11institutetext:  Shanghai Key Lab for Astrophysics, Shanghai Normal
University, Shanghai 200234, China 22institutetext: Key Laboratory for
Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese
Academy of Sciences, Shanghai 200030, China 33institutetext: Key Laboratory of
Polar Science, Polar Research Institute of China, Ministry of Natural
Resources, Shanghai 200136, China 44institutetext: Key Laboratory for the
Structure and Evolution of Celestial Objects, Yunnan Observatories, Chinese
Academy of Sciences, Kunming, Yunnan 650011, China 55institutetext: Key
Laboratory for Research in Galaxies and Cosmology, Department of Astronomy,
University of Science and Technology of China, Chinese Academy of Sciences,
Hefei, Anhui 230026, China 66institutetext: School of Astronomy and Space
Science, University of Science and Technology of China, Hefei, Anhui 230026,
China

# The 2175 Å Bump Features in FeLoBAL Quasars:
One Indicator of MW-like Dust in the Nuclear Region of Quasars

Shaohua Zhang 11 Jian Ge 22 Tuo Ji 33 Peng Jiang 33 Zhijian Luo 11 Xiang Pan
33 Xiheng Shi 33 Chenggang Shu 11 Jianguo Wang 44 Hubing Xiao 11 Hongyan Zhou
335566

(Received xx xx, xxxx; accepted xx xx, xxxx)

To investigate the properties of dust in the nuclear region of quasars, we
explored the extinction curves of the iron low-ionization broad absorption
line (FeLoBAL) quasar SDSS J163004.29+311957.6 and its two analogues. The
parameterized extinction curves indicated the Milky Way-like 2175 Å bump
features in underlying extinction, which are similar to those seen in the
Local Group and a subset of high-redshift star-forming galaxies. Compared to
the bump features in the Large Magellanic Clouds (LMC), the detections in this
work are much closer to those in the Milky Way (MW). These bump features, as
well as those in the high- and low-ionization broad absorption line (BAL)
quasars of Zhang et al., are probably the counterpart of the 2175 Å bump
features in the quasar environment. This type of dust grain is generally
small, easily disrupted by high-energy photons and has difficulty surviving in
the radiation field of the active galactic nucleus (AGN). However, due to the
presence of absorption-line outflows, the 2175 Å bump feature in quasars,
which should be rare, is seen many times in BAL quasars. The shielding effect
of outflow clouds allows the MW-like dust grains to be assembled or extends
the survival period in the quasar nuclear region. The process, and physical
and chemical conditions deserve further observational study and investigation.

###### Key Words.:

dust, extinction – galaxies: ISM – quasars: absorption lines – quasars:
individual: SDSS J163004.29+311957.6

##  1 Introduction

Dust is everywhere in the universe, and although this dust is tiny, it plays
an important role in many astrophysics processes. Absorption, scattering, and
re-radiation of the dust reshape the spectral energy distribution (SED) of
galaxies and active galactic nuclei (AGNs). At the same time, as an important
component of the interstellar medium (ISM), the dust grain surface offers a
place to efficiently form molecules as a critical step for star formation,
which creates galaxies (Draine 2003). Understanding dust properties and their
evolution is crucial to providing complete knowledge of ISM processes, star
formation and galaxy evolution.

In the galaxy, dust fills interstellar space. Its distribution is
heterogeneous, and it is known to be clumpy with cold, dense regions of star
formation. In the AGN, the dust generally accumulates on the periphery of the
accretion disk and forms a circumnuclear dusty “torus” as an important
structural element in the AGN “unified” schemes (e.g., Antonucci 1993; Urry &
Padovani 1995). The torus obscures and absorbs high-energy photons emitted
from the central engine and re-radiates at thermal infrared (IR) wavelengths
(e.g., Rees et al. 1969; Barvainis 1987) ; thus, it is also considered to be
the reason for the appearance of Type I/II AGNs. Beyond that, there is still a
large amount of dust distributed in various parts of the nuclear region, such
as the AGN outflows. Studies have found that high-speed outflows have an
unexpectedly significant correlation with hot dust emission, which suggests a
dust outflow scenario (e.g., Wang et al. 2013; Zhang et al. 2014).

Dust attenuation, polarimetry, and thermal radiation are all efficient
observational means for dust. The blocking effect on centre photons implies
the existence of a dusty torus, and polarimetric and near-/mid-IR
spectroscopic/photometric observations have presented abundant information on
the physical and structural characteristics (e.g., Krolik & Begelman 1988;
Pier & Krolik 1992; Dullemond & van Bemmel 2005; Tristram et al. 2007; Nenkova
et al. 2008; Alonso-Herrero et al. 2011; Tristram et al. 2014; García-González
et al. 2017; Zhuang et al. 2018; Lopez-Rodriguez et al. 2020; Lyu & Rieke
2021). Relatively speaking, dust attenuation can delve into the details of
interstellar dust grains, such as the size distribution of dust grains (e.g.,
Cardelli et al. 1989; Whittet 2003) and even the chemical compositions (e.g.,
Li & Draine 2001; Draine 2003). Furthermore, measuring the extinction curve
and making a corresponding reddening correction allow accurately deriving of
the intrinsic SED, luminosity functions, and physical properties of AGNs.

Regarding the AGN extinction law, Czerny et al. (2004) compared the composites
of red and blue quasars, and Gaskell et al. (2004) also checked the composites
of quasars with different radio properties. The above composite comparative
studies found that quasar extinction curves are, on average, significantly
flatter in ultraviolet (UV) than those of local galaxies, and they do not show
any traces of the 2175 Å bump feature, which is ubiquitous in extinction
curves of the Milky Way (MW) and Large Magellanic Clouds (LMC) (see Draine
1989). Similar results have been obtained from the analyses of the quasar
colour distribution (Richards et al. 2003; Hopkins et al. 2004), and the red
“tail” of the colour distribution of SDSS quasars is well described by the
Small Magmatic Cloud (SMC) like reddening. However, Jiang et al. (2013)
reported the quasar intrinsic anomalously steep reddening law via the
exceptional source IRAS 14026+4341, and the extinction curves detected in all
individuals of the high $A_{\rm V}$ quasar sample are also steeper than the
SMC law (Zafar et al. 2015). In the study of polar dust obscuration in nearby
broad-line active galaxies (BLAGNs) from the XMM-XXL field, Buat et al. (2021)
reported that even 60% of sources containing polar dust exhibit a steep
extinction curve. Indeed, the reality is probably more complex with a mix of
different shapes (e.g., Buat et al. 2021), and we are still far from
understanding quasar extinction phenomena. Moreover, Zhang et al. (2017)
derived the SMC-type reddening law of a dusty torus in three narrow-line
Seyfert 1 galaxies (NLS1s). However, the extinction-to-gas ratios
($E{\rm(B-V)}/N_{\rm H}$) of the two cases are comparable to the Galactic
standard value (Bohlin et al. 1978) and approximately ten to twenty times
larger than that of the SMC (Weingartner & Draine 2001). These findings
indicate the dominance of small dust grains in the quasar environment , which
offers the possibility of detecting the 2175 Å bump feature in quasars.

To date, dozens of 2175 Å bump features have been reported at high redshifts.
These detections are mostly found in individual intervening systems towards
background quasars, including metal (e.g., Mg II, Zn II, C I, etc.) absorbers
(e.g., Wang et al. 2004; Srianand et al. 2008; Noterdaeme et al. 2009; Zhou et
al. 2010; Jiang et al. 2010a,b, 2011; Ledoux et al. 2015; Ma et al. 2017) and
DLAs (e.g., Wucknitz et al. 2003; Junkkarinen et al. 2004; Prochaska et al.
2009; Wang et al. 2012; Ma et al. 2015) or towards gamma ray burst (GRB)
afterglows (e.g., Ellison et al. 2006; Vreeswijk et al. 2006; Prochaska et al.
2009; Liang & Li 2010; Schady et al. 2012; Zafar et al. 2011, 2012, 2018).
These systems have a high metallicity and heavy dust depletion level (e.g., Ma
et al. 2017). These detections reveal information about the ISM in the
foreground galaxies of quasars/GRBs or GRB hosts rather than the quasar
itself. The quasar-associated 2175 Å bump features are still rarely detected,
and only in a limited number of quasar intrinsic extinction curves (Zhang et
al. 2015a; Pan et al. 2017; Shi et al. 2020). Theoretically, the carriers of
2175 Å bump features are small-sized carbonaceous and silicate grains (e.g.,
Joblin et al. 1992; Weingartner & Draine 2001; Li & Draine 2001; Steglich et
al. 2010; Blasberger et al. 2017) ; however, they are easily disrupted by
high-energy photons have difficulty existing in the radiation field of the
centre engine (e.g., Voit 1992 and reference therein). Why can this type of
dust grain be produced and survive in this harsh environment? This is an
interesting topic worth further investigation. Exploring individuals, sample
expansion and further detailed studies of important targets may provide a
possibility for researching the formation and destruction of 2175 Å dust
grains in the nuclear region of quasars.

Broad absorption lines (BALs) are the most famous quasar intrinsic absorption
systems, and they are one of the direct performances of AGN outflows. They
appear in the spectra of $\sim 10-26\%$ of optically selected quasars and are
often observed as absorption by the ions of C IV, Si IV, Al III and Mg II
(e.g., Weymann et al. 1991; Reichard et al. 2003; Trump et al. 2006; Gibson et
al. 2009; Scaringi et al. 2009; Zhang et al. 2010; Allen et al. 2011).
Moreover, iron low-ionization broad absorption lines (FeLoBALs) are a rare and
special subclass of BALs, which exhibit absorption from excited fine-structure
levels or excited atomic terms of Fe II or Fe III (e.g., Hazard et al. 1987;
Becker et al. 1997, 2000; Menou et al. 2001; Hall et al. 2002; Zhang et al.
2015b). The mid-IR spectroscopic observation of 6 FeLoBAL quasars detected
unidentified emission signatures of carbonaceous molecules (Farrah et al.
2010), which implies that the carriers of 2175 Å bump features can probably
survive in the environments of BAL quasars. In Zhang et al. (2015a), the
initial exploration of 2175 Å bump features among Mg II absorption line
systems on quasar spectra in the SDSS DR10 led to the identification of excess
broadband extinction near 2250 Å in 7 BAL quasars. These cases are in the
redshift range of $1.78$ to $2.29$, and 5 of them show low-ionization BALs
(LoBALs) of Mg II and Al III. This provides direct evidence of the coexistence
of BALs and 2175 Å bump features in quasars.

In this work, we report the detection of the 2175 Å bump feature in 3 FeLoBAL
quasars. The remainder of this paper is organised as follows: in Sect. 2, we
describe the observational data available and data reduction. In Sect. 3, we
present the derivation of the extinction curve, Sect. 4 provides a discussion,
and Sect. 5 summarises the main conclusions.

##  2 Observations and data reduction

After Zhang et al. (2015a), 2175 Å bump features in the spectra of the SDSS
DR7 FeLoBAL quasars (Shen et al. 2011) were systematically explored following
the search procedure of Zhang et al. (2015a). The quasar composite reddened by
a parameterised extinction curve (introduced in the first two paragraphs of
Sect. 3) was used to fit the observed spectra in the quasar rest frame and
obtain potential bump candidates, and then a simulation technique of the
control sample (Jiang et al. 2010a,b; also see the fifth paragraph of Sect. 3)
was performed to gauge the significance of the bump features. 10 bump
candidates were screened out with 2175 Å bump features at a $>3\sigma$ level
of statistical significance (see Table 1). After performing a visual
examination, 7 “false” signals are rejected. This is simply because their
spectral wavelength ranges only cover part of the bump feature, and no
effective photometric data limit the far-UV models. We finally identified 3
credible candidates, i.e., J102036.10+602339.0 (referred to as SDSS J1020+6023
hereafter), SDSS J134951.93+382334.1 (referred to as SDSS J1349+3823
hereafter), and SDSS J163004.29+311957.6 (referred to as SDSS J1630+3119
hereafter). Their emission-line redshifts given by the SDSS DR7 quasar
catalogue (Schneider et al. 2010) are $0.9940\pm 0.0006$, $1.0943\pm 0.0009$,
and $1.9960\pm 0.0007$, respectively. Since SDSS J1630+3119 has the most
observational data and its spectrum shows the entire bump profile, it is
analysed and reported in the text in detail. For SDSS J1020+6023 and SDSS
J1349+3823, their optical spectra cannot completely cover the bump profiles
because of their lower redshifts; fortunately, the reliability of spectral
fitting is verified by the GALEX data; thus, they are listed in the
appendices.

Table 1:  Best Fitted Parameters of 10 bump candidates in FeLoBAL quasars
$\begin{array}[]{cc ccc cc c}\hline\cr\hline\cr\vskip 3.0pt plus 1.0pt minus
1.0pt\cr{\rm Name\leavevmode\nobreak\
(SDSSJ)}&z&c_{1}&c_{2}&c_{3}&x_{0}&\gamma&{\rm Significance}\\\ \vskip 3.0pt
plus 1.0pt minus 1.0pt\cr\hline\cr\vskip 3.0pt plus 1.0pt minus 1.0pt\cr
101927.37+022521.4&1.3643&-1.95\pm 0.02&0.65\pm 0.01&0.76\pm 0.06&4.46\pm
0.01&0.99\pm 0.04&5.6\sigma\\\ 102036.10+602339.0&0.9940&-1.82\pm 0.02&0.54\pm
0.01&0.95\pm 0.08&4.49\pm 0.01&1.20\pm 0.03&6.9\sigma\\\
102249.27+130125.0&1.2168&-0.87\pm 0.03&0.55\pm 0.01&1.41\pm 0.07&4.78\pm
0.02&1.68\pm 0.04&3.7\sigma\\\ 110711.40+082331.2&1.3875&-1.73\pm 0.04&0.81\pm
0.01&0.57\pm 0.04&4.69\pm 0.01&0.95\pm 0.05&6.2\sigma\\\
112220.76+153927.8&1.0996&-1.38\pm 0.01&0.43\pm 0.01&1.11\pm 0.06&4.56\pm
0.01&1.10\pm 0.02&7.0\sigma\\\ 134951.93+382334.1&1.0943&-0.73\pm 0.02&0.37\pm
0.01&2.02\pm 0.13&4.67\pm 0.01&1.18\pm 0.08&17.2\sigma\\\
142010.28+604722.3&1.3450&-0.58\pm 0.03&0.51\pm 0.01&0.39\pm 0.06&4.58\pm
0.01&0.87\pm 0.04&8.7\sigma\\\ 152438.79+415543.0&1.2299&-1.34\pm 0.01&0.41\pm
0.01&0.70\pm 0.06&4.47\pm 0.01&0.96\pm 0.03&10.9\sigma\\\
155633.78+351757.3&1.4950&-3.46\pm 0.02&1.00\pm 0.01&1.35\pm 0.10&4.56\pm
0.01&1.45\pm 0.02&7.6\sigma\\\ 163004.29+311957.6&1.9960&-3.37\pm 0.05&1.11\pm
0.01&0.90\pm 0.07&4.51\pm 0.01&0.92\pm 0.07&31.2\sigma\\\ \vskip 3.0pt plus
1.0pt minus 1.0pt\cr\hline\cr\hline\cr\end{array}$

SDSS J1630+3119 is an infrared-luminous quasar with an extremely faint UV
emission. The broadband SED for SDSS J1630+3119 from UV to near-IR is
presented by the photometric images taken with the SDSS (York et al. 2000) at
the $u^{\prime}$, $g^{\prime}$, $r^{\prime}$, $i^{\prime}$, and $z^{\prime}$
bands, the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) at the
$J$, $H$, and $Ks$ bands, and the Wide-field Infrared Survey Explorer (WISE;
Wright et al. 2010) at the $W1$, $W2$, $W3$, and $W4$ bands. The optical and
near-IR photometric data of SDSS J1630+3119 join smoothly, suggesting that the
quasar did not vary significantly between the SDSS and 2MASS observations. The
multiband magnitudes are summarised in Table 2.

Table 2:  Broadband photometric data of SDSS J1630+3119
$\begin{array}[]{cccc}\hline\cr\hline\cr\vskip 3.0pt plus 1.0pt minus
1.0pt\cr{\rm Band}&{\rm Magnitude}&{\rm Date-Observation}&{\rm Survey}\\\
\vskip 3.0pt plus 1.0pt minus 1.0pt\cr\hline\cr\vskip 3.0pt plus 1.0pt minus
1.0pt\cr u^{\prime}&23.15\pm 0.46&04/29/2003&{\rm SDSS}\\\ g^{\prime}&21.41\pm
0.04&04/29/2003&{\rm SDSS}\\\ r^{\prime}&20.12\pm 0.02&04/29/2003&{\rm
SDSS}\\\ i^{\prime}&19.16\pm 0.02&04/29/2003&{\rm SDSS}\\\ z^{\prime}&18.31\pm
0.03&04/29/2003&{\rm SDSS}\\\ J&17.420\pm 0.221&04/05/1998&{\rm 2MASS}\\\
H&16.178\pm 0.170&04/05/1998&{\rm 2MASS}\\\ Ks&15.871\pm 0.205&04/05/1998&{\rm
2MASS}\\\ W1&15.217\pm 0.205&02/17/2010&{\rm WISE}\\\ W2&14.080\pm
0.044&02/17/2010&{\rm WISE}\\\ W3&10.720\pm 0.076&02/17/2010&{\rm WISE}\\\
W4&8.191\pm 0.206&02/17/2010&{\rm WISE}\\\ V&-&07/06/2005-10/22/2013&{\rm
CSS}\\\ g&-&06/17/2009-06/27/2013&{\rm Pan-STARRS}\\\
r&-&06/07/2010-06/20/2013&{\rm Pan-STARRS}\\\ i&-&06/09/2009-05/11/2014&{\rm
Pan-STARRS}\\\ z&-&03/01/2010-08/16/2013&{\rm Pan-STARRS}\\\
y&-&02/20/2010-08/22/2013&{\rm Pan-STARRS}\\\ zr&-&06/11/2018-09/02/2019&{\rm
ZTF}\\\ \vskip 3.0pt plus 1.0pt minus 1.0pt\cr\hline\cr\hline\cr\end{array}$
Figure 1: Light curves of SDSS J1630+3119 from CSS, Pan-STARRS, and ZTF
surveys. The raw data are shown with errors in different colours. The CSS
continually monitored SDSS J1630+3119 for more than 8 years (170 epochs) at
the $V$ band beginning on 6 July 2005, in which the light curve means that
there is no apparent change in the optical continuum within large measurement
errors. Pan-STARRS has performed another extensive monitor since 9 June 2009,
and the magnitudes of 68 epochs confirm the latter half of the light curve
observed by the CSS. After approximately 5 years, SDSS J1630+3119 was
monitored again by the ZTF and has 18 new observations with a $zr$ filter. The
light curves with a high photometric accuracy from the Pan-STARRS and ZTF
suggest that SDSS J1630+3119 does not have significant long-term variability
in optical and near-infrared bands.

SDSS J1630+3119 was targeted for spectroscopic observations by the SDSS as a
high-redshift quasar candidate ($LEGACY\\_TARGET1=QSO\\_HIZ$). Two archival
observations for SDSS J1630+3119 from the SDSS and SDSS-III/BOSS were
performed on 4 April 2005 (DR7; Abazajian et al. 2009) and 7 July 2011 (DR16;
Dawson et al. 2016), and the 1D spectra were accessed from the SDSS Science
Archive Server. The BOSS spectrum multiplied by approximately 1.2 times is in
excellent agreement with the SDSS spectrum. Meanwhile, the Catalina Sky
Surveys (CSS; Drake et al. 2009), the Panoramic Survey Telescope & Rapid
Response System (Pan-STARRS; Chambers et al. 2016), and the Zwicky Transient
Facility (ZTF; Masci et al. 2018) present that SDSS J1630+3119 does not have
significant long-term variability in either optical or near-IR bands within
the current measurement errors for 14 years beginning on 6 July 2005 (Fig. 1).
Thus, the spectral flux density differences between the two spectroscopic
observations probably come from the spectrophotometric calibration of the
different SDSS Optical Spectroscopy Pipelines (Version ‘$26$’ and
‘$v5\\_13\\_0$’ ). Moreover, the scaled BOSS spectrum agrees very well with
the SDSS photometric data , which indicates that the recalibration is reliable
and that SDSS J1630+3119 did not vary significantly between the SDSS
photometric and spectroscopic observations. In the following analysis, the
scaled BOSS spectrum is used to derive the extinction curve because of its
wider wavelength coverage. All of the data have been corrected for the
Galactic reddening of $E{\rm(B-V)}=0.032$ (Schlegel et al. 1998) and the
Fitzpatrick (1999) reddening curve before any further analysis.

The spectrum of SDSS J1630+3119 shows unusual absorption features and has been
classified as a BAL quasar in Gibson et al. (2009) and Shen et al. (2011). In
Fig. 2, we present the observed spectrum of SDSS J1630+3119 and the absorption
troughs of C IV, Al III, Mg II and iron multiplets (marked by grey dot-dashed
lines and characters). A typical FeLoBAL quasar with absorption troughs
similar to those of SDSS J1630+3119 and SDSS J144002.24+371058.5 (referred to
as SDSS J1440+3710 hereafter) is over-plotted for a comparison, in which the
spectrum is multiplied by a factor to match that of SDSS J1630+3119 in the
wavelength ranges of $1600-1900$ Å and $2800-3300$ Å. The high-ionization
absorption lines of C IV $\lambda\lambda 1548,1551$ in the spectrum of SDSS
J1630+3119 are revealed by troughs near the centre of the corresponding lines,
which have an extremely broad velocity width from approximately 4000 to
$-13,000$ km s-1 with respect to the systemic velocity. The numerous
absorption lines from low-ionization species, such as Mg II, Al III, and Fe
II, that characterise the spectrum show some different appearances that are
narrower and heavily blended together for most of them in the lower-resolution
spectrum. In the figure, we identify some strong absorption troughs of Fe II
multiplets UV 1, UV 2, UV 3, and UV 62,63 near 2600 Å, 2382 Å, 2344 Å, and
2750 Å (in the quasar rest frame), and other weak multiplets of Fe II UV 8 and
UV 44,45 on the longward of the C IV emission line, and Fe II UV 64, UV 145,
and UV 263 on either side of the UV 1 multiplets are not marked.

Figure 2:  Observed spectrum of SDSS J1630+3119 and BAL troughs. The spectrum
was slightly smoothed with a Gaussian kernel with a sigma of 3 spectral pixels
to help show the absorption features. The spectrum of SDSS J1630+3119 is
dominated by a strong broad and blueshifted absorption troughs from Mg II, Al
III, C IV, and Fe II UV 1, UV 2, UV 3, UV 62,63, Opt. 6,7 and Opt. 8 multiplet
transitions, the vertical dashed-dotted grey marks indicate the expected rest
frame positions of the resonant lines in these features. The comparison
spectrum (shown in green) with similar absorption troughs to SDSS J1630+3119
from the FeLoBAL quasar SDSS J1440+3710 was smoothed and multiplied by 0.75 to
match that of SDSS J1630+3119 in the wavelength ranges of $1600-1900$ Å and
$2800-3300$ Å.

##  3 Extinction Curve and Bump

In Fig. 3(a), we present the observed data of SDSS J1630+3119, including the
original spectrum (black curve) and multiband magnitudes (orange squares). The
dramatic flux drop shortward of the Mg II broad emission line (BEL) appears to
slow down somewhere between the SDSS $r^{\prime}$ and $i^{\prime}$ bands,
suggesting that a broad absorption bump occurs there. In the MW and other
galaxies in the Local Group, the interstellar extinction is most commonly
obtained through the “pair method” by comparing the spectra of two stars of
the same spectral type, one of which is reddened ($f_{\rm obs}$) while the
other is unreddened ($f_{\rm model}$). A similar technique, called the “quasar
spectrum pair method”, was used to derive the quasar extinction curve and
identify 2175 Å bump features in the quasar spectrum (e.g., Wang et al. 2004;
Zhou et al. 2010). The reddened spectrum is an observed quasar spectrum, and
the unreddened spectrum is replaced by the quasar composite or an individual
blue quasar spectrum. In this work, the unreddened spectrum is set to a quasar
composite, which is obtained by combining the SDSS DR7 quasar composite
($\lambda<3000$ Å; Jiang et al. 2011) and the near-IR quasar template
($\lambda\geq 3000$ Å; Glikman et al. 2006).

We borrowed the Fitzpatrick & Massa (1990) parametrization of the optical/UV
extinction curve; however, the extinction curve we measured in this work is
$A^{*}(x)$ rather than $k(\lambda-V)$ ($\equiv E{\rm(\lambda-V)}/E{\rm(B-V)}$,
used in the original Fitzpatrick & Massa formula). The reason for this will be
explained in the next paragraph. In fact, the above two parameters are
equivalent for a given extinction curve. $A^{*}(x)$ is described as a
combination of a linear background and a Drude component, representing the
underlying extinction and the possible 2175 Å bump feature, respectively,

$\displaystyle A^{*}(x)={\rm-2.5log}\frac{f_{\rm obs}}{f_{\rm
model}}=c_{1}+c_{2}x+c_{3}\frac{x^{2}}{(x^{2}-x_{0}^{2})^{2}+x^{2}\gamma^{2}},$
(1)

where $x\leavevmode\nobreak\ (\equiv\lambda^{-1})$ is the inverse wavelength
and $x_{0}$ and $\gamma$ are the peak position and full width at half maximum
(FWHM) of the Drude profile, respectively. The UV linear component is set by
the slope $c_{2}$ and intercept $c_{1}$, which depend on the relative
intensity of both model and observed spectra. In this work, the model and
observed spectra are not normalized. If they are normalized at the IR
wavebands where the extinction is negligible, the parameter $c_{1}$ will be
zero. Meanwhile, the far-UV curvature term ($x\geq 5.9\leavevmode\nobreak\
\rm\mu m^{-1}$) in the Fitzpatrick & Massa formula is excluded. The limited
wavelength coverages and the low signal-to-noise ratio (S/N) blueward spectra
of SDSS J1630+3119 are insufficient for properly identifying the extinction in
the far-UV band. Here, the 2175 Å bump feature is supposed to have the same
redshift as the quasar. If the devised peak position is much larger than the
mean value of $x_{0}=4.59\pm 0.027$ $\mu$m-1 in galactic interstellar curves,
then the bump in the quasar spectrum would be the intervening system, which is
not associated with the quasar. After masking the wavelength range with Mg II,
Al III, C IV, and strong iron absorption troughs, the spectrum is fitted with
models as the quasar composite that is reddened by dust with the parameterised
extinction curve. We perform the least squares minimization using the
Interactive Data Language (IDL) procedure MPFIT111The Markwardt IDL Library is
available at http://cow.physics.wisc.edu/$\sim$craigm/idl/idl.html. developed
by Markwardt (2009). The best-fitting model yields $c_{1}=-3.37\pm 0.05$,
$c_{2}=1.11\pm 0.01$, $c_{3}=0.90\pm 0.07$, $x_{0}=4.51\pm 0.01$ $\mu$m-1, and
$\gamma=0.92\pm 0.07$ $\mu$m-1. The uncertainties are the formal MPFIT errors.

In general, the extinction curves are exhibited in their native normalization
($E{\rm(\lambda-V)}/E{\rm(B-V)}$) or are normalised to an absolute scale
($A_{\lambda}/A_{\rm V}$). The visual/near-UV curves would be approximated by
an analytic formula with one parameter $R_{\rm V}$ ($\equiv A_{\rm
V}/E{\rm(B-V)}$) (Cardelli et al. 1989). The parameter $R_{\rm V}$ can also
serve as an indicator of galactic environmental characteristics, and its value
in the dense region is larger than that in diffuse ISM (Whittet 2003). In this
work, the derived curve is not normalised, because the optical/UV SEDs of
quasars are directly related to the fundamental plane of black hole activity,
and the continuum slope is very different among different quasars. We do not
know the unreddened intrinsic continuum for a given quasar, and it is set to a
quasar composite in the fitting. Obviously, there must be some deviation
between them, and it will be reflected in the parameter $c_{2}$. Thus, in the
extinction measure process of this work, we cannot actually precisely extract
the conventionally defined extinction parameters, such as $A_{\rm V}$,
$E{\rm(B-V)}$ and $R_{\rm V}$, from the derived extinction curve. Fortunately,
the peak position and width of the bump feature are not correlated with one
another (Fitzpatrick & Massa 1986), but both are correlated with the slope of
the extinction curve (Cardelli et al. 1989). Thus, the detection of the 2175 Å
bump feature from the derived extinction curve is viable, and we can
investigate the extinction properties of these special dust grains. The bump
strength can be defined using these parameters: $A_{\rm
bump}\leavevmode\nobreak\ =\pi c_{3}/2\gamma=1.53\pm 0.16$ $\mu$m-1, which can
be interpreted as rescaling the integrated apparent optical depth of the bump
feature ($A_{\lambda}=\frac{2.5}{\rm ln10}\tau_{\lambda}$). Likewise, $A_{\rm
bump}$ is also not normalised. In Gordon et al. (2003) and Fitzpatrick & Massa
(2007), the bump strength is defined as the normalised strength by the colour
excess $(A_{\rm bump}/E{\rm(B-V)})$.

Figure 3: Panel (a): observed data of SDSS J1630+3119 and 2175 Å bump feature
in spectrum. The quasar spectra, including the original spectrum (black curve)
and reddened quasar composites with the best-fitting extinction curves
with/without the bump component (solid and dashed green curves), are
overplotted for a comparison. The sky blue region between them is the
extinction bump of the MW-like dust. The multiband magnitudes of SDSS
J1630+3119 from the SDSS, 2MASS and WISE are presented by orange squares.
Panel (b): Observed extinction (orange squares and black curve) and modelled
extinction (green curve) of SDSS J1630+3119. The grey dashed and sky blue
solid lines present the underlying linear and bump components of the best-
fitting extinction curve. The extinction curve rapidly decreases with an
increasing wavelength into the infrared spectrum, and the extinction is an
unlimited approach to zero with $\lambda\to\infty$.

In Fig. 3(a), the best-fitting model for SDSS J1630+3119 is overplotted with
the observed spectra using a green solid cure. To emphasise the requirement of
the 2175 Å bump feature in the extinction curve, we also overplot the reddened
quasar composite with only the linear component of the best-fitting model (the
green dashed curve). The difference between them corresponds to the bump
extinction feature (sky blue shadow). In Fig. 3(b), we present the observed
(orange squares and black curve) and modelled (green curve) extinction of SDSS
J1630+3119. The grey dashed and sky blue solid lines present the underlying
and bump components of the best-fitting extinction curve. One can find that
the extinction curve rapidly decreases with an increasing wavelength into the
infrared spectrum, and $E(x)$ is an unlimitd approach to zero with the a
wavelength of $\lambda\to\infty$.

To confirm the existence of the 2175 Å bump feature in SDSS J1630+3119, we
employ several independent validation tests. The first is the simulation
technique of the control sample developed by Jiang et al. (2010a,b). This
technique begins with the construction of a control sample, including 200
quasars with a minimal redshift differential relative to SDSS J1630+3119 and
an $i^{\prime}-$band spectral $S/N>6$ from the SDSS DR7 quasar catalogue.
Then, we fit each of them by reddening a quasar composite with a parameterised
extinction curve. The parameters ($x_{0}$ and $\gamma$) are fixed to the best-
fitting values of the 2175 Å bump feature in SDSS J1630+3119. Since the
“pseudo-continuum (the complex of nuclear continuum and Fe II emission
multiplets)” diversity between the quasar spectra and the composite can mimic
a false bump feature (e.g., Pitman et al. 2000), the bump strength
distribution of the control sample can be considered as the measurement
fluctuation of the 2175 Å bump feature. The distribution is expected to be a
Gaussian profile, as shown by the pink curve in Fig. 4. In principle, a bump
candidate with a significance level of $>3\sigma$ is considered real
detection, where $\sigma$ is the standard deviation of the Gaussian profile
The bump strength in SDSS J1630+3119 is obviously far from the distribution
($\overline{A}_{\rm bump}=-0.01$ $\mu$m-1 and $\sigma=0.05$ $\mu$m-1), and the
bump detection in SDSS J1630+3119 has an extremely high statistical
significance.

Figure 4: Histogram of fitted bump strengths of control sample for SDSS
J1630+3119. The pink solid line is the best-fitting Gaussian profile. Three
vertical blue dashed lines indicate the 3$\sigma$, 4$\sigma$, and 5$\sigma$
boundaries of the Gaussian profile. The red star indicates the bump strength
derived in the spectrum of SDSS J1630+3119, which has an extremely high
statistical significance level.

The second test is more intuitionistic. It focuses only on the bump features
we are interested in. For the purpose of comparison, we again use the typical
FeLoBALquasar, SDSS J1440+3710. In Fig. 5, we present the corrected spectrum
of SDSS J1630+3119 (sky blue curve) for bump reddening of the Drude component
(sky blue solid curve in Fig. 3 (b)) and the observed spectra of SDSS
J1630+3119 and SDSS J1440+3710. The latter two are also shown in Fig. 2 for a
comparison to identify BAL troughs. In the figure, the corrected spectrum of
SDSS J1630+3119 agrees well with the observed spectrum of SDSS J1440+3710 ,
except for the two sides of the Fe II UV 1 troughs. They share the same
continuum, similar overlapping iron absorption lines and other metal
absorption troughs. In other words, the observed continuum difference between
SDSS J1630+3119 and SDSS J1440+3710 is probably the 2175 Å bump feature.

Figure 5: Comparison of corrected spectrum of SDSS J1630+3119 bump reddening
(sky blue curve) and observed spectra of SDSS J1630+3119 and J1440+3710. The
corrected spectrum agrees very well with the observed spectrum of SDSS
J1440+3710 (also shown in Fig. 2 by the green curve), and both spectra follow
the reddened quasar composite (red curve) by the SMC extinction curve of
$E{\rm(B-V)}=0.46$ very well. The continuum difference between SDSS J1630+3119
and SDSS J1440+3710 is probably 2175 Å bump feature.

In the confirmation of 2175 Å bump features in BAL quasars (Zhang et al.
2015a), we performed continuum fitting by reddening a quasar composite with
different extinction curves, namely, from the SMC, LMC2 Supershell (near the
30 Dor star-forming region; Gordon et al. 2003), LMC, and MW. The bump is
absent in the SMC extinction and gradually increases from the LMC2 Supershell
to the LMC and the MW. In Fig. 5 of this work, the reddened quasar composite
by the SMC extinction curve of $E{\rm(B-V)}=0.46$ is present (red curve), and
either the corrected spectrum of SDSS J1630+3119 or the observed spectrum of
SDSS J1440+3710 could follow the composite continuum. This implies that the
extinction curves without the 2175 Å bump feature are certainly not suitable
for the original spectrum of SDSS J1630+3119. For the extinction curves of the
LMC2 Supershell, LMC, and MW, the addition of the 2175 Å bump feature in the
extinction curve would greatly improve the fitting results relative to the SMC
curve.

Considering that BAL quasars have, on average, redder continua and stronger
optical/UV Fe II emission multiplets than non-BAL quasars (e.g., Boroson &
Meyers 1992; Zhang et al. 2010), a few of the strongest Fe II and Fe III
emission objects may be accidently identified as 2175 Å bump features (Pitman
et al. 2000). In Zhang et al. (2015a), we further mentioned trying to use the
BAL composite to replace the quasar composite. We found that the fitting of
the BAL composite generally shows slightly ($\sim 20\%$) stronger bumps for
all candidates. Meanwhile, we also ruled out the possibility that the 2175 Å
bump feature is only an illusion of unusual Fe II and/or Fe III absorption
troughs through the photoionization simulation of iron absorption. Although
iron absorption multiplets are spread over the wavelength range of $2200-2800$
Å, in either gas density ($n_{\rm H}$) – column density ($N_{\rm H}$) –
ionization parameter ($U$) space, there are not enough iron transitions around
2175 Å to produce wide and strong enough absorption troughs that mimic the
2175 Å bump feature. More details can be found in Section 4 of Zhang et al.
(2015a).

##  4 Discussion

SDSS J1630+3119 is not the only special case; in the appendices, we also
present the analysis of 2175 Å bump features observed toward two FeLoBAL
quasars: SDSS J1020+6023 and SDSS J1349+3823. The parameterised extinction
curves yield prominent bump features at 2175 Å in the underlying extinction.
The bump parameters are $x_{0}=4.49\pm 0.01$ $\mu$m-1, $\gamma=1.20\pm 0.03$
$\mu$m-1, and $A\rm_{bump}=1.24\pm 0.11$ $\mu$m-1 for SDSS J1020+6023 and
$x_{0}=4.67\pm 0.01$ $\mu$m-1, $\gamma=1.18\pm 0.08$ $\mu$m-1, and
$A\rm_{bump}=2.68\pm 0.25$ $\mu$m-1 for SDSS J1349+3823. Independent tests
imply that both detections have extremely high statistical significance
levels. Compared with SDSS J1630+3119, the two cases appear to be the same,
showing similar absorption troughs of Fe II multiplets, extreme reddening and
prominent bump features; thus, they are listed as analogues of SDSS J1630+3119
in the appendices. As Fig. A.1 and Fig. B.1 show, unlike SDSS J1630+3119, the
spectra of SDSS J1020+6023 and SDSS J1349+3823 cover most of the bump
profiles, except the blueward profile. The GALEX NUV magnitudes are high
levels consistent with the reddened quasar composite, which suggests that the
extinction shape and bump are well defined.

###  4.1  Quasars associated or intrinsic systems?

Usually, absorbers present in quasar spectra are classified as intervening or
associated systems based on the blueshift velocity relative to the quasar rest
frame. The former is produced by intergalactic clouds or galaxies located far
from the background quasars, and the latter originates from materials
associated with the host or intrinsically in quasar. The 2175 Å bump features
in SDSS J1630+3119 and its analogues have the same redshifts as quasars, which
indicates that these features are bound to be quasar-associated.

However, the carriers of these 2175 Å bump features are still unable to be
distinguished, whether the star formation region of the host or the nuclear
region of the quasar exists. Although some works suggest that the profile and
shift of the concomitant metal absorption lines might be able to be a
discriminating pointer (e.g., Shi et al. 2020), that undoubtedly gives a
higher requirement for spectroscopic observations. In this work, the spectral
resolution is far from achieving the degree of resolving line profiles in
absorption troughs; meanwhile, there is no narrow emission line in the
wavelength coverage to measure the precise system redshift. We might be able
to try to explore the origin of bump features by means of the simultaneous
occurrence of BALs. On the one hand, the host of quasars may suffer strong
high-energy radiation from the nucleus, while the quasar feedback on hosts may
also produce a disturbance in the ISM. Hence, the probability of 2175 Å bump
features existing in hosts is relatively small compared to quiescent galaxies,
which is why quasar-associated 2175 Å bump features are rarely found. On the
other hand, previous statistical works indicate that approximately 15% of
optically selected quasars show BAL features, and the detection probabilities
of LoBALs and FeLoBALs are two and three powers of the above number (e.g.,
Weymann et al. 1991; Reichard et al. 2003; Trump et al. 2006; Gibson et al.
2009; Zhang et al. 2010). However, the cases found in the literature (Zhang et
al. 2015a; Pan et al. 2017; Shi et al. 2020) and this work give a different
conclusion. From these works, we collected 28 sources whose 2175 Å bump
features have the same redshifts as quasars. Thus, the BAL fractions are
nearly 36% (BALs), 29% (LoBALs), and 11% (FeLoBALs) in this small sample. This
situation suggests that there is a strong correlation between occurrences of
BALs and 2175 Å bump features, and that the 2175 Å bump features we observed
are intrinsic to quasars rather than the hosts.

Admittedly, this small sample has a strong selection effect. The exploration
in Zhang et al. (2005a) is based on the SDSS DR10 Mg II absorption-line
sample, in which the quasars with unusual absorption lines (i.e., FeLoBALs)
are removed through visual inspection. However, this work begins from the
known SDSS DR7 FeLoBAL quasars; therefore, the fraction of the FeLoBAL-2175 Å
bump features is relatively grossly underestimated. Moreover, statistical
studies of optically selected BAL quasars indicate that LoBAL quasars show a
moderate level of reddening and FeLoBAL quasars are heavily reddened (e.g.,
Hall et al. 2002; Reichard et al. 2003; Zhang et al. 2010; Dunn et al. 2015),
and the additional 2175 Å bump features make these objects fainter and easier
to miss in the magnitude-limited sample. If an unbiased systematic exploration
for 2175 Å bump features is performed in homogeneous quasar samples, it is
believed that the foregoing conclusion will be greatly enhanced.

###  4.2 Comparison of the bump parameters

Figure 6:  Comparison of bump parameters for MW (blue circles), LMC average
(navy circles), and LMC2 Supershell (green circles); BAL quasars (pink
squares) and two cases in literature (brown triangles) with intrinsic 2175 Å
bump features, and SDSS J1630+3119 (red filled stars). For visual clarity, the
error bars on the MW, LMC average and LMC2 Supershell bumps are not displayed.
The bump in SDSS J1630+3119 is relatively closer to those in MW, and they
share similar absorption strength and width, but the bump peak in SDSS
J1630+3119 is located at a longer wavelength than the galactic interstellar
curves. By contrast, the bumps in the LMC average, LMC2 Supershell, BAL
quasars and the other two cases present broader absorption features with more
disordered peak positions and weaker absorption strengths. The results of the
two analogues are also shown by red open stars.

Fig. 6 illustrates the bump parameters of the 2175 Å bump features in SDSS
J1630+3119 and its analogues. For a comparison, the panels also include those
in the Local Group and the other 9 quasar intrinsic 2175 Å bump features.
Among the 12 quasar intrinsic cases, 3 objects (SDSS J0916+2921, SDSS
J1020+6023 and SDSS J1349+3823) have redshifts of $z\sim 1.0$, and the others
have higher redshifts ($z\sim 2.0$). The absorbers in the MW (blue circles)
have a large absorption strength ($A_{\rm bump}=2.48\pm 1.15$ $\mu$m-1) and a
constant peak position ($x_{0}=4.59\pm 0.027$ $\mu$m-1), and their widths vary
over a wide range of $\gamma=1.0\pm 0.25$ $\mu$m-1. The 2175 Å bump features
in BAL quasars (pink squares; Zhang et al. 2015a) present broader bump
features at smaller wavenumbers ($\gamma=-1.3\pm 0.39$ $\mu$m-1,
$x_{0}=4.45\pm 0.05$ $\mu$m-1), and their strengths are much weaker than those
of the galactic bumps at 2175 Å on average, but similar to those of the LMC
average (navy circles) and LMC2 Supershell (green circles) bumps. However, two
2175 Å bump features (brown triangles) reported in Pan et al. (2017) and Shi
et al. (2020) are relatively prone to the MW sightlines that deviate most from
the main distribution. The 2175 Å bump features in SDSS J1630+3119 and its
analogues are significantly stronger than the previous detections in BAL
quasars and SDSS J1705+ 3543. Their strengths are very close to the averaged
strength of the galactic bumps. For the bump width, the bump in SDSS
J1630+3119 has almost the same average width as those in the MW, and the bumps
in the two analogues are slightly wider, but they are not out of range of
those in the MW. For the peak position, only in one case (SDSS J1349+3823),
the bump peaks at $2141\pm 4.5$ Å, the blueward of 2175 Å; the bumps in
another two cases (SDSS J1020+6023 and SDSS J1630+3119) tend to peak at larger
wavelengths, and their peak positions are $2227\pm 4.9$ Å and $2217\pm 5.0$ Å,
respectively.

The bump peak position seems to vary in different studies. We note that this
parameter depends on the accuracy of the redshift measurement, which is a
weakness of BAL quasars. In their spectra, there is a lack of narrow emission
lines and not enough complete BEL profiles to measure the precise redshift.
The position deviation of the bumps in all BAL quasars is $0.08$ $\mu$m-1, and
this value corresponding to a redshift is approximately 0.018. Although there
are many disadvantages in the redshift measurement of BAL quasars, the SDSS
redshift accuracy is much higher than the above value. This suggests that the
redshift uncertainty is not sufficient to cause such a large variation.
Moreover, none of the 2175 Å bump features in this work and other quasar
intrinsic cases are out of range of the LMC average and LMC2 Supershell bumps
(Gordon et al. 2003). Thus, we tend to think that the different parameter
distributions of the 2175 Å bump features are intrinsic and are possibly
attributed to variations in the grain-size and/or chemical composition of the
dust (Draine 2003).

###  4.3 Survival environment of 2175 Å dust grains

For these FeLoBAL quasars with 2175 Å bump features, which structure do 2175Å
dust grains survive in: the BAL region, or something else? In the common
sense, outflows may emerge from the outer region of the accretion disk or even
the innermost region of the torus, in which the gas clouds are dusty and
relatively cold. These dusty clouds are uplifted and exposed to the central
engine. Generally, the low density part is rapidly heated and highly ionised,
and the dust in this would be sublimated. The dust grains survive in dense
regions and may even form in dense clouds embedded in outflows (Elvis et al.
2002). However, this does not mean that 2175 Å dust grains must exist in the
outflows. Nearly two-thirds of quasars with quasar-associated 2175 Å bump
features are non-BAL quasars. When the sightline to continuum source
intersects the outflow clouds, the 2175 Å bump feature and BAL troughs would
coexist in the spectrum, which originate from the dust grains and ionised
gases carried by outflows, respectively. In addition, the torus may be the
primary dust warehouse in the nuclear region of quasars. Large amounts of dust
grains gather here, and the stacked structure provides a good shelter for
them. The absorption features of the silicate grains at 9.7 $\mu$m and the
polycyclic aromatic hydrocarbon (PAH) molecules at 3.4 $\mu$m are collected in
the mid-IR spectra of many Type II Seyferts. We suspect that such dust is
widespread; it also certainly requires a proper orientation angle for 2175 Å
bump features in quasars; otherwise, the dust grains either do not obscure the
central engine or are completely obscure, and then the source is transformed
into Type II.

In Zhang et al. (2015a), we proposed another probable picture taken from the
early evolution of quasars. The AGN activity destroys and clears the dust
grains in the surrounding space of the nucleus, which are prepared through the
persistent nucleosynthesis processes of carbon, and the formation of dust
occurs throughout the evolution of stars and galaxies before the black hole is
ignited. Outflow winds shield high-energy photons from the central engine and
extend the survival period of dust in the nuclear region of quasars.

How these dust grains enter into (or are assembled) and survive in the nuclear
region of quasars and the survival environment and the dust grain itself are
interesting questions that deserve further observational study and
investigation. For example, follow-up high-resolution spectroscopy at large
telescopes will present abundant gas absorption lines in association with
dust, which could be used to measure the chemical abundance and dust
depletion, obtain the column density of hydrogen and the dust-to-gas ratio,
and even detect neutral and molecular gases, finally understanding the
chemical and physical conditions in absorber systems.

Furthermore, although the origin of the 2175 Å bump features remains unclear,
carbonaceous dust has long been proposed as a candidate. Laboratory and
theoretical studies confirmed that materials such as graphite, aromatic
hydrocarbon molecules, fullerene, hydrogenated amorphous carbon, and even
carbon nanotubes could produce a broad extinction bump at approximately 2175 Å
(e.g., Stecher & Donn 1965; Wright 1988; Mennella et al. 1998; Aannestad 1992;
Joblin et al. 1992). The carrier of the 2175 Å bump features is probably one
of them or a mixture of several materials. Follow-up near-/mid-IR spectroscopy
will be expected to present the corresponding absorption and emission features
of the materials.

##  5 Summary

In this paper, we reported for the first time the discovery of 2175 Å bump
features in the optical spectra of the FeLoBAL quasars SDSS J1630+3119 and its
two analogues. They are typical FeLoBAL quasars, and their optical spectra
present numerous absorption troughs from high- and low-ionization species,
such as C IV, Al III, Mg II and Fe II multiplets. After masking these broad
absorption features, parameterised extinction curves were used to redden the
quasar composite to fit the observed spectra. The UV to near-IR broadband SEDs
of these objects verify the efficiency of spectral continuum fitting. The
fitting yields prominent extinction bumps at 2175 Å, which are also confirmed
again through several independent validation tests. Compared to the bump
features in the LMC and LMC2 Supershell, the detections in this work are much
closer to those in the MW. Our 2175 Å bump features are detected in the quasar
rest frame. Taking the fractions of high- and (iron) low-ionization BALs into
account, we consider that they, as well as those in the BAL quasars of Zhang
et al. (2015), are intrinsic to quasars rather than the quasar host and
intervening galaxies. These 2175 Å bump features may be related to the bumps
seen in the Local Group and may be a counterpart of the 2175 Å bump features
under different conditions in the quasar environment. Traversing the various
structures of AGNs, we suggest that only a dusty torus can provide a suitable
context for the survival of 2175 Å bump carriers. The dust grains perhaps
survive in the dusty torus, and the shielding effect of outflow clouds allows
the MW-like dust to be assembled or to extend the survival period in the
nuclear region. To further investigate the intrinsic nature of FeLoBAL-2175 Å
bump feature, we need follow-up monitoring of absorption lines and mid-
infrared spectroscopy to study PAH emissions.

###### Acknowledgements.

We thank the anonymous referee for their constructive comments and
suggestions. This work is supported by the National Natural Science Foundation
of China under Grant No. NSFC-12173026 and the Natural Science Foundation of
Shanghai under Grant No. 20ZR1473600. This publication makes use of data
products from the Two Micron All Sky Survey, which is a joint project of the
University of Massachusetts and the Infrared Processing and Analysis
Center/California Institute of Technology, funded by the National Aeronautics
and Space Administration and the National Science Foundation. This publication
makes use of data products from the Wide-field Infrared Survey Explorer, which
is a joint project of the University of California, Los Angeles, and the Jet
Propulsion Laboratory/California Institute of Technology, funded by the
National Aeronautics and Space Administration. This research is based on
observations made with the Galaxy Evolution Explorer, obtained from the MAST
data archive at the Space Telescope Science Institute, which is operated by
the Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-26555. The CSS survey is funded by the National Aeronautics and
Space Administration under Grant No. NNG05GF22G issued through the Science
Mission Directorate Near-Earth Objects Observations Program. The CRTS survey
is supported by the U.S. National Science Foundation under grants AST-0909182.
The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been
made possible through contributions by the Institute for Astronomy, the
University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society
and its participating institutes, the Max Planck Institute for Astronomy,
Heidelberg and the Max Planck Institute for Extraterrestrial Physics,
Garching, The Johns Hopkins University, Durham University, the University of
Edinburgh, the Queen’s University Belfast, the Harvard-Smithsonian Center for
Astrophysics, the Las Cumbres Observatory Global Telescope Network
Incorporated, the National Central University of Taiwan, the Space Telescope
Science Institute, the National Aeronautics and Space Administration under
Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA
Science Mission Directorate, the National Science Foundation Grant No.
AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the
Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. ZTF
is supported by the National Science Foundation and a collaboration including
Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at
Stockholm University, the University of Maryland, Deutsches Elektronen-
Synchrotron and Humboldt University, Lawrence Livermore National Laboratory,
the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee,
Trinity College Dublin, and Institut national de physique nucleaire et de
physique des particules. Operations are conducted by COO, IPAC and University
of Washington. The part of data presented herein were obtained at the W. M.
Keck Observatory, which is operated as a scientific partnership among the
California Institute of Technology, the University of California and the
National Aeronautics and Space Administration. The Observatory was made
possible by the generous financial support of the W. M. Keck Foundation. The
authors wish to recognize and acknowledge the very significant cultural role
and reverence that the summit of Mauna Kea has always had within the
indigenous Hawaiian community. We are most fortunate to have the opportunity
to conduct observations from this mountain. Funding for SDSS-III has been
provided by the Alfred P. Sloan Foundation, the Participating Institutions,
the National Science Foundation, and the U.S. Department of Energy Office of
Science. The SDSS-III web site is http://www.sdss3.org/. SDSS-III is managed
by the Astrophysical Research Consortium for the Participating Institutions of
the SDSS-III Collaboration including the University of Arizona, the Brazilian
Participation Group, Brookhaven National Laboratory, Carnegie Mellon
University, University of Florida, the French Participation Group, the German
Participation Group, Harvard University, the Instituto de Astrofisica de
Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns
Hopkins University, Lawrence Berkeley National Laboratory, Max Planck
Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics,
New Mexico State University, New York University, Ohio State University,
Pennsylvania State University, University of Portsmouth, Princeton University,
the Spanish Participation Group, University of Tokyo, University of Utah,
Vanderbilt University, University of Virginia, University of Washington, and
Yale University.

##  Appendix A SDSS J1020+6023

SDSS J1020+6023 was selected as a candidate of the intrinsic 2175Å bump
feature according to the SDSS spectrum. Its broad extinction bump was
confirmed by UV-to-IR broadband SED and again by a follow-up Keck
spectroscopy.

SDSS J1020+6023 was imaged at five photometric bands on March 3, 2000, and
targeted as a quasar candidate based on its location in the
$u^{\prime}g^{\prime}r^{\prime}i^{\prime}$ colour cube (Richards et al. 2004)
for spectroscopic observations by the SDSS survey. Two observations were taken
on March 05, 2002, and the spectrum we used was extracted from the SDSS DR7.
This object did not vary significantly between the SDSS photometric and
spectroscopic observations, since its spectrum is in excellent agreement with
photometric data. SDSS J1020+6023 is also a typical FeLoBAL quasar, classified
into several existing BAL catalogs (Trump et al. 2006; Gibson et al. 2009;
Zhang et al. 2010; Shen et al. 2011). The blue band spectrum shows overlapping
iron absorption lines (2300-2415Å, 2550-2630Å and 2700-2770Å) and Mg II broad
absorption troughs. Except for iron emission/absorption multiplets and narrow
absorption lines of Mg I and Ca II H/K, no other emission or absorption lines
can be identified from the spectrum.

A follow-up spectroscopic observation was obtained on March 8, 2013, on the 10
m Keck II telescope using the Echellette Spectrographs and Imager (ESI;
Sheinis et al. 2002). The high resolution ($R\sim 30,000$), high signal-to-
noise ratio (median $S/N\approx 11$) spectrum was determined to distinguish
the structure of absorption features. We implemented the $0.75\arcsec$ slit to
match the seeing, where one exposure of 1200 seconds was taken. The data were
reduced and calibrated using the ESIRedux software package. The entire spectra
cover the full optical range from 3900 Å to 1.1 $\mu$m. It is highly
consistent with the SDSS spectrum and $r^{\prime}$-, $i^{\prime}$\- and
$z^{\prime}$-band photometric data. This high-resolution spectroscopic
observation is widely used in extinction curve fitting and all measurements of
broad and narrow absorption lines.

SDSS J1020+6023 is very red ($\Delta(g-i)=1.5180$) with a dramatic flux drop
shortward of Mg II BAL, suggesting the presence of heavy dust extinction. We
also collected broadband photometric data of SDSS J1020+6023 from other
available large surveys at UV wavelengths (the Galaxy Evolution Explorer or
GALEX; Martin et al. 2005) and IR wavelengths (the 2MASS and WISE). The IR and
optical photometric data join smoothly, suggesting that the quasar did not
vary significantly between the 2MASS, WISE and SDSS observations. The
broadband SED broken at the SDSS $r$-band again confirmed heavy dust
extinction. After correcting for galactic reddening using the dust map of
Schlegel et al. (1998) and the Fitzpatrick (1999) reddening curve, we
transformed the magnitudes and optical spectrum into a rest frame with a
quasar redshift. The same fitting process was used for exploring the 2175 Å
bump feature in SDSS J1020+6023. The best-fitting model yields $c_{1}=-1.82\pm
0.02$, $c_{2}=0.54\pm 0.01$, $c_{3}=0.95\pm 0.08$, $x_{0}=4.49\pm 0.01$
$\mu$m-1, and $\gamma=1.20\pm 0.03$ $\mu$m-1. The strength of the 2175 Å bump
feature is $A\rm_{bump}=1.24\pm 0.11$ $\mu$m-1.

This model is consistent with multiband photometry with effective wavelengths
less than the $H$-band. The GALEX NUV magnitude is high, consistent with the
reddened quasar composite spectrum. The $u^{\prime}$-band is affected by the
expected strong Al III absorption and has a lower radiation flux than the
model spectrum. However, the near-IR photometric fluxes ($H$, $K_{s}$, and
$W1$) of SDSS J1020+6023 are under the reddened spectral template. The reason
is that the near-IR composite was constructed from observations of 27
luminous, low-redshift, UV-excess selected quasars, and it was excellently
described by the combined model with a single power-law slope and a black body
from H$\alpha$ to 3.5 $\mu$m (Glikman et al. 2006). This result strongly
suggests the presence of significant quantities of hot dust in the near-IR
composite. We added a dust emission component with a temperature of $\sim
1700$ K to near-IR magnitudes of SDSS J1020+6023, and the correctional fluxes
(open squares) are extremely consistent with the near-IR composite. The
typical dust sublimation temperature is $\sim 1500-2000$ K (e.g., Tuthill et
al. 2001; Monnier & Millan-Gabet 2002), and the extra hot dust in this object
is probably the inner edge of the torus. The combination spectrum together
with the IR through optical to UV photometric data, the best-fitting model and
the observed/modelled extinction curves are presented in Fig. A.1.

Independent tests were employed to check the authenticity of the detection. As
shown in Fig. A.2, the bump strength distribution of the control sample peaks
at $\overline{A}_{\rm bump}=0.13$ $\mu$m-1 with a variance of $\sigma=0.16$
$\mu$m-1, and the bump feature in SDSS J1020+6023 is detected at $>5\sigma$.
In Fig. A.2 (b), the scaled observed spectrum of the FeLoBAL quasar SDSS
J112828.31+011337.9 is overplotted for a comparison. This case has almost the
same Mg II and Fe II absorption troughs as SDSS J1020+6023, and its spectrum
agrees well with the corrected spectrum of SDSS J1020+6023. Both spectra
follow the reddened quasar composite by the SMC extinction curve of
$E{\rm(B-V)}=0.25$.

Figure 7: Same as Fig. 3 but for SDSS J1020+6023. A dust emission with $T\sim
1700$ K (blue dashed line) is added to the near-IR magnitudes to better match
the composite, since the NIR composite from Glikman et al. (2006) has a
stronger IR emission than SDSS J1020+6023. The correctional multiband
magnitudes of SDSS J1020+6023 from the 2MASS and WISE surveys are presented by
orange open squares.

Figure 8: Same as Fig. 4 and Fig. 5, but for SDSS SDSS J1020+6023. The
continuum slopes of the corrected spectrum of SDSS J1020+6023 and the observed
spectrum of SDSS J112828.31+011337.9 follow the reddened quasar composite (red
curve) by the SMC extinction curve of $E{\rm(B-V)}=0.25$.

##  Appendix B SDSS J1349+3823

SDSS J1349+3823 is another analogue of SDSS J1630+3119. The archives provide
multiwavelength photometric magnitudes from the GALEX, SDSS and 2MASS surveys
and spectroscopic fluxes from the SDSS survey. Its 1D spectrum from 3800 Å to
9200 Å was accessed from the SDSS DR7. After correcting for galactic reddening
using the dust map of Schlegel et al. (1998) and the Fitzpatrick (1999)
reddening curve, we transformed the magnitudes and optical spectrum into a
quasar rest frame. The photometric and spectroscopic data are shown using
orange squares and black curves in Fig. B.1 (a). The optical spectrum
simultaneously shows a remarkable intrinsic 2175 Å bump feature and broad
absorption troughs of Mg II and Fe II multiplets UV 1, UV 2, UV 3 and UV
62,63. The extremely low flux at the $u^{\prime}$ band implies that there are
possible strong absorption troughs of Al III and even Fe III UV 34
$\lambda\lambda\lambda$1895.46, 1914.06, and 1926.30, and UV 48
$\lambda\lambda\lambda$2062.21, 2068.90, and 2079.62.

The same fitting process was used to explore the 2175 Å bump feature in SDSS
J1020+6023. The best-fitting model yields $c_{1}=-0.73\pm 0.02$,
$c_{2}=0.37\pm 0.01$, $c_{3}=2.02\pm 0.13$, $x_{0}=4.67\pm 0.01$ $\mu$m-1, and
$\gamma=1.18\pm 0.08$ $\mu$m-1 , where the bump strength is
$A\rm_{bump}=2.68\pm 0.25$ $\mu$m-1. This case, similar to SDSS J1020+6023,
has a low redshift of $\sim 1.0$; therefore, the SDSS spectrum cannot cover
the shortward of the 2175 Å bump feature. However, the GALEX NUV and 2MASS
near-IR photometric data fall right on the modelled spectrum, which suggests
that the shape and bump of the extinction curve are well defined. In Fig. B.1
(a) and (b), the best-fitting model and observed/modelled extinction curves
are presented. We also performed tests as for J1630+3119 to confirm the bump
detection in SDSS J1349+3823. As shown in Fig. B.2, the bump feature in SDSS
J1349+3823 is detected at $\gg 5\sigma$. The corrected spectrum of SDSS
J1349+3823 agrees with the observed spectrum of the FeLoBAL quasar SDSS
J084044.41+363327.8, and their spectral continua follow the reddened quasar
composite by the SMC extinction curve of $E{\rm(B-V)}=0.17$.

Figure 9: Same as Fig. 3 but for SDSS J1349+3823.

Figure 10: Same as Fig. 4 and Fig. 5, but for SDSS J1349+3823. The continuum
slopes of the corrected spectrum of SDSS J1349+3823 and observed spectrum of
SDSS J084044.41+363327.8 follow the reddened quasar composite (red curve) by
the SMC extinction curve of $E{\rm(B-V)}=0.17$.

## References

  * Abazajian et al. (2009) Abazajian, K. N., et al. 2009, ApJS, 182, 543
  * Allen et al. (2011) Allen, J. T., Hewett, P. C., Maddox, N., et al. 2011, MNRAS, 410, 860
  * Alonso-Herrero et al. (2011) Alonso-Herrero, A., Ramos Almeida, C., Mason, R., et al. 2011, ApJ, 736, 82
  * Aannestad (1992) Aannestad P. A., 1992, ApJ, 386, 627
  * Antonucci (1993) Antonucci, R. 1993, ARA&A, 31, 473
  * Barvainis (1987) Barvainis, R. 1987, ApJ, 320, 537
  * Becker et al. (1997) Becker, R. H., Gregg, M. D., Hook, I. M., et al. 1997, ApJ, 479, L93
  * Becker et al. (2000) Becker, R. H., White, R. L., Gregg, M. D., et al. 2000, ApJ, 538, 72
  * Blasberger et al. (2017) Blasberger, A., Behar, E., Perets, H. B., et al. 2017, ApJ, 836, 173
  * Bohlin et al. (1978) Bohlin, R. C., Savage, B. D., & Drake, J. F. 1978, ApJ, 224, 132
  * Boroson & Meyers (1992) Boroson, T. A. & Meyers, K. A. 1992, ApJ, 397, 442
  * Buat et al. (2021) Buat, V., Mountrichas, G., Yang, G., et al. 2021, A&A, 654, A93
  * Cardelli et al. (1989) Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245
  * Chambers et al. (2016) Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv:1612.05560
  * Czerny et al. (2004) Czerny, B., Różańska, A., Dovčiak, M., et al. 2004, A&A, 420, 1
  * Dawson et al. (2016) Dawson, K. S., Kneib, J.-P., PerC IVal, W. J., et al. 2016, AJ, 151, 44
  * Draine (1989) Draine, B. 1989, Interstellar Dust, 135, 313
  * Draine (2003) Draine, B. T. 2003, ARA&A, 41, 241
  * Drake et al. (2009) Drake, A. J., Djorgovski, S. G., Mahabal, A., et al. 2009, ApJ, 696, 870
  * Dullemond & van Bemmel (2005) Dullemond, C. P. & van Bemmel, I. M. 2005, A&A, 436, 47
  * Dunn et al. (2015) Dunn, J. P., Wasik, B., Holtzclaw, C. L., et al. 2015, ApJ, 808, 94.
  * Ellison et al. (2006) Ellison, S. L., Vreeswijk, P., Ledoux, C., et al. 2006, MNRAS, 372, L38
  * Elvis et al. (2002) Elvis, M., Marengo, M., & Karovska, M. 2002, ApJ, 567, L107
  * Farrah et al. (2010) Farrah, D., Urrutia, T., Lacy, M., et al. 2010, ApJ, 717, 868
  * Fitzpatrick & Massa (1986) Fitzpatrick, E. L. & Massa, D. 1986, ApJ, 307, 286
  * Fitzpatrick & Massa (1990) Fitzpatrick, E. L. & Massa, D. 1990, ApJS, 72, 163
  * Fitzpatrick (1999) Fitzpatrick, E. L. 1999, PASP, 111, 63
  * Fitzpatrick & Massa (2007) Fitzpatrick, E. L., & Massa, D. 2007, ApJ, 663, 320
  * García-González et al. (2017) García-González, J., Alonso-Herrero, A., Hönig, S. F., et al. 2017, MNRAS, 470, 2578
  * Gaskell et al. (2004) Gaskell, C. M., Goosmann, R. W., Antonucci, R. R. J., et al. 2004, ApJ, 616, 147
  * Gibson et al. (2009) Gibson, R. R., Jiang, L., Brandt, W. N., et al. 2009, ApJ, 692, 758
  * Glikman et al. (2006) Glikman, E., Helfand, D. J., & White, R. L. 2006, ApJ, 640, 579
  * Gordon et al. (2003) Gordon, K. D., Clayton, G. C., Misselt, K. A., et al. 2003, ApJ, 594, 279
  * Hall et al. (2002) Hall, P. B., Anderson, S. F., Strauss, M. A., et al. 2002, ApJS, 141, 267
  * Hazard et al. (1987) Hazard, C., McMahon, R. G., Webb, J. K., et al. 1987, ApJ, 323, 263
  * Hopkins et al. (2004) Hopkins, P. F., Strauss, M. A., Hall, P. B., et al. 2004, AJ, 128, 1112
  * Jiang et al. (2010) Jiang, P., Ge, J., Prochaska, J. X., et al. 2010a, ApJ, 720, 328
  * Jiang et al. (2010) Jiang, P., Ge, J., Prochaska, J. X., et al. 2010b, ApJ, 720, 328
  * Jiang et al. (2011) Jiang, P., Ge, J., Zhou, H., et al. 2011, ApJ, 732, 110
  * Jiang et al. (2013) Jiang, P., Zhou, H., Ji, T., et al. 2013, AJ, 145, 157
  * Joblin et al. (1992) Joblin, C., Leger, A., & Martin, P. 1992, ApJ, 393, L79
  * Junkkarinen et al. (2004) Junkkarinen, V. T., Cohen, R. D., Beaver, E. A., et al. 2004, ApJ, 614, 658
  * Krolik & Begelman (1988) Krolik, J. H. & Begelman, M. C. 1988, ApJ, 329, 702
  * Ledoux et al. (2015) Ledoux, C., Noterdaeme, P., Petitjean, P., et al. 2015, A&A, 580, A8
  * Li & Draine (2001) Li, A. & Draine, B. T. 2001, ApJ, 550, L213
  * Liang & Li (2010) Liang, S. L. & Li, A. 2010, ApJ, 710, 648
  * Lopez-Rodriguez et al. (2020) Lopez-Rodriguez, E., Alonso-Herrero, A., García-Burillo, S., et al. 2020, ApJ, 893, 33
  * Lyu & Rieke (2021) Lyu, J. & Rieke, G. H. 2021, ApJ, 912, 126
  * Ma et al. (2015) Ma, J., Caucal, P., Noterdaeme, P., et al. 2015, MNRAS, 454, 1751
  * Ma et al. (2017) Ma, J., Ge, J., Zhao, Y., et al. 2017, MNRAS, 472, 2196
  * Markwardt (2009) Markwardt, C. B. 2009, Astronomical Data Analysis Software and Systems XVIII, 411, 251
  * Martin et al. (2005) Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJ, 619, L1
  * Masci et al. (2018) Masci, F., Kulkarni, S. R., Graham, M., et al. 2018, The Astronomer’s Telegram, 11685
  * Mennella et al. (1998) Mennella, V., Colangeli, L., Bussoletti, E., et al. 1998, ApJ, 507, L177
  * Menou et al. (2001) Menou, K., Vanden Berk, D. E., Ivezić, Ž., et al. 2001, ApJ, 561, 645
  * Monnier & Millan-Gabet (2002) Monnier, J. D. & Millan-Gabet, R. 2002, ApJ, 579, 694
  * Nenkova et al. (2008) Nenkova, M., Sirocky, M. M., Nikutta, R., et al. 2008, ApJ, 685, 160
  * Noterdaeme et al. (2009) Noterdaeme, P., Ledoux, C., Srianand, R., et al. 2009, A&A, 503, 765
  * Pan et al. (2017) Pan, X., Zhou, H., Ge, J., et al. 2017, ApJ, 835, 218
  * Pier & Krolik (1992) Pier, E. A. & Krolik, J. H. 1992, ApJ, 399, L23
  * Pitman et al. (2000) Pitman, K. M., Clayton, G. C., & Gordon, K. D. 2000, PASP, 112, 537
  * Prochaska et al. (2009) Prochaska, J. X., Worseck, G., & O’Meara, J. M. 2009, ApJ, 705, L113
  * Rees et al. (1969) Rees, M. J., Silk, J. I., Werner, M. W., et al. 1969, Nature, 223, 788
  * Reichard et al. (2003) Reichard, T. A., Richards, G. T., Schneider, D. P., et al. 2003, AJ, 125, 1711
  * Richards et al. (2004) Richards, G. T., Nichol, R. C., Gray, A. G., et al. 2004, ApJS, 155, 257
  * Richards et al. (2003) Richards, G. T., Hall, P. B., Vanden Berk, D. E., et al. 2003, AJ, 126, 1131
  * Scaringi et al. (2009) Scaringi, S., Cottis, C. E., Knigge, C., et al. 2009, MNRAS, 399, 2231
  * Schady et al. (2012) Schady, P., Dwelly, T., Page, M. J., et al. 2012, A&A, 537, A15
  * Schlegel et al. (1998) Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525
  * Schneider et al. (2010) Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, AJ, 139, 2360
  * Shen et al. (2011) Shen, Y., Richards, G. T., et al. 2011, ApJS, 194, 45
  * Sheinis et al. (2002) Sheinis, A. I., Bolte, M., Epps, H. W., et al. 2002, PASP, 114, 851
  * Shi et al. (2020) Shi, X.-. cao ., Pan, X., Dou, L.-. ming ., et al. 2020, Chinese Astron. Astrophys., 44, 196
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., et al. 2006, AJ, 131, 1163
  * Srianand et al. (2008) Srianand, R., Gupta, N., Petitjean, P., et al. 2008, MNRAS, 391, L69
  * Stecher & Donn (1965) Stecher, T. P. & Donn, B. 1965, ApJ, 142, 1681
  * Steglich et al. (2010) Steglich, M., Jäger, C., Rouillé, G., et al. 2010, ApJ, 712, L16
  * Tristram et al. (2007) Tristram, K. R. W., Meisenheimer, K., Jaffe, W., et al. 2007, A&A, 474, 837
  * Tristram et al. (2014) Tristram, K. R. W., Burtscher, L., Jaffe, W., et al. 2014, A&A, 563, A82
  * Trump et al. (2006) Trump, J. R., et al. 2006, ApJS, 165, 1
  * Tuthill et al. (2001) Tuthill, P. G., Monnier, J. D., & Danchi, W. C. 2001, Nature, 409, 1012
  * Urry & Padovani (1995) Urry, C. M. & Padovani, P. 1995, PASP, 107, 803
  * Voit (1992) Voit, G. M. 1992, MNRAS, 258, 841
  * Vreeswijk et al. (2006) Vreeswijk, P. M., Smette, A., Fruchter, A. S., et al. 2006, A&A, 447, 145
  * Wang et al. (2004) Wang, J., Hall, P. B., Ge, J., et al. 2004, ApJ, 609, 589
  * Wang et al. (2012) Wang, J.-G., Zhou, H.-Y., Ge, J., et al. 2012, ApJ, 760, 42
  * Wang et al. (2013) Wang, H., Xing, F., Zhang, K., et al. 2013, ApJ, 776, L15
  * Weingartner & Draine (2001) Weingartner, J. C. & Draine, B. T. 2001, ApJ, 548, 296
  * Weymann et al. (1991) Weymann, R. J., Morris, S. L., Foltz, C. B., et al. 1991, ApJ, 373, 23
  * Whittet (2003) Whittet, D. C. B. 2003, Dust in the galactic environment
  * Wright (1988) Wright, E. L. 1988, Nature, 336, 227
  * Wright et al. (2010) Wright, E. L., Eisenhardt, P. R. M., et al. 2010, AJ, 140, 1868
  * Wucknitz et al. (2003) Wucknitz, O., Wisotzki, L., Lopez, S., et al. 2003, A&A, 405, 445
  * York et al. (2000) York, D. G., Adelman, J., et al. 2000, AJ, 120, 1579
  * Zafar et al. (2011) Zafar T., Watson D., Fynbo J. P. U., Malesani D., Jakobsson P., de Ugarte Postigo A., 2011, A&A, 532, A143
  * Zafar et al. (2012) Zafar, T., Watson, D., Eliasdottir, A., et al. 2012, ApJ, 753, 82
  * Zafar et al. (2015) Zafar, T., Møller, P., Watson, D., et al. 2015, A&A, 584, A100
  * Zafar et al. (2018) Zafar, T., Heintz, K. E., Fynbo, J. P. U., et al. 2018, ApJ, 860, L21
  * Zhang et al. (2010) Zhang, S., Wang, T.-G., Wang, H., et al. 2010, ApJ, 714, 367
  * Zhang et al. (2014) Zhang, S., Wang, H., Wang, T., et al. 2014, ApJ, 786, 42
  * Zhang et al. (2015) Zhang, S., Ge, J., Jiang, P., et al. 2015a, ApJ, 802, 92
  * Zhang et al. (2015) Zhang, S., Zhou, H., Wang, T., et al. 2015b, ApJ, 803, 58
  * Zhang et al. (2017) Zhang, S., Zhou, H., Shi, X., et al. 2017, ApJ, 845, 126
  * Zhuang et al. (2018) Zhuang, M.-Y., Ho, L. C., & Shangguan, J. 2018, ApJ, 862, 118
  * Zhou et al. (2010) Zhou, H., Ge, J., Lu, H., et al. 2010, ApJ, 708, 742


11institutetext: LIA - Avignon Université , 339 chemin des Meinajaries, 84140,
Avignon, France
11email:  {carlos-emiliano.gonzalez-gallardo, eric.sanjuan, juan-
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>22institutetext: Département de GIGL, Polytechnique
Montréal,
C.P. 6079, succ. Centre-ville, Montréal (Québec) H3C 3A7 Canada

# Audio Summarization with Audio Features and Probability Distribution
Divergence

Carlos-Emiliano González-Gallardo 11 Romain Deveaud 11 Eric SanJuan 11 Juan-
Manuel Torres-Moreno 1122

###### Abstract

The automatic summarization of multimedia sources is an important task that
facilitates the understanding of an individual by condensing the source while
maintaining relevant information. In this paper we focus on audio
summarization based on audio features and the probability of distribution
divergence. Our method, based on an extractive summarization approach, aims to
select the most relevant segments until a time threshold is reached. It takes
into account the segment’s length, position and informativeness value.
Informativeness of each segment is obtained by mapping a set of audio features
issued from its Mel-frequency Cepstral Coefficients and their corresponding
Jensen-Shannon divergence score. Results over a multi-evaluator scheme shows
that our approach provides understandable and informative summaries.

###### Keywords:

Audio Summarization JS divergence Informativeness Human Language Understanding

##  1 Introduction

Multimedia summarization has become a major need since Internet platforms like
Youtube111https://www.youtube.com/ provide easy access to massive online
resources. In general, automatic summarization intends to produce an abridged
and informative version of its source [17]. The type of automatic
summarization we focus in this article is audio summarization, which source
corresponds to an audio signal.

Audio summarization can be performed with the following three approaches:
directing the summary using only audio features [2, 9, 10, 21], extracting the
text inside the audio signal and directing the summarization process using
textual methods [1, 13, 16] and an hybrid approach which consists of a mixture
of the first two [15, 19, 20]. Each approach has advantages and disadvantages
with regard to the others. Using only audio features for creating a summary
has the advantage of being totally transcript independent; however, this may
also be a problem given that the summary is based only on how things are said.
By contrast, directing the summary with textual methods benefits from the
information contained within the text, dealing to more informative summaries;
nevertheless, in some cases transcripts are not unavailable. Finally, using
both audio features and textual methods can boost the summary quality; yet,
disadvantages of both approaches are present.

The method we propose in this paper consists of an hybrid approach during
training phase while text independent during summary creation. It resides on
using textual information to learn an informativeness representation based on
probability distribution divergences that standard audio summarization with
audio features does not consider. During the summarization process this
representation is used to obtain an informativeness score without a textual
representation of the audio signal to summarize. To our knowledge, probability
distribution divergences have not been used for audio summarization.

The rest of this article is organized as follows. In Section 2 we give an
overview of what audio summarization is, we include its advantages and
disadvantages comparing it with other summarization techniques. During Section
3 we explain how the probability distribution divergence may be used over an
audio summarization framework and we describe in detail our summarization
proposal. In Section 4 we describe the dataset used during training and the
summary generation phases as well as the evaluation metric that we adopted to
measure the quality of the produced summaries and the results from the
experimental evaluation of the proposed method. Finally, Section 5 concludes
the article.

##  2 Audio Summarization

Audio summarization without any textual representation aims to produce an
abridged and informative version of an audio source using only the information
contained in the audio signal. This kind of summarization is challenging
because the available information corresponds to how things are said, this is
advantageous in terms of transcripts availability. Hybrid audio summarization
methods or text based audio summarization algorithms need automatic or manual
speech transcripts to select the pertinent segments and produce an informative
summary [19, 20]. Nevertheless, speech transcripts may be expensive, non
available or of low quality, this creates repercussions over the summarization
performance.

Duxans et al. [2] managed to generate audio based summaries of a soccer match
using re-transmissions that detect highlighted events. They based their
detection algorithm on two acoustic features: the block energy and the
acoustic repetition indexes. The performance was measured in terms of goal
recall and summary precision, showing high rates for both categories.

Maskey et al. [10] presented an audio based summarization method using a
Hidden Markov Model (HMM) framework. They used a set of different
acoustic/prosodic features to represent the HMM observation vectors: speaking
rate; F0 min, max, mean, range and slope; min, max and mean RMS energy; RMS
slope and sentence duration. The hidden variables represented the inclusion or
exclusion of a segment within the summary. They performed experiments over 20
CNN shows and 216 stories previously used in [9]. Evaluation was made with
standard Precision, Recall and F-measures information retrieval measures.
Results show us that the HMM framework had a very good coverage
($Recall=0.95$) but a very poor precision ($Precision=0.26$) when selecting
pertinent segments.

Zlatintsi et al. [21] addressed the audio summarization task by exploring the
potential of a modulation model for the detection of perceptually important
audio events. They performed a saliency computation of audio streams based on
a set of saliency models and various linear, adaptive and nonlinear fusion
schemes. Experiments were performed over audio data extracted from six
30-minute movie clips. Results were reported in terms of frame-level precision
scores showing that nonlinear fusion schemes perform best.

Audio summarization based only on acoustic features like fundamental
frequencies, energy, volume change and speaker turn, has the big advantage
that no textual information is needed. This approach is especially useful when
human transcripts are not available for the spoken documents and Automatic
Speech Recognition (ASR) transcripts have a high word error rate. However, for
high informative contexts like broadcast news, bulletins or reports, most
relevant information resides on the things that are said while audio features
are limited to how things are said.

##  3 Probability Distribution Divergence for Audio Summarization

All presented methods in the previous section omit the informativity content
of the audio streams. In order to overcome the lack of information, we propose
an extractive audio summarization method capable of representing the
informativeness of a segment in terms of its audio features during training
phase; informativeness is mapped by a probability distribution divergence
model. Then, when creating a summary, textual independence is reached using
only audio based features.

Divergence is defined by Manning [8] as a function which estimates the
difference between two probability distributions. In the framework of
automatic text summarization evaluation, [6, 14, 18] have used divergence
based measures such as Kullback–Leibler and Jensen–Shannon (JS) to compare the
probability distribution of words between automatically produced summaries and
their sources. Extractive summarization based on the divergence of probability
distributions has been discussed in [6] and a method has been proposed in [17]
(DIVTEX).

Our proposal, based on an extractive summarization approach aims to select the
most pertinent audio segments until a time threshold is reached. A training
phase is in charge of learning a model that maps a set of 277 audio features
to an informativeness value. A big dataset is used to compute the
informativeness by obtaining the divergence between the dataset documents and
their corresponding segments. During the summarization phase, the method takes
into account the segment’s length, position and the mapped informativeness of
the audio features to rank the pertinence of each audio segment.

###  3.1 Audio signal pre-processing

During the pre-processing step, the audio signal is split into background and
foreground channels. This process is normally used on music records for
separating vocals and other sporadic signals from accompanying
instrumentation. Rafii et. al [12] achieved this separation for identifying
recurrent elements by looking for similarities instead of periodicities.

Rafii et. al approach is useful for those song records where repetitions
happen intermittently or without a fixed period; however, we found that
applying the same method to newscasts and reports audio files made much easier
to segment them using only the background signal. We assume this phenomena is
due to the fact that newscasts and reports are heavily edited with a low
volume of background music playing while the journalist speak and louder
music/noises for transitions (foreground).

Following [12], to suppress non-repetitive deviations from the average
spectrum and discard vocal elements, audio frames are compared using the
cosine similarity. Similar frames separated by at least two seconds are
aggregated by taking their per-frequency median value to avoid being biased by
local continuity. Next, assuming that both signals are additive, a pointwise
minimum between the obtained frames and the original signal is applied to
obtain a raw background filter. Then, a foreground and background time-
frequency mask is derived from the raw background filter and the input signal
with a soft mask operation. Finally, foreground and background components are
obtained by multiplying the time-frequency masks with the input signal.

###  3.2 Informativeness model

Informativeness is learned from the transcripts of a big audio dataset such as
newscasts and reports. A mapping between a set of 277 audio features and an
informativeness value is learned during the training phase. It corresponds to
the Jensen-Shannon divergence ($D_{JS}$) between the segmented transcripts and
their source.

The $D_{JS}$ is based on the Kullback-Leibler divergence [4] with the main
difference that is symmetric. The $D_{JS}$ between a segment $Q$ and its
source $P$ is defined by [7, 18] as:

$\displaystyle D_{JS}(P||Q)$ $\displaystyle=$
$\displaystyle\frac{1}{2}\sum_{w\in
P}\left[P_{w}\log_{2}\left(\frac{2P_{w}}{P_{w}+Q_{w}}\right)+Q_{w}\log_{2}\left(\frac{2Q_{w}}{P_{w}+Q_{w}}\right)\right]$
(1) $\displaystyle P_{w}$ $\displaystyle=$
$\displaystyle\frac{C^{P}_{w}+\delta}{|P|+\delta\times\beta}$ (2)
$\displaystyle Q_{w}$ $\displaystyle=$
$\displaystyle\frac{C^{Q}_{w}+\delta}{|Q|+\delta\times\beta}$ (3)

where $C^{(P|Q)}_{w}$ is the frequency of word $w$ over $P$ or $Q$. To avoid
shifting the probability mass to unseen events, the scaling parameter $\delta$
is set to $0.0005$. $|P|$ and $|Q|$ corresponds to the number of tokens on $P$
and $Q$. Finally $\beta=1.5\times|V|$, where $|V|$ is the vocabulary size on
$P$.

Each segment $Q$ has a length of 10 seconds and is represented by 277 audio
features where 275 corresponds to 11 statistical values of 25 Mel-frequency
Cepstral Coefficients (MFCC) and the other two correspond to the number of
frames in the segment and its starting time. The 11 statistical values can be
seen in Table 1, where $\phi^{\prime}$ and $\phi^{\prime\prime}$ corresponds
to the first and second MFCC derivative.

Feature | MFCC
---|---
|  $\phi$ |  $\phi^{\prime}$ |  $\phi^{\prime\prime}$
min |  • |  |
max |  • |  |
median |  • |  |
mean |  • |  • |  •
variance |  • |  • |  •
skewness |  • |  |
kurtosis |  • |  |
Table 1: MFCC based statistical values

A linear least squares regression model ($LR(X,Y)$) is trained to map the 277
audio features ($X$) into a informativeness score ($Y$). Figure 1 shows the
whole training phase (informativeness model). All audio processing and feature
extraction is performed with the Librosa library
222https://librosa.github.io/librosa/index.html [11].

Figure 1: Informativeness model scheme

###  3.3 Audio Summary Creation

The summary creation of a document $P$ follows the same audio signal pre-
processing steps described in Section 3.1. During this phase, only the audio
signal is needed and informativeness of each candidate segment $Q_{i}\in P$ is
predicted with the $LR(Q_{i},Y_{Q_{i}})$ model. Figure 2 shows the full
summarization pipeline to obtain a $\theta$ threshold length summary of an
audio document $P$.

After the background signal is isolated from the main signal a temporally-
constrained agglomerative clustering routine is used to partition the audio
stream into $k$ contiguous segments.

$\displaystyle k=\frac{P_{length}}{60}\times 20$ (4)

being $P_{length}$ the length in seconds of $P$.

To rank the pertinence of each segment $Q_{1}...Q_{k}$, a score $S_{Q_{i}}$ is
computed. Audio summarization is performed by choosing those segments which
contain higher $S_{Q_{i}}$ scores in order of appearance until $\theta$ is
reached. $S_{Q_{i}}$ is defined as:

Figure 2: Summary creation scheme

$\displaystyle S_{Q_{i}}$ $\displaystyle=$
$\displaystyle\frac{1}{1+e^{-(\Delta_{t_{i}}-5)}}\times\frac{|Q_{i}|}{|P|}\times
e^{-\frac{t_{Q_{i}}}{\Delta_{t_{i}}}}\times e^{1-LR_{Q_{i}}}$ (5)

Here $\Delta_{t_{i}}=t_{Q_{i}+1}-t_{Q_{i}}$, being $t_{Q_{i}}$ the starting
time of the segment $Q_{i}$ and $t_{Q_{i}+1}$ the starting time of $Q_{i+1}$.
$|Q_{i}|$ and $|P|$ corresponds to the length in seconds of the segment
$Q_{i}$ and $P$ respectively.

##  4 Experimental Evaluation

We trained the informativeness model explained in Section 3.2 with a set of
5,989 audio broadcasts which corresponds to more than 310 hours of audio in
French, English and Arabic [5]. Transcripts were obtained with the ASR system
described on [3].

During audio summary creation we focused on a small dataset of 10 English
audio samples. In this phase no ASR system was used given the text
independence our systems achieves once the informativeness model has been
obtained. Selected sample lengths vary between 102 seconds (1m42s) and 584
seconds (9m44s) with an average length of 318 seconds (5m18s).

Similar to Rott et al. [13], we implement a 1-5 subjective scaled opinion
metric to evaluate the quality of the generated summaries and their parts.
During evaluation, we provided a set of five evaluators with the original
audio, the generated summary, their corresponding segments and the scale shown
in Table 2.

###  4.1 Results

Summary length was set to be the 35% of the original audio length during
experimentation. Evaluation was performed over the complete audio summaries as
well as over each summary segment. We are interested on measuring the
informativeness of the generated summaries but also on measuring the
informativeness of each one of its segments.

Score | Explanation
---|---
$5$ | Full informative
$4$ | Mostly informative
$3$ | Half informative
$2$ | Quite informative
$1$ | Not informative
Table 2: Evaluation scale

Table 3 shows the length of each video and the number of segments that were
selected during the summarization process. “Full Score” corresponds to the
complete audio summaries evaluation while “Average Score” to the score of
their corresponding summary segments. Both metrics represent different things
and seem to be quite correlated. “Full Score” quantifies the informativeness
of all the summary as a whole while “Average Score” represents the summary
quality in terms of the information of each of its segments. To validate this
observation, we computed the linear correlation between these two metrics
obtaining a PCC value equal to $0.53$.

The average scores of all evaluators can be seen in Table 3. The lowest “Full
Score” average value obtained during evaluation was $2.75$ and the highest
$4.67$, meaning that the summarization algorithm generated at least half
informative summaries. “Average Score” values oscillate between $2.49$ and
$3.76$. An interesting case is sample #6, which according to its “Full Score”
is “mostly informative” (Table 2) but has the lowest “Average Score” of all
samples. This difference is given because 67% of its summary segments has an
informativity score $<3$, but in general it achieves to communicate almost all
the relevant information. Figure 3 plots the average score of each one of the
30 segments for sample #6.

Sample | Length | Segments | Full Score | Average Score
---|---|---|---|---
1 | 3m19s | 8 | 4.20 | 2.90
2 | 5m21s | 13 | 3.50 | 2.78
3 | 2m47s | 5 | 3.80 | 3.76
4 | 1m42s | 5 | 3.60 | 2.95
5 | 8m47s | 22 | 4.67 | 3.68
6 | 9m45s | 30 | 4.00 | 2.49
7 | 5m23s | 8 | 3.20 | 3.75
8 | 6m24s | 20 | 3.75 | 2.84
9 | 7m35s | 18 | 3.75 | 3.19
10 | 2m01s | 4 | 2.75 | 2.63
Table 3: Audio summarization performance over complete summaries and summary
segments Figure 3: Audio summarization performance for sample #6

A graphical representation of the audio summaries and their performance can be
seen in Figure 4. Full audio streams are represented by white bars while
summary segments are represented by the gray zones. The height of each summary
segment corresponds to their informativeness score.

Figure 4: Graphical representation of audio summarization performance

From Figure 4 it can be seen that samples #2, #3, ,#7, #8 and #10 have all
their summary segments clustered to the left. This is due to the preference
that the summarization technique is given to the first part of the audio
stream region whereby, within a standard newscast, is gathered the major part
of the information. The problem is that in cases where different topics are
covered over the newscast (multi-topic newscast, interviews, round tables,
reports, etc.), relevant information is distributed all over the video. If a
big amount of relevant segments are grouped in this region, the summarization
algorithm uses all the space available for the summary very fast, discarding a
large region of the audio stream. This is the case of samples #7 and #10 which
“Full Scores” are less to $3.50$.

Concerning sample #5, a well distribution of its summary segments is observed.
From its 22 segments, only 4 had an informativeness score $\leq 3$, achieving
the highest “Full Score” of all samples and a good “Average Score”.

##  5 Conclusions

In this paper we presented an audio summarization method based on audio
features and on the hypothesis that mapping the informativeness from a pre-
trained model using only audio features may help to select those segments
which are more pertinent for the summary.

Informativeness of each segment was obtained by mapping a set of audio
features issued from its Mel-frequency Cepstral Coefficients and their
corresponding Jensen-Shannon divergence score. Summarization was performed
over a sample of English newscasts, demonstrating that the proposed method is
able to generate at least half informative extractive summaries. We can deduce
that there is not a clear correlation between the quality of a summary and the
quality of its parts. However this behavior could be modeled as a recall based
relation between both measures.

As future work we will validate this hypothesis as well as expand the
evaluation dataset from a multilingual perspective to consider French an
Arabic summarization.

## Acknowledgments

We would like to acknowledge the support of CHIST-ERA for funding this work
through the Access Multilingual Information opinionS (AMIS), (France - Europe)
project.

## References

  * [1] Christensen, H., Gotoh, Y., Renals, S.: A cascaded broadcast news highlighter. IEEE transactions on audio, speech, and language processing 16(1), 151–161 (2008)
  * [2] Duxans, H., Anguera, X., Conejero, D.: Audio based soccer game summarization. In: Broadband Multimedia Systems and Broadcasting, 2009. BMSB’09. IEEE International Symposium on. pp. 1–6. IEEE (2009)
  * [3] Jouvet, D., Langlois, D., Menacer, M., Fohr, D., Mella, O., Smaïli, K.: Adaptation of speech recognition vocabularies for improved transcription of youtube videos. Journal of the International Science and General Applications 1(1), 1–9 (2018)
  * [4] Kullback, S., Leibler, R.A.: On information and sufficiency. The annals of mathematical statistics 22(1), 79–86 (1951)
  * [5] Leszczuk, M., Grega, M., Koźbiał, A., Gliwski, J., Wasieczko, K., Smaïli, K.: Video summarization framework for newscasts and reports – work in progress. In: Dziech, A., Czyżewski, A. (eds.) Multimedia Communications, Services and Security. pp. 86–97. Springer International Publishing, Cham (2017)
  * [6] Louis, A., Nenkova, A.: Automatic summary evaluation without human models. In: TAC (2008)
  * [7] Louis, A., Nenkova, A.: Automatically evaluating content selection in summarization without human models. In: 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. pp. 306–314. ACL (2009)
  * [8] Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA, USA (1999)
  * [9] Maskey, S., Hirschberg, J.: Comparing lexical, acoustic/prosodic, structural and discourse features for speech summarization. In: Ninth European Conference on Speech Communication and Technology (2005)
  * [10] Maskey, S., Hirschberg, J.: Summarizing speech without text using hidden markov models. In: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers. pp. 89–92. Association for Computational Linguistics (2006)
  * [11] McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., Nieto, O.: librosa: Audio and music signal analysis in python. In: 14th python in science conference. pp. 18–25 (2015)
  * [12] Rafii, Z., Pardo, B.: Music/voice separation using the similarity matrix. In: ISMIR. pp. 583–588 (2012)
  * [13] Rott, M., Červa, P.: Speech-to-text summarization using automatic phrase extraction from recognized text. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) Text, Speech, and Dialogue. pp. 101–108. Springer International Publishing, Cham (2016)
  * [14] Saggion, H., Torres-Moreno, J.M., Cunha, I.d., SanJuan, E.: Multilingual summarization evaluation without human models. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. pp. 1059–1067. COLING ’10, Association for Computational Linguistics, Stroudsburg, PA, USA (2010), http://dl.acm.org/citation.cfm?id=1944566.1944688
  * [15] Szaszák, G., Tündik, M.Á., Beke, A.: Summarization of spontaneous speech using automatic speech recognition and a speech prosody based tokenizer. In: KDIR. pp. 221–227 (2016)
  * [16] Taskiran, C.M., Pizlo, Z., Amir, A., Ponceleon, D., Delp, E.J.: Automated video program summarization using speech transcripts. IEEE Transactions on Multimedia 8(4), 775–791 (Aug 2006). https://doi.org/10.1109/TMM.2006.876282
  * [17] Torres-Moreno, J.M.: Automatic text summarization. John Wiley & Sons (2014)
  * [18] Torres-Moreno, J., Saggion, H., da Cunha, I., SanJuan, E., Velázquez-Morales, P.: Summary evaluation with and without references. Polibits 42, 13–19 (2010), http://polibits.cidetec.ipn.mx/ojs/index.php/polibits/article/view/42-2/1781
  * [19] Zechner, K.: Spoken language condensation in the 21st century. In: Eighth European Conference on Speech Communication and Technology (2003)
  * [20] Zlatintsi, A., Iosif, E., Marago, P., Potamianos, A.: Audio salient event detection and summarization using audio and text modalities. In: Signal Processing Conference (EUSIPCO), 2015 23rd European. pp. 2311–2315. IEEE (2015)
  * [21] Zlatintsi, A., Maragos, P., Potamianos, A., Evangelopoulos, G.: A saliency-based approach to audio event detection and summarization. In: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European. pp. 1294–1298. IEEE (2012)