# Narrow absorption line Outflow in Seyfert 1 galaxy J1429+4518:
Outflow’s distance from the central source and its energetics

M. Dehghanian,1 N. Arav,1 D. Byun1 G. Walker1 and M. Sharma1
1Department of Physics, Virginia Tech, Blacksburg, VA 24061, USA
E-mail<EMAIL_ADDRESS>0000-0002-0964-7500 0000-0003-2991-4618
0000-0002-3687-6552 0000-0001-6421-2449 0009-0001-5990-5790

(Accepted 2023 November 24. Received 2023 October 16; in original form 2023
August 25)

###### Abstract

In the HST/COS spectrum of the Seyfert 1 galaxy 2MASX J14292507+4518318, we
have identified a narrow absorption line (NAL) outflow system with a velocity
of $-$151 km s-1. This outflow exhibits absorption troughs from the resonance
states of ions like C iv, N v, S iv, and Si ii, as well as excited states from
C ii∗, and Si ii∗. Our investigation of the outflow involved measuring ionic
column densities and conducting photoionization analysis. These yield the
total column density of the outflow to be estimated as $\log N_{H}$=19.84
[cm${}^{-2}]$, its ionization parameter to be $\log U_{H}$=-2.0 and its
electron number density equal to $\log n_{e}$= 2.75[cm-3]. These measurements
enabled us to determine the mass-loss rate and the kinetic luminosity of the
outflow system to be $\dot{M}$=0.22 [$M_{\astrosun}~{}yr^{-1}$] and
$\log\dot{E_{K}}$=39.3 [erg s-1], respectively. We have also measured the
location of the outflow system to be at $\sim$275 pc from the central source.
This outflow does not contribute to the AGN feedback processes due to the low
ratio of the outflow’s kinetic luminosity to the AGN’s Eddington luminosity
($\dot{E_{K}}/{L_{Edd}}\approx 0.00025\%$). This outflow is remarkably similar
to the two bipolar lobe outflows observed in the Milky Way by XMM-Newton and
Chandra.

###### keywords:

galaxies: active –galaxies: Seyfert– galaxies: absorption lines – galaxies:
individual: 2MASX J14292507+4518318

††pubyear: 2023††pagerange: Narrow absorption line Outflow in Seyfert 1 galaxy
J1429+4518: Outflow’s distance from the central source and its
energetics–Narrow absorption line Outflow in Seyfert 1 galaxy J1429+4518:
Outflow’s distance from the central source and its energetics

##  1 Introduction

Absorption outflows from active galactic nuclei (AGN) are frequently suggested
as potential contributors to the AGN feedback processes. Various studies have
discussed this idea, e.g. Silk & Rees (1998); Scannapieco & Oh (2004); Yuan et
al. (2018); Vayner et al. (2021); He et al. (2022). Absorption lines found in
the rest-frame UV spectra are typically grouped into three main categories
based on their characteristics: broad absorption lines (BALs) which have a
width of $\geq$2000 km s-1, narrow absorption lines (NALs) with a width of
$\leq$500 km s-1, and a middle subgroup known as mini-BALs (Itoh et al.,
2020). When it comes to BALs, it is clear that the absorbing gas is connected
to the AGN (Vestergaard, 2003) and could indeed have a noticeable impact on
suppressing the star formation rate within the host galaxies (Chen et al.,
2022); however, the nature of NAL outflows is less understood. This is due to
the challenge of discerning intrinsic NALs (associated NALs) from those NALs
that are unrelated to the quasars (intervening NALs). Intervening NALs can
originate from various sources, including intervening galaxies, intergalactic
clouds, Milky Way gas, or gas within the host galaxies of the quasars (Misawa
et al., 2007a). Several studies (e.g. Misawa et al. (2007a); Chen & Qin
(2013); Chen et al. (2018, 2020)) have highlighted the point that variations
in the absorption lines serve as a robust indicator to distinguish between
narrow absorption lines arising from the outflow system, or those called
intervening NALs. These variations in absorption lines typically arise from
changes in the column density of absorbing gas as part of the intrinsic
mechanism.

Shen & Ménard (2012) and Chen et al. (2018) explain that a velocity of 3000 km
s-1 is often used to limit associated NALs; however, it should also be noted
that some NAL outflows with velocity being obviously larger than 3000 km s-1
have been observed (Hamann et al., 2011; Chen & Qin, 2013). Using large
samples of the SDSS quasar C iv and Mg ii narrow-line absorptions, Chen & Pan
(2017) discuss that the velocity boundary is associated with the quasar
luminosity. In the study referred to, it is proposed that velocity thresholds
of 4000 km s-1 be used to identify quasar CIV-associated NALs and velocity
thresholds of 2000 km s-1 be employed to identify quasar MgII-associated NALs.

Despite the fact that NALs have received less attention than BALs, they hold
great potential as a useful tool for investigating the physical properties of
outflows because of these two significant reasons (Misawa et al., 2007b):

  * •

NALs do not have the problem of self-blending, which is defined as blending
blue and red components of doublets like C iv $\lambda\lambda$ 1548,1551Å.

  * •

NALs appear in a larger variety of AGNs, whereas BALs are mostly detected in
radio-quiet quasars.

This paper identifies and analyzes the NAL outflow system of the AGN 2MASX
J14292507+4518318 (hereafter J1429+4518) using the observations performed by
the Hubble Space Telescope (HST) in 2021. For this object, the intrinsic
nature of the NAL is deduced from the presence of several excited state
absorption narrow lines observed in the spectrum. The paper’s structure is as
follows:

In the next Section, we described the observations and data acquisition of the
target. It is followed by Section 3, which includes the analysis, and explains
the methods used in the paper. Section 3 also details the approaches used to
calculate the ionic column densities of the NALs. Calculations of the electron
number density and location of the outflow, along with the best
photoionization solution, are all included in Section 3. Part of Section 3 is
dedicated to finding the central source’s mass and the AGN’s Eddington
luminosity. These calculations are followed by estimating the energetics of
the outflow system. Section 4 discusses the stability of the outflow and
indicates the importance of the NALs in general. In this Section, we have also
compared our NAL outflow system with two lobe outflows detected in the Milky
Way. Finally, Section 5 gives a summary of what we have done here and
concludes the paper.

Here we adopted a cosmology with h= 0.696, $\Omega$= 0.286, and $\Omega_{\nu}$
= 0.714 (Bennett et al., 2014). We used the Python astronomy package Astropy
(Astropy Collaboration et al., 2013, 2018) for our cosmological calculations,
as well as Scipy (Virtanen et al., 2020), Numpy (Harris et al., 2020), and
Pandas (Reback et al., 2021) for most of our numerical computations. For our
plotting purposes, we used Matplotlib (Hunter, 2007).

##  2 Observations

J1429+4518 is a Seyfert 1 galaxy, located at Redshift z=0.07454, with J2000
coordinates at RA=14:29:25.07 and DEC=+45:18:31.93. The galaxy was observed
using the HST Cosmic Origins Spectrograph (HST/COS) on three separate
occasions: September 19th 2021, December 20th of 2021, and December 21st of
the same year, as part of HST Proposal 16301 (Putman et al., 2020). The first
observation utilized the G130M grating, while the subsequent two observations
used G160. For this study, we excluded the observation taken on December 21st
of 2021, as it covered the same wavelength range as the other December
observation but had a shorter exposure time. The spectral data for both
observations are displayed in Figs 1 and 2 .

After obtaining the relevant data from the Mikulski Archive for Space
Telescopes (MAST), we detected an outflow at a velocity of v= –151 km s-1,
with blue-shifted ionic absorption lines denoted by red vertical lines in
Figures 1 $\&$ 2. Among the identified absorption lines, several well-known
resonance doublets, such as C iv and N v, were observed. Additionally, we
identified absorption lines arising from excited states, such as C ii∗ and Si
ii∗.

###  2.1 Redshift considerations

The redshift assigned to the object (z=0.07454) is the sole value documented
in the SIMBAD Astronomical Database 111SIMBAD Astronomical Database:
http://simbad.cds.unistra.fr/simbad/ and is derived from optical data
analysis. Despite the NASA/IPAC Extragalactic Database
(NED)222NED:https://ned.ipac.caltech.edu/ reporting seven different redshift
values (within the range of z=0.0745 to z=0.0748) for this object, their
preferred redshift in their databases is also documented as 0.07454.
Furthermore, the SDSS Science Archive Server
(SAS)333SAS:https://dr18.sdss.org/ confirms this redshift for the same object.
In the interest of maintaining consistency with these databases, we have
adopted the same redshift value. It is essential to acknowledge the inherent
uncertainties associated with this value:

To ensure that we have picked a reliable redshift and to mitigate the
potential larger uncertainties associated with C iv emission line models (Chen
et al., 2019; Shen et al., 2011), we opted to utilize the SDSS data directly
and perform modeling on [O iii]$\lambda$5007Å. For this purpose, we have used
the optical spectrum obtained in March 2003 (MJD 52728) and used two Guassians
to fit [O iii] emission line. Our modeling yielded to a redshift of 0.0747, a
value only 0.00016 larger than the NED estimate. However, this will increase
the velocity of the outflow to be v= –195 km s-1. Taking all these
considerations into account, we utilize the initially assumed redshift
throughout this paper, and we assume an uncertainty of $\Delta$v$\sim$44 km
s-1 when needed.

##  3 Analysis

###  3.1 Ionic Column Densities

Determining the ionic column densities (Nion) of the outflow absorption lines
is crucial for understanding the physical properties of the outflow system.
While there are two methods to estimate the ionic column densities, we
employed "the apparent optical depth" (AOD) method for all detected absorption
lines since all of the identified doublets appear to be saturated (see
Sections 3.1.1). In this method, the main assumption is that the outflow
uniformly and fully covers the source (Spitzer, 1968; Savage & Sembach, 1991).
When using the AOD method, one can infer the column density via the equations
1$\&$ 2 below (Spitzer, 1968; Savage & Sembach, 1991):

$I(\lambda)=I_{0}(\lambda)e^{-\tau(\lambda)},$ (1)

in which $I(\lambda)$ and $I_{0}(\lambda)$ are the intensities with and
without the absorption, respectively. $\tau(\lambda)$ is the optical depth of
the absorption trough. Then, the ionic column densities can be measured using
equation 2 (Arav et al., 2001):

$N_{ion}=\frac{3.7679\times
10^{14}}{\lambda_{0}f_{ik}}\times\int\tau(\nu)~{}d\nu[cm^{-2}],$ (2)

where $\lambda_{0}$ is the transition’s wavelength and $f_{ik}$ is the
oscillator strength of the transition. For more details regarding the AOD
method, please see Arav et al. (2001), Gabel et al. (2003), and Byun et. al
(2022c). The other method is called the partial covering (PC) method, and it
will not be used in this paper. For a detailed explanation of both methods and
to better understand the logic behind them and get a sense of their
mathematics, please refer to Barlow et al. (1997); Arav et al. (1999a, b); de
Kool et. al (2002); Arav et al. (2005); Borguet et al. (2012a); Byun et. al
(2022b, c).

Below we explain how various absorption lines were treated and why we applied
the AOD method approach in each case. For each absorption line that we were
interested in, we used the redshift of the outflow (z${}_{\textrm{outflow}}$)
to transfer the spectrum from wavelength space to velocity space (please refer
to Fig. 3). As shown by the dashed lines in Fig. 3, we have chosen a velocity
range of -210 km s-1 to -90 km s-1 as our integration range when performing
column density calculations. This region was chosen based on the centroid
velocity and the absorption trough width of Si iv. Since all the absorption
lines result from the same outflow system, we use the same integration range
for all.

####  3.1.1 C iv, Si iv, and N v doublets

The C iv $\lambda\lambda$1548, 1551Å, Si iv $\lambda\lambda$1394, 1403Å and N
v $\lambda\lambda$1239, 1243Å absorption lines are prominent spectral features
observed in AGN outflows. All of these lines are easily detectable in the
spectrum of J1429+4518 (see Figs 1 and 2) indicating the presence of an
absorption outflow system. However, while these lines are recognized as
doublets (and expected to be treated using the PC method), they are all very
saturated as their blue-to-red trough ratio is very close to 1:1 for all three
cases. For this reason, in our calculations, we adopted the AOD results of the
red doublet (since it has the lower oscillator strength) and considered them a
lower limit to the total ionic column density for each species. Note that for
the case of N v, the high-velocity wing of the red doublet is actually
Galactic C ii $\lambda$1334Å absorption.

####  3.1.2 Ly$\alpha$

As shown in Fig. 3, Ly$\alpha$ is also suspected of being highly saturated
since it has a very large optical depth. For this reason, while we use the AOD
method to calculate its ionic column density, we take it as a lower limit and
not an accurate measurement.

####  3.1.3 C ii $\lambda$ 1334 Å and C ii∗ $\lambda$ 1336 Å

Since these two lines are from different energy levels, we employ the AOD
method to calculate the column density for each line individually. However, as
depicted in Fig. 3, the flux ratio of C ii to C ii∗ appears to be
approximately 1:1. While it is evident that the lines are not fully saturated
(optical depth is not very large), a 1:1 ratio might have resulted from a mild
saturation effect. For this reason, we proceed to utilize their column density
resulted from the AOD method as a lower limit, not an accurate measurement. In
later steps, we use the ratio of
$\frac{\textrm{N}_{ion}(\text{C\,{ii}}^{*})}{\textrm{N}_{ion}(\text{C\,{ii}})}$
to extract a lower limit for the electron number density of the outflow.

####  3.1.4 Si iii $\lambda$ 1206Å

As marked in Fig. 1, Si iii $\lambda$ 1206Å is easily identifiable. Since this
line is a singlet, we use our usual AOD method to infer its ionic column
density. However, since it appears to be very deep (see Fig. 3) and hence
probably saturated, we consider the results as a lower limit, not an actual
measurement.

####  3.1.5 Si ii $\lambda$1260 Å and Si ii∗ $\lambda$1265 Å

Fig. 3 shows that both resonance and excited state absorption lines of Si ii
ion are detectable. Since the Si ii trough is significantly shallower than the
Si iii trough, we cautiously accept its AOD measurement as a real measurement
as opposed to a lower limit. The fact that Si ii∗ is shallower than Si ii
suggests that we can use their ionic column densities ratio to estimate the
electron number density of the outflow (Arav et al., 2018). Later in Section
3.3.1 we discuss this process in more detail.

Figure 1: The spectrum of J1429+4518 observed by the HST/COS in Sep
2021(Putman et al., 2020). The absorption features of the outflow system with
a velocity of –151 km/s are marked with red lines, while the grey lines show
the absorption from the interstellar medium (ISM). The dashed blue line shows
our continuum emission model. The question marks indicate some absorption
lines that we could not identify (we confirm they are not CIV resulting from
an intervening system). Figure 2: Same as Fig.1 but the data are from HST/COS
observations in Dec 2021 (Putman et al., 2020). Note that since the C iv lines
are black at the bottom, the covering fraction of the NAL outflow is estimated
to be 1.0. Figure 3: Normalized flux versus velocity for blue-shifted
absorption lines detected in the spectrum of J1429+4518. The horizontal green
dashed horizontal line shows the continuum level, and the vertical black
dashed lines show the region between -210 and -90 km s-1. The vertical solid
black line indicates the centroid velocity of v${}_{\textrm{centroid}}$=–151
kms-1.

####  3.1.6 S iv $\lambda$1063 Å and S iv∗ $\lambda$1073 Å

While we were able to accurately measure the column density of the S iv using
the AOD method, S iv∗ is absent (Fig. 3). For this reason, we used a Gaussian
fit based on the S iv absorption line and estimated an upper limit for S iv∗
ionic column density. Then the ratio of Nion(S iv∗) to Nion(S iv) can be used
to extract an upper limit for the electron number density of the outflow
system.

Fig. 4 illustrates the Gaussian fit for both the resonance and excited states
of the S iv line. It is important to note that we modeled the S iv∗ state
using the best-fit parameters obtained for the S iv resonance line as the
template. The depth of the curve for S iv∗ was allowed to adjust within a
3$\sigma$ error range during the fitting process. We have checked the spectrum
for any signs of the C iii∗ $\lambda$ 1175Å narrow absorption line and we were
not able to identify any traces of the mentioned line. Table 1 summarizes the
results we got from our Nion measurements, along with the numbers we chose to
use in the next step, which will assess the outflow characteristics. The
adopted errors include the corresponding AOD errors and a systematic error
(20$\%$ of the adopted value), which are quadratically added together (e.g. Xu
et al., 2018; Miller et al., 2018, 2020c).

Figure 4: Gaussian modeling (green) of the S iv∗$\lambda$1073 Å absorption trough, created by fitting S iv $\lambda$1063 Å line (orange) and using its shape as a template, where we have allowed the optical depth to adjust within 3$\sigma$ error range. The horizontal dashed line shows the continuum level. Ion | AOD | Adopted
---|---|---
C iv | 580${}^{+50}_{-40}$ | >580-122
Si iv | 63${}^{+7}_{-6}$ | >63-14
Ly$\alpha$ | 105${}^{+2}_{-2}$ | >105-21
N v | 503${}^{+24}_{-31}$ | >503-103
C ii | 187${}^{30}_{-27}$ | >187-46
Si ii | 9${}^{+1}_{-1}$ | 9${}^{+1}_{-1}$
Si iii | 18${}^{+1}_{-1}$ | >18-4
S iv | 262${}^{+31}_{-29}$ | 262${}^{+61}_{-60}$
Si ii∗ | 4${}^{+1}_{-1}$ | 4${}^{+1}_{-1}$
Table 1: The ionic column densities of the absorption lines detected in the
J1429+4518 outflow system. Please note that the reported/adopted value for C
ii includes the column densities of C ii and C ii∗. We did not add the
systematic error to Si ii and Si ii∗ since these two lines are only 5Å apart
and will not be affected much by a systematic error. All of the column density
values are in units of 1012 cm-2.

###  3.2 Photoionisation Solution

We calculated the ionic column densities of the mentioned absorption lines for
the primary purpose of estimating the characteristics of the outflow,
including the total hydrogen column density (NH) and the ionization parameter
of the system (UH) (e.g. Xu et al., 2019; Byun et. al, 2022a, b, c; Walker et.
al, 2022). To do so, we use Cloudy simulations (Ferland et al., 2017) in which
a grid of NH and UH are used to predict the abundance of various ions. Then we
constrain the NH and UH values based on our measured ionic column densities.
The top panel of Fig. 5 shows the results of this process. To produce this
Figure, we used the spectral energy distribution (SED) of AGN HE0238-1904
(hereafter HE0238, Arav et al., 2013) in Cloudy and set a large range of grids
on NH and UH. In Fig. 5-top panel, the coloured contours show the values
labeled "adopted" in Table 1. As this Figure shows, a single- phase solution
is sufficient to satisfy the constraints from the ionic column densities.
Based on these results, we narrowed down the column density-ionization space
to a pair of NH and UH for the absorption outflow system:
U${}_{H}=$–2.0${}^{+0.1}_{-0.1}$ and NH=–19.84${}^{+0.20}_{-0.20}$ [cm-2]
(reduced $\chi^{2}$ = 2.5). Please note that to produce these results, a solar
abundance was assumed.

While the above solution satisfies all the constraints, we examined the
dependency of the results on the SED and metalicity. For this purpose, we
repeated the same process of prediction NH and UH using another two SEDs,
namely the MF87 SED (Mathews & Ferland, 1987), and UV-soft SED (Dunn et al.,
2010). For all three SEDs, we have also investigated the effects of super-
solar metalicity (Z=4.68Z☉) (Ballero et al., 2008; Miller et al., 2020b). The
lower panel of Fig. 5 shows the effects of different SEDs and super-solar
metalicity on the results. In this Figure, each solid line belongs to the
calculations considering a solar metalicity while dashed lines are resulted
from a super-solar metalicity. These results show that using MF87 and UV-soft
SEDs results in a slightly smaller $\chi^{2}$ (2.3 and 1.5, respectively).
However, as discussed by Arav et al. (2013), we prefer to continue using
HE0238 SED since it extends quite far into the extreme UV rest (EUV)
wavelength range.

Figure 5: Top panel: single phase photoionization solution for the absorption
outflow system. Each coloured contour indicates the ionic column densities
consistent with the observations (available in Table 1), assuming the HE0238
SED and solar metallicity. Solid lines within the contours show the ionic
column densities taken as measurements, while dashed lines contours belong to
lower limits. The shaded bands are the uncertainties added for each contour.
The black circle shows the best $\chi^{2}$-minimization solutions. Bottom
Panel: The photoionization solution for a total of six models, including three
SEDs (HE0238, MF87, and UV–soft) and two metallicities for each: solar
metallicity and super-solar metallicity, for a total of six models.

###  3.3 Determining the electron number density and the distance of the
outflow from the central source

####  3.3.1 Electron Number Density

We ran a model of Cloudy using HE0238 SED and the NH and UH values to predict
the outflow’s temperature. This simulation resulted in a temperature of
T$\approx$14000 K. Assuming this temperature, we used Chianti 9.0.1 atomic
database (Dere et al., 1997, 2019) to estimate the abundance ratios of excited
state to the resonance state for Si ii, C ii, and S iv as a function of
electron number density (ne). Fig. 6 illustrates the results. Since Si ii’s
absorptions in both excited and resonance states were reliably measured
(section 3.1.5), we took its ratio as a diagnostics for the electron number
density of the outflow (blue curve in Fig. 6). These considerations result in
$\log$ ne= 2.75${}^{+0.20}_{-0.25}$ [cm-3]. As described in Osterbrock &
Ferland (2006) and for a highly ionized plasma, ne$\approx$1.2nH, resulting in
$\log$ nH= 2.67${}^{+0.20}_{-0.25}$ [cm-3]. We have also used C ii and S iv to
predict lower and higher limits for ne (details in sections 3.1.3 and 3.1.6,
respectively). These upper and lower constraints on ne are fully consistent
with the Si ii∗/Si ii measurement.

Figure 6: The excited state to resonance state column density ratio vs. the
electron number density from Chianti atomic database. We have shown the
calculated value from our modeling and marked its corresponding electron
number density by a vertical dashed line. Please note that these results are
sensitive to the temperature, and to produce this plot, we assumed T=14000 K.

####  3.3.2 Location of the Outflow

Once NH, nH, and UH are known, we can use equation 14.4 from Osterbrock &
Ferland (2006) to extract the location of the outflow:

$U_{H}\equiv\frac{Q(H)}{4\pi R^{2}c~{}n_{H}}\Rightarrow
R=\sqrt{\frac{Q(H)}{4\pi c~{}n_{H}U_{H}}}$ (3)

In which Q(H) [s-1] is the number of hydrogen-ionizing photons emitted by the
central object per second, R is the distance between the outflow and the
central source, nH is the hydrogen density and c is the speed of light.

To calculate R, we must first calculate the number of hydrogen-ionizing
photons Q(H). To do so, we followed several steps explained below (please
refer to the works done by Miller et al. (2020a), Byun et. al (2022a, b), and
Walker et. al (2022)):

  * •

First, We scaled the SED of HE0238 to match the continuum flux of J1429+4518
at observed wavelength of $\lambda$=1350Å. Based on the HST observations we
are using in this paper, at the mentioned wavelength F${}_{\lambda}\approx
3\times 10^{-15}$ erg s-1 cm-2 Å-1.

  * •

Then, we integrated over the scaled SED for all energies above 1 Ryd (Hydrogen
ionization potential) to get a value of 1.2${}^{+0.1}_{-0.1}\times 10^{54}$
s-1 for Q(H) and a bolometric luminosity of Lbol= 2.13${}^{+0.2}_{-0.2}\times
10^{44}$ erg s-1.

  * •

Finally, using these values and equation 3, we predict that the outflow system
is located at a distance of R$\approx$275${}^{+53}_{-46}$ pc from the source.

###  3.4 Black hole mass and outflow’s energetics

####  3.4.1 Black Hole mass and the Eddington luminosity

To get an estimate of the outflow’s energetics, including its mass-loss rate
and the kinetic luminosity, we need to get a hand on the mass of central BH
and its Eddington luminosity first.

In 2006, Vestergaard & Peterson showed that C iv emission line could be used
to measure the mass of the BH via this equation:

$\log
M_{BH}(\textrm{\text{C\,{iv}}})=\log({[\frac{\textrm{FWHM(\text{C\,{iv}})}}{1000\textrm{kms}^{-1}}]^{2}[\frac{\lambda
L_{\lambda}(1350\AA)}{10^{44}\textrm{ergs}^{-1}}]^{0.53}})$ (4)

Later in 2017, Coatman et al. revised equation 4 as below:

$\displaystyle M_{BH}(\textrm{\text{C\,{iv}},
Corr.})=10^{6.71}[\frac{\textrm{FWHM(\text{C\,{iv}},
Corr.)}}{1000\textrm{kms}^{-1}}]^{2}\times$ (5) $\displaystyle[\frac{\lambda
L_{\lambda}(1350\AA)}{10^{44}\textrm{ergs}^{-1}}]^{0.53}$

where:

$\displaystyle\textrm{FWHM(\text{C\,{iv}},
Corr.)}=\frac{\textrm{FWHM(\text{C\,{iv}}, Meas.)}}{(0.41\pm
0.02)\frac{\textrm{\text{C\,{iv}} blueshift}}{1000\textrm{kms}^{-1}}+(0.62\pm
0.04)}$ (6)

As shown in Fig. 2, we can easily identify the C iv emission and use its FWHM
to achieve the mass of the black hole. Fig. 7 shows our Gaussian model to fit
the spectra at C iv region, in which we have modeled the C iv emission with a
single Gaussian. Based on this Figure, the FWHM is measured to be
$\approx$3100 km s-1 for the C iv emission line. We also measured an average
flux of 1$\times$10-15 erg s-1 cm-2 Å-1 for $\lambda_{\textrm{rest}}$=1350Å,
which for our narrow outflow system results in $\lambda$Lλ=1.16$\times$1042
erg s-1. Using these values in the equation 6 $\&$ 5, we get a black hole mass
of MBH=7.5$\times$106M☉, resulting in an Eddington luminosity of
LEdd=8.8$\times$1044 erg s-1.

Figure 7: C iv emission line region. The blue line shows our Gaussian fit to
the emission spectra. The Red dashed line indicates where the half maximum is,
while the green dashed lines show the full width of the half maximum. The
continuum level is shown in the Figure, and it is subtracted for the purpose
of FWHM determination.

####  3.4.2 Outflow’s Energetics

Using outflow’s R and NH obtained above, we can estimate the mass-loss rate
and the kinetic luminosity of the outflow using the following
equations(Borguet et al., 2012b):

$\dot{M}\simeq 4\pi\Omega RN_{H}\mu m_{p}\nu$ (7)
$\dot{E}_{K}\simeq\frac{1}{2}\dot{M}v^{2}$ (8)

In equation 7, $\mu$ = 1.4 is the mean atomic mass per proton, v is the
velocity of the outflow, and mp is the mass of a proton (Borguet et al.,
2012a). In the mentioned equation, $\Omega$ is the global covering factor
defined as the percentage of the source covered by the outflow in all
directions (not only line-of-sight). We assume $\Omega=0.5$ as Misawa et al.
(2007b) explain that intrinsic NALs are observed in at least 50$\%$ of AGNs.
In an earlier study published in 2003, Vestergaard discusses more than half of
the AGNs embed NALs. All these studies are in agreement with the 50-70$\%$
population ratio of NALs among Seyfert 1 galaxies as reported by Crenshaw et
al. (1999). Assuming all these values and solving equations 7 and 8 for the
mass-loss rate and the kinetic luminosity results in
$\dot{M}$=0.22${}^{+0.09}_{-0.06}$$M_{\astrosun}yr^{-1}$ and
$\log\dot{E_{K}}$=39.3${}^{+0.1}_{-0.2}$ [erg s-1]. Table 2 summarizes all
these results along with the results from section 3.

As reported in Table 2, the outflow system’s kinetic luminosity ratio to the
source’s Eddington luminosity is $\approx 0.0002\%$. Hopkins & Elvis (2010)
explain that an outflow system must have a $\dot{E_{K}}/{L_{Edd}}$ of at least
$\approx 0.5\%$ to contribute to the AGN outflow, verifying that the NAL
outflow system identified here does not contribute to AGN feedback processes.

It is worth mentioning that if we take the redshift considerations into
account (Section 2.1) and assume that the outflow has a velocity of
approximately –195 km s-1, then $\dot{M}$=0.27$M_{\astrosun}yr^{-1}$ and
$\log\dot{E_{K}}$=39.5[erg s-1]. Based on these values, the outflow system’s
kinetic luminosity ratio to the source’s Eddington luminosity would be
$\approx 0.0004\%$. This indicates that for the [OIII]-based redshift, the
above discussion remains valid, and this outflow contributes minimally or not
at all to the AGN feedback process.

$\log N_{H}$ [cm-2] | 19.84${}^{+0.20}_{-0.20}$
---|---
$\log U_{H}$ [dex] | -2.0${}^{+0.1}_{-0.1}$
$\log n_{e}$ [cm-3] | 2.75${}^{+0.20}_{-0.20}$
R [pc] | 275${}^{+53}_{-46}$
$\dot{M}$ [$M_{\astrosun}yr^{-1}$] | 0.22${}^{+0.09}_{-0.06}$
$\log\dot{E_{K}}$ [erg s-1] | 39.3${}^{+0.1}_{-0.2}$
$\dot{E_{K}}/{L_{Edd}}$ | 2.0${}^{+0.9}_{-0.7}$ $\times$10-6
$\dot{E_{K}}/{L_{Bol}}$ | 8.5${}^{+3.0}_{-2.0}$$\times$10-6
Table 2: Physical properties of the narrow absorption line outflow system

##  4 Discussion

###  4.1 The thermal stability curve

When gas is photoionized, it will reach the equilibrium defined by an
ionization parameter (Tarter et. al, 1969; Kallman & Bautista, 2001):

$\xi\equiv\frac{L_{\rm ion}}{n_{H}r^{2}}[\textrm{erg~{}cm~{}s}^{-1}]$ (9)

and a specific temperature. This equilibrium arises due to the interplay
between various heating and cooling mechanisms that depend on the gas’s
physical and chemical attributes and the characteristics of the intrinsic
radiation of the source.

It is common to represent these balanced states on a stability curve, depicted
in a diagram with temperature (T) on the vertical axis and the ratio of
$\xi$/T on the horizontal axis (for example, Krolik et. al (1981)). When we
assume that L/n(H)r2 remains constant , the latter parameter, $\frac{\xi}{T}$
$\propto$ $\frac{p_{rad}}{p_{gas}}$, indicates the gas pressure. The thermal
stability curve (S-curve) for J1429+4518 is produced using Cloudy and is shown
in Fig. 8. To create the stability curve, we have used the previously
mentioned HE0238 SED and the outflow’s characteristics are adopted from Table
2. In this Figure, the region on the left-hand side of the curve is dominated
by cooling processes, while heating processes dominate the right-hand side. A
positive slope on the S-curve happens when an increase in T (due to an
isobaric perturbation) will move the gas to the cooling region and so revert
the gas to the same equilibrium state. Such equilibrium states are considered
to be thermally stable. The green circle in Fig. 8 indicates where our narrow
absorption line outflow is located regarding the stability. Based on this
plot, the outflow is on the positive slope and hence it is assumed to be
thermally stable.

Figure 8: The photoionization stability curve (s-curve) for J1429+4518. The
green circle indicates the narrow absorption outflow system discussed in this
paper. Yellow rectangles indicate the negative-slope regions, meaning any gas
with those characteristics would be unstable.

###  4.2 The Importance of the NALs

Hamann et al. (2011) explain that to understand AGN outflows better, we need
clearer information about the different types of outflows, including NAL
outflows. For a while now, there has been an ongoing discussion about whether
the NALs and BALs (Broad Absorption Lines) have any connection or relationship
(Weymann et al., 1979). Furthermore, there’s an ongoing debate about how these
associated NALs and BALs might fit into the overall development or evolution
of AGNs (Briggs et al., 1984; Hamann et al., 2001).

It’s not straightforward to detect NAL outflows because they’re hard to
identify: most narrow lines we see in AGN spectra come from unrelated cosmic
gas, not from the AGN themselves. However, some studies suggest that a
significant part of these narrow absorption lines actually come from AGN
outflows (e.g. Nestor et. al, 2008; Wild et al., 2008; Richards, 2001).

###  4.3 Similarity to Milky Way Outflows

The narrow absorption line outflow system in J1429+4518 shows some remarkable
similarity to the two bipolar lobe outflows observed in the Milky Way by XMM-
Newton and Chandra (see Ponti et al. (2019), figure 1). One of these lobe
outflows extends up to 160 pc from the Galactic Center (GC) in the northern
direction. The second lobe, situated to the south, extends even further,
reaching beyond the southern Fermi Bubble’s base and spanning approximately
250 pc (Veilleux et al., 2020). Notably, the distances from their central
source align closely with the 275 pc measured for our study’s NAL outflow
system. Furthermore, their kinematic luminosities are estimated at
$\log\dot{E_{K}}$=39.6 [erg s-1] (Ponti et al., 2019)—merely 0.3 dex larger
than that of our outflow system—these similarities are striking. Finally, the
comparability of the Milky Way’s black hole mass (MBH=4.15$\times$106M☉;
GRAVITY Collaboration et al. (2019)) with that of J1429+4518
(MBH=6.7$\times$106M☉) along with the mentioned similarities in the outflow’s
measurements, suggest a common origin for the two outflows. It’s worth noting
that other outflows in the Milky Way are located farther away or have much
larger kinetic luminosity(Veilleux et al., 2020).

##  5 Conclusions

In this paper, we analysed two datasets from HST/COS 2021 observations of
Seyfert 1 galaxy J1429+4518. We successfully identified a narrow absorption
line outflow system located $\sim$275 pc away from the central source. This
outflow was identified through narrow absorption troughs of C iv, Si iv, N iv,
Ly$\alpha$, C ii, C ii∗, Si ii, Si ii∗, Si iii, and S iv. We measured the
centroid velocity of the outflow system to be –151 km s-1 and extracted the
ionic column densities of the absorption lines using the AOD method.

As the next step, measured ionic column densities were used to determine the
total hydrogen column density ($\log$ NH=19.84 erg s-1) and the ionization
state ($\log$ UH=-2.0) of the NAL outflow. This was done by using
$\chi^{2}$-minimization solutions and performing Cloudy simulations. We also
used the ratio of the ionic column densities of Si ii∗ to Si ii for measuring
the electron number density ($\log$ ne=2.75 cm-3) and hence its hydrogen
density. Having the hydrogen density enabled us to estimate the outflow
location mentioned above. We followed all these calculations by estimating the
energetics of the outflow: this outflow has a kinetic luminosity of
$\log\dot{E_{K}}$=39.3[erg s-1] and its mass-loss rate is measured to be
$\dot{M}$=0.22$M_{\astrosun}yr^{-1}$.

The NAL outflow of J1429+4518 is thermally stable, but it does not contribute
to AGN feedback processes since its kinetic luminosity is only $\approx
0.00025\%$ of the Eddington luminosity of the central source. Finally, we note
that this NAL outflow exhibits a striking similarity to the two bipolar lobe
outflows detected in the Milky Way through observations conducted by XMM-
Newton and Chandra. The similarities in their energetics and location, and
having central black holes with similar masses, strongly suggest that these
outflows might have a common origin.

## Acknowledgements

We express our appreciation to the anonymous reviewer whose feedback has
contributed to the enhancement of this manuscript. We acknowledge support from
NSF grant AST 2106249, as well as NASA STScI grants AR- 15786, AR-16600,
AR-16601, and HST-AR-17556.

## Data Availability

The data of J1429+4518 described in this paper may be obtained from the MAST
archive at https://dx.doi.org/10.17909/mzeb-fm44

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MIDLMedical Imaging with Deep Learning 2019 MIDL 2019 – Extended Abstract
Track Neda Azarmehr1,2<EMAIL_ADDRESS>
Xujiong Ye1<EMAIL_ADDRESS>
Faraz Janan1<EMAIL_ADDRESS>
James P Howard2<EMAIL_ADDRESS>
Darrel P Francis2<EMAIL_ADDRESS>
Massoud Zolgharni2,3<EMAIL_ADDRESS>
1 School of Computer Science, University of Lincoln, Lincoln, UK
2 National Heart and Lung Institute, Imperial College London, London, UK
3School of Computing and Engineering, University of West London, London, UK

# Automated Segmentation of Left Ventricle in 2D Echocardiography using Deep
Learning

###### Abstract

Following the successful application of the U-Net to medical images, there
have been different encoder-decoder models proposed as an improvement to the
original U-Net for segmenting echocardiographic images. This study aims to
examine the performance of the state-of-the-art proposed models claimed to
have better accuracies, as well as the original U-Net model by applying them
to an independent dataset of patients to segment the endocardium of the Left
Ventricle in 2D automatically. The prediction outputs of the models are used
to evaluate the performance of the models by comparing the automated results
against the expert annotations (gold standard). Our results reveal that the
original U-Net model outperforms other models by achieving an average Dice
coefficient of 0.92$\pm 0.05$, and Hausdorff distance of 3.97$\pm 0.82$.

###### keywords:

Echocardiography, Segmentation, Deep Learning

##  1 Introduction

To assess the cardiac function in 2D ultrasound images, quantification of the
Left Ventricle (LV) shape and deformation are crucial, and this relies on the
accurate segmentation of the LV contour in end-diastolic (ED) and end-systolic
frames (Raynaud et al., 2017). At present, the manual segmentation of the LV
suffers from various complications: (i) it needs to be carried out only by an
experienced clinician; (ii) inevitable inter- and intra-observer variability
in the annotations; (iii) and it is laborious and must be repeated for each
patient. Consequently, the automatic segmentation methods have been designed
to resolve this issue that can lead to increased patient throughput and can
reduce the inter-user discrepancy. There are many suggested methods for 2D LV
segmentation. Recently Deep Convolutional Neural Networks have shown promising
results for image segmentation (Jafari et al., 2018; Leclerc et al., 2019),
specifically U-Net, which has been successfully applied to multiple medical
image segmentation problems. As explained in the abstract, this study aims to
investigate and compare the performance of U-Net 1 and U-Net 2 models reported
by (Leclerc et al., 2019) with the original U-Net (Ronneberger et al., 2015).
More details in Appendix A.

##  2 Methods and dataset

The study population consisted of 61 patients with Apical 4-chamber views, who
were recruited from patients who had undergone echocardiography with Imperial
College Healthcare NHS Trust. The study was approved by the local ethics
committee and written informed consent was obtained. All data acquired with
the same equipment. To achieve the gold-standard (ground-truth) measurements,
one accredited and experienced cardiology expert manually traced the LV
borders. Out of 1098 available frames (61 patients $\times 3$ positions
$\times 3$ heartbeats $\times 2$ ED/ES frames), a total of 992 frames were
annotated. To investigate the inter-observer variability, a second operator
repeated the LV tracing on 992 frames, blinded to the judgment of the first
operator. From the total of 992 images, 60% were selected for training, 20% of
total data used for validation, and the remaining 20% was used for testing.
All three datasets comprised images from different patients and no images from
the same patient were shared between the datasets.

All images were resized to a smaller dimension of $256\times 256$ pixels (as
needed by model U-Net 1 and U-Net 2), leading to a fair comparison. All models
produce the output with the same spatial size as the input image. Pytorch was
used for the implementations (Paszke et al., 2017), where Adam optimiser with
250 epochs and learning rate of 0.00001 were used for training the models. The
negative log-likelihood loss is used as the network’s objective function. All
computations were carried using an Nvidia GeForce GTX 1080 Ti GPU.

##  3 Experiment results and discussion

All models were trained separately using the annotations provided by either of
the operators. The Dice Coefficient (DC) and Hausdorff distance (HD) were
employed to evaluate the performance and accuracy of the models in segmenting
the LV region. Fig 1A displays output examples from the three models when
trained using annotation provided by Operator-A (i.e., OA). To specify the LV
endocardium border, the contour of the predicted segmentation was used. The
solid blue line indicates the manual annotation while the yellow line shows
the automated results. As can be seen, the U-Net model achieved a higher DC
(0.97) and lower HD (4.24) score. A visual inspection of the automatically
detected LV border also confirms this. The LV border obtained from the U-Net 1
and U-Net 2 models seems to be less smooth compared to that in the U-Net
model. However, all three models look to perform with reasonable accuracy.

Figure 1:  Good and failed case example outputs from three different models.

Fig 1B illustrates the results for a failed case example, for which all 3
models seem to struggle. It is evident that image quality tends to be lower in
failed cases due to the missing borders, the presence of speckle noise or
artefacts, and poor contrast between the myocardium and blood pool. The left
side of Table LABEL:table:_Table_1, provides average DC and HD for the 3
models, across all 199 testing images. Plausible scenarios for manual or
automated (U-Net only) are provided on the right side of the table; for each
image, there were 4 assessments of the LV border; 2 human and 2 automated
(trained by annotation of either of human operators). The automated model
performs similarly to human operators. The model disagrees with the
Operator-A, but so does the Operator-B. Since different experts make different
judgments, it is not possible for any automated model to agree with all
experts. However, it is desirable for the automated models not to have larger
discrepancies when compared with the performance of human judgments; that is,
to behave approximately as well as human operators.

##  4 Conclusion

Our study compared the performance of three models which appear to perform no
worse than human experts. However, the automated models, when encountered with
the lower image qualities, demonstrate larger discrepancies with the gold-
standard annotations. This is potentially caused by the lack of balanced data
in terms of different levels of image qualities in our dataset. Future
investigations will examine the correlation between the performance of the
automated model and the image qualities.

N.A. was supported by the School of Computer Science PhD scholarship at the
University of Lincoln.

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## Appendix A

Details of models summaries in the following table. The “number of feature
maps” given in table corresponds to the number of convolutions in the
convolution layers. For each U-Net implementation, the values for the first,
the bottom (where the spatial information is the most compressed), and the
last convolution layers indicated (Leclerc et al., 2019).

Architectures |  Number of feature maps |  Upsampling scheme |  Normalization scheme
---|---|---|---
U-Net | $64\downarrow 1024\uparrow 64$ | Deconvolution | None
U-Net 1 | $32\downarrow 128\uparrow 16$ |  2 $\times 2$ repeats | None
U-Net 2 | $48\downarrow 768\uparrow 48$ | Deconvolutions | BatchNorm