# Serendipitous Catch of a Giant Jellyfish: an Ionized Nebula around 3C 275.1
with 170 kpc Long Tails

Qinyuan Zhao  Department of Astronomy, Xiamen University, Xiamen, Fujian
361005, China  Junfeng Wang Department of Astronomy, Xiamen University,
Xiamen, Fujian 361005, China  Zhenzhen Li Key Laboratory for Research in
Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of
Sciences, 80 Nandan Road, Shanghai 200030, China  Key Laboratory of Polar
Science, MNR, Polar Research Institute of China, Shanghai 200136, China

###### Abstract

3C 275.1 is a blue quasar at $z=0.55522$, hosting powerful outflows and
residing in a complex environment. We present a serendipitously detected giant
nebula surrounding 3C 275.1, which shows morphological features resembling
those of objects known as “jellyfish galaxy”, with extremely long tails of
ionized gas extending to 170 kpc in projection. We analyze its optical spectra
taken by the MUSE on the VLT. The brighter part of this giant nebula exceeds
100 kpc, whose rotation curve does not flatten out, is very different from
those of normal spiral galaxies. This system shares some characteristics
common to those formed via ram pressure stripping (RPS), yet its long narrow
tails and higher ionization are unusual compared to known tails in jellyfish
galaxies, not fully consistent with a simple RPS scenario. Our photoionization
simulation and the inferred short recombination timescale both suggest that
besides the quasar 3C 275.1, an extra source of ionization is necessary to
keep the gas ionized at such distance from the nucleus, which could be related
to RPS, tidal interaction or AGN outflow, providing new evidence of active
dynamical interaction of a galaxy with the intracluster medium.

Galaxy evolution (594); Intracluster medium (858); Galaxy clusters (584); Ram
pressure stripped tails (2126)

††facilities: VLT (MUSE), HST (WFPC2)††software: astropy (Collaboration et
al., 2013, 2018)

##  1 Introduction

Environmental effects play a key role in galaxy evolution, specifically the
transformation from late-type to early-type galaxies. Galaxies evolve
primarily through mergers (Toomre & Toomre, 1972; Perry et al., 2023), tidal
interactions (Mayer et al., 2006; Chung et al., 2007), ram-pressure stripping
(RPS; Gunn & Gott 1972; Luo et al. 2023), viscous stripping (Nulsen, 1982;
Quilis et al., 2000), galaxy harassment (Moore et al., 1996), and starvation
(Larson et al., 1980; Peng et al., 2015). These interactions can remove the
gas to rapidly quench star formation (Roberts et al., 2019; Ciocan et al.,
2020) or increase interstellar medium (ISM) density to enhance star formation
(Roberts & Parker, 2020; Cramer et al., 2021).

Among these mechanisms, RPS has been known as the most efficient mechanism of
gas removal from galaxies in cluster environments (Dressler, 1980). When a
galaxy falls into a cluster through the intergalactic space filled with hot
and diffuse gas, ram pressure from intra-cluster medium (ICM) gas applies a
force to the gas component in the galaxy that can potentially unbind its gas
reservoir. This process, which results in disturbed galaxy morphologies and
trailing tails of stripped gas, is referred to as RPS (see Boselli et al. 2022
for a review). Bekki (2009) named these objects “jellyfish galaxies” due to
the morphologies of their tails. Observations and simulations both demonstrate
important effects of RPS on galaxy evolution such as disk truncation, star
formation quenching, central bulge build-up, formation of flocculent arms, the
transformation of dwarf galaxies, and long filamentary structures (Okamoto &
Nagashima 2003; Tonnesen & Bryan 2012; Ruszkowski et al. 2014; Kenney et al.
2014).

In the past several years, more signatures of RPS have been observed in
molecular gas (e.g. Sivanandam et al. 2010, 2014; Moretti et al. 2018, 2020),
and ionized gas in the optical (e.g. Fumagalli et al. 2014; Fossati et al.
2016; Bellhouse et al. 2019; Cramer et al. 2019; Azevedo et al. 2023) and
X-ray bands (e.g. Sun et al. 2006, 2010; Poggianti et al. 2019). JO201 with 94
kpc long tails which is falling into Abell 85, undergoes both RPS and AGN
feedback at low redshift (Bellhouse et al., 2019; George et al., 2019).
Boselli et al. (2019) detected two massive star forming galaxies with tails
that extend to 100 kpc at $z=0.7$. Recent studies further reveal that RPS can
also help compressing the gas (e.g., Roberts & Parker 2020; Troncoso-Iribarren
et al. 2020), which may briefly enhance star formation during the early
stages, and eventually remove the full ISM from the galaxy, leading to the
quenching of star formation (Vollmer et al., 2001; Tonnesen et al., 2007).

It is critical to observe jellyfish galaxies at high physical resolution to
better understand star formation activities at small-scale. Integral field
spectroscopy (IFS) observations provide both spatial and spectral information,
therefore it is very advantageous to investigate various physical properties
of jellyfish galaxies with IFS observations. However, most IFS studies have
been limited to the local universe, whereas jellyfish galaxies in massive
clusters are mostly at intermediate redshift (e.g. Kalita & Ebeling 2019).
Hence case study of their local analogues can provide more insights.

In this paper, we present VLT/MUSE IFS observations of quasar 3C 275.1 at
$z\sim 0.55$ and discovery of a giant nebula surrounding it, aiming to
characterize its intergalactic environment and ISM. This system was
serendipitouly identified during our study on galaxy scale quasar outflow
using IFS archival data (Zhao & Wang, 2023). We carry out emission line
analysis of the ionized gas in this newly identified jellyfish galaxy, which
includes [O ii], H$\beta$ and [O iii]. This paper is structured as follows: we
describe observations and data reduction in Section 2. In Sections 3 and 4, we
measure the group environment and the nebula. In Section 5, we discuss the
possible origin of the nebula. We conclude with a summary in Section 6.
Throughout this paper, we adopt a cosmology with $H_{0}$=70 km s-1 Mpc-1,
$\Omega_{m}=0.3$, $\Omega_{\Lambda}=0.7$.

##  2 Observation and Data Reduction

3C 275.1 was observed by VLT/MUSE in 2021 February under ESO program ID
106.2142.001, i.e., 0106.B-0564(A) (PI: Balmaverde). Results on the optical
spectral properties of the nuclei of some radio-loud AGNs have been published
(Capetti et al., 2023). The spectra were taken in the optical band (wavelength
coverage $\lambda$ $\sim$ 4750 $\mathrm{\AA}$ \- 9350 $\mathrm{\AA}$ in the
observer’s frame), covering 3054 $\mathrm{\AA}$ to 6012 $\mathrm{\AA}$ in the
rest frame for 3C 275.1 at $z\sim 0.555$. The field of view (FOV) covers an
average of $1^{\prime}\times 1^{\prime}$, and medium spectral resolution of R
= 3500. The seeing varied during the observations from 0.65 arcsec to 1.2
arcsec, and the full width at half maximum (FWHM) of the point-spread function
(PSF) in the averaged data set was $0.9\arcsec$. The total on-source
integration time is 4 $\times$ 562 s.

After removing cosmic rays from the raw data using the L.A. Cosmic procedure
(van Dokkum, 2001), we reduce the raw data using the ESO-MUSE pipeline. The
final data cubes have a spatial scale of $0.2\arcsec\times 0.2\arcsec$. The
estimated angular resolution is $\sim$ 0.9$\arcsec$ based on the FWHM of the
PSF map, which roughly corresponds to 5.8 kpc at the redshift of $z=0.55522$.
We estimated the PSF for this observation using surface brightness profiles of
broad emission lines (BELs), i.e., using a 2D Gaussian to fit the BELs map.

##  3 The Quasar 3C 275.1 and Its Environment

###  3.1 Properties of 3C 275.1

3C 275.1 with a central black hole of 108.3 M⊙ (McLure et al., 2006) at
$z=0.55522\pm 0.00004$ (Adelman-McCarthy et al., 2008) is a FR II radio
quasar. The radio morphology of 3C 275.1 is a dogleg structure, and the two
sides display an asymmetry in the Faraday depolarization (Garrington & Conway,
1991). Several authors have argued that the source lies in a rich cluster
which could account for its distorted shape (Stocke et al., 1985; Ellingson et
al., 1991; Hardcastle & Worrall, 1999; Crawford & Fabian, 2003), Liu & Pooley
(1990) note that there is a galaxy near the southern component which may be
interacting with the southern lobe.

3C 275.1 is hosted by an elliptical galaxy although the colors are somewhat
bluer than normal (Hintzen & Romanishin, 1986), and is surrounded by a number
of faint galaxies (Hintzen et al., 1981). Ellingson & Yee (1994) measured 35
of these galaxies and only found five to be within about 300 km s-1 of the
quasar redshift. Previous work suggests that 3C 275.1 is located at the centre
of the gravitational potential well of the group (Krempec-Krygier et al.,
1998). An extremely large elliptical nebula whose major axis roughly
perpendicular to the radio axis (e.g. Stocke et al. 1985) and exceeding 100
kpc (14″) underlying 3C 275.1 (Hintzen et al., 1983), shows a “solid-body
rotation curve” extending 40 kpc from the quasar nucleus (Hintzen & Stocke,
1986). The nebula may be relic from multiple tidal interactions with
cluster/group members, or it may be the cooling flow centered on the quasar.
In the latter case, the quasar was considered to be a possible “proto-cD”
galaxy (Hintzen & Stocke, 1986). In this IFS observation, we also identified a
large nebula around 3C 275.1, confirming the results of Hintzen et al. (1983)
and Hintzen & Stocke (1986). Moreover, the nebula we detected is much larger
than previous results, extending to $\sim$170 kpc. Crawford & Fabian (2003)
reported on the spatially extended soft X-ray emission surrounding 3C 275.1.
The X-ray emission is most likely thermal emission from the ICM of a cluster,
or possibly associated with the radio plasma (Crawford & Fabian, 2003; Belsole
et al., 2006). We overlays the X-ray, radio, and optical nebula contours
(Figure 2e) and find that the extended X-ray emission is preferentially
aligned along the radio jet direction, while the optical filamentary nebula is
located between radio/x-ray jet hotspots and is roughly perpendicular to jet
axis.

###  3.2 Group Membership

In order to determine which galaxies belong to the “group” and which are
foreground or background sources, we have estimated spectroscopic redshifts of
the sources in the region covered by the ionized gas using their emission or
absorption spectral features. Using available MUSE data (Figure 1). Thirty
continuum sources are identified, and five group members are unambiguously
spectroscopically identified with redshifts between $0.5535\leq z\leq 0.5595$
in the FOV. Figure 1 shows an image from the MUSE observations of the field
where we mark the quasar and surrounding galaxies with their IDs, together
with the morphology of nebula. Blue, white and red rectangles represent that
the galaxy is a group member, a foreground or background source, and uncertain
source, respectively. The IDs, galaxy coordinates, photometric redshifts from
SDSS Data Release 12 (Alam et al., 2015), spectroscopic redshifts and $\Delta
v$ with 3C 275.1 are reported in Table 1. There could be some dwarf galaxies
with weak continuum that have not been identified. The velocity range of these
5 group members is $\sim$ 1200 km s-1. The 4 closest galaxies have a velocity
range of only 650 km s-1, possibly consistent with being members of a group.
Such complex environment could result in galaxy interactions that contribute
to redistribution of gas to large scales (e.g. Johnson et al. 2024).

Figure 1: MUSE white-light image in linear scale over the full field of view of the observations. The five group members that are unambiguously identified are marked with blue rectangles, while the foreground or background sources are marked with white rectangles, and sources with no identified redshifts are marked with red rectangles. The yellow star marks the position of the quasar 3C 275.1. The morphology of [O iii] is marked with green contours. The physical scale at the redshift of the group is indicated at the top left.  Table 1: Summary of Galaxies in the Field of 3C 275.1. ID | RA | Dec |  photo $z$ |  spec $z$ | $\Delta v$
---|---|---|---|---|---
G1 | 12:43:58.31 | +16:23:10.27 |  0.409 $\pm$ 0.1071 | 0.2367 |
G2 | 12:43:57.55 | +16:23:09.78 |  0.511 $\pm$ 0.0626 |  |
G3 | 12:43:57.65 | +16:23:13.38 | - | 0.4909 |
G4 | 12:43:58.71 | +16:23:01.87 |  0.241 $\pm$ 0.1293 | 0.1974 |
G5 | 12:43:58.56 | +16:23:01.46 | - | 0.9489 |
G6 | 12:43:57.89 | +16:23:01.70 | - | 0.8162 |
G7 | 12:43:56.20 | +16:23:06.20 | - |  |
G8 | 12:43:58.82 | +16:22:57.36 | - |  |
G9 | 12:43:58.45 | +16:22:57.12 | - |  |
G10 | 12:43:57.27 | +16:22:57.82 |  0.286 $\pm$ 0.0890 | 0.2395 |
G11 | 12:43:56.42 | +16:23:00.71 | - | 0.8877 |
G12 | 12:43:56.48 | +16:22:58.21 | - | 0.5586 | 650
G13 | 12:43:55.99 | +16:22:59.96 |  0.031 $\pm$ 0.1285 | 0.8888 |
G14 | 12:43:58.32 | +16:22:53.00 |  0.624 $\pm$ 0.0390 | 0.6942 |
G15 | 12:43:57.76 | +16:22:58.88 | - | 0.5580 | 535
G16 | 12:43:59.36 | +16:22:49.21 |  0.466 $\pm$ 0.0562 |  |
G17 | 12:43:58.46 | +16:22:50.61 | - |  |
G18 | 12:43:57.84 | +16:22:48.03 |  0.553 $\pm$ 0.0361 | 0.5570 | 342
G19 | 12:43:57.31 | +16:22:55.04 | - |  |
G20 | 12:43:57.23 | +16:22:51.42 | - |  |
G21 | 12:43:56.13 | +16:22:54.02 |  0.658 $\pm$ 0.2108 | 0.0956 |
G22 | 12:43:56.24 | +16:22:46.73 |  0.571 $\pm$ 0.0412 | 0.5595 | 823
G23 | 12:43:57.40 | +16:22:42.94 |  0.113 $\pm$ 0.0730 |  |
G24 | 12:43:58.15 | +16:22:37.84 |  0.126 $\pm$ 0.0423 | 0.1663 |
G25 | 12:43:57.89 | +16:22:35.82 | - | 0.5039 |
G26 | 12:43:59.00 | +16:22:33.73 | - |  |
G27 | 12:43:58.78 | +16:22:22.42 | - |  |
G28 | 12:43:57.54 | +16:22:24.81 |  0.470 $\pm$ 0.1262 | 0.5535 | -332
G29 | 12:43:56.58 | +16:22:28.29 |  0.457 $\pm$ 0.0232 | 0.4651 |
G30 | 12:43:59.47 | +16:22:26.28 |  0.540 $\pm$ 0.0942 | 0.4936 |

##  4 Kinematic Maps of the Giant Nebula

In order to understand the the main properties of this giant ionized nebula,
we fit the most prominent gas emission lines by minimizing $\chi^{2}$ using
the Python package MPFIT. After extracting the spectrum in each spaxel,
subtracting the continuum and removing the broad emission line, we fit the [O
ii], H$\beta$ and [O iii] lines with a combination of Gaussian profiles, so
that all fitted lines share the same central velocity and velocity dispersion.

###  4.1 Non-parametric Measurements

We performed non-parametric measurements of the emission line profiles
following the method described in Liu et al. (2013), and obtained three maps:

(i) intensity map of the [O iii] $\lambda$5007 $\mathrm{\AA}$ line.

(ii) $v_{med}$, median velocity map.

(iii) $W_{80}$, line width map. This is defined as the difference between the
velocities at 10 and 90 per cent of the cumulative flux:
$W_{80}=v_{90}-v_{10}$. For a purely Gaussian velocity profile, $W_{80}$ =
2.563$\sigma$.

These non-parametric measurements were obtained using the best-fit profiles.
Figure 2 shows an overview of the flux distribution and kinematical properties
of the ionized gas, as obtained from the fit of the [O iii] $\lambda$5007
line. The maps were obtained by selecting only those spatial pixels with a
signal-to-noise ratio (S/N) equal to or higher than 1.5.

The resulting morphology and kinematics of the nebula are complex. The flux
distribution (Figure 2a) shows multiple clumps around the quasar, as well as
filamentary structures in the west regions. Based on the morphology,
kinematics and locations relative to the quasar, we initially refer to
different parts of the nebula as the Host nebula (“A-G”), the Half-ring nebula
(“H-K”), the West filaments (“L-Q”), and the faint nebula (ellipse region).
The Host nebula surrounds the quasar and extends to a projected radii of about
50 kpc from the quasar with [O iii] surface brightness levels ranging from 0.1
to 9 $\times$ 10-17 erg s-1 cm-2 arcsec-2, with velocities ranging from -300
to 400 km s-1, and distinct rotating disk-like structure (see § 5.2). It is
attached with two long tails over 100 kpc to the west with [O iii] surface
brightness levels ranging from 0.01 to 0.1 $\times$ 10-17 erg s-1 cm-2
arcsec-2. The flux for the faint nebula in the southwest (in ellipse) is
obtained from the data-cube for [O iii] in velocity ranges of $\sim$ 300 - 800
km s-1 rather than fitting [O iii] due to the weak signal. Therefore, the
velocity and line width maps do not show this nebula.

The large scale velocity structure is rather complex. The velocity
distribution (Figure 2b) of Host nebula is remarkably well organized. One part
of the nebula is redshifted, while the other is blueshifted. The velocity map
shows evidence for a velocity gradient along the northeast-southwest
direction, with velocity ranging between $\sim$ +400 km s-1 and $-$300 km s-1,
possibly associated with a rotating disk. The velocity of the Half-ring nebula
relative to the systemic is approximately zero, except that the “M” and “K”
regions are redshifted and blue-shifted, respectively. The velocity of “K” is
different with that of other nearby regions, likely representing an
independent system.

The map of $W_{80}$ (Figure 2c) shows elevated velocity dispersion in the
central (“A”) region, and the value of $W_{80}$ reaches 1000 km s-1, which is
comparable to that of known quasar outflows (e.g. Zakamska et al. 2016),
indicating possible association with an AGN outflow. The other regions have a
lower velocity dispersion of $\sim$ 150-400 km s-1 at the tail.

Figure 2: Non-parametric measurements of [O iii] nebula. The maps are (a) flux
intensity of [O iii] (erg s-1 cm-2 arcsec-2); (b) median velocity (km s-1);
(c) line width ($W_{80}$, km s-1); (d) [O iii] surface brightness contours
overlaid on the HST WFPC2/F675W image of the field marking surface brightness
levels of 5, 150, 1500, and 4000 $\times$ 10-20 erg s-1 cm-2 arcsec-2; (e) [O
iii] (red) surface brightness contours and 2.99 GHz radio (green) contours
observed by VLASS overlaid on the Chandra/ACIS-S image. The maps were obtained
by selecting only those spaxels with a S/N of [O iii] $\lambda$5007 line equal
to or higher than 1.5. The red cross marks the position of the quasar 3C
275.1. The PSF (0.9 $\arcsec$) of MUSE data is depicted by the open circle in
the lower left of the panels. Note that, because of the weak signal, the faint
nebula image in the ellipse is generated from the data-cube for [O iii] in
velocity ranges of $\sim$ 300 - 800 km s-1 in panel (a). Therefore, the
velocity and line width maps (b and c) do not show the nebula in the ellipse.

###  4.2 The Host Nebula

The kinematics of the Host nebula are more complex than the canonical velocity
map expected for rotating disks. We modeled the Host nebula kinematics using a
tilted-ring model, and fit with the BBarolo (Di Teodoro & Fraternali, 2015)
tool, to test whether the Host nebula kinematics are compatible with a
rotation-supported system. This fit has some limitations. Since the Host
nebula has more extension in the northeast, and the disk model is symmetric,
it cannot be further extended to cover the entire nebula. The best-fit plots
for a rotating disk are shown in Figure 3. Based on these, we infer an
inclination $i=55^{\circ}$, PA = 61∘, and $v_{rot}\sim 240$ km s-1. The best-
fit velocity maps still show significant residuals at northeast of the nucleus
with $\Delta v\sim 200$ km s-1, and $\Delta v\sim-100$ km s-1 at nucleus, east
and northwest of the nucleus. These features might be associated with a
companion (G15) and quasar outflows. We produced position-velocity (PV)
diagram, as shown in Figure 4. In the PV diagram, the range of detected
velocities well exceed the projected maximum rotational disk velocity given by
the BBarolo model. In addition, the velocity amplitude of the Host nebula
($\sim$350 km s-1) is higher than that of the normal spiral galaxies
($\sim$200 km s-1, Vulcani et al. 2018), even the massive spiral galaxies
($\sim$250 km s-1), and the size of these nebula is larger than that of normal
galaxy disks. Although PV diagram shows solid body rotation, as reported by
Hintzen & Stocke (1986), the overall kinematics are more complex than this,
and the pattern of large velocity residuals in Figure 3 is unlikely to be
explained by any simple disk model. It is likely to be a 3D gas distribution
with some complex motions. It implies that additional factors may be causing
its kinematics to deviate from a normal rotating disk, such as inflow,
outflow, and the interaction with the companion galaxies.

To examine if the nebula has any relationship with galaxies in the quasar
environment, we show [O ii] and [O iii] emission contours over the HST optical
image in Figure 2d. The northeast region (“F”) is close to galaxy G15, while
the eastern (“D”) region is close to G9 and G17. There appears to be weak
continuum emission in “D” region (Figure 2d). The line width in “F” region is
narrower than that from nearby parts of the gas, indicating that the emission
line likely arises in its ISM. The wider line width at “D” region is probably
the result of the interaction between the companion galaxies and 3C 275.1.
However, the continuum and the emission lines of G9 and G17 are too faint to
obtain spectroscopic redshifts. There may be more dwarf galaxies around the
quasar 3C 275.1 with weak continuum, but current imaging data cannot indicate
if there are any interaction between these galaxies and the quasar.

Figure 3: The observed kinematics, the best-fit model using a rotating disk,
and the corresponding residual velocity of the Host nebula. Note that the
northeast region of the nuclear region is not fitted. The red cross identifies
the position of the quasar 3C 275.1.  Figure 4:  Position-velocity diagram of
the model (red) and the observed (blue) taken along the disk major axis (PA =
61∘).

###  4.3 The Half-ring Nebula

3C 275.1 is surrounded by multiple clumps (“H-M”), these clumps are $\sim$
30-40 kpc away from the quasar. Clumps “L” and “M” are more continuously
connected to the West filaments and are discussed together next. “H” region is
spatially coincident with companion galaxy G18, and the plus marks the
position of G18 (Figure 2). The surface brightness contours of clump “H” is
somewhat elongated in the southeast–northwest direction, exhibiting an offset
(0.5 ″) between its position of surface brightness peak and position of the
G18 (Figure 2d). Together, the morphology and spatial coincidence with G18
suggest that this elongated clump likely arises from tidal stripping of the
ISM of G18 (e.g. Decarli et al. 2019). G18 is tidally stretched along its
course, thus leading to the spatial extent between the southeast and northwest
wings of the G18. The other half-ring of clumps might be consistent with a
stream of tidal debris.

The majority of the entire nebula is dominated by [O iii] emission, but the
“I” region has greater [O ii] emission (Figure 6 and 7). “I” also shows a
spatial trend in the ionization-state-sensitive O32 and [O iii]/H$\beta$ line
ratio maps (Figure 7), which implies the ionization degree is lower in the
south and higher near the quasar. In addition, [O iii] line profile is
different from H$\beta$ which is wider (Figure 6). This may be related to the
presence of jet, because the projection of the radio lobe is in the “I” region
(Figure 2e). However, the southward radio jet is expected to be moving away
from observer(Liu & Pooley, 1990), while the gas in “I” region is slightly
redshifted or at the systemic velocity. The line width of “I” is also narrower
than that of other nearby clumps. Weak continuum emission can be detected
after stacking the spectra, but its surface brightness peak is not coincident
with any continuum sources in the HST image. It is plausible that the emission
line of “I” arises from the ISM of the dwarf or young stellar populations in
the tidal tail.

The velocity distribution of “K” shows that it is likely an independent
velocity system, more blueshifted than other regions. The morphology of “K” is
somewhat elongated in an east-west direction. As “K” is spatially adjacent to
the galaxy G20, the emission line could be associated with G20. This nebula
could also arise from RPS of the ISM of a faint dwarf galaxy as it moves
through the hot halo of the quasar host group (e.g., Chen et al. 2019). We do
not rule out the possibility that “K”, like “M” and “L”, is associated with
the filament to the east and west.

###  4.4 The West Filaments

Together, the flux distribution and velocity maps all suggest that “M” -“N”,
“L”-“O”-“P”/“Q”, and the faint nebula in the ellipse can be regarded as a
whole diffuse structure. “M” and “L” are probably the footpoints of the long
filaments. “K” may belong here as well. Hence we define these as the West
filaments.

These filamentary structures are long and narrow with a width of $\sim$
1.5″(10 kpc). They exhibit a line width of $W_{80}\sim$ 200 - 400 km s-1. The
“M”-“N” filament extends to $\sim$ 100 kpc west of the quasar and intersects
with the Host nebula at $r\sim$20 kpc. The velocity at “M” is consistent with
the velocity at the Host nebula it connects to, and the velocity at “N” is
close to 0 km s-1. The velocity of this filamentary structure (“M” -“N”)
increases as it gets closer to the quasar. The morphology and kinematics of
this filament can be explained by the gases arising from ongoing RPS of ISM
(e.g. Azevedo et al. 2023) or cool, filamentary accretion (e.g. Johnson et al.
2022). However, this velocity gradient does not seem to apply to the
“L”-“O”-“Q” filament, which has an irregular velocity gradient that is
inconsistent with a simple case of RPS or filamentary accretion. In addition,
the “L”-“O”-“P” structure shows regular velocity gradient, but the morphology
of this structure is neither like accretion inflow nor like ram pressure
stripped tails.

The faint nebula is redshifted with velocity ranging from 300 to 800 km s-1,
and the velocity increases with distance (Figure 5). Such one-sided tail is a
signpost RPS feature, and the known jellyfish galaxies with such feature are
often observed in galaxy clusters and groups (e.g. Bekki 2009). There is no
clear velocity gradient along the filaments, as expected for a typical RPS
case. Given the complexities in the western filaments, we suggest that this
system is inconsistent with a simple RPS scenario. It remains plausible that
this system is experiencing RPS with some complicating factors that are not
yet understood given the current data.

Figure 5:  Spectra (black) and best-fit spectral (red) for three regions in
faint nebula. Locations of these regions are labelled by their IDs in Figure
2).

##  5 Discussion

###  5.1 Physical Conditions of the Giant Nebula

AGN, shocks and young stellar populations all may contribute to ionize the
nebula at such a large scale (e.g. Liu et al. 2013; Epinat et al. 2018; Chen
et al. 2019). To better explore the properties of the nebula, we selected
several representative regions and extracted their spectra to infer physical
conditions and the origin of the ionized gas.

We measure the the electron density using [O ii] doublet , and quantify the
degree of ionization using [O iii]/H$\beta$ and O32 (O32=$\frac{\hbox{[O\,{\sc
iii}]}\lambda 5007+\hbox{[O\,{\sc iii}]}\lambda 4959}{\hbox{[O\,{\sc
ii}]}\lambda 3727+\hbox{[O\,{\sc ii}]}\lambda 3726}$) which are ionization
state-sensitive strong and weak line ratios . These line ratio measurements
and electron density are reported in Table 2, and O32, [O iii]/H$\beta$ maps
shown in Figure 7. We use Python package Pyneb, and assume an electron
temperature of 10000K, to estimate the electron density. The Host nebula where
the [O ii] doublet is resolved, are in the high density state except for “C”
and “D”, while the other nebulas are in a low density limit except for “H”,
“I”, “P” and “Q”.

The inferred electron density in the Host nebula range from $\sim$ 200 to 300
cm-3, which is consistent with the electron density of radio galaxies at a few
100 cm-3 (Nesvadba et al., 2006, 2008). The mean value of electron density is
$n_{\rm e}\simeq 100\rm cm^{-3}$ measured on the integrated spectrum of the
tail, which is comparable to the mean value of electron density in the tail of
the jellyfish galaxy (Boselli et al., 2019). However, this value is a light-
weighted mean, and is thus probably biased towards the brightest and highest
density regions. This gas density matches that observed in star forming
regions of galaxies at z $\simeq$ 2 (Sanders et al., 2016). we can also
estimate the typical recombination time of the ionized gas,

$\tau_{\mathrm{rec}}=\frac{1}{n_{e}\times\alpha_{A}}$ (1)

where $\alpha_{A}$ is the total recombination coefficient ($\alpha_{A}$ = 4.2
$\times$ 10-13 cm3 s-1, Osterbrock & Ferland 2006). $\tau_{\mathrm{rec}}\sim
10^{3}$ yr, which is much shorter than the typical dynamical timescale of the
tail ($\tau\sim 10^{8}$ yr, e.g. Smith et al. 2022). This very short
recombination time suggests that, as in local galaxies, a source of gas
excitation within the tail must be present. This off-nuclear ionizing source
could be young stellar population, which is similar to the observed jellyfish
galaxies with extended tails of star forming regions (e.g. Poggianti et al.
2017), or it could be shocks or collisional ionization (e.g. Fossati et al.
2016), also in line with theoretical expectations (Tonnesen et al., 2007).

Figure 6:  Examples of nebular spectra (stronger lines) and best-fit spectral
models for multiple regions, including central (“A”-“G”), and outer regions
(“H” - “Q”). The median and zero velocity is marked by green dotted and grey
dashed lines, respectively. Locations of these regions are labelled by their
IDs in Figure 2. The extracted spectrum is shown as solid black lines and the
best-fit models are shown as red solid lines.

Figure 7: The intensity ratio of [O iii] 5007 to the narrow component of
H$\beta$ image and the line ratio O32 (O32=[O iii]$\lambda\lambda$5007+4959/[O
ii]$\lambda\lambda$3727+3726 image). The ratio persists at a constant level
($\sim$10) in the whole field of view, except for the “I” and “Q” where the
ratio persists $\sim$ 3\. Due to the SNR of the emission line in the outer
region, we bin each of the clumps in the outer region (“N-Q”) of [O
iii]/H$\beta$ map while we bin the region between “O” and “Q” in O32 map, and
give the average values of these regions.

The nebula also shows a spatial trend in [O iii]/H$\beta$ and O32 line ratio.
The distributions of the [O iii]/H$\beta$ ratio diagnostics are almost
constant across the whole nebula ($\sim$ 10), except for the “H-J” ($\sim$ 5)
and “Q” ($\sim$ 2) region (see Table 2 and Figure 7). These values ([O
iii]/H$\beta$ $\sim$ 10) indicate that the nebula has a higher degree of
ionization than other jellyfish galaxies ([O iii]/H$\beta$ $<$ 3, e.g.
Poggianti et al. 2017; Boselli et al. 2019; Bellhouse et al. 2019). We note
that the degree of ionization of “C” and “D” are lower than that of regions
“F” -“E” -“B” -“G”, which forms an arc-like structure. In addition to AGN
photoionization, such an arc-like structure could be heated by shock waves
associated with motion in the group, therefore they show higher degree of
ionization. The level of ionization is lower in “I” and “Q” region, indicating
the gas in these region may be ionized by young stellar populations.

Spatial trend of O32 is similar to [O iii]/H$\beta$, the higher [O
iii]/H$\beta$, the larger O32. The arc-like structure has an O32 of around 5,
“A” has a higher O32, which is related to AGN, and “C” and “D” has a lower O32
(see Table 2). The majority of the nebula is [O iii] dominated but the “I”
region has greater [O ii] emission. Both [O iii]/H$\beta$ and O32 indicate
that the Half-ring nebula (“H-K”) has lower ionization degree than that of the
West filaments (“L-Q”) except “Q”, which may be related to electron density
and shocks.

To better understand its origin, we performed a photoionization simulation
assuming that the nebula is illuminated by a central ionizing source with a
typical SED of AGN defined by Mathews & Ferland (1987) (hereafter MF87). Using
the observed quasar continuum at 5100Å, we have an estimated bolometric
luminosity of the ionization source, $L_{\rm bol}\approx 10^{46}$ erg s-1. For
simplicity, we assume that the nebula has a slab shape, and a metallicity of
solar abundance. The electron density of the nebula is roughly fixed to
$n_{\rm e}=100$ cm-3, as a typical estimate of the electron density (see Table
2). The total column density is fixed to a large value of $N_{\rm H}=10^{22}$
cm-2, assuming that the nebula is thick enough. The simulation is executed
using the CLOUDY C17.01 photoionization code (Ferland et al., 2017). We export
the model-predicted [O iii]/H$\beta$ line ratio for each nebula region, and
compare them with observed line ratio (Figure 8). It can be seen that the
observed line ratio in the whole nebula is higher than its model-predicted
values, especially in the tail, possibly because this nebula have additional
ionized source besides the quasar, such as young stellar populations or
contribution from shocks.

Figure 8:  Comparison between the model-predicted [O iii]/H$\beta$ line ratio and the spectrum-observed line ratio in different regions. The dashed line represents a 1:1 relationship.  Table 2: Summary of emission-line flux, electron density and line ratio measurements for extracted regions in the nebula Region | Distance |  ne |  [O iii]/H$\beta$ |  O32 ([O iii]/[O ii]) |  Flux${}_{\hbox{[O\,{\sc iii}]}\lambda 5007}$ |  Flux${}_{\hbox{[O\,{\sc ii}]}\rm doublet}$
---|---|---|---|---|---|---
| (arcsec/kpc) |  (cm-3) |  |  |  $(10^{-17}$ erg s-1 cm-2 arcsec-2) |  (10-18 erg s-1 cm-2 arcsec-2)
A | 0 |  213 $\pm$ 237 |  12.67 $\pm$ 0.02 |  11.8 $\pm$ 0.12 |  148.14 $\pm$ 1.21 |  167.32 $\pm$ 20.68
B | 2.16/13.94 |  198 $\pm$ 15 |  9.83 $\pm$ 0.01 |  5.25 $\pm$ 0.12 |  46.64 $\pm$ 0.06 |  118.41 $\pm$ 14.32
C | 2.01/12.95 |  26 $\pm$ 13 |  7.97 $\pm$ 0.06 |  2.92 $\pm$ 0.06 |  17.83 $\pm$ 0.14 |  81.53 $\pm$ 4.69
D | 3.4/21.90 |  $<$ 5 |  6.87 $\pm$ 0.03 |  2.33 $\pm$ 0.05 |  18.11 $\pm$ 0.10 |  103.53 $\pm$ 4.70
E | 3.56/22.94 |  400 $\pm$ 21 |  10.89 $\pm$ 0.02 |  3.94 $\pm$ 0.07 |  27.19 $\pm$ 1.75 |  91.99 $\pm$ 0.001
F | 4.69/30.19 |  325 $\pm$ 26 |  11.01 $\pm$ 0.02 |  4.98 $\pm$ 0.01 |  26.69 $\pm$ 0.22 |  71.42 $\pm$ 0.93
G | 2.55/16.40 |  339 $\pm$ 33 |  9.60 $\pm$ 0.03 |  3.90 $\pm$ 0.22 |  19.55 $\pm$ 2.27 |  66.86 $\pm$ 13.30
H | 5.13/33.02 |  165 $\pm$ 51 |  4.99 $\pm$ 0.09 |  1.15 $\pm$ 0.27 |  4.58 $\pm$ 1.14 |  52.96 $\pm$ 9.4
I | 4.82/31.02 |  91 $\pm$ 20 |  4.83 $\pm$ 0.04 |  0.50 $\pm$ 0.09 |  4.71 $\pm$ 0.12 |  124.55 $\pm$ 11.23
J | 6.28/40.43 | - |  5.78 $\pm$ 0.22 |  1.48 $\pm$ 0.18 |  5.24 $\pm$ 0.36 |  47.21 $\pm$ 7.89
K | 6.93/44.67 |  $<$ 24 |  7.54 $\pm$ 0.12 |  2.32 $\pm$ 0.32 |  3.11 $\pm$ 0.19 |  17.87 $\pm$ 5.6
L | 5.46/35.16 | - |  8.13 $\pm$ 0.11 |  1.60 $\pm$ 0.22 |  3.60 $\pm$ 0.06 |  30.06 $\pm$ 6.60
M | 5.23/33.72 |  $<$ 127 |  7.97 $\pm$ 0.12 |  2.21 $\pm$ 0.03 |  4.27 $\pm$ 0.07 |  25.72 $\pm$ 0.85
N | 13.61/87.62 | - |  20.56 $\pm$ 0.73 |  6.29 $\pm$ 0.56 |  1.33 $\pm$ 0.06 |  2.82 $\pm$ 1.58
O | 10.20/65.72 | - |  10.46 $\pm$ 0.23 |  10.6 $\pm$ 0.22 |  1.57 $\pm$ 0.04 |  1.95 $\pm$ 0.44
P | 11.64/74.97 |  182 $\pm$ 142 |  8.89 $\pm$ 0.23 |  5.25 $\pm$ 0.14 |  1.35 $\pm$ 0.04 |  3.44 $\pm$ 0.49
Q | 26.88/173.14 |  219 $\pm$ 185 |  1.97 $\pm$ 0.15 |  1.04 $\pm$ 0.09 |  0.65 $\pm$ 0.05 |  8.30 $\pm$ 0.56

###  5.2 Origin of the Ionized Gas

Due to the complex dynamics, morphology and presence of some faint galaxies
surrounding 3C 275.1, it is reasonable to expect that this giant nebula is
related to its group environment, possibly explained by CGM, IGM or ICM in the
presence of interactions.

Firstly, RPS can readily produce the large scale ionized gas seen here,
shaping the morphology that resembles a jellyfish galaxy with the striking
westwards tails. The tails, which have a cometary shape with a typical surface
brightness of a few times 10-19 erg s-1 cm-2 arcsec-2, extend up to $\simeq$
170 kpc from the nucleus. A natural initial interpretation given its one-
sided, linear tail is the result of RPS, and there is at least one reported
RPS feature that is nearly as long ($\sim$ 100 kpc; e.g. Yagi et al. 2007).

In addition, the host nebula exhibit arc-like structure (“F” -“E” -“B” -“G”).
This observed feature is similar to that of the jellyfish galaxies (e.g.
Bellhouse et al. 2017), in which an arc-like structure is bent into the same
direction of their stripped gas trails, possibly suggesting the ram pressure
compressed this side of the disk. Therefore, this arc-like structure would be
ionized by shocks, which is consistent with the [O iii]/H$\beta$ line ratio of
our CLOUDY simulation lower than that of the observation(Figure 8). If 3C
275.1 falls to the cluster center with high velocity, it would undergo extreme
RPS (e.g. Poggianti et al. 2016; Azevedo et al. 2023). Krempec-Krygier et al.
(1998) found that 3C 275.1 is located at the center of the group. Based on the
group members that have been identified, 3C 275.1 is likely to suffer from the
ram pressure, as redshift of 3C 275.1 differs significantly from the average
redshift of these group members, indicating that there is a large velocity
difference between 3C 275.1 and its group members.

It is difficult to conclude if a galaxy is undergoing RPS based solely on its
optical morphology, especially since there are other cluster-specific
processes that also lead to disturbed morphologies, for example tidal effects
and/or harassment (e.g. Mayer et al. 2006; Chung et al. 2007). The elongated
morphology could well be the result of tidal interaction as the group members
and surrounding gas fall toward the quasar host. The current data is still not
sufficient to determine whether 3C 275.1 is located at the center of the
group, and future observations including X-rays will be needed. It is also
possible that surrounding galaxies are falling into 3C 275.1, nebula arises
from RPS of their ISM as they move through the hot halo of the quasar host
group (e.g. Boselli et al. 2019).

Moreover, part of the ionized gas can be attributed to AGN outflow. There is
presence of an outflow in the nucleus (“A”), which is indicated by the high
velocity dispersion of gas and the blueshifted line asymmetry. However, except
for the nuclear region, the velocity dispersion is not high (W${}_{80}\lesssim
400$ km s-1), which is inconsistent with the AGN-driven outflow with wide
opening angles (e.g. Liu et al. 2013).

In principle, the ionized gas may also arise from stellar feedback. The
interaction between galaxies could increase their star formation and supernova
rate in situ, such as in gas rich RPS tails. However, most regions except for
“A” exhibit low velocity dispersion, which is inconsistent with high velocity
dispersion expected from stellar feedback (Rupke et al., 2019). While “C-D”,
“H-M” and “Q” regions exhibit lower [O iii]/H$\beta$ ratio, line ratio $\sim
10$ in most regions, implying a high-ionization state in general (Baldwin et
al., 1981).

The kinematics and morphology of the nebula are complex, with rotating
structure and striking long tails. We suggest that more processes besides RPS
are responsible for the nebula, which may experience tidal stripping and AGN
outflow.

##  6 Summary

In this paper, we present discovery of a giant nebula around a radio quasar 3C
275.1 at redshift $z=0.55522$ using VLT/MUSE observation. The flux
distribution shows multiple clumps and the kinematic of the nebula are
complex. Based on morphology of the nebula, we separate the large nebula into
three substructures including a Host nebula, the Half-ring nebula, and the
West filaments. We summarise our results below:

(1) A giant nebula around 3C 275.1 is identified, extending a projected
distance up to 170 kpc from the nucleus and resembling a jellyfish galaxy
formed via RPS. The nebula emits strongly in [O ii], H$\beta$, and [O iii],
enabling a good estimation of density and ionization state.

(2) Through spectroscopic measurements, we confirm that 3C 275.1 is located in
a dense environment with identified group members. The southeastern region and
the westernmost region are spatially coincident with at least four group
member galaxies.

(3) The kinematics and morphology of the nebula are more complex than a simple
RPS case. In contrast to other jellyfish galaxies, the nebula of this galaxy
has a larger size and higher ionization. The rotation curve of the Host nebula
does not flatten in the outer, which is different from those of normal spiral
galaxies. It also exhibits an arc-like structure which might be a possible
sign of RPS with a higher degree of ionization. This is consistent with our
CLOUDY simulation, indicating that the arc-like structure needs additional
ionizing source besides AGN. Overall, there are various possibilities for the
origin of the ionized gas in addition to RPS, such as outflow and tidal
stripping during galaxy interactions.

##  7 Acknowledgments

We sincerely thank the anonymous referee for thorough and helpful comments
that significantly improved the clarity of our work. We thank Dr. Chong Ge for
helpful discussion. We thank Dr. Enrico Di Teodoro for suggestions on fitting
the disk with BBarolo. J.W. acknowledges the National Key R&D Program of China
(Grant No. 2023YFA1607904). We acknowledge the NSFC grants 12033004, 12333002,
12221003 and the science research grants from CMS-CSST-2021-A06. This work is
supported by the Fund (KP202105) from the Key Laboratory of Polar Science,
MNR, Polar Research Institute of China. Q.Z. acknowledges the support from the
China Postdoctoral Science Foundation (2023M732955). This research has made
use of the services of the ESO Science Archive Facility. Based on observations
collected at the ESO under program 0106.B0564(A). This research used
observations made with the NASA/ESA Hubble Space Telescope, and obtained from
the Hubble Legacy Archive, which is a collaboration between the Space
Telescope Science Institute (STScI/NASA), the Space Telescope European
Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre
(CADC/NRC/CSA).

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# Car-Driver Drowsiness Assessment through 1D Temporal Convolutional Networks

Francesco Rundo  ADG Central R&D Division
STMicroelectronics
Catania, Italy
<EMAIL_ADDRESS>Concetto Spampinato  Perceive Lab
University of Catania
Catania, Italy
<EMAIL_ADDRESS>Michael Rundo  Perceive Lab
University of Catania
Catania, Italy
<EMAIL_ADDRESS>
###### Abstract

Recently, the scientific progress of Advanced Driver Assistance System
solutions (ADAS) has played a key role in enhancing the overall safety of
driving. ADAS technology enables active control of vehicles to prevent
potentially risky situations. An important aspect that researchers have
focused on is the analysis of the driver’s attention level, as recent reports
confirmed a rising number of accidents caused by drowsiness or lack of
attentiveness. To address this issue, various studies have suggested
monitoring the driver’s physiological state, as there exists a well-
established connection between the Autonomic Nervous System (ANS) and the
level of attention. For our study, we designed an innovative bio-sensor
comprising near-infrared LED emitters and photo-detectors, specifically a
Silicon PhotoMultiplier device. This allowed us to assess the driver’s
physiological status by analyzing the associated PhotoPlethysmography (PPG)
signal.Furthermore, we developed an embedded time-domain hyper-filtering
technique in conjunction with a 1D Temporal Convolutional architecture that
embdes a progressive dilation setup. This integrated system enables near real-
time classification of driver drowsiness, yielding remarkable accuracy levels
of approximately 96%.

###### Index Terms:

Drowsiness, Deep learning, D-CNN, Deep-LSTM, PPG (PhotoPlethySmography)

##  I Introduction

In the medical domain, the term ”drowsiness” refers to a state characterized
by a reduced level of alertness and a tendency to sleep. The advancement of
technology has motivated researchers to develop effective methods for
detecting critical levels of driver drowsiness, aiming to prevent serious road
traffic accidents. Numerous studies have extensively explored the correlation
between attention level and Heart Rate Variability (HRV) [1]. HRV serves as an
indicator of the autonomic regulation of the heart and can be derived from the
frequency analysis of the ElectroCardioGraphy (ECG) or PhotoPlethysmoGraphic
(PPG) signals obtained from the subject. Essentially, HRV reflects the
variability in the time intervals between consecutive heartbeats,
predominantly influenced by the dynamic interplay between the Autonomous
Nervous System (ANS) and the heart [1]. Recent scientific investigations have
focused on analyzing the relationship between drowsiness and ANS activity
through HRV. In this study, we propose an innovative pipeline for evaluating
the attention level of car drivers using PPG signals. The organization of this
paper is as follows: Section II presents an overview of related works. In
Section III, we provide detailed information about the hardware framework
employed to acquire the PPG signals, along with the associated processing
pipeline. Section IV describes the Deep Learning framework utilized for
classifying the collected PPG waveforms. Finally, we present the experimental
results and discuss future avenues of research.

##  II Related Works

Numerous studies have focused on utilizing physiological signals to evaluate
the driver’s condition. In a study by Vicente et al. [1], the authors employed
Electrocardiography (ECG) signals to classify the driver’s drowsiness status.
However, ECG signals are susceptible to artifacts that can distort the
accurate acquisition of Heart Rate Variability (HRV). To mitigate this issue,
the authors proposed a pipeline to filter and stabilize the ECG signal. Many
existing approaches concentrate on detecting the attention level through the
analysis of Lower-Frequency (LF) and Higher-Frequency (HF) features. In a
study by Szypulska et al. [2], the authors developed a drowsiness detection
system that assessed fatigue and sleep onset by examining the LF/HF ratio
derived from frequency analysis of the ECG’s R-R tachogram. The results
demonstrated the efficacy of this approach in Advanced Driver Assistance
Systems (ADAS) applications. However, these methods require the acquisition of
ECG signals for HRV analysis. Traditional ECG signal acquisition involves the
configuration of Einthoven’s Triangle [3], necessitating the driver’s contact
with three specific electrodes to ensure accurate signal acquisition.
Consequently, this introduces challenges in maintaining the robustness of the
ECG signal sampling system, which in turn affects the quality of the derived
HRV signal [3]. In light of these challenges, recent studies have shifted
towards the utilization of PhotoPlethysmoGraphic (PPG) signals as an
alternative to ECG signals [4]. Unlike ECG signals, PPG signals only require a
single contact point on the subject’s skin for accurate sampling [4].In recent
years, Deep Learning (DL) approaches have gained significant attention due to
their effectiveness in estimating a subject’s drowsiness. For instance,
studies by Hong et al. [5] and Alshaqaqi et al. [6] propose DL architectures
to track changes in eye state for estimating driver drowsiness. However, these
approaches face challenges in recording video sequences under adverse
conditions, such as varying illumination and occlusions.Driven by the rapid
advancements in Machine Learning, researchers have developed effective
architectures for classifying the driver’s physiological status. Cheon et al.
[7] introduced a pipeline that addresses the classification problem of driver
drowsiness by utilizing a variety of sensors on the steering wheel.
Additionally, Choi et al. [8] employed a Multimodal Deep Learning model to
recognize the driver’s vigilance level by analyzing both visual and
physiological data. While previous methods have shown impressive results, they
often involve substantial computational costs, limiting their applicability on
resource-constrained automotive-grade devices.

Figure 1: The proposed PPG sampling system. TABLE I: Low-Pass and High-Pass
Filter Design for the PPG Signal

Type | Frequency pass [Hz] | Frequency stop [Hz] | Passband Attenuation [dB] | Stopband Attenuation [dB]
---|---|---|---|---
LP | 4.8 | 10 | 0.001 | 100
HP | 1 | 0.3 | 0.01 | 40

##  III Methods and Materials

In this investigation, our focus was on developing a system to capture the PPG
signal of car drivers, enabling us to evaluate and monitor their corresponding
attention levels. The PPG signal is a non-invasive approach widely used for
analyzing the heart’s pulse rate. By extracting features from the PPG signal,
we can monitor various factors such as heart pulse, respiratory rate, as well
as vascular and cardiac disorders [4].The PPG waveform consists of two main
components: the pulsatile ’AC’ signal, which represents the cardiac-
synchronous changes in blood volume, and the ’DC’ component, which is
influenced by the processes of respiration and thermo-regulation. During a
cardiac cycle, as the heart pumps blood towards the peripheral regions of the
body, it generates pressure that causes the arteries and arterioles in the
subcutaneous tissue to expand. This expansion, in turn, results in an increase
in blood volume. To capture these changes in volume, we utilize a specialized
device equipped with a light-emitting component and a detector that is placed
on the skin. By illuminating the skin and measuring the amount of back-
scattered light received by the detector [4], we can effectively detect the
heart’s pressure pulse, which is manifested as a peak in the PPG waveform. In
Fig. 1, the process underlying PPG waveform(s) formation is reported. To
perform the PPG acquisition, we used the PPG sampling device composed of the
Silicon Photomultiplier sensor [9, 10]. The proposed PPG probes comprises an
array device, called Silicon Photomultipliers (SiPMs) [11], characterized by a
total area of $4.0\times 4.5$ $mm^{2}$ and $4871$ square microcells with $60$
µm pitch. The devices present a geometrical fill factor of $67.4\%$ and are
packaged in a surface mount housing (SMD) with about $5.1\times 5.1$ $mm^{2}$
total area [4].For the PPG signal acquisition system, we employed a Pixelteq
dichroic bandpass filter with specific characteristics. The filter was
designed with a pass-band centered at approximately $540$ nm and a Full Width
at Half Maximum (FWHM) of $70$ nm. Its optical transmission in the pass-band
range exceeded $90-95\%$. To secure the filter onto the SMD package, we used a
Loctite 352TM adhesive. The PPG detector comprises a light emitter and a
detector based on advanced technology. We utilized OSRAM LT M673 LEDs, which
integrate InGan technology, in a compact SMD package. These LEDs offer a wide-
angle view of 120° and cover an area of $2.3\times 1.5$ $mm^{2}$. With a
spectral bandwidth of 33 nm, they emit lower power in the standard range.To
optimize the functionality of the PPG probe, we designed a printed circuit
board (PCB) that incorporates a user-interface based on National Instruments
(NI) instrumentation. The PCB features a 4V portable battery, power management
circuits, a conditioning circuit for the output signals of the SiPMs, as well
as multiple USB connectors for PPG probes and corresponding SMA output
connectors. In our previous works [4, 11], we provided additional information
regarding the hardware setup for PPG signal acquisition. The PPG Sensor Probe
consists of the SiPM sensor and the corresponding LEDs. To manage the power
consumption of the SiPM device, we implemented a Power Management Circuit [9,
10, 11]. For PPG signal acquisition, we placed the PPG sensor probe on the
car’s steering wheel. The driver was instructed to maintain their hand in
contact with the probe to trigger the signal. The acquired PPG raw signal was
processed internally by the NI device, which includes multiple 24-bit ADCs.
Furthermore, the NI device is equipped with a Windows-based operating system
and utilizes the LabView software framework [4]. Figure 2 illustrates the
pipeline employed to process the PPG signal using the NI device. We developed
a LabView-based algorithm to filter the raw PPG data, implementing both low-
pass and high-pass FIR (Finite Impulse Response) filters. Additionally, we
computed the first and second derivatives of the PPG signal as part of the
preprocessing step, enabling the evaluation of the minimum and maximum
extremes for each waveform. The rendered PPG signal was displayed on the
monitor connected to the NI device, as depicted in Figure 2. More detailed
descriptions regarding the NI device and LabView software framework can be
found in our previous publications [4, 9]. For the purpose of this study, we
utilized the MATLAB framework to process the PPG raw data, as outlined in the
validation session.

Figure 2: The proposed driver drowsiness monitoring pipeline.

###  III-A The Hyper Filtering Layers

To mitigate the impact of motion and noise artifacts in the acquired PPG
signal, we employed a frequency filtering approach and developed a signal
stabilization algorithm. In our bio-inspired pipeline, which has shown
promising results in our previous works [4], [9, 10, 11], we utilized a series
of FIR filters to implement a low-pass and high-pass filtering scheme within
the frequency range of $1-10$ Hz. This effectively eliminated unwanted noise
components, including the interference caused by the 50 Hz power line
frequency. To further enhance the robustness of the pipeline, we introduced a
set of hyper-filtering layers. The objective was to explore the potential of
hyperspectral imaging concepts in the context of PPG signal processing.
Drawing inspiration from Chang’s work [12], we adapted the concept of
hyperspectral imaging to analyze 1D signals, specifically the PPG signal.
Hyperspectral imaging is a technique used to capture visual information from
the entire electromagnetic spectrum, allowing for detailed spectral analysis
of each pixel in an image. While traditionally applied to visual imagery for
object recognition and material identification, we explored its application to
1D signals. Our objective was to investigate whether collecting information
from multiple frequency ranges of the PPG signal, akin to the concept of
hyperspectral imaging, could provide more comprehensive features related to
the driver’s attention level. Instead of applying a single set of filters
(e.g., low-pass and high-pass), we analyzed a range of frequencies within the
1-10 Hz range to characterize the individual PPG waveforms. Through our
investigation, we discovered that the informative frequency range for
attention assessment fell within this range. To further enhance the hyper-
filtering process, we explored the possibility of subdividing the frequency
range into sub-intervals. This subdivision aimed to simulate a hyper-spectral
process, where each sub-interval captured specific frequency components
related to attention. Consequently, we incorporated both low-pass and high-
pass filters in our configuration, implementing two layers of hyper-filtering.
In the hyper low-pass filtering layer, we adjusted the frequencies in the low-
pass part while keeping the cutoff frequency of the high-pass filter constant,
and vice versa. To ensure optimal performance and minimize the introduction of
noise artifacts, we opted to use Butterworth filters in both layers of hyper-
filtering [13, 14]. To determine the optimal number of sub-intervals within
the 1-10 Hz frequency range, we employed a ”try-and-error” approach combined
with heuristic tests. Our goal was to strike a balance between computational
load and discriminative capacity, ensuring that the sub-intervals effectively
captured the relevant frequency components while avoiding excessive
computational complexity. Through iterative experimentation and analysis, we
determined that a total of 11 sub-intervals provided the most favorable
results. Once subdividing the frequency into $11$ sub-bands, we designed a
Reinforcement Learning (RL) algorithm. The implementation details of this
approach are reported in the following items:

  * •

We defined an action $a_{t}$ as the sub-band frequency selected in the range
reported in Table I and according to the type of filtering (low-pass or high-
pass);

  * •

an Agent is defined selecting the action $a_{t}$

  * •

We defined a next state $S_{t+1}$ as a set of pre-processed signals obtained
collecting the value of each input PPG samples (in a windows of 5 sec sampling
at 1 Khz as sampling frequency) of the filtered PPG raw signal at specific
sub-band frequency of the action $a_{t}$;

  * •

We define an environment Reward as $R(.|s_{t},a_{t})$ i.e., a measure of
drowsiness of the car driver. We indicated as $R(.|s_{t},a_{t})$ the distance
of the output of the deep learning system (regression layer plus SoftMax
classification) with respect car-driver’s level of attention.

We determined the optimal policy $P_{o}$ that minimizes the cumulative
discount reward by applying the following formula:

$P_{o}=argmax_{P_{o}}\ E\left[\sum_{t\geq
0}{\gamma^{t}R\left(.\middle|s_{t},a_{t}\right)|P_{o}\ }\right]$ (1)

Where ${\gamma}$ is a proper discounted coefficient in (0,1). In order to
evaluate the the goodness of a state st and the goodness of a state-action
couple $(s_{t},a_{t})$, we denoted the Value function and the Q-value function
respectively:

$V^{P_{0}}(s_{t})=E\left[\sum_{t\geq
0}{\gamma^{t}R\left(.\middle|s_{t}\right)|P_{o}\ }\right]$ (2)
$Q^{P_{0}}(s_{t},a_{t})=E\left[\sum_{t\geq
0}{\gamma^{t}R\left(.\middle|s_{t},a_{t}\right)|P_{o}\ }\right]$ (3)

To determine the optimal set of sub-band frequencies for each hyper-filtering
layer, we employed Q-learning algorithms [15]. By utilizing this reinforcement
learning technique, we iteratively trained our system to select the most
suitable frequencies based on the observed states and rewards. The results of
the RL algorithm, which specify the frequency sets for the two hyper-filtering
layers, are summarized in Table II and III. Formally, let be
${W}_{{PPG}}^{i}\left({t}_{k}\right)$ the single segmented waveform of each
hyper-filtered PPG time-series (i.e. by using a specific frequency values both
in low-pass and high-pass). To capture the dynamic characteristics of the
hyper-filtered PPG signals, we computed signal patterns for each sample
$s(t_{k})$ in the waveform. These signal patterns were derived from analyzing
the variations in the hyper-filtered PPG time-series. By examining the changes
in signal samples, we obtained a large dataset of signal patterns
corresponding to each hyper-filtered PPG signal. The size of this dataset
matched the number of filtering frequencies, which was 11 as indicated in
Table II and III. To detect the car driver’s drowsiness, we extracted a
substantial amount of signal patterns from the hyper-filtered PPG time-series.
These signal patterns were collected over a time window of 4 seconds, allowing
us to capture relevant temporal variations in the PPG signal. The extracted
signal patterns served as input to our designed Deep Learning block. In Fig.
3, we provide a visualization of the generated signal patterns obtained from
the temporal variations of the samples $s(t_{k})$ for each hyper-filtered
signal. These signal patterns effectively capture the unique characteristics
of the PPG signal and will be utilized to characterize the driver’s level of
attention through the subsequent Deep Learning block.

Figure 3: Some instances of the hyper-filtered PPG generated patterns

Figure 4: The proposed driver drowsiness monitoring pipeline.

###  III-B The Deep Learning block

As mentioned earlier, we developed a customized Deep 1D Temporal Dilated
Convolutional Neural Network (1D-CNN)[16] specifically tailored for our task.
This network is designed to process the signal patterns $s(t_{k})$ derived
from each hyper-filtered PPG signal. The overall architecture of the proposed
model can be seen in Fig.4. The key innovation of our architecture lies in the
utilization of dilated causal convolution layers. The term ”causal” indicates
that the activation at a given time step depends only on the preceding time
step, enhancing the temporal modeling capability of the network. The 1D-CNN
comprises a series of residual blocks, with a total of 12 blocks stacked
together. Each block consists of a dilated convolution layer, followed by
batch normalization, ReLU activation, and spatial dropout. The dilated
convolution layer performs a convolution operation with a $3\times 3$ kernel.
The dilation factor progressively increases for each block, starting from a
value of 2. To complete the pipeline, a two-class softmax layer is added as
the final output of the 1D-CNN. This layer predicts the driver’s drowsiness
level based on the input signal-patterns generated from the hyper-filtered PPG
signals. By leveraging the hierarchical structure of the 1D-CNN and
incorporating dilated causal convolutions, our proposed architecture
demonstrates the capability to effectively process the hyper-filtered PPG
signal-patterns and accurately predict the driver’s drowsiness level.

TABLE II: Hyper Low-Pass Filtering Setup (in Hz)

F | f1 | f2 | f3 | f4 | f5 | f6 | f7 | f8 | f9 | f10 | f11
---|---|---|---|---|---|---|---|---|---|---|---
HP | 0.5 | / | / | / | / | / | / | / | / | / | /
LP | 0 | 1.4 | 2.9 | 2.5 | 3.8 | 3.9 | 4 | 4.5 | 5 | 5.3 | 6.9

TABLE III: Hyper High-Pass Filtering Setup (in Hz)

F | f1 | f2 | f3 | f4 | f5 | f6 | f7 | f8 | f9 | f10 | f11
---|---|---|---|---|---|---|---|---|---|---|---
HP | 0.5 | 1.2 | 2.6 | 2.7 | 3.3 | 3.5 | 4 | 4.4 | 5 | 5.7 | 6.4
LP | 7 | / | / | / | / | / | / | / | / | / | /

##  IV Experimental Results

In order to evaluate the performance of our proposed pipeline, we conducted
experiments using a dataset consisting of PPG measurements obtained from
seventy patients. The age range of the recruited patients was between 21 and
70 years. To validate the effectiveness of our 1D-CNN framework, we collected
PPG signals under both drowsy and wakeful conditions. These conditions were
supervised and confirmed by experts in physiology, who also acquired the
corresponding ECG signals to ensure the subject’s level of awareness. Previous
studies have shown that EEG signals can provide insights into the subject’s
level of attention by analyzing the presence of alpha and beta waves [17].
Therefore, we collected PPG signals alongside EEG signals to establish the
correlation between physiological signals and drowsiness level. The PPG
signals were collected at a sampling frequency of 1 KHz, and the data
collection duration was set to 5 minutes. To perform the experiments, we
divided the dataset into a training set (70% of the total dataset) and a
testing/validation set (30% of the collected data). This division allowed us
to assess the robustness and efficiency of our proposed approach.
Additionally, we utilized specially designed PPG signals that represented both
high and low attention levels to further evaluate the pipeline’s
discriminative capabilities. In Table IV, we present the performance of our
proposed pipeline compared to other deep learning-based approaches [13]. The
table showcases the effectiveness of our pipeline in accurately classifying
different attention levels. Furthermore, in Fig. 5, we provide the dynamic
learning error of the 1D-CNN, which demonstrates the network’s ability to
capture the correlation between the hyper-filtered PPG samples and the
corresponding attention level of the monitored subjects. This graph
illustrates the progressive improvement of the network’s performance during
the learning process. Overall, the experimental results validate the
robustness and efficiency of our proposed pipeline in accurately identifying
and classifying different levels of attention based on the hyper-filtered PPG
samples.

Figure 5: The 1D-CNN Learning Loss dynamic. TABLE IV: Benchmark Performance of the Proposed Pipeline Table | Car Driver Attention Estimation
---|---
| Drowsy Driver | Wakeful Driver
Proposed | $98.71\%$ | $99.03\%$
[13] | $96.50\%$ | $98.40\%$

##  V Conclusion and Discussion

Developing a deep learning architecture for classifying driver’s attention
levels is a highly challenging task, and in our research, we have successfully
proposed a 1D-CNN model that outperforms other deep learning approaches in
this domain. One major advantage of our method is that it does not require the
acquisition of ECG or EEG signals to assess the driver’s drowsiness level.
This eliminates the issues associated with motion and noise artifacts that can
affect the analysis of HRV. Additionally, our approach avoids the need for
frequency domain analysis of PPG data, which is required by other HRV-based
methods. We have emphasized the benefits of acquiring PPG signals in a vehicle
environment by strategically placing embedded sensors on the steering wheel.
The experimental results also demonstrate that our pipeline achieves accurate
classification of the driver’s attention level with only one minute of PPG
acquisition, significantly shorter than the 10-12 minutes typically required
by HRV-based methods. Furthermore, our pipeline can be combined with other
promising solutions, such as imaging methods in the visible and infrared
spectrum, to further enhance accuracy. The effectiveness of the deep learning
framework is evident in its ability to learn signal-patterns generated from
the pre-processing of acquired PPG waveforms. Our results clearly demonstrate
the effectiveness of the proposed pipeline in assessing the driver’s
drowsiness level, as evidenced by the reliable performance on the collected
dataset of 70 recruited subjects. Currently, we are in the process of porting
the implemented deep learning algorithm to an embedded system based on the
STMicroelectronics SoC STA1295 ACCORDO 5, which offers powerful computing
capabilities with ARM Cortex cores and a dedicated GPU for graphics and image
processing tasks. We are also porting the PPG data filtering and stabilization
pipeline to a hardware/software environment based on the SPC5x CHORUS
microcontroller technology provided by STMicroelectronics. In our future work,
we plan to explore additional advanced solutions based on deep architectures
that have shown promising results in other applications. This includes
investigating nonlinear models and oral-based approaches, as mentioned in our
previous works [18, 19]. By continuously expanding and refining our pipeline,
we aim to make significant advancements in assessing and monitoring driver
attention levels for enhanced road safety.

## Acknowledgment

The authors thank the physiologists belonging to the Department of Biomedical
and Biotechnological Sciences (BIOMETEC) of the University of Catania, who
collaborated in this work in the context of the clinical study Ethical
Committee CT1 authorization n.113 / 2018 / PO. This research was funded by the
National Funded Program 2014-2020 under grant agreement n. 1733, (ADAS +
Project).

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