# Properties of post-shock solar wind deduced from geomagnetic indices
responses after sudden impulses

###### Abstract

Interplanetary (IP) shock plays a key role in causing the global dynamic
changes of the geospace environment. For the perspective of Solar-Terrestrial
relationship, it will be of great importance to estimate the properties of
post-shock solar wind simply and accurately. Motivated by this, we performed a
statistical analysis of IP shocks during 1998-2008, focusing on the
significantly different responses of two well-used geomagnetic indices (SYMH
and AL) to the passive of two types of IP shocks. For the IP shocks with
northward IMF (91 cases), the SYMH index keeps on the high level after the
sudden impulses (SI) for a long time. Meanwhile, the change of AL index is
relative small, with an mean value of only -29 nT. However, for the IP shocks
with southward IMF (92 cases), the SYMH index suddenly decreases at a certain
rate after SI, and the change of AL index is much significant, of -316 nT.
Furthermore, the change rate of SYMH index after SI is found to be linearly
correlated with the post-shock reconnection E-field (EKL). Based on these
facts, an inversion model of post-shock IMF orientation and EKL is developed.
The model validity is also confirmed by studying 68 IP shocks in the period of
2009-2013. The inversion accuracy of IMF orientation is 88.24%, and the
inversion efficiency of EKL is as high as 78%.

Gindraft=false LI ET AL. POST-SHOCK PROPERTIES DEDUCED FROM GEOMAGNETIC
INDICES Corresponding author: H. Li, State Key Laboratory of Space Weather,
Center for Space Science and Applied Research, Chinese Academy of Sciences,
P.O. Box 8701, Beijing 100190, China<EMAIL_ADDRESS>
11affiliationtext: State Key Laboratory of Space Weather, Center for Space
Science and Applied Research, Chinese Academy of Sciences, Beijing, China.

##  1 Introduction

As one of the major interplanetary (IP) disturbances, IP shocks link events
occurred at the Sun with moderate to extraordinary disturbances at the Earth,
and greatly affect the near-Earth environment. Several geoeffectivenesses of
IP shocks can be summarized as follows: (1) IP shocks are accompanied by large
changes of solar wind dynamic pressure and thus significantly compress the
Earth’s magnetosphere, causing the sudden impulses (SIs) observed by ground-
based magnetometers (Chao and Lepping, 1974; Araki, 1994); (2) IP shocks with
southward interplanetary magnetic field (IMF) are important interplanetary
causes of geomagnetic storms (e.g. Tsurutani et al., 1988, 1992; Gonzalez et
al., 1999; Echer et al., 2008a, b); (3) IP shocks can accelerate the energetic
particles during their propagation (e.g. Rao et al., 1967; Lee, 1984; Desai et
al., 2003) and trigger substorms (e.g. Tsurutani and Zhou, 2001; Yamauchi et
al., 2006; Yue et al., 2010). Besides, most of the IP shocks observed within 1
AU can be identified with Interplanetary Coronal Ejections (ICMEs) (Sheeley et
al., 1985; Gopalswamy, 2006). Thus, IP shocks can also signal the impending of
arrivals of ICMEs.

When an IP shock collides with the Earth’s magnetopause, the great compression
of magnetosphere will cause a SI of the geomagnetic H-component at middle and
low latitudes on the ground. When a SI is followed by a magnetic storm, it is
then called storm sudden commencement (SSC). Observations show that almost all
SIs are caused by IP shocks (Chao and Lepping, 1974; Smith et al., 1986). The
SI amplitude is found to be linearly correlated to the the change in the
square root of the solar wind dynamic pressure in many quantitative studies
(e.g. Russell et al., 1992, 1994a, 1994b; Wang et al., 2009). And the rise
time of SI generally ranges from 2 to 10 min centered around 4 min (Maeda et
al, 1962) and is affected by IP shock strength and orientation (Takeuchi et
al., 2002; Wang et al., 2006).

The primary cause of intense geomagnetic storms ($<$ -100 nT) is associated
with interplanetary structures with intense and long-duration southward IMF
(Gonzalez et al., 1994). Except for ICME or Magnetic Cloud (MC), another major
origin of these structures is the sheath region (SH) behind an IP shock,
caused by draping effects or by amplification of the pre-existing southward
IMF by shock compression (Gosling et al., 1991). Thus, IP shocks can have an
important role in causing geomagnetic storms. Echer et al. (2008b) identified
the interplanetary causes of intense geomagnetic storms during solar cycle 23
(1996-2006). Based on their storm list, we obtained some simple statistics of
intense storms for different drivers (Corotating Interaction Region (CIR)
only; ICME only; SH only; combination of SH and ICME, SH+ICME) as shown in
Table 1. The intensity of purely SH-driven storm (-172 nT) is significantly
greater than that of CIR-driven storm (-118 nT), and is on the same level of
purely ICME-driven storm (-166 nT). The storms caused by the combination of SH
and ICME have the largest intensity (-223 nT). About 45.2% of the intense
storms are caused by the contribution of IP shocks (SH, SH+ICME). For great
storms ($<$ -200 nT), this percentage increases to 72.2%. Similar conclusion
is also made by Huttunen and Koskinen (2004).

Based on the SI amplitude observed on the ground, Wang et al. (2010) made the
first effort to estimate IP shock characteristics (such as the change in the
square root of solar wind dynamic pressure and the shock speed along the Sun-
Earth line) and associated geosynchronous magnetic field variation. Although
the estimations are not unique solutions, they still are of particular use in
studying events before the space era and could provide proxies for future if
we do not have monitoring spacecraft at the L1 point.

The dynamic pressure change across an IP shock affects the large-scale change
of magnetosphere structure. Compared with the estimation of post-shock dynamic
pressure, the estimations of post-shock IMF orientation and reconnection
E-field (EKL, proposed by (Kan and Lee, 1979)) are of greater importance in
magnetospheric storms/substorms. The post-shock IMF orientation determines
whether the sheath region can drive a magnetospheric storm/substorm or not,
while EKL controls the magnitude of storm/substorm and the duration of
substorm growth phase (Li et al., 2010, 2013). Motivated by this, we will
develop an inversion model to estimate the properties of post-shock solar
wind, such as IMF orientation and EKL. This paper is organized as follows: The
different responses of two geomagnetic indices after SI for IP shocks with
northward and southward IMF are shown in section 2, both from the perspective
of case study and of superposed epoch analysis. The inversion model for post-
shock solar wind properties is summarized in section 3. The validation of the
inversion model is checked up in section 4. At last, section 5 gives the
summary.

##  2 Different Responses of SYMH/AL Index after SI

###  2.1 Two Comparative Case Study

Figure 1 shows two IP shocks with northward IMF (left column) and with
southward IMF (right column), and the corresponding responses of SYMH/AL index
after SIs. The top four panels show successively the sudden changes of IMF
magnitude ($B_{T}$), solar wind bulk speed ($V_{SW}$), solar wind number
density ($N_{SW}$), and solar wind temperature ($T$) observed by ACE satellite
at L1 point, which represent the occurrence of an IP fast forward shock. The
next two panels give the Z component of IMF ($B_{Z}$) and the reconnection
E-field (EKL). The bottom two panels show the responses of SYMH index and AL
index. Note that, both the SYMH index and AL index have been time-shifted to
the shock occurring onset marked as the vertical dashed line.

For the IP shock with northward IMF on June 4 2000, IMF $B_{Z}$ increased from
4 nT to 10 nT across the shock. The sheath region kept northward IMF for a
long time. Meanwhile, EKL had a very minor enhancement and kept on an extreme
low level of about 0.2 mV/m. This shock then caused a SI on the ground. The
SYMH index increased from -6 nT to 8 nT, and then kept almost invariant. The
AL index had no obvious change after the shock collided the magnetosphere.
Except for a compression effect of magnetosphere, this IP shock and its sheath
region with northward IMF did not caused any magnetic storm or magnetospheric
substorm.

For the IP shock with southward IMF on October 28 2001, IMF $B_{Z}$ decreased
from -5 nT to -15 nT across the shock. The sheath region kept southward IMF
for more than half an hour. Meanwhile, EKL had a significant enhancement and
kept on a high level of about 9 mV/m. Similarly, this shock then caused a SI
on the ground, with the SYMH index increasing from -16 nT to 19 nT. Compared
to the previous shock event with northward IMF, this shock then led to some
different geoeffectivenesses after the SI. The SYMH index decreased suddenly
after the peak value of SI at an almost constant rate, which implied the
formation of the partial ring current during a magnetic storm as studied by Li
et al. (2011). The AL index also decreased suddenly and significantly from
-200 nT to -700 nT, implying the enhancement of westward electrojet on the
high-latitude ionosphere and the possible occurrence of a substorm.

###  2.2 Superposed Epoch Analysis

To check up whether the different responses of SYMH/AL index after SI shown in
Figure 1 are common phenomena, a superposed epoch analysis of IP shocks with
northward IMF and with southward IMF is performed. During the period of
1998-2008, 91 IP shocks with northward IMF and 92 IP shocks with southward IMF
are studied. Note that, the IP shocks with the absolute value of post-shock
$B_{Z}$ less than 2 nT are all excluded to show the differences more clear.

Figure 2 shows the results of superposed epoch analysis of IP shocks with
northward IMF (left column) and with southward IMF (right column). Instead of
the SYMH index and AL index, the relative values ($\delta$SYMH and $\delta$AL)
are processed here, which have subtracted the values at SI rising onset.
Except for the bottom two panels, the other panels are the same as those in
Figure 1. It is clear that the general changes of $B_{T}$, $V_{SW}$, $N_{SW}$
and the absolute value of $B_{Z}$ across the shocks are quite similar for the
IP shocks with northward IMF and with southward IMF. However, as shown in
Figure 1, the changes of EKL across the shocks and the responses of
$\delta$SYMH and $\delta$AL after SIs are obviously different for these two
types of IP shocks. For the IP shocks with northward IMF: 1) EKL changed from
0.4 mV/m to 0.7 mV/m across the shocks; 2) the SYMH index kept almost
invariant after SIs; 3) the AL index decreased very few after SIs, mostly
greater than -100 nT. For the IP shocks with southward IMF: 1) EKL changed
significantly from 1.6 mV/m to 4.2 mV/m across the shocks; 2) the SYMH index
decreased at a certain rate after SIs; 3) the AL index decreased significantly
after SIs.

##  3 Inversion Model of Post-shock Solar Wind Properties

###  3.1 Post-shock IMF Orientation Estimation

As shown in Figure 2, the responses of AL index after SIs are quite different
for the IP shocks with northward/southward IMF. To give more details, Figure 3
shows the histogram distribution of $\Delta$AL for these two types of IP
shocks. $\Delta$AL = ALM \- ALO, represents the change of AL index after SIs,
where ALM is the mean value of AL index after SI, and ALO is the AL index at
the SI onset. The vertical axis represents the occurrence rate. For the IP
shocks with northward IMF, $\Delta$AL ranges mainly from -80 nT to 40 nT,
peaking at 0$\sim$40 nT. For the IP shocks with southward IMF, $\Delta$AL
ranges mainly from -600 nT to -40 nT, peaking at -600$\sim$-200 nT. The mean
value of $\Delta$AL is -29 nT (-316 nT) for the IP shocks with northward
(southward) IMF. Inspired by this fact, we try to use $\Delta$AL to be the
criterion for estimating post-shock IMF orientation. For practice, the
criterion of $\Delta$AL is chosen to be -80 nT. Thus, the inversion model of
post-shock IMF orientation is given as follows:

$\left\\{\begin{array}[]{lr}\Delta\textnormal{AL}\geq\textnormal{-80
nT},&\textnormal{Northward IMF}\\\ \Delta\textnormal{AL}<\textnormal{-80
nT},&\textnormal{Southward IMF}\\\ \end{array}\right.$ (1)

Based on this criterion, the post-shock IMF orientations for the 183 IP shocks
during 1998-2008 studied in subsection 2.2 is estimated by using the
corresponding observations of $\Delta$AL after SIs. The inversion result is
shown in Table 2. For 80 out of 91 IP shocks with northward IMF, the post-
shock IMF orientations were estimated correctly, with the inversion accuracy
of 87.91%; for 75 out of 92 IP shocks with southward IMF, the post-shock IMF
orientations were estimated correctly, with the inversion accuracy of 81.52%.
For all the 183 IP shocks, the inversion accuracy is 84.70%.

###  3.2 Post-shock Reconnection E-field Estimation

As shown in Figure 2, the SYMH index kept on the high level after SIs for IP
shocks with small post-shock reconnection E-field, and decreased at a certain
rate for the IP shocks with large post-shock reconnection E-field. Figure 4
shows the relationship between the change rate of SYMH index after SIs
($\delta$SYMH/$\delta$t) and the post-shock reconnection E-filed (EKL). To
obtain the change rate of SYMH index, we chose the 30-min duration time series
of SYMH index after SI and used the least square method to do the linear
fitting. The slope of linear fitting is regarded as the change rate of SYMH
index. It is clear that $\delta$SYMH/$\delta$t is negatively correlated to the
post-shock EKL, with the correlation coefficient of -0.86. From this linear
relationship, the post-shock EKL can be estimated from $\delta$SYMH/$\delta$t
observation. The empirical formula is summarized as follows:

$\textnormal{E}_{KL}=\left\\{\begin{array}[]{lr}0.22-7.70\
\delta\textnormal{SYMH}/\delta\textnormal{t},&\delta\textnormal{SYMH}/\delta\textnormal{t}\leq
0.028\\\ 0.0,&\delta\textnormal{SYMH}/\delta\textnormal{t}>0.028\\\
\end{array}\right.$ (2)

##  4 Validation of Inversion Model

To check up the validity of our inversion model of post-shock solar wind
properties, we performed a statistical test of 30 IP shocks with northward IMF
and 38 IP shocks with southward IMF identified during the period of 2009-2013.

Table 3 shows the inversion accuracy of post-shock IMF orientation. For 28 out
of 30 IP shocks with northward IMF, the post-shock IMF orientations were
estimated correctly, with the inversion accuracy of 93.33%; for 32 out of 38
IP shocks with southward IMF, the post-shock IMF orientations were estimated
correctly, with the inversion accuracy of 84.21%. For all the 68 IP shocks,
the inversion accuracy is 88.24%. On the whole, the post-shock IMF orientation
estimation is pretty good.

Figure 5 shows the results of post-shock EKL estimation. The horizontal axis
shows the observed EKL and the vertical axis shows the estimated EKL. The
dashed diagonal line indicates the perfect inversion result. The closer to the
diagonal line, the better inversion is. We adopt the inversion efficiency (PE)
(Agterberg, 1984) to quantify the accuracy of the estimated value compared
with the measurement, which is defined as:

$\textnormal{PE}=1-\frac{\textnormal{variance of the
residual}}{\textnormal{variance of the data}}$ (3)

The residual is the difference between the measured data and the estimation.
The closer of PE to 1, the better inversion is. Although there are some
deviations, the estimated EKL are scatted around the diagonal line. The
estimations are in good agreement with the observations, with an inversion
efficiency of 78%.

##  5 Summary

As one of the major IP disturbances, IP shock plays a key role in causing the
global dynamic changes of the geospace environment. IP shocks are usually
accompanied by large changes of solar wind dynamic pressure and thus
significantly compress the Earth’s magnetosphere. Besides, the sheath regions
after IP shocks are another major origins of IP structures with intense and
long-duration southward IMF, which are important sources of geomagnetic
storms. In addition, IP shocks can also trigger magnetospheric substorms.
Thus, it will be of great importance to estimate the properties of post-shock
solar wind simply and accurately.

Motivated by this, we performed a statistical analysis of IP shocks with
northward (91 cases) and southward (92 cases) interplanetary magnetic field
(IMF) during 1998-2008. The responses of two well-used geomagnetic indices,
SYMH and AL index, are found to be significantly different to the passive of
these two types of IP shocks. For the IP shocks with northward IMF, the SYMH
index keeps on the high level after the sudden impulses (SI) for a long time.
Meanwhile, the change of AL index is relative small, with the mean value of
only -29 nT. However, for the IP shocks with southward IMF, the SYMH index
suddenly decreases at a certain rate after SI, and the change of AL index is
much significant, of -316 nT. Furthermore, the change rate of SYMH index is
found to be linearly correlated with the post-shock reconnection E-field
(EKL). Based on these facts, an inversion model of post-shock IMF orientation
and EKL is developed. For the post-shock IMF orientation, the inversion model
is summarized as follows:

$\left\\{\begin{array}[]{lr}\Delta\textnormal{AL}\geq\textnormal{-80
nT},&\textnormal{Northward IMF}\\\ \Delta\textnormal{AL}<\textnormal{-80
nT},&\textnormal{Southward IMF}\\\ \end{array}\right.$ (4)

And for the post-shock reconnection E-field, the inversion model is summarized
as follows:

$\textnormal{E}_{KL}=\left\\{\begin{array}[]{lr}0.22-7.70\
\delta\textnormal{SYMH}/\delta\textnormal{t},&\delta\textnormal{SYMH}/\delta\textnormal{t}\leq
0.028\\\ 0.0,&\delta\textnormal{SYMH}/\delta\textnormal{t}>0.028\\\
\end{array}\right.$ (5)

The validity of this inversion model is also verified by studying 68 IP shocks
in the period of 2009-2013. The inversion accuracy of IMF orientation is
88.24%, and the inversion efficiency of EKL is as high as 78%.

These estimations of post-shock IMF orientation and reconnection E-field are
an extension of our previous work (Wang et al., 2010), which made the first
effort to estimate IP shock characteristics, such as the change in the square
root of solar wind dynamic pressure, the shock speed along the Sun-Earth line,
and associated geosynchronous magnetic field variation, from SI amplitude
observed on the ground. These estimations are of particular use in studying
events before the space era and could provide proxies for future times if we
do not have a solar wind monitoring spacecraft at the L1 point.

###### Acknowledgements.

We acknowledge the use of 64-s ACE data combined the magnetic field and solar
wind plasma from Coordinated Data Analysis Web (CDAWeb). We also thank the
Kyoto World Data Center for providing the SYMH/AL indices. This work was
supported by 973 program 2012CB825602, NNSFC grants 41204118 and 41231067, and
in part by the Specialized Research Fund for State Key Laboratories of China.

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Table 1: Statistics of intense magnetic storms ($<$ -100 nT) during solar
cycle 23 (1996-2006) for different drivers: (1) CIR only; (2) ICME only; (3)
SH only; (4) SH+ICME.

| CIR | ICME | SH | SH+ICME
---|---|---|---|---
Case number | 12 | 33 | 22 | 15
Percentage | 14.6% | 40.2% | 26.9% | 18.3%
Storm intensity | -118 nT | -166 nT | -172 nT | -223 nT
Great storma | 0 (0.0%)b | 5 (27.8%) | 6 (33.3%) | 7 (38.9%)

aGreat storm represents the magnetic storm with the peak SYMH index $<$ -200
nT. bThe value precedes the bracket is the case number, and the value in the
bracket represents its percentage.

Table 2: Inversion accuracy of post-shock IMF orientation for the IP shocks during 1998-2008. | Northward (Estimated) | Southward (Estimated)
---|---|---
Northward (observed) | 80 (87.91%) | 11 (12.09%)
Southward (observed) | 17 (18.48%) | 75 (81.52%)

Table 3: Inversion accuracy of post-shock IMF orientation for the IP shocks during 2009-2013. | Northward (Estimated) | Southward (Estimated)
---|---|---
Northward (observed) | 28 (93.33%) | 2 (6.67%)
Southward (observed) | 6 (15.79%) | 32 (84.21%)

Figure 1: Two comparative events of IP shocks with northward IMF on June 4,
2000 (left column) and with southward IMF on October 28, 2001 (right column).
Note that, both the SYMH index and AL index have been time-shifted to the
shock occurring onset marked as the vertical dashed line.

Figure 2: Superposed epoch analysis of IP shocks with northward IMF (left
column) and with southward IMF (right column). The gray line is the
observation for each IP shock, and the black line represent the mean value.
Instead of the SYMH index and AL index, the relative values ($\delta$SYMH and
$\delta$AL) are processed here, which have subtracted the values at SI rising
onset. Note that, both the $\delta$SYMH and $\delta$AL have been time-shifted
to the shock occurring onset marked as the vertical dashed line.

Figure 3: Histogram distribution of $\Delta$AL for two types of IP shocks: (A)
IP shocks with northward IMF; (B) IP shocks with southward IMF.

Figure 4: Relationship between the change rate of SYMH index after SIs and the
post-shock reconnection E-field. The red line represents the linear fitting
result. CC represents the correlation coefficient.

Figure 5: Results of post-shock reconnection E-field estimation. The dashed
diagonal line indicates the perfect inversion. PE represents the inversion
efficiency.


# Optical Variability of the very Radio-Loud Narrow line Seyfert 1 galaxy, 1H
0323+342

Clay S. Turner,1 Hugh R. Miller,1 Jeremy D. Maune1 and Joseph R. Eggen2,3,4
1Georgia State University, Department of Physics and Astronomy, Atlanta,
Georgia 30303, USA
2Department of Astronomy, University of Maryland, College Park, MD 20742, USA
3Center for Research and Exploration in Space Science and Technology,
NASA/GSFC, Greenbelt, MD 20771, USA
4NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
E-mail<EMAIL_ADDRESS>(CST)

(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

1H 0323+342 is optically one of the nearest and brightest very radio loud
narrow line Seyfert 1 galaxies (vRL NLSy1). It is also one of the first vRL
NLSy1s detected at gamma-ray energies by the Fermi-LAT. We report the results
of monitoring the optical flux of 1H 0323+342 during more than six and a half
years. In some cases, we, for the first time, simultaneously use two
telescopes to monitor the optical flux of 1H 0323+342 on timescales ranging
from minutes to hours, demonstrating the reality of low amplitude
microvariability whole events with durations of a few hours for this object.
Based on the present results, as well as those of earlier studies, we suggest
that this represents a fundamental timescale associated with the underlying
source region We also present an enhancement of Howell’s comparison star
method for detecting microvariability.

###### keywords:

galaxies:Seyfert - galaxies:photometry - galaxies:active

††pubyear: 2022††pagerange: Optical Variability of the very Radio-Loud Narrow
line Seyfert 1 galaxy, 1H 0323+342–Optical Variability of the very Radio-Loud
Narrow line Seyfert 1 galaxy, 1H 0323+342

##  1 Introduction

The very radio loud narrow line Seyfert 1 galaxy (vRL NLSy1) 1H 0323+342 is
one of the first of this class to be detected at gamma-ray energies by the
Fermi-LAT (Abdo et al., 2009). Previously, gamma ray loud active galactic
nuclei (AGN) have been identified most often with BL Lac objects and flat
spectrum radio quasars, i.e. blazars. However, recently a small number of
radio galaxies have also been detected with Fermi. The host galaxies for all
these classes are elliptical galaxies. The host galaxies associated with most
Seyfert galaxies are typically spiral galaxies. Since gamma ray emission is
thought to be the signature of the presence of a relativistic jet oriented
near the line-of-sight to the observer, this indicates, for the first time,
that spiral galaxies may also host these highly energetic jets. vRLNLSy1s are
a class of AGN with unusual characteristics when compared with those
associated with Seyfert 1 galaxies (Komossa, 2018; Pogge, 2000). The host
galaxies for NLSy1 galaxies are usually spiral or barred spirals (Ferrara,
2000; Crenshaw et al., 2004; Deo et al., 2006). The $H\beta$ emission line has
FWHM<2000 $kms^{-1}$ (Goodrich, 1989) and their spectra contain strong Fe II
emission (Osterbrock & Pogge, 1985). They have steep soft X-ray spectra, and
exhibit strong, large amplitude X-ray variability, (e.g., Boller et al. 1996
). These extreme properties can be understood in terms of vRL NLSy1s having
relatively low-mass super massive black holes (SMBH) and accretion rates near
the Eddington accretion rate (Komossa, 2008).

Yuan et al. (2008) defined vRL NLSy1 galaxies as those NLSy1 galaxies which
have R>100, where R is defined as the ratio of rest frame flux at 1.4 GHz to
the optical flux in the B band at 4400Å. Thus, those members of vRL NLSy1s
detected at gamma ray energies share many properties similar to those seen for
blazars (Abdo et al., 2009; Foschini et al., 2015). In particular, blazars
exhibit large amplitude, rapid variations with timescales as short as minutes-
to-hours. Rapid and large amplitude optical variability is a well-established
property for blazars and blazar-like AGN such as vRL NLSy1s (Smith, 1996).
Indeed, rapid optical variations on timescales as short as minutes to hours,
known as microvariability (Miller et al., 1989) or intranight optical
variability (INOV) (Wagner & Witzel, 1995), have been reported for several vRL
NLSy1 galaxies including 1H 0323+342, e.g. Ojha et al. (2019); Paliya et al.
(2013, 2014); Maune et al. (2014). 1H 0323+342 is one of the nearest vRL NLSy1
galaxies detected at gamma ray energies and is among the brightest of this
class observed at optical wavelengths Foschini et al. (2015); Doi (2020). As
such, it is an attractive object to monitor for rapid variations at optical
wavelengths. Variability studies, such as those reported in the present study,
provide valuable insight into the physical mechanism(s) responsible for the
emission processes and the structure and size of the emitting region(s).
However, none of the earlier investigations demonstrated the reality of well-
defined whole events (i.e., variations which exhibit both the rise and decline
in flux defining the event) with optical ranges as small as $\approx 0.03$ mag
and a duration as short as a few hours such as are presented in this present
investigation.

In this paper, we report the results of a monitoring program begun in 2010,
during which we have monitored the optical flux of 1H0323+342 on timescales as
short as minutes to as long as years. On one night we obtained a unique set of
observations using multiple telescopes to provide quasi-simultaneous
monitoring of the optical flux on minute timescales. Previously Miller et al.
(2017) presented preliminary results using a small, early segment of the data.
The present paper builds upon those earlier reported results and includes a
substantial body of new unpublished observations.

##  2 Optical Observations and Data Reduction

We obtained the majority of the photometric observations included here using
the 31-inch NURO telescope, 42-inch Hall telescope, and the 72-inch Perkins
telescope at Lowell Observatory in Flagstaff, AZ. We collected additional
optical data using the 24-inch Plane Wave Instruments CDK telescope located at
Georgia State University’s Hard Labor Creek Observatory (HLCO) near Rutledge,
GA. The Perkins, Hall, and NURO telescopes are equipped with custom designed
cryogenically chilled CCD cameras fitted with Johnson filters for photometry.
Similarly, the 24-inch Planewave telescope uses a commercially available
Peltier chilled CCD camera (Apogee Alta U230) with Johnson filters.

The observational data presented here for 1H 0323+342 consists of nearly 5000
individual observations obtained on 85 different nights from November, 2010 to
March, 2017. Nightly summary data is given in Table 2. In the table we provide
mean Julian date, the common date, number of nightly observations, time span
in minutes for the observing run, mean R band magnitude, and, for nights with
five or more observations, the modulation index percentage defined in
Quirrenbach et al. (2000). These observations were included as part of our
larger blazar monitoring program which scheduled approximately one week of
observations, less than a week away from the new moon (dark time), each month.
The observations consist of nights when as few as two observations per night
were obtained to nights when hundreds of images were taken at minute-scale
time resolution for a duration of several hours during that night. On several
occasions, quasi-simultaneous observations were also taken with two different
telescopes in an effort to provide independent confirmation for the reality of
any small amplitude (< 0.03 mag) microvariability events which might have
timescales from minutes to hours.

The R-band magnitudes were derived using differential aperture photometry with
in-field comparison stars. We selected the comparison stars based on each of
them having a brightness comparable to 1H 0323+342 and for their exhibiting
photometric stability over long periods of time. The R-band magnitudes for the
comparison stars were calibrated using Landolt (1992) standard stars. The
finding chart for 1H 0323+342 along with the comparison stars which we used
and their R-band magnitudes may be found on our group’s dedicated web pages at
https://sites.google.com/site/jdmaune/seyfert-fields/0323-342. At the
beginning of each night, we collected a set of 10 bias frames and subsequently
combined them into a single super bias frame. In addition, we also collected a
set of 10 flat field images during the night and combined them to form a
single super flat field frame. The super bias frame was then subtracted from
each individual object frame and was then divided by the super flat field
frame. We utilized IDL for most of the data reduction. However, we used MaxIm
DL and IRAF for a small portion of the data. Maxim DL and IRAF have been shown
to produce nearly identical results (Blackwell et al., 2005). Similarly, we
have reduced some data using IDL and Maxim DL (specifically for the nights of
simultaneous observations), compared the results, and found there was good
agreement in the results for IDL and Maxim DL. The similar results for the
various programs is not surprising since differential aperture photometry is a
basic form of analysis. For consistency, we standardized to a seven arcsecond
diameter aperture for the analyses of all the data taken with any of the four
telescopes.

$2011$$2012$$2013$$2014$$2015$$2016$$2017$$15$$15{,}25$$15{,}5$$15{,}75$$16$$16{,}25$$16{,}5$YearR
Magnitude1H 0323+342 R Magnitude Figure 1: 1H 0323+342 R band light curve

##  3 R Band Light Curve

The range for the optical variability for 1H 0323+342, covering an interval of
$\approx 6$ years, is shown in Fig. 1. This source is seen to be
extraordinarily active from late 2010 to early 2013 with several rapid, large
amplitude events observed where the source varies $\approx 1.0$ mag on the
timescale of less than a week. However, the variations appear to be of lower
amplitude and tend to occur when the object is near the fainter end of the
range exhibited in Fig. 1 after 2013. Although this seems to be true for the
overall variability patterns shown in figure 1, there are exceptions to this
general trend. For example, 1H 0323+342 appears to exhibit extremely rapid
large-amplitude variations on Feb 19, 2012 while it is in a bright state and
similar, large-amplitude variations are observed on Feb 21, 2012 when the
source was near a minimum of its brightness. The source seems to be unusually
active during this period. Unfortunately, the monitoring of 1H 0323+342 is not
sufficiently dense and continuous to determine if there is any relationship
between the brightness/faintness of the object and the presence of rapid large
amplitude variations.

##  4 Test for the Presence of Microvariability

A visual inspection of the light curve shown in Fig. 1 clearly indicates that
1H 0323+342 exhibits variability over timescales ranging from less than a day
to years. Several earlier variability studies of 1H 0323+342, e.g., (Paliya et
al., 2013; Ojha et al., 2019), seeking to identify microvariations, have found
positive evidence for the existence of small amplitude ($\approx 0.1$ mag)
variations on the timescale of minutes to hours. We propose to investigate the
question of whether small, lower amplitude flares with durations of only a few
hours or less can be identified with events that are statistically significant
and therefore real. We designed a set of observations using two geographically
separated telescopes, where each telescope observed 1H 0323+342 during the
same time on the same night. Specifically, the telescopes we used were the
72-inch and the 31-inch telescopes located at Lowell Observatory. The
telescopes are located just over 250 meters apart on Anderson Mesa, AZ. On 23
Sept 2014, we monitored 1H 0323+342 with both telescopes for an interval of
just over 4 hours. During the time 1H 0323+342 was monitored, a small, low-
amplitude flare was observed. Figure 2 shows the R band magnitude vs the time
in hours measured with each telescope. The time scale on the x-axis is
measured relative to the common start time. Upon inspection of Fig. 2, one
sees that the 31-inch telescope data and the 72-inch data are sampled
asynchronously with respect to each other. The 31-inch telescope’s data set
consists of a total of 297 observations and the 72-inch telescope’s data set
contains a total of 115 observations. This is due to each telescope using
different exposure times and different sampling rates. However the flare
present in each data set is visually discernible nonetheless. In order to
determine the statistical significance of the variations, we have analyzed
this event in two different ways. First we wish to see if the observed
variance during the flare for the AGN is larger than the expected variance a
reference star would have if its magnitude were the same as the AGN’s mean
magnitude during this period.

Following Howell & Jacoby (1986), we obtained a series of images with each
telescope. Each image is reduced, as described above, with the magnitude data
being placed into vectors based upon which reference star, AGN and which
telescope was used. E.g., the 31" telescope data during the flare consists of
297 measurements for each of the reference stars and for the AGN. Thus the 297
values for Reference star "A" are used to create a vector. Likewise the 297
values for reference star "B" are used to make a vector. This is done in turn
for each reference star and for the AGN. Then a mean magnitude and variance is
calculated per vector. The 72" data is treated similarly. Howell et al.
(1988), shows the variances of the reference stars’ magnitudes scale with
their magnitudes. I.e., the brighter the star, the lower the variance will be
for a given exposure time. Thus, we can fit a power law to the set of
magnitudes and variances and use it to predict the variance of a reference
star as if its magnitude were the same as the AGN’s mean magnitude. The
results of the power law fits and the variances are shown in Figs. 3 and 4.
The numerical values for the predicted and measured variances are in Table 1.
We can use an F-test (Johnson et al., 1995) to compare the AGN’s observed
variance and the predicted reference star’s variance to see if the observed
AGN’s variability during the flare is likely due to the variances observed
with the reference stars’ data or if the AGN has its own variance over and
above that of the reference stars’ measured variances. Using a 99.5 per cent
confidence interval (right hand $\alpha=0.005$) and our particular sample
sizes we found the critical thresholds for the F scores and calculate the F
scores. These are shown in Table 1. For both sets of data, the AGN’s variance
is significantly larger than the critical threshold, thus we are confident
that the object’s brightness is actually changing during this time frame.

$0$$1$$2$$3$$4$$15.8$$15.85$$15.9$$15.95$$16$Hours Post Common Start Time (JD2456923.797)R Magnitude31" Telescope72" Telescope Figure 2: 31" and 72" R band magnitudes during flare Table 1: F-test and variance results Scope | $N$ | $F$ | $F_{\alpha=0.005}$ | Predicted Var. | Measured Var.
---|---|---|---|---|---
31" | 297 | 4.675 | 1.35 | $8.011\times 10^{-5}$ | $3.745\times 10^{-4}$
72" | 115 | 21.717 | 1.62 | $7.173\times 10^{-6}$ | $1.558\times 10^{-4}$
$14$$14.5$$15$$15.5$$16$$16.5$$17$$17.5$$10^{-5}$$10^{-4}$$10^{-3}$Mean R
MagnitudeVariance31" Data VariancesRegression LineComparison StarsAGN Figure
3: 31-inch telescope variance vs R band magnitude
$14.5$$15$$15.5$$16$$16.5$$17$$17.5$$10^{-6}$$10^{-5}$$10^{-4}$Mean R
MagnitudeVariance72" Data VariancesRegression LineComparison StarsAGN Figure
4: 72-inch telescope variance vs R band magnitude

Secondly, we recognize that the intrinsic variability associated with 1H
0323+342 should be the same when detected at either telescope and therefore
the light curves observed at each telescope should be highly correlated if
there are intrinsic variations present. However the signal associated with the
noise detected in the light curves produced at each telescope should be
uncorrelated and not contribute to any correlation between the two light
curves. In order to determine if a correlation is present, a discrete cross
correlation test is used to find if both data sets varied together. We did
this with the Z transformed discrete correlation function (ZDCF), (Alexander,
1997), which is an enhancement of Edelson & Krolik (1988). The extension
includes population sampling and Fisher’s z transformation of the Pearson
coefficient. The DCF peaked at a time lag of zero minutes with a correlation
coefficient (Pearson’s r value) of 0.806 and using Fisher’s z transformation
we find the confidence interval of one standard deviation to be [0.767,
0.838]. This supports the reality of the 1H 0323+342 variability which is
present in the data recorded from both telescopes. See fig. 5 for the DCF with
time lags up to $\pm 60$ minutes.

The reader may notice that the mean magnitudes of 1H 0323+342 indicated in
Figures 3 and 4 differ. This was caused by targeting an extended object and
using too short exposures on the 31" telescope. Thus parts of the galaxy’s
light falls below threshold of measure resulting in the 31" telescope data for
this run to be low. So we end up with an effective offset of under 0.2
magnitudes between the two data sets. Even with this difference, correlation
of the two data sets is unaffected by such an offset.

$-60$$-45$$-30$$-15$$0$$15$$30$$45$$60$$-0.5$$-0.25$$0$$0.25$$0.5$$0.75$$1$Relative
Time Difference (minutes)Z Transformed Discrete Cross CorrelationZ Transformed
Discrete Cross Correlation Figure 5: ZTDCT vs time lag ($\pm$ 60 minutes)
$2011$$2012$$2013$$2014$$2015$$2016$$2017$$0$$5$$10$$15$$20$$25$YearModulation
Index $m[\%]$1H 0323+342 Modulation Indices $m[\%]$ Figure 6: 1H 0323+342
Modulation Indices

##  5 Discussion

In earlier investigations of microvariations for 1H 0323+342, positive
detections of microvariations were reported with amplitudes of the variations
ranging from $\approx 0.07$ mag (Paliya et al., 2013) to >0.35 mag (Paliya et
al., 2014). In the first set of observations, 1H 0323+342 was monitored on
four separate nights. On three of those nights, there were significant
variations in the seeing (as evidenced from the variations in the FWHM of the
point spread function), and thus there is no strong evidence supporting
intrinsic variations for 1H 0323+342 on those nights. However, on the night of
01-26-2012, there was significant variability detected with a variation of
0.07 mag which has a duration of $\approx 2$ hours. The shape of this event is
mostly rounded with the peak occurring near 16.0 U.T. In a second campaign in
2012,Paliya et al. (2014) reported a large microvariability flare on
12-09-2012 when the flux was observed to increase $\approx 0.35$ mag and then
approximately 2 hours later it returned to the level prior to the flare. The
duration of this flare is $\approx 2$ hours with an overall flat-topped shape
to this flaring event. Note that the large variations observed in the FWHM
during the night does somewhat reduce the confidence one has in the detail
structure of the light curve, but the magnitude of this variation is likely
real. Ojha et al. (2019) reported observations of 1H 0323+342 on 02-12-2016
when a rise and decline by $\approx 0.07$ magnitudes were observed with high
signal-to-noise observations. The shape of this flare is flat-topped with a
rapid rise and fall and an overall duration of $\approx 1.25$ hours. In
summary, the duration of these microvariability events in these earlier
investigations are typically on the order of a few hours and the range in
optical flux is on the order $\approx 0.1$ mag.

In the present investigation, we have, for the first time, identified and
positively detected the presence of a low amplitude flux variations with an
amplitude of $\approx 0.03-0.04$ mag with a duration of this complete event
$\approx 2-3$ hours. It is important to note that the duration of all the
detected microvariability events discussed here is quite similar, typically a
few hours. This raises the possibility that this represents a basic,
fundamental timescale (or physical size) in the source associated with the
emitting region.

We know that low amplitude, small timescale events such as the one present in
this data can arise from small, localized events in the source region. Events
of this form may be due to turbulence that is present in standing shocks in
the relativistic jet (Marscher et al., 1992). We also note that the flare has
an approximately 3:1 ramp up to ramp down time. This could be due to a density
gradient across the standing shock in the jet. These low amplitude variations
could also be produced by disturbances other than those in the jet, e.g. hot
spots or flares in the accretion disk (Mangalam & Wiita, 1993).

1H 0323+342’s long term optical variations indicate that this AGN underwent a
general decline in level of activity over an approximately 3 years interval
and then it remained in this lower activity state for the following 4 years.
This is seen in both figures 1 and 6. However, our limited time-coverage from
2010 to the present does not allow us to determine if this is part of a longer
term decline. Near the beginning of our observing time, the AGN exhibited
rapid, shorter timescale variations with an amplitude of nearly a magnitude.
However, after 3 years, the observed amplitude of the shorter term variations
are reduced to around 0.2 magnitudes. During our last year of observation, the
short term variations have further declined in amplitude to around 0.1
magnitudes. This long term trend implies observations of this AGN need to be
carried out over many years to gain a fuller understanding of the nature of
the character of the variations observed for this source and to provide for
fuller understanding of both the low frequency and high frequency portion of
the power density spectrum, particularly with this light curve containing many
multiyear trends.

## Data Availability

The data for the charts used in this paper may be downloaded from the authors’
webpage.

https://www.astro.gsu.edu/~turner/1H0323+342_TotalSet.csv

https://www.astro.gsu.edu/~turner/1H0323+342_Flare_31inchTelescope.csv

https://www.astro.gsu.edu/~turner/1H0323+342_Flare_72inchTelescope.csv

Table 2: Nightly Summary Mean JD | Date | $N_{obs}$ | Span(min) | R Mag | $m[\%]$
---|---|---|---|---|---
2455530.810 | 11/30/10 | 72 | 539 | 15.922 | 0.98
2455531.806 | 12/1/10 | 72 | 534 | 15.911 | 1.59
2455532.808 | 12/2/10 | 72 | 534 | 15.879 | 0.91
2455533.803 | 12/3/10 | 69 | 534 | 15.934 | 1.06
2455566.648 | 1/5/11 | 144 | 173 | 15.481 | 19.74
2455567.691 | 1/6/11 | 245 | 387 | 15.537 | 22.15
2455568.693 | 1/7/11 | 260 | 387 | 15.538 | 21.53
2455923.689 | 12/28/11 | 96 | 354 | 16.059 | 1.25
2455924.698 | 12/29/11 | 86 | 347 | 16.135 | 0.52
2455925.715 | 12/30/11 | 48 | 325 | 16.129 | 2.07
2455926.735 | 12/31/11 | 42 | 317 | 16.134 | 0.66
2455927.689 | 1/1/12 | 96 | 354 | 16.119 | 1.22
2455951.729 | 1/25/12 | 93 | 187 | 15.558 | 2.42
2455952.633 | 1/26/12 | 93 | 156 | 15.803 | 19.70
2455984.633 | 2/27/12 | 100 | 62 | 16.206 | 6.04
2455986.615 | 2/29/12 | 31 | 23 | 16.310 | 1.88
2455987.655 | 3/1/12 | 19 | 68 | 16.315 | 0.98
2455988.624 | 3/2/12 | 15 | 53 | 16.302 | 1.18
2455989.654 | 3/3/12 | 20 | 94 | 16.302 | 1.64
2456187.904 | 9/17/12 | 541 | 230 | 16.216 | 1.72
2456188.896 | 9/18/12 | 37 | 272 | 16.182 | 0.82
2456189.893 | 9/19/12 | 39 | 278 | 16.187 | 5.49
2456190.902 | 9/20/12 | 38 | 271 | 16.184 | 1.98
2456191.902 | 9/21/12 | 38 | 269 | 16.170 | 2.82
2456218.802 | 10/18/12 | 6 | 274 | 16.015 | 0.24
2456219.854 | 10/19/12 | 90 | 505 | 16.039 | 0.44
2456220.846 | 10/20/12 | 87 | 497 | 16.026 | 0.56
2456221.847 | 10/21/12 | 151 | 505 | 16.020 | 0.55
2456222.731 | 10/22/12 | 36 | 177 | 16.011 | 1.17
2456243.808 | 11/12/12 | 9 | 520 | 16.007 | 1.75
2456244.781 | 11/13/12 | 3 | 14 | 16.013 | -
2456245.778 | 11/14/12 | 3 | 14 | 15.961 | -
2456246.791 | 11/15/12 | 3 | 14 | 16.030 | -
2456248.791 | 11/17/12 | 3 | 13 | 16.025 | -
2456249.787 | 11/18/12 | 3 | 14 | 16.068 | -
2456281.804 | 12/20/12 | 6 | 294 | 15.888 | 6.67
2456304.831 | 1/12/13 | 3 | 13 | 16.049 | -
2456305.659 | 1/13/13 | 3 | 48 | 16.078 | -
2456310.642 | 1/18/13 | 4 | 53 | 16.030 | -
2456311.634 | 1/19/13 | 4 | 52 | 16.067 | -
2456312.635 | 1/20/13 | 4 | 53 | 16.067 | -
2456336.752 | 2/13/13 | 3 | 13 | 15.785 | -
2456337.652 | 2/14/13 | 12 | 181 | 16.029 | 0.99
2456338.652 | 2/15/13 | 12 | 183 | 16.045 | 0.59
2456339.595 | 2/16/13 | 3 | 13 | 16.070 | -
2456340.595 | 2/17/13 | 3 | 13 | 16.073 | -
2456341.596 | 2/18/13 | 3 | 14 | 16.082 | -
2456342.596 | 2/19/13 | 3 | 13 | 15.791 | -
2456533.884 | 8/29/13 | 3 | 1 | 15.888 | -
2456540.882 | 9/5/13 | 3 | 1 | 15.808 | -
2456567.956 | 10/2/13 | 3 | 7 | 15.988 | -
2456625.793 | 11/29/13 | 3 | 1 | 16.035 | -
2456658.719 | 1/1/14 | 3 | 1 | 15.960 | -
2456663.770 | 1/6/14 | 3 | 3 | 16.011 | -
2456711.621 | 2/23/14 | 3 | 3 | 15.958 | -
2456922.871 | 9/22/14 | 88 | 189 | 15.945 | 2.81
2456923.883 | 9/23/14 | 415 | 376 | 15.923 | 2.25
2456924.876 | 9/24/14 | 381 | 380 | 16.003 | 1.92
2456925.884 | 9/25/14 | 302 | 380 | 16.021 | 1.11
2456958.927 | 10/28/14 | 158 | 524 | 15.892 | 0.85
2457040.716 | 1/18/15 | 20 | 253 | 15.963 | 0.73
2457041.681 | 1/19/15 | 34 | 295 | 15.918 | 2.04
2457042.686 | 1/20/15 | 34 | 300 | 15.944 | 2.05
Table 3: continued

Nightly Summary  Mean JD Date $N_{obs}$ Span(min) R Mag $m[\%]$ 2457261.552
8/27/15 48 111 15.816 0.78 2457283.841 9/18/15 36 187 15.926 0.91 2457308.713
10/13/15 18 92 15.792 0.55 2457338.824 11/12/15 88 588 15.755 1.70 2457339.820
11/13/15 88 589 15.775 0.93 2457340.819 11/14/15 90 589 15.771 0.77
2457372.705 12/16/15 4 16 15.947 - 2457427.609 2/9/16 2 3 15.738 - 2457428.609
2/10/16 2 3 15.744 - 2457429.610 2/11/16 2 3 15.735 - 2457430.610 2/12/16 2 3
15.738 - 2457460.636 3/13/16 2 4 15.764 - 2457462.635 3/15/16 2 4 15.738 -
2457607.885 8/7/16 3 10 15.773 - 2457608.882 8/8/16 3 10 15.780 - 2457609.880
8/9/16 3 10 15.773 - 2457630.821 8/30/16 3 10 15.768 - 2457631.820 8/31/16 3
10 15.779 - 2457717.807 11/25/16 79 544 15.812 0.90 2457718.807 11/26/16 79
544 15.822 0.68 2457837.629 3/25/17 3 10 15.843 - 2457839.630 3/27/17 3 10
15.777 -

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