, Hara

and Nakakubo-Morimoto, 2003). The visibility effect includes two components. First, the

overall brightness of a high temperature corona may be significantly affected by the presence

of bright active regions (e.g., Pevtsov and Acton, 2001), and the enhanced brightness of

the corona may hinder identification of dimmer CBPs. Second, areas close to active regions

could be “occulted” by a canopy of bright active region corona. In both cases, the visibility

effect implies that the bright points are still present, but they cannot be identified because

of the enhanced background coronal brightness in their vicinity. Sattarov et al. (2002) drew

conclusions similar to Hara and Nakakubo-Morimoto (2000) about the role of visibility effect

on the basis of the lack of solar-cycle variation in a number of photospheric bipoles. The

number of bright points in data due to Sattarov et al. (2002) showed clear cycle variations,

while the number of magnetic bipoles with a particular size and separation did not show

such variations. Assuming that the fraction of magnetic bipoles associated with CBPs does

not change with solar cycle, Sattarov et al. (2002) have concluded that the cycle variation

of CBPs is apparent, and it is probably caused by changes in overall brightness of the solar

corona as suggested by Hara and Nakakubo-Morimoto (2000).

The advancement of computational capabilities and the open-data policy led to the development

of several methods for automatic identification of coronal bright points. The details

of these methods can be found elsewhere (e.g., Hara and Nakakubo-Morimoto, 2003; Sattarov

et al., 2005a, 2005b; McIntosh and Gurman, 2005; Karachik, Pevtsov, and Sattarov,

2006). Using automatic algorithms and objective criteria enables massive statistical studies

of coronal bright points previously unavailable. As the first step, it is natural to re-examine

previously discovered tendencies and properties of CBPs using significantly larger and more

uniform data sets. In this paper, we use the automatic procedure developed by us to identify

the coronal bright points over the period of the complete solar cycle 23, 1996 – 2008. We

apply this method to SOHO/EIT 195 Å data to study the statistical distribution of CBPs with

the solar cycle. To investigate the significance of the visibility effect, we analyze background

intensity around CBPs and its change with sunspot activity. We compare background intensity

and CBP number at equator, activity belts, and high latitudes, where the consequences

of the visibility effect should be different. In addition to cycle variations in CBPs, we study

their latitudinal distributions. The rest of the paper is organized as follows. In Section 2,

we describe our data set and provide a detailed description of our automatic procedure to

identify coronal bright points. In Section 3, we review our major findings, in Section 4 we

discuss the relative role of the visibility effect and provide an alternative explanation for

cycle variation of the coronal bright points, and in Section 5, we summarize our findings.

2. Data Sets and Automatic Procedure for Identification of CBPs

In this work we use full disk images observed by the Extreme-ultraviolet Imaging Telescope

(EIT, Delaboudinière et al., 1995) on board of SOHO.We utilize EIT full disk synoptic data

with spatial resolution of 2.64 arcsec per pixel and six hours cadence observed in 195 Å from

1996 – 2008. The data are calibrated following the standard EIT data reduction routine. The

calibration routine normalizes the exposure time and takes into account change in response

of CCD camera over the time of mission.

To identify coronal bright points, we employ the automatic procedure developed by us

(first presented in June 2004 at the IAU Symposium 223, see also Sattarov et al., 2005a,

2005b). The procedure was modified by Karachik, Pevtsov, and Sattarov (2006) to improve

the categorization of CBPs either as previously existing or newly emerged bright points.

We implement the following steps in the CBP identification:

Subtraction of large scale variations in the corona.

Exclusion of compact brightenings above active regions from the CBP search.

Identification of potential CBPs.

Analysis of shape and length of potential CBPs to exclude hot pixels, cosmic ray streaks,

and loop-like features not associated with active regions.

The following sections provide a detailed description of each step.

2.1. Step 1: Subtraction of Large Scale Variations in the Corona

The X-ray and Extreme Ultra-Violet (EUV) corona observed on solar disk exhibits variations

in brightness on different spatial scales. For example, the areas of coronal holes and

filament channels are darker, while the areas of strong closed magnetic fields have an enhanced

brightness. Thus, using a fixed intensity threshold (as was done, for example, in

Zhang, Kundu, andWhite, 2001) will only work satisfactorily on images with a low sunspot


324 I. Sattarov et al.

We have adopted an alternative approach and identify CBPs relative to brightness of

background corona near potential CBP. To remove large-scale intensity variations, we subtracted

a median-smoothed image from the original full disk data. The size of the median

filter was determined by comparing the location and number of CBPs found on the difference

image with CBPs selected using the fixed intensity threshold applied to selected

images with no active regions. This procedure was repeated on several EIT images taken

during 1996 – 1997 when sunspot activity was low. The validity of the median filter found

using the 1996 – 1997 data was tested on selected full disk images with various levels of

coronal activity (with active regions present on the disk). In the latter case, coronal bright

points identified after subtraction of images smoothed by median filter were compared with

a manual identification of CBPs.

This process had yielded a very similar size for the median filter for different images, and

finally a median filter with 30 pixels in width was adopted as the standard median filter for

our CBP selection routine. Figure 1 shows an example of an EIT image and identification of

CBPs after applying the median filter.

2.2. Step 2: Exclusion of Compact Brightenings Above Active Regions from the CBP


Subtracting a median-filtered image from the original image improves the identification

of bright points. Unfortunately, in some cases it might also lead to false identifications of

bright portions of active region (AR) loops as coronal bright points. To prevent these false

identifications, we excluded areas of active regions from search for CBPs. Active regions are

excluded based on their brightness and total area. Using original image, the routine identifies

potential active regions as areas whose brightness exceeds AR threshold. This threshold

was fixed at the level of 600 Data Numbers per second (DN s1) based on a trial-and-error

approach on many images. In selecting this threshold, we used data with various levels of

sunspot activity (both during a minimum and maximum of the activity cycle). In part, the

threshold selection was made to optimize selection of CBPs and minimize false-positives

such as bright loop-tops situated inside of active regions.

Next, potential active regions were verified based on their total continuous area (area

within a closed contour). If the total area of a potential active region had exceeded the area

of a circle with a radius of 25 arcsec (maximum radius of CBP), the area was labeled as an

active region, and all potential bright points identified within the area were excluded from

consideration. Bright areas that were not classified as active regions remained as valid locations

for potential bright points. The above two-step approach in identifying active regions

allows us to avoid the exclusion of bright areas in the solar corona not associated with active

regions such as, for example, areas of relatively bright but diffuse corona. A size threshold

for active regions was adopted from the manual survey of CBPs by Longcope et al. (2001).

2.3. Step 3: Identification of Potential CBPs

After subtracting the median-average, we computed the average brightness Iavg and its standard

deviation σ using the difference image. In computing Iavg, all pixels corresponding to

potential bright points are excluded as explained below.

A brightness level (Ithresh) that separates “quiet Sun” and “active” corona was found interactively

by exclusion of the brightest pixels and using the remaining pixels to compute

Iavg until the condition Iavg 0 was satisfied.

After determining Iavg and σ , we selected all closed contours with intensity exceeding

Iavg + σ for further examination as potential bright points. Next, we selected a small subimage

around each potential bright point and computed the center of gravity of the potential


Figure 1 EIT full disk image (reversed colors) taken on 2 April 1996. For demonstration purposes, panel (b)

shows an area of 300 pixels by 300 pixels near disk center, the same area smoothed by median filter (c), and

coronal bright points selected using the fixed intensity threshold (contours) and by our method (crosses). Note

that the brightest feature on panel (d) did not pass the “active region” threshold and, hence, is not selected as

a CBP.

bright point (Cg) and its radius (Rcbp). Rcbp is the maximum distance between Cg and the

contour outlining the CBP boundary. Each selected sub-image is a square centered at Cg

with sides 2× Rcbp in size, but not less than 26 pixels. Using these sub-images we further

refined our definition of potential CBP as a region of sub-image with an area of more than

one pixel and an intensity above Isubavg + 2σsub, where Isubavg and σsub are the average and

its standard deviation of intensity of the sub-image containing the potential bright point.

2.4. Step 4: Analysis of Shape and Length of Potential CBPs

The final selection of coronal bright points was refined using several additional criteria:

total area of potential CBPs, their shape, and angular distance from the disk center (ρ <

0.95RSun). For example, we did not count as CBPs features whose radius is smaller than

2 Mm (potential hot pixels) or larger than 20 Mm (potential ephemeral active regions). We


Figure 2 EIT full disk images for two periods of higher sunspot activity in the years (a) 2000 and (b) 2001.

Color contours show CBPs identified by our program.

also excluded highly elongated and crescent-like structures that may correspond to bright

portions of isolated long coronal loops. If the maximum radius of potential CBP was five

times larger than its minimum radius, the feature was excluded because of its excessive

elongation. Similarly, when the geometric center of a potential CBP was located outside

of closed contour outlining CBP, the feature was excluded because of its crescent-like or

irregular shape. The stated limits of the maximum size of the potential bright point were

adopted from the manual survey by Longcope et al. (2001). The above size-limits were

supported by the distribution of sizes of CBPs in Sattarov et al. (2005c).

According to current knowledge about the coronal bright points, many if not all of these

features may, in fact, be associated with small loops connecting opposite polarity footpoints.

Thus, exclusion of elongated and crescent-like features because of their potential relation

with the coronal loops might appear counter-intuitive. However, it should be emphasized

that we only excluded such features when they were related to long loops, which in our

opinion, do not fit the traditional definition of the coronal bright point as a compact feature.

Figure 2 provides two examples of CBP selection during periods of higher sunspot activity

as compared with the example shown on Figure 1.

3. Coronal Bright Points and Solar Cycle

Figure 3 shows the monthly-averaged number of coronal bright points per solar visible disk

during 1996 – 2008. The CBP number exhibits a clear decrease associated with a maximum

of the sunspot activity cycle; this decrease is at odds with some previous studies of coronal

bright points. For example, data due to McIntosh and Gurman (2005) show an unexplained

jump in CBP number in early 1998 as well as a slight increase towards 2002, a year on

he sunspot cycle maximum. On the other hand, Hara and Nakakubo-Morimoto (2003) have

found a small (20%) increase in the total number of X-ray bright points associated with

the minimum of solar cycle 22.

The total number of CBPs in our data set is about 280 – 450 per image, which exceeds the

number of bright points found in McIntosh and Gurman (2005) for Fe XIII 195 Å (230 – 300


Figure 3 Cycle variation of coronal bright points (monthly-averaged, squares) and monthly sunspot number

(lower curve). The solid line surrounded by two dashed lines represents the change in CBP number with solar

cycle derived on the basis of a Gaussian fit to the distribution of dim and bright CBPs shown in Figure 5. The

two dashed lines represent one sigma standard deviation of the fit shown by the solid line.

per image). Assuming a uniform distribution of CBPs over the solar surface, the number

of CBPs found in McIntosh and Gurman (2005) translates to a density of coronal bright

points of 0.8 – 1.0 × 104 Mm2. Our data yield a density of coronal bright points of about

0.9 – 1.5 × 104 Mm2. For comparison, Longcope et al. (2001) had estimated a density

of CBPs as high as 1.3 × 104 Mm2 and Golub, Krieger, and Vaiana (1976) found 0.6 –

2.0×104 Mm2. Hara and Nakakubo-Morimoto (2003) had reported the density of X-ray

bright points to be 0.2 – 0.7 × 104 Mm2. According to Zhang, Kundu, and White (2001)

the XBP number is about 3 – 4 times smaller than the number of CBPs observed by EIT.With

this correction, the XBP number density found in Hara and Nakakubo-Morimoto (2003) is

in general agreement with Longcope et al. (2001) and Golub, Krieger, and Vaiana (1976).

The density of coronal bright points found by us is in agreement with Longcope et al. (2001)

and Golub, Krieger, and Vaiana (1976). A slight disagreement between the number of bright

points with McIntosh and Gurman (2005) can be attributed to a difference in size of CBPs

identified by the two procedures. Sattarov et al. (2005c) have given an example of a distribution

of CBPs by their size. In that example, the total number of CBPs was about 460 per

image, and there were only about 200 CBPs with area larger than 20 pixels.

The CBP data shown in Figure 3 exclude the smallest CBPs (2 pixels in total area) identified

by our routine. The data set that includes these smallest CBPs yields about 400 –

600 CBPs per image. Although the solar-cycle variation is still present (albeit at a reduced

amplitude of about 20% change between 1996 and 2002), the data show significantly larger

scatter and “jumps” in CBP number. We see the latter as an indication that the EIT spatial

resolution is only ; line-height: 100%">As part of our CBP identification procedure, we have analyzed the average brightness of

the corona in the vicinity of each CBP as well as the maximum brightness of the CBPs

themselves. Figure 4 shows the monthly-averaged brightness of the background in subimages

that were used in the final CBP identification (Isubavg, see Section 2.3). Not surprisingly,

the brightness of the background corona varies with sunspot activity. The increase

in background brightness near the sunspot maximum is evident in areas near the equator,

mid latitudes (activity belts), and high latitudes (outside activity belts), and in our opinion,

Figure 4 Cycle variation of

background intensity for: (a) full

disk, (b) equatorial region

(latitudinal range 0 Ѓ} 5 degrees),

(c) active region belts (+20 Ѓ} 5

degrees and 20 Ѓ} 5 degrees),

and (d) high latitudes (+50 Ѓ}5

degrees and 50 Ѓ} 5 degrees).


it is the direct effect of bright active regions, whose presence may increase the brightness

of a diffuse corona even at a significant distance away from an active region (e.g., Pevtsov

and Acton, 2001). At the rising phase of cycle 23, when the decaying fields of sunspots of

cycle 22 were still present near the equator, we see an enhanced coronal brightness both

at mid-latitudes (Figure 4c) and near the equator (Figure 4b). As the cycle progresses and

the sunspot activity drifts to low latitudes, we see a sharper decrease in coronal brightness

in mid-latitudes and a more gradual decrease near the equator. This behavior supports our

interpretation of active regions as the source of an enhanced corona in the vicinity of CBPs.

It is interesting to note that our CBP identification routine appears to be relatively insensitive

to changes in the sensitivity of the EIT detectors. For example, a drastic change in

quantum efficiency of EIT CCD in 1997 – 1999 did not result in a significant change in background

intensity (see Figure 4) or total number of CBPs (Figure 3). The EIT response graph

(http://umbra.nascom.nasa.gov/eit/EIT.html#RESPONSE) indicates that between 1996 and

1997, the detector’s response for Fe XII 195 Å had decreased by 50%. Despite this drastic

change, however, the CBP number remains nearly constant (Figure 3). Between 1999 and

2008, the detector’s response had gradually decreased from 80% (of pre-launch value) to

about 15%. Contrary to that, however, Figure 3 shows a gradual increase (not decrease) in

CBP numbers during same period. To further evaluate the effects of changes in the detector’s

response, we have conducted the following experiment. We have selected a typical EIT image

and reduced its overall intensity by 50%. Because CCD is a linear detector, one should

expect that a loss of sensitivity will have a linear effect with respect to the image brightness.

The total number of CBPs for this degraded image is only about 10% smaller as compared

with the original image. To simulate a non-linear change in the CCD response, we reduced

the contrast of our test image by 10%. This resulted in only a minor reduction (about 1%)

of the total number of CBPs as compared with the original image. Thus, we believe that a

change in the EIT detector’s response over the lifetime of the SOHO Mission does not have

a significant effect on the CBP number returned by our routine.

In addition to the enhanced background surrounding CBPs, we also see cycle-related

variations in maximum brightness of CBPs (not shown). The average CBPs brightness is

higher near the maximum of sunspot cycle, and it is lower during the sunspot minima. The

enhanced brightness of CBPs near the maximum of a sunspot cycle may indicate the presence

of an additional population of bright points associated with stronger fields of active

regions. The presence of two populations of CBPs was previously suggested by several

researchers (e.g., Golub, Krieger, and Vaiana, 1975; Sattarov et al., 2002; McIntosh and

Gurman, 2005).

To investigate the cycle behavior of two CBP populations, we have divided our data

set into two categories: dim CBPs with maximum intensity Imax 150 DNs1 and bright

CBPs with Imax > 150 DNs1. The maximum brightness threshold, Imax = 150 DNs1, was

selected on the basis of the variation of Imax during solar cycle. Thus, for example, in 1996,

when the sunspot activity was extremely low, the maximum CBP intensity peaked at about

150 DNs1 (see Figure 4 in Sattarov et al., 2005c), which we have adopted as a threshold

for dim CBPs.

Figure 5 shows the cycle variation of two types of CBPs. The number of dim CBPs decreases

as the sunspot activity grows, while the number of bright CBPs increases. A similar

behavior of bright and dim CBPs with solar cycle was previously reported by Hara and

Nakakubo-Morimoto (2003). The numbers of dim (Ndim) and bright (Nbright) CBPs show

a linear relation, albeit with a significant scatter. By fitting the first degree polynomial, we

found that Nbright = (274Ѓ}4) (0.59Ѓ}0.02)Ndim. The proportionality coefficient being less

than unity suggests that an increase in the number of bright CBPs cannot not fully compensate

for a decrease in the number of dim CBPs, and hence, one could expect to find a slight


Figure 5 Cycle variation of (a) “bright” and (b) “dim” CBPs. Open squares with error bars show

monthly-averaged numbers of CBPs and their standard deviations. Dashed lines are approximations of two

distributions by Gaussian functions.

decrease in the total number of CBPs at/near maximum of sunspot activity. Figure 5 shows

a Gaussian fit to distributions of dim and bright CBPs with solar cycle. A sum of these two

Gaussian fits is shown as a solid line in Figure 3; two dashed lines correspond to a standard

deviation of this fit. The trend suggests a solar-cycle variation of total CBP number at about

30% level. The trend is statistically significant, well exceeding the scatter in data points in

Figure 3.

The above relation between number of bright and dim CBPs provides an indirect support

to early findings by Webb et al. (1993) and Harvey-Angle (1993) who showed that a

majority of CBP are associated with canceling bipoles and about one third of them are associated

with ephemeral active regions. Assuming that dim CBPs are primarily associated

with quiet Sun bipoles, and bright CBPs are “active Sun” features, one can arrive to the

conclusion that overall the quiet Sun features well-outnumber the active Sun CBPs. The lat-


itudinal distribution of two types of CBPs is also different. Dim CBPs are distributed more

or less uniformly with solar latitudes (Figure 6, year 1996). The latitudinal profile of bright

CBPs exhibits a well-defined hump during years when the sunspot activity is concentrated

at mid-latitudes. The latitudinal distribution of bright CBPs shows the migration toward the

equator. When the activity belts migrate to the equatorial region, a two-hump pattern in the

latitudinal distribution of CBPs disappears (Figure 7).We would like to clarify, however, that

our method separates coronal bright points into two types based on their brightness alone.

It does not take into account the magnetic properties of CBPs, and therefore, we cannot be

certain whatever “quiet Sun” CBPs are indeed associated with canceling bipoles in quiet

Sun, nor can we state that “active Sun” CBPs are a product of ephemeral active regions. We

plan to investigate such associations in a separate study.

4. Role of Visibility and Other Effects in Cycle Variation of CBP Number

The distribution of CBP number with latitude shown in Figures 6 and 7 suggests a different

origin of the two types of CBPs. Bright CBPs are associated with active region activity

(for example, an enhanced production of magnetic bipoles around developing/decaying active

regions), and dim CBPs are linked to a quiet Sun corona outside of active regions’

sphere of influence”. The decrease in number of dim CBPs can be contributed to the visibility

effect, as proposed by several researchers (e.g., Hara and Nakakubo-Morimoto, 2000;

Sattarov et al., 2002). As a result of enhanced activity, the overall brightness of solar corona

increases (Figure 4), which coincides with a decrease in number of CBPs at all latitudes.

On the other hand, the number of dim CBPs may also decrease because the area of

quiet Sun that hosts these CBPs decreases as the area occupied by active regions increases.

The effect can be seen in the latitudinal distribution of CBPs. For example, in low (0 Ѓ} 5

degrees) and high latitudes (50 Ѓ} 5 degrees), the latitudinal distribution of dim CBPs in

1998 (Figure 6, rising phase of solar cycle) has about the same number of CBPs as the

distribution for 1996 (solar minimum). However, the number of dim CBPs is suppressed in

mid-latitudes, which approximately corresponds to active region latitudes in Figure 7.

One might argue that if the CBP number changes because of the change in total disk

area of an active region, there should be a close relation between the total area of active

region excluded by our routine and the number of CBPs. Indeed, we do see a significant

cycle variation in the area of active regions in our data (Figure 8). However, the effect could

be more complicated than a simple reduction of solar disk area due to the presence of active

regions. In fact, a reduction in the area of a quiet Sun can be larger than the area of the

active region subtracted by our program. For example, CBP production may be significantly

reduced in areas of relatively strong unipolar fields such as plages. However, because of

their lower coronal brightness, plages might not be excluded from the CBP search.

Therefore, to better estimate the effect of the changing area occupied by active regions

(defined in the broad sense as discussed above) on the number of quiet Sun CBPs, we adopt

bright Ca K plages as a proxy for an active region area. Using plages allows for a better estimate

of the effects of the changing area of active regions on the total number of CBPs. Ca K

plages correspond to areas of relatively strong magnetic fields around active regions; quiet

Sun CBPs are not typically present in the close proximity to active regions. In contrast, using

bright coronal areas associated with active regions would mix the visibility effect (blockage


of CBPs by bright active region corona) and the changing area occupied by magnetic field

of active regions. Because active regions are excluded from the CBP search, these areas of

the Sun are effectively “unavailable” for quiet Sun CBPs. In a solar maximum, the fraction

of visible solar disk occupied by Ca K plages can reach about 10% of solar visible disk (e.g.,

Tlatov, Pevtsov, and Singh, 2009), which approximately corresponds to a circle of radius of

20 (solar) degrees centered at the solar disk center. Taking, for example, the distribution of

CBPs for 1996 (Figure 6) and subtracting CBPs from the Ѓ}20 degrees latitudinal range reduces

the total number of CBPs by about 50%. This reduction in CBP number is comparable

to the change in total number of the coronal bright points between the solar minimum and

maximum (Figure 5a), although it cannot completely explain the change in number of dim

CBPs from solar minimum to solar maximum. Thus our estimate agrees with Giovanelli

(1982), who found that the area occupied by a mixed polarity field may vary by as much as

80% between solar minimum and maximum.

Thus, the decrease in number of dim CBPs shown in Figure 5b can be explained largely

by a decrease in the area of quiet Sun “available” for CBPs, suggesting that the visibility

effect may play only a minor role in the cycle variation of coronal bright points.

However, to better evaluate the relative contribution of the visibility effect and the effect

of the decrease in the fraction of quiet Sun area would require a detailed study of the

magnetic properties of areas hosting CBPs. We plan to address this issue in a separate


The decreased fraction of solar surface where CBPs can develop is different from the

visibility effect. The former implies that magnetic bipoles and coronal bright points are

not present in the locations occupied by strong or unipolar fields, while in latter case,

CBPs could be present, but they are not detectable because of the presence of a bright

corona around or above them. (Although it is not clearly suggested by Hara and Nakakubo-

Morimoto (2003), one might interpret their reference to “occultation by active regions” both

as a visibility effect and as a decrease in fraction of the solar surface available for CBPs


Sattarov et al. (2002) used the lack of variation in the number of magnetic bipoles to

support the visibility effect as an explanation for the apparent cycle variation of number of

coronal bright points. It will be instructive to repeat their analysis using a higher threshold

for the magnetic flux. Using the National Solar Observatory (NSO) at Kitt Peak, full

disk longitudinal magnetograms and 50 gauss (G) threshold for magnetic fluxes, we have

identified magnetic bipoles from 1996 –mid-2003. In this determination, we followed the

procedure described by Sattarov et al. (2002), except that we used a higher flux threshold to

reduce the relative contribution of quiet Sun bipoles with respect to bipoles associated with

stronger fields of ephemeral active regions. Figure 9 shows that the number of these bipoles

does vary in unison with sunspot cycle. Sattarov et al. (2002) used lower (20 G) threshold

for magnetic fluxes, and hence, their data may represent a mixture of bipoles associated

with dim (quiet Sun) and bright (active region) CBPs. The fact that bipoles with a low flux

threshold do not show cycle variation but bipoles with stronger flux do show such a variation

suggests that the number of bipoles associated with dim CBPs may decrease near the

maximum of a sunspot cycle, thus supporting the explanation that the cycle variation of dim

CBPs is due to the change in area of the solar disk occupied by a quiet Sun and the magnetic

field of active regions. Similar to Sattarov et al. (2002), Figure 9 does not establish a causal

relation between CBPs and magnetic bipoles. The question of a CBP – bipole association

will be investigated separately.


5. Conclusions

In this study we use an automatic procedure to identify coronal bright points in EIT full disk

images observed in the 195 Å spectral range.We find a slight decrease of the total number of

CBPs near the maximum of the sunspot cycle.We have divided the CBPs into two categories

based on their maximum brightness, and we found opposite trends in cycle variation of dim

and bright CBPs. Dim CBPs, associated with a quiet Sun, vary inversely with sunspot cycle,

while the variation of bright CBPs (active Sun CBPs) shows a positive correlation with

sunspot cycle in agreement with previous studies. The latitudinal distribution of dim CBPs

suggests their uniform distribution over the Sun, while bright CBPs show an enhancement at

latitudes associated with sunspot activity belts. We approximate cycle variation of dim and

bright CBPs by Gaussian functions. The sum of these two functions shows a modest 30%

dip around the maximum of the sunspot cycle, thus supporting the notion of inverse variation

of CBP number with sunspot cycle. Finally, we argue that the decrease in number of dim

CBPs at a solar maximum is caused mainly by the reduction in area of mixed polarity quiet

Sun fields (because the area occupied by active regions increases at the solar maximum).

In our opinion, the visibility effect, when the bright corona obscures CBPs, plays only an

auxiliary role.

Acknowledgements We thank Dr. J. Gurman for his advise on EIT CCD camera response and its correction.

The National Solar Observatory is operated by the Association of Universities for Research in Astronomy

(AURA Inc.) for the National Science Foundation. SOHO/EIT is the result of cooperation between

NASA and ESA. NSO/Kitt Peak data used here are produced cooperatively by NSF/NOAO, NASA/GSFC,

and NOAA/SEL. I. Sattarov and N. Karachik acknowledge partial support for this study from grant UZB-

54(J) of Science and Technology Center in Ukraine. This study was partially supported by NASA’s IAT

NNH09AL04I. This research has made use of NASA’s Astrophysics Data System Bibliographic Services.

Figures 2 and 8 were included at the request of the anonymous referee.


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