# The Best of Both Worlds: Using Automatic Detection and Limited Human
Supervision to Create a Homogenous Magnetic Catalog Spanning Four Solar Cycles

A. Muñoz-Jaramillo1, Z. A. Werginz12, J. P. Vargas-Acosta32, M. D. DeLuca42,
J. C. Windmueller2, J. Zhang5,
D. W. Longcope2, D. A. Lamb6, C. E. DeForest6, S. Vargas-Dominguez3, J. W.
Harvey7, & P. C. H. Martens1
1Georgia State University, Atlanta, GA 30303, USA. Email<EMAIL_ADDRESS>2Montana State University, Bozeman, MT 95717, USA.  3Universidad Nacional de
Colombia, Bogotá, DC 111321, Colombia.  4University of Colorado, Boulder, CO
80309, USA.  5George Mason University, Fairfax, VA 22030, USA.  6Southwest
Research Institute, Boulder, CO 80302, USA.  7National Solar Observatory,
Boulder, CO 80303, USA.

###### Abstract

Bipolar magnetic regions (BMRs) are the cornerstone of solar variability. They
are tracers of the large-scale magnetic processes that give rise to the solar
cycle, shapers of the solar corona, building blocks of the large-scale solar
magnetic field, and significant contributors to the free-energetic budget that
gives rise to flares and coronal mass ejections. Surprisingly, no homogeneous
catalog of BMRs exists today, in spite of the existence of systematic
measurements of the magnetic field since the early 1970’s. The purpose of this
work is to address this deficiency by creating a homogenous catalog of BMRs
from the 1970’s until the present.

For this purpose, in this paper we discuss the strengths and weaknesses of the
automatic and manual detection of BMRs and how both methods can be combined to
form the basis of our Bipolar Active Region Detection (BARD) code and its
supporting human supervision module. At present, the BARD catalog contains
more than 10,000 unique BMRs tracked and characterized during every day of
their observation. Here we also discuss our future plans for the creation of
an extended multi-scale magnetic catalog combining the SWAMIS and BARD
catalogs.

###### Index Terms:

pattern recognition; data mining; object oriented databases; astrophysics;
sun;

##  I Introduction: Bipolar Magnetic Regions vs. Active Regions

One of the most prominent characteristics of the solar cycle is the emergence
of bipolar patches of strong magnetic fields (often with a visible signature
in the form of sunspots). These bipolar magnetic regions (BMRs) have an
important, but subtle duality that changes the way they are studied depending
on the application. On the one hand, the fact that they have large amounts of
free energy that is eventually released in the form flares and coronal mass
ejections (CMEs) makes them one of the cornerstones of space weather studies.
On the other hand, there is now strong evidence that their emergence and decay
during the course of a given solar cycle plays a critical role in building the
large-scale magnetic field that seeds the subsequent cycle[1, 2].

Figure 1: (a) SDO/HMI magnetogram taken on 2012-Jul-07. (b) Automatic
detection and masking of active regions by the HARP algorithm. (c) Manual
circling of bipolar magnetic regions.

Due to the fact that these magnetic patches are directly related with the vast
releases of energy that drive heliospheric variability, people often refer to
them as _active regions (ARs)_. Unfortunately, this has led to a loosening of
terminology because there is a subtle but important difference between BMRs
and ARs: _BMRs denote the bipolar patches that appear on the surface of the
Sun after a flux-emergence event. ARs denote active complexes from which space
weather events originate, which often include more than one BMR._ This
difference becomes quite important for designing automatic detection
algorithms because their success relies heavily on a clear definition of the
nature of the detection targets.

The difference between ARs and BMRs is nicely illustrated in Figure 1, which
shows an SDO/HMI magnetogram (Fig. 1-a), the output of applying the HMI Active
Region Patches (HARP) automatic detection algorithm[3] to that magnetogram
(Fig. 1-b), and the same magnetogram showing individual BMRs circled by us by
hand (Fig. 1-c): even though there is one perfect match between a HARP and a
BMR, two of the HARPs contain more than one BMR and one HARP contains a
predominantly unipolar patch.

To this date, and to the extent of our knowledge, the great majority of
automatic detection algorithms used in solar physics are optimized for the
detection of ARs (i.e. they are mainly designed with a space weather
application in mind; see [4] for an exception). Additionally, in spite of the
availability of systematic measurements of the magnetic field since the early
1970’s, there is no homogenous (i.e. cross-calibrated catalog spanning 40
years of observations) BMR or AR catalog extending that far. Given that our
main interest is studying the role of BMRs as one of the critical building
blocks of the solar cycle, the main goal of this paper is to showcase the
results of our attempts to address this deficiency.

The core of our detection method is the Bipolar Active Region Detection (BARD)
code, which we introduce in Section II. In Section III we discuss the
strengths and weaknesses of purely automatic and purely manual methods of BMR
detection, as well as how to interface a human supervision module with BARD in
order to maximize the accuracy of our catalog. Our preliminary results are
showcased in Section IV and a discussion of our future plans to combine the
BARD and SWAMIS catalogs is included in Section V. We finish with our summary
and concluding remarks in Section VI.

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Figure 2: BARD Algorithm. (a) Input KPVT/512 magnetogram taken on 1991-Aug-18.
(b) Kernel pixels after applying a 400G threshold. (c) Kernel pixels after
morphological opening operation. (d) Detected regions after growing kernels to
encompass all adjacent pixels above a 150G threshold. (e) Magnetogram after
setting the detected regions to zero. (f) Kernel pixels after applying a 200G
threshold. (g) Kernel pixels after morphological opening operation. (h)
Detected regions after growing kernels to encompass all adjacent pixels above
a 150G threshold. (i) Detected regions found with both thresholds. (j)
Detected regions after growing kernels to encompass all adjacent pixels above
a 50G threshold. (k) Detected regions after merging regions that are too close
together. (l) Detected bipolar magnetic regions after using pairing module.

Figure 3: Man vs. Machine. The top panels show a comparison between a fully
automatic detection (a) of BMRs executed on KPVT/512 magnetogram taken on
1991-Aug-18 and a human vetted detection that involves the fragmentation of
large regions in smaller ones and the re-pairing of negative and positive
regions. BMRs that retain the same coloring across panels were not touched by
the human observer. The bottom panels show total unsigned flux for each
uniquely detected BMR at maximum development for the SOHO/MDI mission using a
logarithmic scale. (c) Results using manual detection. (d) Results using the
BARD algorithm.

##  II The Bipolar Active Region Detection (BARD) code

The BARD code is based on the algorithm of Zhang, Wang and Liu[5], which we
will refer to as ZWL from now on. ZWL was originally designed for the
detection of ARs and, as it is the case for most AR detection algorithms, it
operated on unsigned magnetic field. The main differences between BARD and ZWL
are:

  * •

BARD treats positive and negative polarities separately

  * •

BARD uses multiple thresholds of detection to ensure the proper capture of
both large and small positive and negative regions (a single detection merges
many small regions into neighboring large ones).

  * •

BARD includes a module that merges regions of the same sign to give rise to
larger objects.

  * •

BARD has a pairing module that links positive and negative regions to form
BMRs.

###  II-A BARD algorithmic framework

Figure 2 illustrates the main steps of BARD’s algorithmic framework starting
with an input magnetogram (Fig. 2-a):

  1. 1.

Find all pixels above a threshold of detection $k_{th1}$ (Fig. 2-b).

  2. 2.

Apply a morphological opening operation using erosion size $e_{sz}$ and
dilation size $d_{sz}$ to clean small cores out of the set (Fig. 2-c).

  3. 3.

Grow kernels to encompass all adjacent pixels above a threshold $r_{th1}$
(Fig. 2-d).

  4. 4.

Remove detected regions from magnetogram by setting magnetic field to zero
(Fig. 2-e).

  5. 5.

Find all pixels above a second threshold of detection $k_{th2}$ (Fig. 2-f).

  6. 6.

Apply a morphological opening operation using erosion size $e_{sz}$ and
dilation size $d_{sz}$ to clean small cores out of the set (Fig. 2-g).

  7. 7.

Growing kernels to encompass all adjacent pixels above a threshold $r_{th1}$
(Fig. 2-h).

  8. 8.

Combine the results of step 3 and step 7 (Fig. 2-i).

  9. 9.

Grow all regions to encompass all adjacent pixels above a lower threshold
$r_{th2}$ (Fig. 2-j).

  10. 10.

Merge positive and negative regions based on a combination of size and
proximity (Fig. 2-k). The decision to merge two regions is based on the
intersection between circles with radius equal to the average radius of each
region. If there is at least $o_{lim}$ proportional area overlap, the regions
are merged.

  11. 11.

Pair positive and negative regions into BMRs. First priority is given to
matches between the current frame’s regions and BMRs detected on the previous
magnetogram. The remaining positive and negative regions are paired by
minimizing a function that depends on distance between positive and negative
regions, size difference, and flux balance (Fig. 2-l).

TABLE I: Instrument-Specific Positive and Negative Region Detection Parameters | $k_{th1}$ | $k_{th2}$ | $e_{sz}$ | $d_{sz}$ | $o_{lim}$
---|---|---|---|---|---
KPVT/512 | 400G | 200G | 10px | 20px | 0.2
KPVT/SPMG | 400G | 200G | 10px | 20px | 0.2
SOHO/MDI | 500G | 275G | 9px | 18px | 0.2
SDO/HMI | 200G | 200G | 10px | 30px | 0.0

Most of the parameters that control the detection of positive and negative
regions (steps 1 through 10) are instrument specific and can be found on Table
I, the exceptions are $r_{th1}=$150G and $r_{th2}=$50G. The function used to
pair positive and negative regions is:

$F(d,\Delta A,\Delta\Phi)=d^{a}\Delta A^{b}\Delta\Phi^{c},$ (1)

where $d$ is the distance between centroids in degrees, $\Delta A$ is the
difference in area in cm2, $\Delta\Phi$ is the flux imbalance in Mx, and
$a=4$, $b=1$, and $c=1$. These parameters are chosen empirically to obtain the
best match between human and automatic detections performed on the same set of
observations.

Figure 4: Interface for BARD’s human supervision module. This interface was
used to transform Fig. 3-a into Fig. 3-b.

##  III Human vs. Machine: The Best of Both Worlds

As BARD grew in complexity and sophistication, we realized that regardless of
our algorithmic complexity, from time to time there would always BMR be
pairings in which we disagreed with the result of the algorithm. This is
illustrated in Figures 3-a & b which show a side by side comparison of the
output of the BARD code when applied to one of the most complex magnetograms
of the last 40 years (Fig. 3-a) vs. a human realization of the BMR pairing
(Fig. 3-b). In this most difficult case, about half of the detected BMRs
needed to be re-paired (untouched BMRs retain their colors).

To test the other side of the automatic vs. manual spectrum, we did an
exercise where we placed the entire burden of detection on a human observer.
We found that there is a clear limit in complexity beyond which a human
observer begins to miss BMRs. This can be seen very clearly in Figures 3-c & d
which show a side by side comparison of the logarithm of BMR flux for all
detected objects by a human observer (Fig. 3-c) and by the BARD code (Fig.
3-d) on SOHO/MDI magnetograms. There is a very clear deficit of smaller
objects in the human detection as solar activity ramps up.

###  III-A Human Supervision of the BARD Code

In order to take advantage of the strengths of automatic and manual forms of
detection, we implemented a module, shown in Figure 4, that allows a human
observer to interact with the output of the BARD code. Using this module, the
human observer can change BMR pairing and labeling (the most common mistakes
of the BARD code), while placing all the detection burden on the automatic
component. Additionally, in order to keep the catalog as homogeneous as
possible and the supervision exercise trackable, we limited the cadence of
observations to one magnetogram per day – even for modern high cadence
instruments like SOHO/MDI and SDO/HMI.

The great majority of human interaction is performed by
disassociation/association of positive-negative region pairs, followed by a
relabeling step ensuring that a given object is properly tracked in time.
However, sometimes the algorithm merges adjacent, but separate BMRs. When this
is the case, the human observer can fragment a region into smaller objects by
reversing step 10 (Fig. 2-k).

After having gone through the exercise of supervised detection with three
different observers and for different magnetogram sources, our estimated
proportion of human intervention is around 10% of all objects in our database.

Figure 5:  Butterfly diagram of each individually tracked object currently in
the BARD catalog. (a) Marker color denotes the instrument that was used as
data source. (b) Marker color denotes the sign of each BMR’s leading polarity
(solid markers indicate hemispheric exceptions to Hale’s law). (c) Marker
color denotes the sign of each BMR’s contribution to the Sun’s dipole moment.

##  IV Preliminary Results

The assembly of the BARD catalog used magnetograms from four different
instruments: the 512-channel of the Kitt Peak Vacuum Telescope (KPVT/512;
1976-1993), KPVT/SPMG (1992-1999), SOHO/MDI (1996-2011) and SDO/HMI
(2010-2016). Three different undergraduate research assistants worked over a
period of three years (2014-2016) to complete it.

Figure 5-a shows a butterfly diagram in which every dot represents a unique
BMR detected and tracked as it crosses the solar disk, colored according to
the instrument it was measured on. In spite of our data not yet being cross-
calibrated, something on which we are working at the moment, it is reassuring
to see agreement between instruments in terms of the time-latitude coordinates
of the detected objects.

At the moment, the BARD catalog contains nearly 10,000 unique objects, all
tracked across the disk, containing for each day of observation: a pixel mask
of its positive and negative regions, positive and negative magnetic fluxes,
latitude and longitude of each polarity’s centroid, area covered by each
polarity, and tilt. This information is displayed as an example in Figure 5-b,
which shows the systematic East-West hemispheric orientation of BMRs (also
known as Hale’s law[6]). The combination of this systematic East-West
orientation and the tendency of the leading (trailing) polarity of BMRs to be
closer to the equator (pole), also known as Joy’s law, results in the synergic
buildup of a large-scale north-south magnetic field shown in Figure 5-c.

##  V Future Work: Building a Multi-Scale magnetic Catalog using BARD and
SWAMIS

As mentioned in Section IV, we purposely limited the scope of our catalog to
maximize consistency and accuracy when detecting and characterizing BMRs. The
result is a catalog with lower cadence (one measurement per day) than SOHO/MDI
(16 measurements per day) and SDO/HMI (one measurement every 10 minutes), as
well as limited to the top three observable orders of magnitude in flux (i.e.
$10^{20}-10^{22}$Mx) – a limit imposed both by our chosen set of thresholds
and the difficulty of determining bipolarity for smaller objects. While this
is sufficient to seed solar dynamo and surface flux transport models, as well
as to characterize the large scale statistical properties of BMRs and their
dependence of solar activity, there is immense value in building catalogs that
take advantage of full instrumental cadence and scale. The reason is that such
a catalog will enable a wide array of statistical analyses that can shed light
in the scaling and origin of surface magnetism[7, 8, 9, 10], the nature of
observable dynamo action[11, 10], dependence of surface magnetism on solar
activity[12, 13], and characterization of the solar extended cycle[14, 15],
among many other examples.

###  V-A SWAMIS: A multi-scale Feature Tracking Algorithm

Starting development in 2001, SWAMIS[16] has evolved to be a truly multi-scale
detection algorithm that has been used to study flux balance in solar surface
magnetism[17, 18], flux distribution of magnetic elements[9], dissipation of
surface magnetic elements[19], and locality of surface dynamo action[11].

There are two main differences between BARD and SWAMIS. The first one is the
algorithm used for fully determining the size of each feature. As mentioned in
Section II, BARD uses an algorithm that groups all adjacent pixels above a
certain threshold of detection (also referred to as clumping; see Figure 6-a).
This algorithm has a tendency to join large regions, which is not a problem
for large BMRs, but becomes a significant problem for small flux scales. To
address this problem, SWAMIS combines the use of a clumping algorithm for
large objects with a downhill algorithm for small objects(see Figure 6-b),
which seeds each region around a local maximum in flux and grows each feature
until their boundaries meet at saddle points.

---
Figure 6: Feature growing algorithms. Clumping (a) groups all adjacent pixels above a certain threshold of detection. Downhill (b) seeds each region around a local maximum in flux and grows each element until their boundaries meet at saddle points. The current version of SWAMIS uses a hybrid method: clumping in areas of high average field strength, and downhill in areas of low average field strength. Adapted from[16]. |  |
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Figure 7: (a) Original-resolution SDO/HMI magnetogram, degraded by a factor of
8 (b) and 16 (c). Feature detection and tracking of these reduced-resolution
magnetograms allows for the association of smaller-scale features (d) with
larger-scale features (e).

The second main difference, which makes SWAMIS unique among magnetic feature
tracking codes, is the application of the feature detection and tracking to
resampled versions of the same original image. This process, illustrated in
Figure 7, determines the algorithmic make up of SWAMIS. Each of the following
steps are applied by SWAMIS to every magnetogram:

  1. 1.

Downscale the original image (Fig. 7-a) to one half, one quarter, one eighth
(Fig. 7-b), and one sixteenth (Fig. 7-c) of the original resolution.

  2. 2.

Identify which pixels in the images are in features by flagging pixels that
are above a scale-dependent high threshold, and flagging adjacent (in space
and time) pixels that are above a scale-dependent low threshold, and
continuing the search until all such pixels have been flagged

  3. 3.

Apply the hybrid clumping/downhill feature identification method to segment
each image into magnetic features (Figs. 7-d& e show the results for 1/8th and
1/16th of the original resolution), creating masks with feature IDs.

  4. 4.

If necessary based on image resolution and cadence, differentially rotate the
masks of the previous magnetogram using the difference in time between current
and previous frames.

  5. 5.

Use a dual-maximum flux-weighted overlap method[16] to identify feature to
feature matches between the previous and current frames, updating current
feature IDs if a match is found.

  6. 6.

Associate overlapping features across subsequent downsized resolutions. This
step is what makes SWAMIS a multi-scale code as it allows the user to connect
each large-scale structure with its constituent smaller-scale features.

###  V-B BARD & SWAMIS: A Match Made in Heaven

One of the important lessons that we have learned in making the BARD catalog
is the difficulty of automatically pairing positive and negative magnetic
elements to form BMRs when the tracking and pairing module operates using
frame to frame comparisons. The main problem resides on the fact that BMRs not
only span multiple spatial scales, but also different temporal scales. This
difficult, but tractable exercise for a human supervisor becomes too complex
for the current state of the art feature detection algorithms used in solar
physics. However, the existence of a human vetted BARD catalog lends itself
perfectly to the role of anchor for a SWAMIS catalog of unpaired positive and
negative magnetic elements. Our plan for the creation of this hybrid catalog
goes as follows:

  1. 1.

Create a SWAMIS catalog of tracked elements for each of the instruments used
by the BARD catalog at full cadence.

  2. 2.

For each pair of subsequent magnetograms used by the BARD catalog identify the
positive and negative elements, as well as their associated multi-scale
components, that have been identified as linked.

  3. 3.

Using SWAMIS tracking information follow the evolution of the identified
regions to see if there are mismatches connecting the two frames.

  4. 4.

Update SWAMIS flux element IDs in order to better match BARD constituencies,
or flag the event for human supervision in case of failure.

  5. 5.

Create internal links marking the identified positive/negative pair in BARD
and SWAMIS.

##  VI Summary and Concluding Remarks

In this paper we have reported the status of our effort to create a
homogeneous BMR catalog by applying the BARD automatic detection code to
magnetograms taken by the KPVT/512 (1976-1993), KPVT/SPMG (1992-1999),
SOHO/MDI (1996-2011) and SDO/HMI (2010-2016) instruments. In order to increase
the accuracy of the catalog, we couple the automatic detection of BMRs with a
human supervision module that allows a human observer to correct mislabels
(introduced by errors in the automatic tracking module) and incorrect pairing
of positive and negative regions to form BMRs (common when dealing with highly
complex magnetic regions). This arrangement allows us to take advantage of the
strengths of automatic detection (consistency) and of manual detection
(optimal detection of BMRs close enough to form active complexes).

The resulting catalog contains nearly 10,000 unique objects fully
characterized and tracked at a cadence of one measurement per day, 10% of
which required some form of human intervention. Our future plans involve the
combination of the BARD and SWAMIS catalog to take advantage of the full
cadence of the SOHO/MDI and SDO/HMI instruments, and the detection of magnetic
features at smaller scales than those in the current BARD catalog. The
resulting composite catalog will be made public through the solar dynamo
dataverse (https://dataverse.harvard.edu/dataverse/solardynamo) and will prove
instrumental for enabling a new generation of statistical studies shedding
light on the scaling and origin of surface magnetism, the nature of observable
dynamo action, the dependence of surface magnetism on solar activity, and the
characterization of the solar extended cycle.

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