11institutetext:  Kavli Institute for Cosmology, University of Cambridge,
Madingley Road, Cambridge, CB3 0HA, UK
22institutetext: Cavendish Laboratory, University of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, UK
33institutetext: Department of Physics and Astronomy, University College
London, Gower Street, London WC1E 6BT, UK
44institutetext: Centre for Astrophysics Research, Department of Physics,
Astronomy and Mathematics, University of Hertfordshire, Hatfield AL10 9AB, UK
55institutetext: Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126
Pisa, Italy
66institutetext: Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique
de Paris, 98 bis bd Arago, 75014 Paris, France
77institutetext: European Southern Observatory, Karl-Schwarzschild-Strasse 2,
85748 Garching, Germany
88institutetext: Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km.
4, 28850- Torrejón de Ardoz, Madrid, Spain
99institutetext: European Space Agency (ESA), European Space Astronomy Centre
(ESAC), Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid,
Spain 1010institutetext: School of Physics, University of Melbourne, Parkville
3010, VIC, Australia
1111institutetext: ARC Centre of Excellence for All Sky Astrophysics in 3
Dimensions (ATRO 3D), Australia
1212institutetext: Department of Physics, University of Oxford, Denys
Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
1313institutetext: Harvard University, Center for Astrophysics $|$ Harvard &
Smithsonian, 60 Garden St., Cambridge 2138, USA
1414institutetext: Steward Observatory, University of Arizona, 933 N. Cherry
Avenue, Tucson, AZ 85721, USA
1515institutetext: Department of Physics and Astronomy, The Johns Hopkins
University, 3400 N. Charles St., Baltimore, MD 21218
1616institutetext: AURA for European Space Agency, Space Telescope Science
Institute, 3700 San Martin Drive. Baltimore, MD, 21210
1717institutetext: Department of Astronomy, University of Wisconsin-Madison,
475 N. Charter St., Madison, WI 53706 USA
1818institutetext: Department of Astronomy and Astrophysics, University of
California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
1919institutetext: NSF’s National Optical-Infrared Astronomy Research
Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719, USA
2020institutetext: NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7,
Canada

#  JADES: A large population of obscured, narrow line AGN at high redshift

Jan Scholtz , Corresponding authors<EMAIL_ADDRESS>
1122 Roberto Maiolino 112233 Francesco D’Eugenio Emma Curtis-Lake 112244
Stefano Carniani 55 Stephane Charlot 66 Mirko Curti 77 Maddie S. Silcock 44
Santiago Arribas 88 William Baker 1122 Rachana Bhatawdekar 99 Kristan Boyett
10101111 Andrew J. Bunker 1212 Jacopo Chevallard 1212 Chiara Circosta 99
Daniel J. Eisenstein 1313 Kevin Hainline 1414 Ryan Hausen 1515 Xihan Ji 1122
Zhiyuan Ji 1414 Benjamin D. Johnson 1313 Nimisha Kumari 1616 Tobias J. Looser
1122 Jianwei Lyu 1414 Michael V. Maseda 1717 Eleonora Parlanti 55 Michele
Perna 99 Marcia Rieke 1414 Brant Robertson 1818 Bruno Rodríguez Del Pino 99
Fengwu Sun 1414 Sandro Tacchella 1122 Hannah Übler 1122 Giacomo Venturi 55
Christina C. Williams 1919 Christopher N. A. Willmer 1414 Chris Willott 2020
Joris Witstok 1122

We present the identification of 42 narrow-line active galactic nuclei (type-2
AGN) candidates in the two deepest observations of the JADES spectroscopic
survey with JWST/NIRSpec. The spectral coverage and the depth of our
observations allow us to select narrow-line AGNs based on both rest-frame
optical and UV emission lines up to z=10. Due to the metallicity decrease of
galaxies, at $z>3$ the standard optical diagnostic diagrams (N2-BPT or
S2-VO87) become unable to distinguish many AGN from other sources of
photoionisation. Therefore, we also use high ionisation lines, such as He ii
$\lambda$4686, He ii $\lambda$1640, [Ne iv] $\lambda$2422, [Ne v]
$\lambda$3420, and N V $\lambda$1240, also in combination with other UV
transitions, to trace the presence of AGN. Out of a parent sample of 209
galaxies, we identify 42 type-2 AGN (although 10 of them are tentative),
giving a fraction of galaxies in JADES hosting type-2 AGN of about $20\pm 3$%,
which does not evolve significantly in the redshift range between 2 and 10.
The selected type-2 AGN have estimated bolometric luminosities of
$10^{41.3-44.9}$ erg s-1 and host-galaxy stellar masses of $10^{7.2-9.3}$ M⊙.
The star formation rates of the selected AGN host galaxies are consistent with
those of the star-forming main sequence. The AGN host galaxies at z=4-6
contribute $\sim$8-30 % to the UV luminosity function, slightly increasing
with UV luminosity.

###### Key Words.:

Galaxies: active, Galaxies: high-redshift, Galaxies: ISM

##  1 Introduction

It has been widely accepted that supermassive black holes (SMBHs) reside in
the centre of most (perhaps all) massive galaxies. During their accretion
phases, SMBHs are observed as active galactic nuclei (AGN; Rees et al., 1982;
Lynden-Bell, 1969; Soltan, 1982; Merloni et al., 2004). The tight correlation
between a SMBH mass and host-galaxy bulge properties (such as velocity
dispersion and mass) at $z\sim 0$ indicates a strong connection between the
growth of a SMBH and its host galaxy (e.g., Magorrian et al., 1998; Kormendy &
Ho, 2013). Furthermore, (radiative and mechanical) feedback from AGNs is a key
ingredient of galaxy evolution, as AGNs can inject a significant amount of
energy into the interstellar and circumgalactic medium (ISM and CGM,
respectively) of their host galaxies. AGN feedback is necessary to reproduce
key galaxy properties such as: colour bi-modality, galaxy sizes, and a broader
range of specific star formation rates and enrichment of the intergalactic
medium (IGM) by metals, as well as the high-mass drop-off of the stellar mass
function compared to the halo mass function (e.g., Silk & Rees, 1998; Di
Matteo et al., 2005; Alexander & Hickox, 2012; Dubois et al., 2013b, a;
Vogelsberger et al., 2014; Hirschmann et al., 2014; Crain et al., 2015; Segers
et al., 2016; Beckmann et al., 2017; Harrison, 2017; Choi et al., 2018;
Scholtz et al., 2018).

The identification and study of AGN at high redshift is essential to
understanding not only the co-evolution of SMBHs and galaxies, but also the
formation of galaxies at early epochs. Although over the past 15 years there
has been significant progress in the identification of active SMBHs at high
redshift ($z>3$) (e.g. Merloni et al., 2010; Bongiorno et al., 2014;
Trakhtenbrot et al., 2017; Mezcua et al., 2018; Lyu et al., 2022), the
majority of these detections have been limited to bright quasars identified in
large-volume ground-based surveys (e.g., Bañados et al. 2016; Shen et al.
2019, see Inayoshi et al. 2020; Fan et al. 2022 for a review). These include
the most distant identified quasars at $z\sim 7.5$ (Bañados et al., 2018; Yang
et al., 2020; Wang et al., 2021).

With the launch of the James Webb Space Telescope (JWST; Gardner et al., 2023;
Rigby et al., 2023), we now have access to rest-frame UV-to-optical emission
lines of galaxies up to $z\sim 12$. These lines allow us to identify and study
AGNs, even at low masses and luminosities, via optical and UV diagnostic
diagrams. A number of AGN candidates have already been identified by
performing Spectral Energy Distribution (SED) analyses of broad-band
photometry from NIRCam and MIRI aboard JWST (Furtak et al., 2022; Onoue et
al., 2023; Barro et al., 2023; Yang et al., 2023a; Bogdan et al., 2023;
Juodžbalis et al., 2023; Lyu et al., 2023; Yang et al., 2023b). Significant
progress has been made using deep spectroscopy from JWST/NIRSpec (Böker et
al., 2022; Jakobsen et al., 2022) and the JWST/NIRCam grism, tracing the
presence of a broad line region (BLR; Furtak et al., 2023; Greene et al.,
2023; Harikane et al., 2023; Kocevski et al., 2023; Maiolino et al., 2023b, a;
Matthee et al., 2023; Onoue et al., 2023; Übler et al., 2023). These
observations revealed a previously unseen population of AGN at z$>4$, and out
to z$\sim$11, with estimated black hole masses (MBH) in the range $\rm 10^{6}$
to $\rm 10^{8}\leavevmode\nobreak\ M_{\odot}$ and bolometric luminosities $\rm
10^{44}-10^{45}\leavevmode\nobreak\ erg\leavevmode\nobreak\ s^{-1}$. These
black hole masses and luminosities are 2–3 orders of magnitude lower than
those inferred for quasars at the same redshifts (Mazzucchelli et al., 2023;
Zappacosta et al., 2023).

However, identification of narrow-line (i.e. type-2, as opposed to type-1 AGN
showing BLR emission) AGN has remained unexplored with JWST data. This has
been the main AGN identification tool at low redshift, however, so far has had
limited success at high redshift. The reason is most likely the rapid
evolution of the metallicity and ionisation parameter at z¿3 towards more
metal poor and higher ionisation parameter (Hirschmann et al., 2022; Curti et
al., 2023b, a; Tacchella et al., 2023; Trump et al., 2023). Indeed, Harikane
et al. (2023); Kocevski et al. (2023); Maiolino et al. (2023a); Übler et al.
(2023) have shown that the type-1 AGN found by JWST have narrow line ratios on
the classical emission-line diagnostic diagrams (such as BPT; Baldwin et al.
1981a; Kauffmann et al. 2003; Kewley et al. 2013 and VO87; Veilleux &
Osterbrock 1987) that overlap with the local SF sequence, and not with the
region occupied by nearby and low-z AGN. This displacement has been predicted
by photoionization models for low metallicity AGN (Groves et al., 2006;
Nakajima & Maiolino, 2022). Additionally, Feltre et al. (2016) and Gutkin et
al. (2016) ran photo-ionisation grid models to assess the effect of low
metallicity and high ionisation parameter on rest-frame optical and UV
emission lines, and found that the nebular emission of star-forming galaxies
at high redshift become similar to that of AGN, further complicating the AGN
selection via narrow emission line diagnostics. The identification of type-2
AGN at high redshift can still rely on high ionization lines, such as the [Ne
iv] $\lambda$2424 (with ionisation potential $>$63.45eV) detected by
Brinchmann (2023) in a galaxy at z = 7.66. Luckily, the identification of
type-2 AGN with high metallicity through standard BPTs is still possible (see
Perna et al. 2023)

Despite these newly discovered AGN being far less luminous than the previously
known quasar population and being hosted in galaxies with lower masses, they
can play a major role in shaping galaxy evolution (via positive and negative
feedback; Koudmani et al., 2021, 2022) and could potentially significantly
contribute to the reionisation of the Universe. Indeed, we have already
observed these processes in GN-z11 (Bunker et al., 2023b) believed to host an
AGN (Maiolino et al., 2023b) with detected outflow in the system, while
Scholtz et al. (2023) observed heating and ionisation of the circum-galactic
medium around this unique galaxy. These examples show the presence of AGN
feedback at high z, which can explain the early emergence of quiescent
galaxies and high burstiness of star formation (Carnall et al., 2023b, a;
Endsley et al., 2023; Looser et al., 2023c, a; Dome et al., 2023; Strait et
al., 2023); indeed, Gelli et al. (2023) showed that supernovae feedback is not
sufficient to rapidly suppress the star formation in some of these rapidly
quenched systems.

In this paper, we leverage two of the deepest spectroscopic observations from
JWST Advanced Deep Extragalactic Survey (JADES, Proposal ID: 1210 & 3215;
Bunker et al. 2023a; Curtis-Lake et al. 2023; Eisenstein et al. 2023b, a;
Robertson et al. 2023). Using this deep spectroscopy with JWST/NIRSpec, we aim
to identify type-2 AGN using deep spectroscopy with JWST/NIRSpec in galaxies
with stellar masses M${}_{*}\sim 10^{6.5}-10^{9.5}$ M⊙ from redshift z = 1 and
out to the highest redshifts for which rest-frame UV and optical nebular lines
are accessible to NIRSpec (z$\sim$12.0). We reassess the demarcation lines
between AGN and star-forming galaxies at high redshift and further refine
selection criteria using the photo-ionisation modelling introduced by Feltre
et al. (2016) and Nakajima & Maiolino (2022).

The paper is organized as follows. In Section 2, we describe the observations,
data reduction, spectral fitting procedures, and SED analysis to derive
stellar masses and star formation rates (SFRs). In Section 3 we identify AGN
using their narrow line properties of rest-frame UV and optical emission
lines. In Section 4, we estimate the properties of the AGN and their host
galaxies and discuss our results. In Section 5, we draw our conclusions.
Throughout this paper, we use the AB magnitude system and assume a flat
$\Lambda$CDM cosmology with $\Omega$m = 0.315 and H0 = 67.4 km/s/Mpc Planck
Collaboration et al. (2020) cosmology and a Chabrier (2003) initial mass
function.

##  2 Observations, Data reduction and Analysis

The observations of our targets were obtained as part of the JADES survey,
utilising the multi-object spectroscopic capabilities of the _JWST_ /NIRSpec
micro-shutter array (MSA; Jakobsen et al. 2022; Ferruit et al. 2022) across
two programmes: PID 1210 & PID 3215.

Five disperser/filter combinations were used: the low-resolution PRISM/CLEAR
($0.6<\lambda<5.3\leavevmode\nobreak\ \mu$m$,R=30-300$; Jakobsen et al.,
2022), the medium-resolution gratings G140M/F070LP, G235M/F170LP and
G395M/F290LP ($0.6<\lambda<5.3\leavevmode\nobreak\ \mu$m, $R=1,000$), and the
high-resolution grating G395H/F290LP ($2.8<\lambda<5.1\leavevmode\nobreak\
\mu$m, R=2,700). For the 1210 program, the observations consist of individual
visits with a per-visit duration of 33.6 ks for the prism, and of 8.4 ks for
each grating. Each galaxy was assigned a minimum of one and a maximum of three
visits depending on its priority (Bunker et al., 2023a). This resulted in a
maximum integration time of $\sim 100$ ks for the prism and of 25 ks for each
grating. The 3215 program consisted only of three configurations: PRISM/CLEAR,
G140M/F070LP, and G395M/F290LP, with a maximum resulting integration time of
168 ks, 42 ks, and 168 ks.

The observations were performed in the three-shutter nod mode, so that common
targets were observed in different shutters and different locations on the
detector. Therefore, each visit required a unique MSA configuration. Each
target allocation (performed using the eMPT tool; Bonaventura et al.
2023111https://github.com/esdc-esac-esa-int/eMPT_v1) was designed to maximise
the number of targets that have all the disperses/filters in common between
all the dither positions, however, not all targets have the full on source
integration time outlined above. In total, we observed 481 unique targets.

The MSA configurations have been designed to avoid spectral overlap for the
prism mode. However, since the spectra taken with the medium or high-
resolution gratings occupy a significantly larger portion of the detector, and
to avoid removing targets with overlapping spectra, we allowed spectral
overlap for these modes. The MSA configurations were, however, designed to
minimize the negative effect of spectral overlap on our science, since the
highest priority targets are not allowed to be contaminated by neighboring
spectra. We show examples of the acquired PRISM/CLEAR spectra in Figure 1.

Figure 1: Example of low-resolution PRISM/CLEAR spectrum of galaxy (JADES-NS-
GS-00022251) at z = 5.804 included in our parent sample. The spectrum is shown
in units of Fλ normalized to the [O iii] $\lambda$5008 peak. We highlight
major emission lines used in our analysis.

###  2.1 Data Reduction

The JWST/NIRSpec MSA observations were processed with the data reduction
pipeline of the ESA NIRSpec Science Operations Team (SOT) and the NIRSpec GTO
Team, further detailed description of the GTO pipeline will be presented in
Carniani et al. (in prep), and is summarised in Bunker et al. (2023a). Here,
we briefly summarize the procedure. We retrieve the level-1a data products
from the MAST archive and estimate the count-rate slopes per pixel, using the
unsaturated groups in the ramps. We remove any jumps in the ramps due to
cosmic rays by estimating the slope of each ramp. During this stage, we
perform the master dark and bias subtraction, as well as the flagging of
saturated pixels. The background subtraction is performed pixel by pixel by
combining the three nod exposures of each pointing. We note that for some
targets we excluded one of the 3-shutter nods in the background subtraction
stage as a serendipitous source contaminated the open shutters. We performed
the flat-field correction of the spectrograph optics and disperser corrections
on the 2D-dimensional cutouts of each of the three-shutter slits. The path
loss corrections are calculated assuming point sources and taking into account
the source location on the shutter.

Finally, the individual 2D maps are interpolated onto an irregular wavelength
grid for the PRISM/CLEAR observations (to avoid oversampling the line spread
function below 2 $\mu$m) and onto a regular grid for the gratings. We
extracted the 1D spectra from the 2D maps adopting a box-car aperture centered
on the relative position of the targets. We combined all 1D spectra and
removed the bad pixels by adopting a sigma-clipping approach.

###  2.2 Spectral fitting using PPXF

As the continuum is well detected in all our targets in the PRISM
observations, we employed pPXF (Cappellari, 2017, 2022) to fit the continuum
and emission lines simultaneously. As we are interested in inactive galaxies
or type-2 AGN, there is no contamination of the continuum by the AGN, making
pPXF an ideal tool for this analysis. The continuum is fitted as a linear
superposition of simple stellar-population (SSP) spectra, using non-negative
weights and matching the spectral resolution of the observed spectrum. As
input stellar templates we used the synthetic library of simple stellar
population spectra (SSP) from fsps (Conroy et al., 2009; Conroy & Gunn, 2010).
This library uses MIST isochrones (Choi et al., 2016) and C3K model
atmospheres (Conroy et al., 2019). We also used fit a 5th-order multiplicative
polynomial, to capture the combined effects of dust reddening, residual flux
calibration issues, and any systematic mismatch between the data and the input
stellar templates. We find that the NIRSpec/MSA data are fit adequately by
low-order polynomials. Increasing the polynomial degree does not improve the
fit results (as quantified by the value of the reduced $\chi^{2}$). To
simplify the fitting, any flux with a wavelength shorter than Lyman break is
manually set to 0. The full description of the pPXF fitting will be presented
in D’Eugenio et al. (in prep).

For the emission lines fitting we use the redshifts published in the Bunker et
al. (2023a), which used redshifts determined from the medium resolution
gratings available and PRISM spectra otherwise. All emission lines are modeled
as single Gaussian functions, matching the observed spectral resolution. We
use vacuum wavelengths for the emission lines throughout the paper. In order
to remove degeneracies in the fitting and reduce the number of free
parameters, the emission lines are split into four separate kinematic groups,
bound to the same redshift and _intrinsic_ broadening. These groups are as
follows:

  * •

UV lines with rest-frame $\lambda<3000$ Å.

  * •

The hydrogen Balmer series.

  * •

Non-hydrogen optical lines with rest-frame $\lambda<9000$ Å.

  * •

Near infra-red lines.

The stellar kinematics are tied to the Balmer line kinematics. For emission
line multiplets arising from the same level, we fixed the emission line ratio
to the value prescribed by atomic physics (e.g., [O iii] $\lambda$5008/[O iii]
$\lambda$4959 = 2.99). For multiplets arising from different levels, the
emission line ratio can vary. This is relevant for five multiplets: C iv
$\lambda\lambda$ 1548,1551, C iii] $\lambda\lambda$1907,1909, [O ii]
$\lambda\lambda$3727,3730 and [S ii] $\lambda\lambda$6718,6733. However, in
practice, the spectral resolution of the PRISM/CLEAR observations is
insufficient to resolve the individual lines in each multiplet. For this
reason, we model each of these as a single Gaussian in the PRISM observations.
In addition, as the He ii $\lambda$1640 and [O iii] $\lambda$$\lambda$1661,66
are blended together we are unable to use their measured fluxes from the PRISM
observations and instead, choose to use the R1000 observations. The fits are
performed in two steps. In the first run, we tie the kinematics as described
above, and identify robust detections (above 5-$\sigma$ significance) for re-
fitting. In the second run, we only fit for these detected lines, but allow
for their kinematics to vary independently from one another.

The R1000 gratings were fitted using the same procedure as for the PRISM
observations, by combining all three gratings and fitting them simultaneously.
However, we did not stack the spectra from different gratings, because this
would combine the highest-resolution end of one grating with the poorest-
resolution start of the next grating.

####  2.2.1 Fitting the UV lines

The high ionisation UV lines were excluded from the pPXF fitting as they are
extremely faint and blended in the PRISM data. Since the AGN high ionisation
UV lines are considerably fainter than the optical emission lines, we fitted
only objects that are well-detected in [OIII] or H$\alpha$ emission
(SNR$>10$).

We fitted the [Ne iv] $\lambda$2424, [Ne v] $\lambda$3420 and N V
$\lambda$1240 using QubeSpec’s222https://github.com/honzascholtz/Qubespec
fitting module. Each emission line was fitted using a single Gaussian
component and the continuum was fitted as a power law. This simplistic
approach is sufficient for describing a narrow range of the continuum around
an emission line of interest. As QubeSpec is a Bayesian code implemented using
emcee (Goodman & Weare, 2010), it is necessary to also supply priors on each
quantity. The peak of the Gaussian and continuum normalization are given a
log-uniform prior, while the FWHMs are set to a uniform distribution spanning
from the minimum resolution of the NIRSpec/MSA ($\sim$200 kms-1) up to a
maximum of 1200 km/s. The prior on the redshift was a normal distribution
centered on the redshift obtained from pPXF with a standard deviation of 300
km/s. This is done because as the high ionisation UV lines originate from
close to the accretion disc, there can be a significant velocity offset
between the low and high ionisation lines in an AGN. We report the fluxes of
each of these high ionisation lines in the Appendix in Table 5. Throughout
this work we derived upper limits as 3$\sigma$.

###  2.3 SED fitting using BEAGLE

In order to compare our selected AGN with the rest of the galaxy population,
it is necessary to measure the stellar masses and star-formation rates of our
sample. We use the full fitting of the slit-loss corrected PRISM spectra using
the BEAGLE tool (Chevallard & Charlot, 2016). We assume a delayed-exponential
star-formation history (SFH), while decoupling the current SFR from the
previous SFH by allowing a recent duration of 10Myr of constant star formation
to vary independently. A Chabrier (2003) initial mass function (IMF) with an
upper mass cut-off of 100 M⊙ was adopted using the updated Bruzual & Charlot
(2003) stellar population models described in Vidal-García et al. (2017a). We
define the total stellar mass as the mass currently locked into stars. This
definition accounts for the fraction of mass returned to the ISM during
stellar evolution. The SFRs of our objects are averaged over 10 million years.

We note that the SFRs of the whole sample estimated with BEAGLE are in
excellent agreement with those estimated from the dust-corrected Balmer lines
(H$\alpha$ and H$\beta$; see Curti et al. 2023b for more information).

###  2.4 Comparison to photoioinization models

At high redshifts, galaxies become more metal-poor and show an increasingly
higher ionisation parameter (see Hirschmann et al., 2022; Schaerer et al.,
2022; Cameron et al., 2023b; Curti et al., 2023b; Trump et al., 2023),
resulting in standard diagnostics diagrams used to identify AGNs at low
redshift becoming less useful (see Kewley et al., 2013, 2019; Hirschmann et
al., 2022). We, therefore, consider the photoionisation models initially
described in Feltre et al. (2016) and Gutkin et al. (2016), and updated with
more recent stellar spectra and with a better description of AGN cloud
microturbulence (Vidal-García et al., 2017b; Mignoli et al., 2019; Hirschmann
et al., 2019). These works consider a large grid of photoionisation models
computed using the CLOUDY code (Ferland et al., 2013) for star formation and
AGN narrow-line regions, and for various gas metallicities, dust content, and
ISM densities. From these model grids, we selected all models with
metallicities between 0.001 and 0.02 (corresponding to 0.06-1.3 solar) and a
dust-to-metal mass ratio of 0.3. This value is intermediate between the range
observed in the most metal-poor absorbers (e.g., Konstantopoulou et al., 2023)
and the Milky-Way value of 0.45. We consider all models with carbon-to-oxygen
abundance ratio in the range 0.38-1.00 solar to describe a variety of
different or less common star-formation grids. We further restrict the grids
to only include models with an IMF upper mass cut-off of 300 M⊙, similar to
the SED fitting above.

We also compare our observed emission line ratios with the models of Nakajima
& Maiolino (2022). They investigated the emission line ratios of star-forming
galaxies, AGN, PopIII stars, and Direct Collapse black holes (DCBHs) using the
Cloudy code and the BPASS stellar population models (Eldridge et al., 2017).
In this work, we only use the models for star-forming galaxies and AGN host
galaxies, as our objects do not have low enough metallicities to host either
PopIII or DCBHs (based on values from Curti et al., 2023b). We selected the
same metallicities, dust-to-metal mass ratio and IMF upper cutoff as for the
Gutkin et al. (2016) and Feltre et al. (2016) models.

Throughout this work, we present these models on our diagnostics plots
(Figures 2, 3, 4 and 6) as light blue and yellow circles for AGN and star-
formation, respectively. We will further discuss these points and use them to
redefine the selection of AGN based on to-be-introduced N2-BPT and S2-VO87
diagnostic diagrams in §3.1.

##  3 Selection of AGN in JADES deep spectroscopic data

In order to select AGN based on their narrow line properties we require a
3$\sigma$ detection of the following lines: H$\alpha$, H$\beta$, [O iii] and C
iii], and wavelength coverage of [S ii], [N ii] and UV and optical He ii and C
iv for the optical and UV line selection. Overall, this yields a sample size
of 110 and 99 sources for the programmes 1210 and 3215, respectively. We
define the line ratios used in this paper in Table 1 (see below) and we
summarise all emission lines, their wavelengths and ionisation potential in
the Appendix in Table 6. We consider a galaxy an AGN candidate if it appears
as an AGN in at least one diagnostic. Furthermore, we report the list of
selected AGN candidates, field, coordinates, redshift, the method with which
we detect it, and other notes in Table 2.

Table 1: Definitions of line ratios adopted throughout the paper. Diagnostics | Line Ratio
---|---
R3 | [O III]$\lambda$5008 / H$\beta$
N2 | [N II]$\lambda$6584 / H$\alpha$
S2 | [S II]$\lambda\lambda$6718,32 / H$\alpha$
Ne3O2 | [Ne III]$\lambda$3869 / [O II]$\lambda\lambda$3727,29
He2 | He II$\lambda$4686 / H$\beta$
C43 | C IV$\lambda\lambda$1549,51/C III]$\lambda\lambda$1906,08
C3He2 | C iii]$\lambda\lambda$1906,08 / He II$\lambda$1640
Ne4C3 | [Ne iv] $\lambda$$\lambda$2422,24/C III]$\lambda\lambda$1906,08
Ne5C3 | [Ne v] $\lambda$$\lambda$3427,29/C III]$\lambda\lambda$1906,08
N5C3 | N V $\lambda$$\lambda$1239,42/C III]$\lambda\lambda$1906,08

Table 2: List of AGN from JADES HST Deep selected in this work. Columns: ID, redshift, detection method, stellar mass, SFR and UV magnitude from BEAGLE, AGN bolometric luminosity and any other additional notes. We note that for bolometric luminosity the uncertainties are dominated by the systematical uncertainties (0.3-0.5 dex). ID | field | z | selection | log10 M∗ | SFR | MUV | log10 Lbol | Notes
---|---|---|---|---|---|---|---|---
|  |  | method | M⊙ | M⊙ yr-1 |  | ergs s-1 |
JADES-NS-GS00004902 | 1210 | 5.123 | S2-VO87* | 8.6${}^{0.03}_{0.02}$ | 1.4${}^{0.21}_{-0.14}$ | -19.0 | 43.0 |
JADES-NS-GS00007099 | 1210 | 2.860 | S2-VO87* | 9.1${}^{0.01}_{0.01}$ | 1.1${}^{0.07}_{-0.06}$ | -17.4 | 42.9 |
JADES-NS-GS00007762 | 1210 | 4.146 | High ion | 8.2${}^{0.02}_{0.02}$ | 4.1${}^{0.29}_{-0.28}$ | -18.8 | 43.6 | outflows
JADES-NS-GS00008083 | 1210 | 4.665 | High ion & HeII$\lambda 4686$ | 7.8${}^{0.01}_{0.01}$ | 7.3${}^{0.12}_{-0.12}$ | -18.5 | 44.5 | Type-1 & LAE
JADES-NS-GS00008456 | 1210 | 1.884 | HeII$\lambda 4686$ | 8.1${}^{0.03}_{0.08}$ | 0.1${}^{0.03}_{-0.01}$ | -16.1 | 41.6 |
JADES-NS-GS00008880 | 1210 | 2.327 | N2-BPT | 7.1${}^{0.25}_{0.70}$ | 0.1${}^{0.03}_{-0.02}$ | -14.9 | 42.0 |
JADES-NS-GS00009422 | 1210 | 5.942 | HeII$\lambda 1640$ & HeII$\lambda 4686$ | 7.7${}^{0.00}_{0.00}$ | 5.4${}^{0.04}_{-0.03}$ | -19.8 | 44.5 | LAE
JADES-NS-GS00009452 | 1210 | 5.135 | N2-BPT | 8.1${}^{0.17}_{0.16}$ | 4.2${}^{1.17}_{-0.97}$ | -17.9 | 43.6 |
JADES-NS-GS00010073 | 1210 | 2.632 | HeII$\lambda 4686$ | 6.9${}^{0.06}_{0.04}$ | 0.8${}^{0.05}_{-0.04}$ | -16.5 | 43.0 |
JADES-NS-GS00016745 | 1210 | 5.574 | S2-VO87* | 8.3${}^{0.03}_{0.04}$ | 6.5${}^{0.26}_{-0.27}$ | -19.5 | 43.7 |
JADES-NS-GS00017072 | 1210 | 4.707 | HeII$\lambda 1640$ | 8.2${}^{0.06}_{0.15}$ | 0.4${}^{0.11}_{-0.06}$ | -17.9 | 42.3 |
JADES-NS-GS00017670 | 1210 | 2.350 | HeII$\lambda 4686$ | 8.4${}^{0.01}_{0.01}$ | 0.9${}^{0.04}_{-0.04}$ | -17.8 | 43.0 |
JADES-NS-GS00021842 | 1210 | 7.981 | High ion | 7.4${}^{0.03}_{0.02}$ | 2.4${}^{0.08}_{-0.07}$ | -18.6 | 43.8 | LAE
JADES-NS-GS00022251 | 1210 | 5.804 | HeII$\lambda 1640$ | 7.9${}^{0.03}_{0.03}$ | 4.7${}^{0.12}_{-0.13}$ | -18.9 | 44.1 |
JADES-NS-GS10000626 | 1210 | 4.468 | HeII$\lambda 4686$ & High ion | 6.8${}^{0.29}_{0.20}$ | 0.3${}^{0.01}_{-0.01}$ | -16.8 | 42.5 |
JADES-NS-GS10008071 | 1210 | 2.227 | S2-VO87 | 8.8${}^{0.01}_{0.01}$ | 7.2${}^{0.15}_{-0.17}$ | -17.9 | 43.1 |
JADES-NS-GS10011849 | 1210 | 2.686 | S2-VO87* | 8.1${}^{0.03}_{0.04}$ | 2.5${}^{0.06}_{-0.06}$ | -18.0 | 43.6 |
JADES-NS-GS10012477 | 1210 | 0.665 | S2-VO87 | 7.8${}^{0.01}_{0.02}$ | 0.1${}^{0.00}_{-0.00}$ | -15.7 | 41.5 |
JADES-NS-GS10012511 | 1210 | 2.019 | N2-BPT | 8.2 ${}^{0.3}_{-0.4}$ | 3.2 ${}^{0.7}_{-0.9}$ | -16.5 | 41.7 |
JADES-NS-GS10013597 | 1210 | 3.320 | HeII$\lambda 4686$ | 7.5${}^{0.13}_{0.06}$ | 0.8${}^{0.20}_{-0.12}$ | -17.4 | 42.6 |
JADES-NS-GS10013609 | 1210 | 6.931 | High ion | 7.7${}^{0.06}_{0.07}$ | 3.9${}^{0.19}_{-0.18}$ | -18.7 | 44.0 | outflows
JADES-NS-GS10013905 | 1210 | 7.206 | HeII$\lambda 4686$ | 7.4${}^{0.05}_{0.03}$ | 2.2${}^{0.14}_{-0.12}$ | -18.6 | 43.7 |
JADES-NS-GS10015338 | 1210 | 5.073 | HeII$\lambda 4686$ | 7.9${}^{0.04}_{0.04}$ | 3.6${}^{0.15}_{-0.19}$ | -19.4 | 43.9 | LAE
JADES-NS-GS10035295 | 1210 | 3.588 | HeII$\lambda 1640$ | 7.4${}^{0.07}_{0.03}$ | 2.3${}^{0.06}_{-0.05}$ | -18.0 | 43.7 |
JADES-NS-GS10036017 | 1210 | 2.016 | N2-BPT & S2-VO87 | 9.3${}^{0.01}_{0.02}$ | 85.5${}^{1.69}_{-1.95}$ | -20.7 | 44.1 |
JADES-NS-GS10040620 | 1210 | 1.776 | N2-BPT | 8.1${}^{0.04}_{0.04}$ | 0.1${}^{0.02}_{-0.01}$ | -16.4 | 41.7 |
JADES-NS-GS10056849 | 1210 | 5.821 | High ion | 7.1${}^{0.06}_{0.04}$ | 1.1${}^{0.05}_{-0.04}$ | -18.1 | 43.2 | LAE
JADES-NS-GS10058975 | 1210 | 9.437 | High ion & HeII$\lambda 1640$ | 8.1${}^{0.03}_{0.03}$ | 6.6${}^{0.20}_{-0.20}$ | -20.3 | 44.4 |
JADES-NS-GS00095256 | 3215 | 4.159 | S2-VO87 | 7.4${}^{0.12}_{0.33}$ | 0.3${}^{0.21}_{-0.14}$ | -16.9 | 43.0 |
JADES-NS-GS00099671 | 3215 | 5.936 | HeII$\lambda 4686$ | 7.5${}^{0.05}_{0.05}$ | 1.2${}^{0.07}_{-0.06}$ | -18.0 | 42.9 |
JADES-NS-GS00104075 | 3215 | 3.717 | S2-VO87* | 7.2${}^{0.12}_{0.07}$ | 1.3${}^{0.29}_{-0.28}$ | -17.1 | 43.6 |
JADES-NS-GS00108487 | 3215 | 3.975 | S2-VO87 | 8.9${}^{0.02}_{0.03}$ | 0.5${}^{0.12}_{-0.12}$ | -18.2 | 44.5 |
JADES-NS-GS00111091 | 3215 | 4.497 | S2-VO87 | 8.6${}^{0.03}_{0.04}$ | 0.4${}^{0.03}_{-0.01}$ | -17.4 | 41.6 |
JADES-NS-GS00111511 | 3215 | 3.008 | S2-VO87* | 8.0${}^{0.03}_{0.05}$ | 0.4${}^{0.03}_{-0.02}$ | -16.8 | 42.0 |
JADES-NS-GS00114573 | 3215 | 2.881 | S2-VO87* | 8.0${}^{0.04}_{0.05}$ | 0.2${}^{0.04}_{-0.03}$ | -17.3 | 44.5 |
JADES-NS-GS00132213 | 3215 | 3.012 | S2-VO87 | 7.7${}^{0.04}_{0.03}$ | 0.3${}^{1.17}_{-0.97}$ | -17.6 | 43.6 |
JADES-NS-GS00143403 | 3215 | 0.735 | S2-VO87* | 8.2${}^{0.01}_{0.01}$ | 0.1${}^{0.05}_{-0.04}$ | -15.4 | 43.0 |
JADES-NS-GS00201127 | 3215 | 5.837 | S2-VO87* | 8.2${}^{0.06}_{0.08}$ | 2.1${}^{0.26}_{-0.27}$ | -18.5 | 43.7 |
JADES-NS-GS00202208 | 3215 | 5.450 | HeII$\lambda 1640$ | 8.1${}^{0.02}_{0.02}$ | 11.7${}^{0.11}_{-0.06}$ | -19.7 | 42.3 |
JADES-NS-GS00208643 | 3215 | 5.566 | HeII$\lambda 4686$ | 7.2${}^{0.01}_{0.01}$ | 1.5${}^{0.04}_{-0.04}$ | -18.0 | 43.0 |
JADES-NS-GS00209979 | 3215 | 1.883 | S2-VO87* | 8.5${}^{0.01}_{0.01}$ | 1.8${}^{0.08}_{-0.07}$ | -17.9 | 43.8 |

###  3.1 Selecting AGN based on optical emission lines

We plot our sources as dark blue points on the N2–R3 and S2–R3 planes (also
known as BPT and VO87 diagrams, respectively; Baldwin et al. 1981b; Veilleux &
Osterbrock 1987) in the top and bottom row of Figure 2, respectively. The low
metallicities of high-z objects lead to faint [N ii] emission lines, which are
typically undetected at z$>$4 (Cameron et al., 2023b; Curti et al., 2023b).
Furthermore, the high ionisation parameter pushes star-forming sources to high
R3 values, towards the classical demarcation between star formation and AGN
defined by Kewley et al. (2001); Kauffmann et al. (2003); Kewley et al.
(2013). On the other hand the low metallicity of the Narrow Line Region at
high-z results in the line ratios of AGN to shift away from the locus of AGN
typically populated by AGN and move towards the locus of star forming galaxies
Nakajima & Maiolino (2022); Harikane et al. (2023); Maiolino et al. (2023a).
The combination of these effects makes high-z AGN and SF galaxies to largely
overlap on these diagrams and makes the selection of high-z AGN much more
challenging than in the local Universe. However, these diagrams can still be
used to identify AGN via a conservative selection approach, as discussed in
the following.

In Figure 2, we plot results from different photoionisation models for a wide
range of gas properties (metallicities, ionisation parameter and densities) in
the left and right panels, with star-forming models and AGN models shown in
yellow and blue points, respectively. The left panels show AGN models from
Feltre et al. (2016) and star-forming models from Gutkin et al. (2016), while
the right panels show the AGN and star-forming models of Nakajima & Maiolino
(2022). The photo-ionisation models indeed show that star-forming galaxies can
lie in the AGN part of the BPT diagram at high redshift with log10([O
iii]/H$\beta$)$>0.9$ (as also noted in Figure 14 of Feltre et al. 2016). As
such, we need a clean selection to account for galaxies with low metallicity
and high ionisation parameters. To identify a conservative demarcation line
between star-forming galaxies and AGN, we define the edge of the star-forming
region using the points with the highest [N ii]/H$\alpha$ values from either
the Kewley et al. (2001) line or Gutkin et al. (2016) models. We clarify that
this is a very conservative method to select AGN. Galaxies above this
demarcation line can be safely classified as AGN, however, as discussed above,
certainly there are plenty of AGN also mixed with the galaxy population below
this line, especially at these high redshifts. Indeed the type-1 AGN from
Harikane et al. (2023); Maiolino et al. (2023a) do lie in the star-formation
part of the BPT diagram.

We fit these points, marking the edge of the star-forming region of the BPT
diagram, with the functional form as in Kewley et al. (2001); Kauffmann et al.
(2003):

$Y=\frac{a}{(X-b)}+c$ (1)

where Y = log10([OIII]/H$\beta$) and X=log10([NII]/H$\alpha$). We report the
results for the demarcation line in Table 3. In the BPT diagram, this
functional form well describes the edge of star-forming galaxies and we show
this line as a green dashed line in the top panels of Figure 2. We select five
AGN based on the new demarcation line, all at z¡5. We highlighted these
sources with red circles in the top panel Figure 2.

We repeat the same analysis on the S2-R3 diagram (S2-VO87) to make new
demarcation lines between AGN and star-forming galaxies. However, the previous
functional form no longer fits the edges of the Kewley et al. (2001) line and
Gutkin et al. (2016) points and we need to adapt it as:

$\begin{split}Y=&\frac{a}{(X-b)}+c\;[X>-0.92]\\\ =&d+eX\;[X>-0.92]\end{split}$
(2)

We report the parameters of the AGN demarcation lines in Table 3. Overall, we
detect seventeen AGN in the S2-R3 BPT diagram, using the Kewley et al. (2001)
or Gutkin et al. (2016) line (dashed green line in the bottom panels of Figure
2). We note that there are several AGN candidates close to the demarcation
line. We discuss these selection methods and their reliability in §4.2.

Figure 2:  Typical line ratio diagnostic diagrams used to select AGN. N2-R3 BPT ([N ii]/H$\alpha$ vs [O iii]/H$\beta$; top row) and S2-VO87 ([S ii]/H$\alpha$ vs [O iii]/H$\beta$; bottom row). The ionisation models of Feltre et al. (2016); Gutkin et al. (2016) (left) and Nakajima & Maiolino (2022) (right) are reported as yellow (SF) and blue (AGN) points (see §2.4). The objects from this work are plotted as blue squares. The objects selected as AGN based in these diagrams are highlighted by red circles. We also plot our new demarcation lines as green dashed. The black dashed lines show the star forming versus AGN demarcation lines from Kewley et al. (2001) and Kauffmann et al. (2003). For comparison, we plot SDSS galaxies shown as a grey contour plot. The magenta and cyan squares show a stacked spectrum for AGN and star-forming galaxies (see §4.1). We highlight type-1 AGN from Harikane et al. (2023) and Maiolino et al. (2023a) as green squares and diamonds. The red star shows the X-ray selected AGN in our sample.  Table 3: Fitted parameters for equation 1 when fitted for the N2-R3 and S2-R3 diagnostics plot. We define the edge of star-forming galaxies described in §3.1.  Model | a | b | c | d | e
---|---|---|---|---|---
N2-R3 | 3.08 | 1.03 | 2.78 | - | -
S2-R3 | 0.78 | 0.34 | 1.36 | -0.91 | -1.79
Figure 3:  The He2-N2 (He ii /H$\beta$ vs [N ii]/H$\alpha$) diagram for the
sample of JADES galaxies for our sample, plotted as blue squares. The left and
right plots show ionisation models from Feltre et al. (2016); Gutkin et al.
(2016) and Nakajima & Maiolino (2022), respectively, as yellow and light blue
points (see §2.4). The magenta and cyan squares show a stacked spectrum for
AGN and star-forming galaxies (see §4.1). The black dashed line indicates a
demarcation line between star-forming and AGN galaxies by Shirazi & Brinchmann
(2012). The blue and black contours show the star-forming galaxies and AGN
from SDSS, respectively. We highlight the selected AGN in this diagram with a
red circle.

Shirazi & Brinchmann (2012), Tozzi et al. (2023) and Dors et al. (2023)
investigated the identification of AGN in SDSS using the He ii $\lambda$4686
emission line. This is a recombination line whose flux is nearly independent
of the gas metallicity and ionization parameter, depending instead primarily
on the shape of the ionizing spectrum and, more specifically, on the number of
ionizing photons with energies beyond 54 eV. We plot the He2-N2 diagram in
Figure 3. In this case, we do not redefine the demarcation line as defined by
Shirazi & Brinchmann (2012) as the original line is more conservative than the
photo-ionisation models used in our work. Overall, we detect He ii
$\lambda$4686 in nine galaxies, whose HeII/H$\beta$ ratio indicates the
presence of hard ionising radiation indicating AGN. Despite the deep JWST
observations, the upper limits on He ii $\lambda$4686/H$\beta$ do not provide
any constraints on the presence of an AGN. We note that we selected three
additional sources solely based on their N2 ratio $>0.2$ (see Figure 2).
Although these sources do not have He ii $\lambda$4686 detections, their large
N2 ratio is already identified with the N2-BPT. In summary, based on this
diagram we select 12 AGN in total.

We finally note that the only X-ray detected AGN in our sample (red star in
Figures 2 and 3) is located in the star-forming region of the N2-BPT and
S2-VO87 diagnostic diagrams (hence it would have been missed by this
classification, as most type 1 AGN, as discussed in Harikane et al., 2023;
Maiolino et al., 2023a; Kocevski et al., 2023), while the upper limit on the
HeII$\lambda$4686 would no identify as AGN, further showing complexity of
selecting AGN at high redshift.

###  3.2 Selecting AGN based on UV line diagnostics

Figure 4:  C iii]/He ii $\lambda$1640 vs C iv/C iii](CHe2-C43) diagnostic
diagram. We plot our sample as blue squares. The left and right plots show
ionisation models from Feltre et al. (2016); Gutkin et al. (2016) and Nakajima
& Maiolino (2022), respectively, as yellow and light blue points (see §2.4).
The green diamonds show He ii $\lambda$1640 detection from the VANDELS survey
(Saxena et al., 2020), and all C iv detections from Mascia et al. (2023) as
grey colour with squares and circles representing SF and AGN, respectively.
The green squares show local analogues of high-redshift galaxies (from CLASSY
survey; Mingozzi et al., 2023). We also show the black dashed and dotted
demarcation lines between AGN, star-forming galaxies and composite line ratios
from Hirschmann et al. (2022). Overall, we select five AGN on this diagram and
we highlight these with a red circle. The magenta and cyan squares show a
stacked spectrum for AGN and star-forming galaxies (see §4.1).

In the previous section, we showed that the selection of AGN at high-z using
the BPT and S2-VO87 diagrams is extremely challenging for all but metal-rich
galaxies. In this section, we will focus on the UV emission lines. The
clearest signature of AGN activity is high-ionisation lines such as
NV$\lambda$1240, [Ne iv] $\lambda$2424, [Ne v] $\lambda$3426 (Feltre et al.,
2016). They require high energy photons (77, 98 and 68 eV, respectively), that
can hardly be produced by star-formation processes and are not seen even in
galaxies hosting WR stars (Mingozzi et al., 2023); hence, as already
mentioned, these are clear signatures of AGN. However, they are often very
faint even in powerful AGN (Übler et al., 2023).

Past works on this topic have suggested various UV emission lines ratios based
on C iii]$\lambda\lambda$1907,09, C iv$\lambda\lambda$1548,51, [O iii] 1660,66
and He ii $\lambda$1640 (Feltre et al., 2016; Hirschmann et al., 2022;
Mingozzi et al., 2022; Mascia et al., 2023). These lines are ideal in
identifying AGN at z$>6.8$, where most rest-frame optical emission lines are
redshifted out of the wavelength range of JWST/NIRSpec. However, even in
luminous AGN the majority of these lines are beyond detectability for a 4-7
hour JWST/NIRSpec exposure at high redshift.

Therefore, in this work, we focus mainly on the brightest emission lines of
the UV emission lines that are potentially detectable in our data, or on which
we can at least obtain meaningful upper limits. In Figure 4, we show the ratio
C iii]$\lambda\lambda$(1906+1908)/He ii $\lambda$1640 (C3He2) against C
iv1548,50/C iii]$\lambda\lambda$(1906+1908) (C43). We also plot the models of
AGN and star-forming galaxies as blue and orange points, respectively, from
Feltre et al. (2016); Gutkin et al. (2016) (left plot) and Nakajima & Maiolino
(2022) (right plot). The black and dotted lines show the demarcation lines
between star-forming galaxies, AGN, and composite objects derived by
Hirschmann et al. (2022). They obtain such a line from the post-processing of
the Illustris TNG cosmological simulation using the models of Gutkin et al.
(2016) and Feltre et al. (2016). In our sample, there are six targets for
which we detect He ii $\lambda$1640\. Out of these five objects, one lies in
the AGN part of the diagram. Furthermore, four targets are in the composite
part of the diagram according to Hirschmann et al. (2022), but they are
classified as AGN based on Feltre et al. (2016) and Nakajima & Maiolino
(2022).

We also show the objects from Saxena et al. (2020) with He ii $\lambda$1640
detection in the VANDELS survey (McLure et al., 2018) as green diamonds. This
sample removed all X-ray-detected objects, removing any signs of obvious AGN.
However, most of the type-1 AGN detected with JWST do not show any X-ray
emission even in the deepest observations (Maiolino et al. in prep),
indicating that many of these VANDELS sources might have a significant AGN
contribution. All the VANDELS sources would be classified as AGN by Feltre et
al. (2016); Gutkin et al. (2016); Nakajima & Maiolino (2022), whereas 14 of
them fall in the AGN or composite region defined by Hirschmann et al. (2022).
We also show C iv detections from VANDELs survey (Mascia et al., 2023) as grey
squares and circles representing SF and AGN host (identified from X-ray or as
type-1 AGN) galaxies, respectively. The AGN and SF galaxies from Mascia et al.
(2023) are well separated on our diagram as predicted by the photo-ionisation
models used in our work.

We compare our observations with local high-redshift analogues from the CLASSY
survey (Berg et al., 2022; James et al., 2022), specifically the data from
Mingozzi et al. (2023). The CLASSY sources are classified as star-forming
galaxies based on the BPT and He ii $\lambda$4686 optical emission lines.
Overall, the five objects in our sample with He ii $\lambda$1640 selected as
AGN in our sample have significantly lower C3-He2 ratios than other ”pure”
star-forming galaxy samples, confirming our AGN selection.

We note that Mascia et al. (2023) devised a classification of UV-BPT using the
VANDELS survey. However, most of the diagnostics diagrams used in that work
use both C iv and He ii $\lambda$1640 in the same emission line ratio.
Unfortunately, the majority of our sample is undetected in both C iv and He ii
$\lambda$1640 which does not allow us to place these objects on their
diagnostics diagram. We will further investigate these diagnostics with the
full JADES sample in future works.

We note that the above emission line diagnostic requires the detection of
three UV emission lines. Despite the excellent sensitivity of JWST/NIRSpec,
detecting all three emission lines is still a challenge for exposures $<$10
hours.

Figure 5: Emission line diagnostics plot for UV high ionisation lines. - Top
panel: C3He2 vs [Ne iv] $\lambda$2424/C iii]; Middle Panel: C3He2 vs [Ne v]
$\lambda$3427/C iii]diagnostic diagram; Bottom panel: C3He2 vs N V
$\lambda$1240/C iii]. We plot our objects detected in high ionisation lines as
blue points. The yellow and light blue points show star forming and AGN from
photo-ionisation models from Feltre et al. (2016); Gutkin et al. (2016) (see
§2.4). We highlight the emission line ratios of GN-z11 (Maiolino et al.,
2023b) as the magenta star.

We detect [Ne iv] $\lambda$2424 in five objects. We plot these sources in the
top panel of Figure 5, showing C3He2 vs [Ne iv] $\lambda$2424/C iii] as blue
points, together with the photo-ionisation models of Gutkin et al. (2016) and
Feltre et al. (2016) as yellow and blue points, respectively. All of these
four sources have extremely high log10([Ne iv] $\lambda$2424/C iii] ) ratio
($>-0.6$) indicating that these sources are ionised by AGN. The fifth source
(ID JADES-NS-GS-1000626) is poorly constrained in both C iii] and He ii
$\lambda$1640, and therefore we do not show it on this diagram.

We detect [Ne v] $\lambda$3427 and NV$\lambda$1240 in the objects JADES-NS-
GS-10013609 and JADES-NS-GS-00021842, respectively. In both cases, the
detections are secured at SNR$\sim$4.5. However, the objects do not have solid
detections of either He ii $\lambda$1640 or C iii] . The non-detections of C
iii] put a lower limit on the log([Ne v] $\lambda$3420/C iii] ) or log(N V
$\lambda$1240/C iii] ) of $>$0\. According to Feltre et al. (2016), this lower
limit is on the border of what can be reasonably predicted by AGN photo-
ionisation models. The N V $\lambda$1240 detection can be potentially
explained by star formation, assuming a very high logU=-0.5, which is
inconsistent with that measured from [O ii] and [O iii] (logU=-2). As such, we
identify JADES-NS-GS-00021842 as an AGN. It is necessary to observe these
objects with deep rest-frame UV spectroscopy to help constrain the next
geeneration of photo-ionisation models.

##  4 Discussion

In the previous section, we presented the selection of AGN in the JADES HST
Deep survey. Overall, we selected 42 AGN candidates, from at least one of the
different selection methods investigated in Section 3, in our parent sample of
209 galaxies (defined in §3) in the redshift range of 1.4–9.4. The final
fraction of type-2 AGN in the galaxy population is up to 20%. In this section,
we discuss our results and their implications. In §4.1 we perform spectral
stacking to find average emission line properties of AGN and star-forming
galaxies, in §4.2 we compare the different selection methods used in this
work, in §4.4 we investigated the AGN bolometric luminosities, in §4.5 we
compare the SFR and stellar masses of AGN to those of star-forming galaxies
and finally in §4.6 we discuss the contribution of AGN host galaxies to the UV
luminosity function.

###  4.1 Stacking

In order to get the average emission line properties of our star-forming and
AGN samples, we stacked the R1000 grating spectra for each of the emission
lines used in our emission line diagnostics. Although the PRISM observations
are deeper, the vastly varying spectral resolution as a function of wavelength
makes any stacking efforts challenging. Furthermore, some of the emission
lines we are interested in (such as [NII], He ii 1640) would be blended with
other emission lines. Also, we only stack spectra from the 1210 program, since
the 3215 observations lack band-2 observations (see discussion in §4.2). As
such, this would bias the stack against redshifts where the key emission lines
are redshifted to band-2.

We stack the AGN hosts and star-forming galaxies in two separate stacks: a
rest-frame UV emission line stack (C iv, HeII$\lambda$1640 and C iii]) and a
rest-frame optical one (HeII$\lambda$4685, H$\beta$, [O iii], H$\alpha$, [N
ii] ). For each of these two cases, we stack all sources with available R1000
coverage of these emission lines. Overall, we stack 9 and 20 AGN, and 31 and
55 star-forming galaxies (i.e. the parent sample type-1 and type-2 AGN) in the
UV and optical emission line sets, respectively, using only the 1210 program.
As shown with type-1 AGN, some objects can be low luminosity AGN that is not
selected using any of our diagnostics. As such, the star-forming galaxy sample
can be contaminated by unidentified AGN.

The stacking was performed by first shifting all spectra to the rest-frame and
rebinning them to a common wavelength grid using Python’s spectres package
(Carnall, 2017). We then model and subtract the continuum with a single power
law. This is an appropriate model for the continuum, as we are only fitting a
narrow wavelength range, and the continuum is poorly detected in the R1000
spectra. The individual spectra are weighted using two separate weighting
schemes: a) 1/rms2 weights; and b) 1/(rms${}^{2}\times$ F[OIII]) or
1/(rms${}^{2}\times$ FCIII]) weights for the UV line stacking) weights.
However, given the large range of redshifts and galaxy luminosities, we found
that the final stacked spectra are dominated by bright targets when we do not
normalise by line fluxes. Hence, we will use the stacked spectra weighted by
both the noise and the line fluxes ([O iii] or C iii]), which approximates a
median stacked spectrum.

The measured fluxes and their uncertainties for the HeII$\lambda$4685,
H$\beta$, [O iii], H$\alpha$, [N ii] and UV emission lines: CIV,
HeII$\lambda$1640 and C iii]$\lambda$1907,1909 along with the number of
objects in the stack and their median redshift are summarised in the Table 4.
The final stacked spectra and the best fits are shown in the Appendix in
Figures 12 and 13. We detect all emission lines across all samples at
$>3\sigma$, except for He ii $\lambda 4686$ in the star-forming galaxy sample.
The detection of [N ii] seems like a contradiction compared to Cameron et al.
(2023b), however, this is purely due to the inclusion of galaxies with z¡4,
while Cameron et al. (2023b) restricted their sample to $z>5.5$.

We show the line ratios from the stacked spectra in all diagnostic diagrams
for the AGN and star-forming samples as magenta and cyan squares in Figures 2,
3 and 4.

The stacked spectra on the N2-BPT (see top panel of Figure 2) show that AGN at
high-z are [N ii] weak and have a [O iii]/H$\beta$ ratio very similar to star-
forming galaxies, making the selection of AGN at high-z impossible using this
method. Furthermore, the AGN host galaxies have the same [S ii]/H$\alpha$
ratios as star-forming galaxies (see bottom panel of Figure 2), further
showing the difficulty of using this diagram to select AGN at high-z.

We do not detect the He ii $\lambda$4686 in the star-forming galaxies, and
therefore we place a 3$\sigma$ upper limit on the flux. The He ii
$\lambda$4686/H$\beta$ vs [N ii]/H$\alpha$ diagram shows a separation of star-
forming and AGN galaxies as expected based on the study by Shirazi &
Brinchmann (2012) that focused on SDSS galaxies. As such, the He ii
$\lambda$4686 line is an ideal tracer of AGN activity; however the He ii
$\lambda$4686 is a factor of 7 fainter than He ii $\lambda$1640, making it
extremely difficult to detect, even with JWST.

We detect He ii $\lambda$1640 in both star-forming and AGN-dominated galaxies,
although the flux of He ii $\lambda$1640 is three times higher in AGN compared
to star-forming galaxies. Furthermore, the C iv emission line is a factor of
eight brighter in AGN than in the star-forming galaxies. This indicates a much
higher and harder ionisation field in AGN host galaxies, boosting the high
ionisation lines. However, the stacked spectrum of star-forming galaxies shows
He ii $\lambda$1640, with the star-forming stack in the border of AGN and
star-forming galaxies on the C iii]/C iv vs C iii]/He ii $\lambda$1640
diagnostic plot (see Figure 4). This can be due to some low luminosity AGN in
the star-forming sample that do not have individual He ii $\lambda$1640
detections, or because star-forming galaxies at high-z are creating more hard
ionising photons than previously modelled.

Table 4:  Results of the stacking analysis for UV and optical emission lines of star-forming and AGN samples. Sample | AGN | AGN | SF | SF
---|---|---|---|---
| Optical | UV | Optical | UV
N | 20 | 9 | 55 | 31
zmedian | 3.87 | 5.93 | 4.77 | 5.91
Fluxes | ($\times$10-20 | erg s-1 cm-2) |  |
H$\alpha$ | 114.5${}^{+1.7}_{-1.6}$ | - | 66.5${}^{+0.8}_{-0.8}$ | -
[N ii] | 16.6${}^{+1.0}_{-1.1}$ | - | 2.5${}^{+0.4}_{-0.4}$ | -
[S ii] | 4.5${}^{+2.0}_{-1.7}$ | - | 1.6${}^{+0.5}_{-0.5}$ | -
[O iii] | 222.2${}^{+1.9}_{-2.0}$ | - | 113.3${}^{+0.8}_{-0.8}$ | -
H$\beta$ | 48.8${}^{+1.5}_{-1.5}$ | - | 24.1${}^{+0.6}_{-0.7}$ | -
He ii $\lambda$4686 | 6.6${}^{+1.2}_{-1.1}$ | - | $<10.5$ | -
C iii] | - | 20.7${}^{+3.8}_{-3.7}$ | - | 16.5${}^{+1.9}_{-1.8}$
C iv | - | 59.0${}^{+4.7}_{-4.6}$ | - | 6.1${}^{+1.7}_{-1.5}$
He ii $\lambda$1640 | - | 18.6${}^{+3.3}_{-3.3}$ | - | 4.2${}^{+1.4}_{-1.3}$

###  4.2 Comparing AGN selection methods

Figure 6:  [O iii] /H$\beta$ vs [NeIII]/[OII] diagram (”OHNO”). The black
dashed lines show the boundary between AGN and star forming galaxies from
Backhaus et al. (2022). The orange and light blue points show star forming and
AGN photoionisation models from Feltre et al. (2016) and Gutkin et al. (2016),
respectively. There is a significant overlap between AGN and star forming
galaxies in this diagram, making it unreliable to select AGN.

Regardless of the emission diagnostic used to identify AGN (emission lines,
X-ray observations, mid-infra-red selection), the selection method is reliant
on finding emission that cannot be explained by star-formation processes.
Objects not selected as AGN, the star-forming galaxies, can still have a
significant amount of AGN activity, but it is not dominating the total galaxy
emission. Therefore, AGN selection most likely provides an lower limit on the
total number of AGN, as we are likely missing low luminosity active black
holes, outshone by their host galaxy.

There is little overlap between the individual emission line ratios used to
find AGN in this work, with only five objects being selected in more than one
diagnostic. This can be explained by the different emission line ratios being
more or less sensitive to AGN activity in different observed regimes (e.g.
redshift and metallicity of the ISM). As discussed above, the N2-BPT method
becomes increasingly unreliable towards high redshift as galaxies become more
metal-poor and have higher ionisation parameters. Although the highest
redshift AGN selected by this method is at z=5.135 (JADES-NS-GS-00009452;
12+log(O/H)= 7.92), the bulk of the AGN selected by this method are below
z=2.3.

Topping et al. (2020); Runco et al. (2021) investigated the effect of stellar
population age and metallicity on the BPT and S2-VO87 diagrams. These works
have found that a population of young, metal-poor stellar populations can lie
above the Kewley et al. (2001) line. These low S2 ratio targets would be
excluded from selection with the new definition of the demarcation lines and
the AGN selected in the S2-VO87 diagram are above the points seen in both
Topping et al. (2020); Runco et al. (2021) (see also Strom et al., 2017).
However, many of our selected AGN are very close to the demarcation line,
making their selection tentative. As a result, we select any AGN selected
within a distance of 0.1 dex from the demarcation as tentative, and we mark
this with asterisks in Table 2. We note that our conclusions do not change
whether we include or exclude these tentative AGN in our final sample.

In both sets of observations (1210 and 3215) we select more AGN using the
S2-VO87 diagram (with [S ii] emission line) than the classical BPT diagram
(using [N ii] emission line). The [S ii] doublet is not blended with H$\alpha$
in the PRISM observations at $z<5$, and hence we can use the deeper PRISM
observations to constrain [S ii] doublet. On the contrary, the [N ii] doublet
is blended with H$\alpha$ in the PRISM observations, and hence we require the
shallower R1000 JADES observations to put constraints on it. This is
especially taxing for the 3215 observations, which were designed to observe
galaxies at z$>6$ and hence do not have any R1000 Band 2 observations. This
results in no constraints on the [N ii] doublet for z = 1.8–3.6 in the 3215
program, and hence low detection of AGN on the BPT diagram in this program.

Luckily, as we push our AGN selection to higher redshifts, many useful UV
lines ([Ne iv] $\lambda$$\lambda$2424, [Ne v] $\lambda$3427, N V
$\lambda$1240, He ii $\lambda$1640) are redshifted into the wavelength range
of NIRSpec, and then to a higher sensitivity range of NIRSpec
($\lambda>1.2\mu$m). As a result, the majority of the AGN selected above z=3
are based on [Ne iv] $\lambda$$\lambda$2424, [Ne v] $\lambda$3427, N V
$\lambda$1240, He ii $\lambda$1640\. Still, these essential emission lines for
identifying AGN activity at high redshift remain difficult to detect with
JWST. Many of the emission lines such as N V $\lambda$1240, He ii
$\lambda$1640 are blended with nearby emission lines in the PRISM observations
and require R1000 observations to deblend, which are shallower in JADES survey
compared to the PRISM observations. Dedicated ultra-deep R1000 observations
are required to detect these lines, even in moderate luminosity AGN.

The HeII$\lambda$4686 emission line remains a robust diagnostic to detect AGN
across large redshift and metallicity ranges. However, it is intrinsically
fainter by a factor $\sim$10 compared to the HeII$\lambda$1640, resulting in
it being a challenge to detect even in deep JWST spectra. However, if detected
at high redshift, it is an unambiguous tracer of AGN activity at high
redshift.

It is important to point out that these high-ionization lines do not trace AGN
activity as such, but a hard ionising radiation, with which AGN activity is
the most likely source in galaxy evolution. However, other sources can also
produce He ii emission such as Wolf-Rayet stars, X-ray binaries, and some more
exotic star-formation processes (e.g. very top-heavy IMF; Thuan & Izotov 2005;
Kehrig et al. 2015; Schaerer et al. 2019; Umeda et al. 2022). However, the WR
stars produce very broad He ii forming so-called ”blue bump” (see Brinchmann
et al. 2008) features not observed in any of our sources. Furthermore, Saxena
et al. (2020) indeed reproduced their detections of He ii in the VANDELS
survey using implementations of binary stars using BPASS models. However, the
AGN and SF galaxies from the same VANDELS survey (Mascia et al., 2023) show
the separation of the SF and AGN host galaxies predicted by the Gutkin et al.
(2016) and Feltre et al. (2016) models. Nakajima & Maiolino (2022) models,
which we use as a comparison of our data, also use BPASS to implement hard
ionising photons from binary stars. Meanwhile, high-luminosity AGN at high
redshift, such as type-1 AGN at z$\sim$5.5 from Übler et al. (2023) do show
features of relatively weak He ii $\lambda$4686 (log10(He ii
$\lambda$4686/H$\beta$ )$\sim-1.2$), showing that even objects with relatively
weak HeII can be AGN. Finally, Cameron et al. (2023a) identified one of our
AGN (ID 9422) to be dominated by nebular continuum. This will be addressed in
a separate paper in more detail (Tacchella et al. in prep).

Larson et al. (2023) investigated the use of ”OHNO” diagram ([O iii] /H$\beta$
vs [NeIII]/[OII]) to replace the N2-BPT diagram in selecting AGN. We plot the
Cloudy photoionisation models in Figure 6 with JADES galaxies. There is a
significant overlap between AGN and star-forming galaxies in this diagram. The
one galaxy with [O iii] /H$\beta$¿0.95 lies in the region only populated by
AGN Cloudy models is 16745 at $z=5.56$. Overall, in agreement with Larson et
al. (2023), we confirm that it is difficult to differentiate between AGN and
metal-poor high ionisation star-forming HII regions in the OHNO diagram. For
this reason, we need to rely on higher ionisation lines.

Finally, Maiolino et al. (2023a) recently published a sample of type-1 AGN
observed with two sources overlapping with our observations. We detected only
one as an AGN based on emission line diagnostics (JADES-NS-GS-0008083).
Although it was selected by detection of both HeII$\lambda$4686 and NeIV, the
other AGN (with log(L${}_{\rm Bol}=44.3$)) is not selected in any of the
narrow emission line diagnostics. As already discussed above, it is important
to stress that emission line diagnostics miss AGN at high redshift. This can
be attributed to 1) the physical properties of high-z AGN being either
different from local templates (especially because of low metallicity); 2)
these AGN not properly modelled by photoionization models (hence not predicted
by the diagnostics); or 3) the emission from these sources is dominated by
star-formation in the host galaxy (see Silcock et al. in prep).

###  4.3 AGN fraction with deeper and shallower data

Across the two observational programs, we selected 28 and 14 AGN from the
parent samples of 110 and 99 galaxies, respectively. This corresponds to an
AGN fraction of 24$\pm$5 % and 14$\pm$4 %, and a combined AGN fraction from
the two programs of 20$\pm$3 %. In Figure 7, we show the AGN fraction as a
function of redshift. We plot the AGN fraction from 1210 and 3215 programs as
red stars and squares and the combined dataset as blue points. The AGN
fraction is consistent with being constant across all redshifts within
1$\sigma$.

Figure 7:  Fraction of AGN as a function of redshift in our survey. The blue
points show the fraction resulting from the combined programs (1210+3215)
while the red stars and squares show separately the 1210 and 3215,
respectively. The red points are offset by 0.05 in the x-axis for better
visibility. We see no significant evolution of the AGN fraction as a function
of redshift.

The fraction of AGN between the two tiers is consistent within 1.5$\sigma$ of
each other. Although this difference is statistically not significant, it is
worth discussing the apparent decrease of AGN in the 3215 program, despite
this program being a factor of 1.5-3$\times$ deeper than 1210 program. In the
3215 observations, only two AGN host galaxies were selected based on He ii
$\lambda$4686, compared to nine in the 1210 program. This is primarily due to
the lack of Band 2 R1000 observations in 3215, which results in no constraints
of He ii $\lambda$4686 for redshifts 2.8–5.4. As discussed above, this similar
issue is also plaguing the BPT selection using the [N ii] emission line, and
hence we do not select any AGN using the diagnostics in BPT either.

The Band 1 R1000 observations, which are key to observing rest-frame UV
emission lines such as C iii], He ii $\lambda$1640 and C iv doublet, are only
a factor of $\sim$1.4 deeper (twice the exposure time) in 3215 than in 1210.
Unfortunately, this improvement in sensitivity is not enough to constrain the
He ii $\lambda$1640 in regular SF galaxies or less extreme AGN. We would thus
require deeper rest-frame UV observations to constrain the AGN and star-
forming populations (over 50 hours with JWST/NIRSpec).

###  4.4 AGN luminosities

Figure 8:  Bolometric luminosity vs redshift for our objects, plotted as red
squares. Our bolometric luminosities are estimated from narrow line emission
lines and should be treated as upper limits. We compare our sample with
previous JWST type-1 AGN (various coloured stars Harikane et al., 2023;
Kocevski et al., 2023; Maiolino et al., 2023a, b; Matthee et al., 2023; Übler
et al., 2023), AGN from the KASHz and SUPER surveys at Cosmic Noon (grey and
red crosses Harrison et al., 2016; Kakkad et al., 2020) and QSOs samples at
high at EoR (blue crosses - XQR-30 from Mazzucchelli et al. 2023, green
crosses - HYPERION from Zappacosta et al. 2023).

The bolometric luminosity is one of the key properties of AGN and can be
easily estimated through the luminosity of the X-ray, BLR or the UV continuum
emission from the accretion disc (e.g. Stern & Laor, 2012; Netzer, 2019; Duras
et al., 2020; Saccheo et al., 2023). However, as our sources were not selected
as Type-1 AGN or X-ray detections, but based on their narrow emission line
properties, hence, we do not have the BLR properties of these AGN, nor we have
their UV or X-ray flux. As a result, we are forced to use the narrow line
emission lines to estimate the bolometric luminosity. We calculate the
bolometric luminosities (Lbol) of our sample from the narrow line fluxes of
H$\beta$ , [O iii] and C iii] , using the new calibrations by Hirschmann et
al. (in prep). The estimated Lbol from all three emission lines agree within
2$\sigma$, however, they are systematically lower by $\sim$0.5 dex compared to
the calibration from Netzer (2009).

We present the estimated bolometric luminosities in Figure 8 as a function of
redshift, together with other AGN from the literature (both from JWST
spectroscopic studies and from previous, non-JWST surveys). However, these
luminosities should be considered as upper limits, as they assume that the
H$\beta$ and [O iii] or C iii] emission is dominated by the narrow line
emission from the AGN, with no contribution from star formation. This is not
necessarily true, as AGN are hosted in star-forming galaxies (see §4.5). The
estimated bolometric luminosities are reported in Table 2.

As illustrated in Figure 8, pre-JWST studies used large optical and NIR
surveys to identify sources dominated by bright rest-frame optical and UV
emission, selecting primarily QSOs, or intermediate luminosity AGN at z$<$3\.
Specifically, in Figure 8, we show luminous quasars at the EoR from Hyperion
and XQR-30 samples (Mazzucchelli et al., 2023; Zappacosta et al., 2023) as
green and blue crosses and X-ray AGN from the KASHz and SUPER surveys
(Harrison et al., 2016; Circosta et al., 2018; Kakkad et al., 2020). Since the
launch of JWST, there have been a number of studies searching for AGN using
BLR (plotted in Figure 8 as light green, cyan, gold, magenta, dark green and
red stars for Harikane et al., 2023; Kocevski et al., 2023; Maiolino et al.,
2023a, b; Matthee et al., 2023; Übler et al., 2023, respectively). Our AGN
sample has generally similar luminosities to those selected as type-1 AGN with
JWST spectroscopic surveys.

With the new capabilities of JWST, we are now probing, at z$>$3, AGN that are
2-3 orders of magnitude less luminous than previous surveys, allowing us to
understand the broader demographics of the AGN population in the early
Universe for the first time.

###  4.5 Host Galaxy properties

One of the key questions of studying AGN host galaxies is investigating their
star-formation properties. Until the launch of JWST, these studies have mostly
focused on $z<3$ moderate luminosity AGN and $z>4$ quasars. These studies have
shown that AGN host galaxies have SFR at, or just below, the level of the
star-forming galaxies main sequence (SFMS) at Cosmic Noon when mass-matching
the active and inactive samples ($z\sim 1-3$; Santini et al., 2012; Rosario et
al., 2013; Vito et al., 2014; Mullaney et al., 2015; Stanley et al., 2015;
Azadi et al., 2015; Scholtz et al., 2018; Förster Schreiber et al., 2019).
With the launch of JWST, we can now select and measure the SFR of moderate
luminosity AGN at high redshift. Additionally, unlike previous studies with
JWST focusing on type-1 AGN, we do not suffer from the AGN contaminating the
continuum emission in our spectra, and hence modeling the stellar SED of the
host galaxy is less problematic (although the AGN can still contaminate the
Balmer emission lines hence making them less reliable for the SFR estimation).

Figure 9:  Offset from the star-forming main sequence (SFMS) of our AGN host
galaxies. Top panel: Offset from the SFMS against stellar mass for the JADES
sample (blue points), our selected AGN (red points). We compare our sample to
the previous AGN sample KASHz survey (Harrison et al., 2016, grey points) and
SUPER survey (Circosta et al., 2018, green points). We highlight the 1$\sigma$
scatter of SFMS by yellow shaded region. Bottom panel: Distribution of
SFR/SFRMS of the AGN candidates selected in this work. The blue histogram
shows the data from this work, and the red dashed line shows the best fit to
the distribution of offset from SFMS assuming it is log-normal. The green and
orange dashed lines show the distribution for the SFMS (Schreiber et al.,
2015) and AGN (Mullaney et al., 2015), respectively.

In §2.3, we described the SED fitting approach adopted to derive the SFR and
stellar masses. We use the star-forming main sequence (SFMS) prescription from
Looser et al. (2023b), who estimated the SFMS based on 3 separate redshifts
bins for sources from JADES with M${}_{*}<10^{9.3}$M⊙. For AGN at Cosmic Noon,
(Harrison et al., 2016; Circosta et al., 2018) we use the SFMS prescription of
Schreiber et al. (2015) as it covers the redshift and stellar mass range of
our AGN. It is worth mentioning that Schreiber et al. (2015) includes a
turnover at high stellar masses.

In Figure 9, we show the offset from the star-forming main-sequence
(SFR/SFRMS) against the stellar mass for AGN and star-forming galaxies in the
JADES survey (red and blue points); KASHz AGN survey (grey points) and SUPER
AGN survey (green points). The AGN host galaxies in our sample have the same
stellar mass distributions as the star-forming galaxies in the JADES HST Deep
survey, with a median stellar mass of SF galaxies and AGN are $10^{7.9}$ and
$10^{8}$ M⊙, respectively. The presence of AGN at such low masses indicates
that AGN activity is also important in galaxies with M${}_{*}<10^{10}$, as
previously suggested by at high redshift (Koudmani et al., 2021, 2022) and low
redshift (Burke et al., 2022; Mezcua et al., 2023; Siudek et al., 2023).

The SFRs of our selected AGN are consistent with the star-forming galaxies in
the JADES sample. However, measuring the SFR of AGN host galaxies is
notoriously difficult (see e.g. Stanley et al., 2015, 2018; Riffel et al.,
2021), as AGN can contaminate the emission lines used to estimate SFR.
Therefore, many of the SFRs presented in this work should be considered as
upper limits. Further SED fitting to disentangle the AGN and star-formation
activity will be performed in Silcock et al. (in prep).

To evaluate the difference in the star-formation properties of AGN in our
sample, we investigate the distribution of SFoffset. Following Mullaney et al.
(2015); Scholtz et al. (2018); Bernhard et al. (2019), we fitted the
distribution of SFR/SFRMS assuming that it is log-normal, with mode and width
as free parameters, using an MCMC method. The mode and width of the derived
log-normal distribution are $0.07\pm 0.10$ and $0.61\pm 0.07$, respectively.
The mode of the distribution is consistent with that of SFMS (modeSF = -0.07;
Schreiber et al. 2015), however, the width is 2$\sigma$ higher than that of
SFMS (width of SFMS of 0.3 dex). Some of this difference may be attributed to
the methods used to derive SFR (Caplar & Tacchella, 2019). For example, the
BEAGLE fitting accounts for physical variations that can lead to variation in
the UV to SFR and H$\alpha$ to SFR relationships (see e.g. Curtis-Lake et al.
2021), while the UV and IR SFRs in Schreiber et al. (2015) use simple
conversions to SFR.

Mullaney et al. (2015) have measured the distribution of SFR/SFRMS for X-ray
selected AGN at z$\sim$1.6, finding that the mode and the width of the
distribution as $-0.36\pm 0.07$ and $0.56\pm 0.09$, respectively. The
disagreement between our results and Mullaney et al. (2015) can be attributed
to our selection of AGN as well as the fact that SFRs here should be
considered upper limits. Furthermore, Mullaney et al. (2015) investigated AGN
in massive galaxies (M${}_{*}>10^{10}$M⊙) at z=1-1.5. The majority of the
high-z AGN were selected based on the detection of He ii $\lambda$1640 or He
ii $\lambda$4686 which are more likely to be detected in brighter targets.

However, in this work, we do not have a sufficient number of sources to
further split our AGN sample by redshift, stellar mass or AGN luminosity. This
will be performed in future works with the full JADES sample.

###  4.6 Contribution of AGN host galaxies to the UV luminosity function

Figure 10: UV luminosity function of galaxies at z=5 from Bouwens et al.
(2021) (blue solid line) and inferred contribution of galaxies hosting type-2
AGN, inferred from the JADES survey (red points). Results from other surveys
are also reported, as indicated in the legend. The dashed red line shows the
galaxy luminosity function scaled downward by a factor of 5. The orange-shaded
region shows the range of possible extrapolated luminosity functions for QSOs
from Niida et al. (2020).

With this new selection of a large sample of Type-2 AGN, we can now
investigate the contribution of AGN host galaxies to the UV luminosity
function at high redshifts. We explore the redshift range 4–6, as this is
poorly explored in the literature before JWST.

Since the selection function for allocating targets in JADES is complex, it is
hard to derive the volume density of AGN host galaxies as a function of the UV
luminosity based on the number of AGN that we have identified. More
importantly, we do not have high enough statistics to derive a proper
luminosity function. Therefore, we make the simplified assumption that the
spectroscopic selection function has not preferentially favoured or
disfavoured galaxies hosting AGN. We estimate the contribution of AGN host
galaxies to the UV luminosity function simply as the fraction of AGN
identified in UV luminosity bins.

We choose the parameterised luminosity function from Bouwens et al. (2021) as
the reference luminosity function, choosing a median redshift of our type-2
AGN at z=4–6 of z${}_{\rm median}\sim 5.5$ (14 objects in total).

Following the procedure discussed above, we show in Figure 10 (as red points)
the UV luminosity function of the type-2 AGN host galaxies z$>4$ from the HST
Deep of JADES survey. We split our sample into three separate bins, choosing
the bins to allow at least three objects per bin, centred on $M_{UV}=$ -20.15,
-18.8, -17.6. The estimated contribution of the type-2 AGN host galaxies to
the UV luminosity function is 33 %, 18 % and 20 %, for the $M_{UV}=$ -20.15,
-18.8, -17.6 bins, respectively. Given the limited number of points, we do not
attempt to fit a functional form, as a small number statistics mean that the
parameters are not adequately constrained. Instead, we simply show that the
type 2 AGN host galaxies LF can match the Bouwens et al. (2021) galaxy UV
luminosity scaled by a factor of 5, as shown by the red dashed line.

We also compare our results with other JWST type-1 AGN surveys: Harikane et
al. (2023), Kocevski et al. (2023), Maiolino et al. (2023a), Matthee et al.
(2023), as indicated in the legend. The cyan diamonds and squares show the
luminosity function inferred by Giallongo et al. (2019) based on X-ray
surveys. Given the uncertainties, our estimated type 2 AGN density is slightly
higher (by a factor of $\sim$2) than the one estimated for type-1 AGN from the
JADES and CEERS surveys (Maiolino et al., 2023a; Harikane et al., 2023). This
suggests a type 2 to type 1 AGN ratio of about two at z$\sim$4–6, or possibly
higher (given that it is more challenging to identify type 2 AGN). We note
that the contribution to the UV luminosity function is significantly higher
than the estimate of Kocevski et al. (2023), but which was most likely
suffering from a low number of statistics in the early JWST results. The
contribution is also higher than found by Matthee et al. (2023), however, as
discussed in Maiolino et al. (2023a), that study probe more luminous AGN,
hence likely less abundant.

We also note that our estimated density of type-2 AGN is higher than found in
the deep X-ray observations by Giallongo et al. (2019). As stated by Maiolino
et al. (2023a), this suggests that the optical and UV emission line selection
of AGN is picking a population of faint or X-ray deficient population of AGN
at high-z. The extrapolation of the QSO luminosity function at z$\sim$5.5
(orange shaded region Niida et al., 2020) is 1–2 orders of magnitude lower
than AGN luminosity function from JWST. Together with Figures 8, 9 and 10,
this further illustrates that JWST is probing a new parameter space of low
luminosity AGN activity at high-z redshift.

We finally point out that our finding that the type 2 AGN host galaxies UV LF
can be reproduced by scaling the galaxies LF by a factor of 5, indicates that
type 2 AGN host galaxies contribute to $\sim$20% to the reionisation of the
Universe ($\sim$30% if including the type 1 AGN). Note that this does not mean
that AGN contribute to a similar level to the reionization of the Universe, as
the ionizing AGN radiation is obscured along our line of sight, and the IGM
sees on average the same fraction of type 2 to type 1 AGN as our line of
sight.

###  4.7 JADES-GS+53.11243-27.77461 - type-2 AGN at z$\sim$9.43

The most distant type 2 AGN in our sample is ID 10058975 (JADES-
GS+53.11243-27.77461), which was identified using two separate methods: 1) the
detection of [Ne iv] $\lambda$2422 UV emission line; 2) by identifying He ii
$\lambda$1640, C iii] and C iv emission line ratios diagnostic. He ii
$\lambda$1640 is redshifted to the $\sim$1.71 $\mu$m, which is covered by both
Band-1 and Band-2 R1000 grating observations from 1210 observations and Band-1
observations in 3215 observations. We showed the final stacked spectrum from
all three observations in Figure 11. The He ii $\lambda$1640 emission line is
detected in all three sets of observations, however, we stacked the three
spectra for more robust detections (SNR=6.5). The [Ne iv] $\lambda$2424
emission is detected at 4$\sigma$, and we show the spectrum in Figure 14,
indicating the presence of strong ionising radiation.

As this object is well detected in all gratings and PRISM observations, we
were able to fit the full 1-5 $\mu$m spectrum with BEAGLE. We estimated the
stellar mass of the AGN host galaxy to be $1.5\times 10^{8}$ M⊙ with an SFR of
6.6 M⊙ yr-1 (SFR/M${}_{*}=40$ Gyr-1). The object has a UV magnitude of
M${}_{\rm UV}=-20$ and is most likely going through a starburst. However, as
discussed above, the high SFR can be explained by the contamination of the
emission lines by the AGN, artificially increasing the estimated SFR of the
system.

Furthermore, this object has a strong [O iii] $\lambda$4363 emission
(5.1$\sigma$ Laseter et al., 2023) at 5.1$\sigma$. This allowed the authors to
constrain the electron temperature (Te) of 18400$\pm$2100K and metallicity 12
+ log(O/H)=$7.46\pm 0.11$. Such high temperatures and strong auroral line are
anomalous in HII regions, but seen in the NLR of AGN (e.g. Brinchmann, 2023).
The full detailed analysis of the [O iii] $\lambda$4363 and [O iii]
$\lambda$1661,66 is presented in Curti et al. (in prep.).

The metallicity inferred from the direct method is 4$\sigma$ lower than the
value inferred by BEAGLE. Despite the unambiguous detection of an AGN and
exquisite data provided by NIRSpec we are still hampered in the
characterization of NLR and host for integrated spectra such as this one. More
advanced SED fitting of these objects using e.g. BEAGLE-AGN (Vidal-García et
al., 2022) is outside the scope of this paper and will be further investigated
by Silcock et al. (in prep).

Figure 11: Detection of C iv $\lambda\lambda$ 1548,50, He ii $\lambda$1640 and
[O iii] $\lambda$1666 in object ID 10058975 (JADES-GS+53.11243-27.77461) at
z=9.43. The spectrum is a combined spectrum of the Band-1 and Band-2 data from
program ID 1210 and Band-1 data from program ID 3215 to increase the SNR of
the detection. The blue line shows the continuum subtracted observed spectrum,
while the red dashed line shows the best fit to the data.

##  5 Conclusions

In this work we have presented the identification of obscured, i.e. narrow-
line (type-2), AGN candidates in the two deepest spectroscopic fields of the
JADES survey in GOODS-S, using rest-frame optical and UV emission lines. We
have investigated the presence of AGN ionisation, in narrow line emission, by
using classical optical N2-R3, S2-VO87, N2-He2 diagnostic diagrams, as well
diagrams exploiting UV emission lines: C iii], C iv, He ii $\lambda$1640, [Ne
iv] $\lambda$2424, [Ne v] $\lambda$3420 and N V $\lambda$1240.

Based on our analyses we find:

  * •

At z¿3, the N2-BPT and S2-VO87 diagnostic diagrams ([OIII]5007/H$\beta$ vs
[NII]6584/H$\alpha$, and vs [SII]6730/H$\alpha$, respectively) are no longer
able to clearly distinguish photoionisation due to type-2 AGN and star-forming
galaxies, because low metallicity, high-z AGN and star-forming galaxies occupy
the same space on these diagrams (see Figure 2). However, we redefine a
conservative demarcation line between AGN and star-forming galaxies on the BPT
and S2-VO87 diagrams to allow for the shift high redshift star-forming
galaxies. We identify five and seventeen AGN host galaxies on the N2-BPT and
S2-VO87 diagrams, respectively. We stress that this is a very conservative
selection and that many more AGN are certainly present on these diagrams,
mixed with star forming galaxies.

  * •

Using the He ii $\lambda$4686/H$\beta$ vs [N ii]/H$\alpha$ diagnostic diagram
we selected eleven AGN and an additional six AGN using the C iii]/C iv vs C
iii]/HeII$\lambda$1640 diagram (see Figures 3 & 4). Interestingly, the only
X-ray detected AGN in our sample, is located in the star-forming region of the
BPT diagram, while is confirmed as AGN in the HeII diagnostic diagrams.

  * •

We detected the high ionization transitions [Ne iv] $\lambda$2424, [Ne v]
$\lambda$3420 or N V $\lambda$1240 in seven galaxies. The luminosities of
these high ionisation lines compared to the C iii] emission line classify
these objects as AGN hosts galaxies.

  * •

In total we selected 28 AGN in the PID-1210 programme and 14 AGN in the
PID-3215 programme, resulting in AGN fractions of $24\pm 5$% and $14\pm 4$%,
respectively. Combining the two samples, we find 42 unique AGN, and an overall
AGN fraction of $20\pm 3$%. We investigated the evolution of AGN fraction as a
function of redshift (see Figure 7) and did not find evidence for significant
evolution.

  * •

By stacking the AGN and star-forming galaxies’ rest-frame UV and optical
spectra we confirmed that both samples have similar emission line ratios in
the rest-frame optical N2, S2 and R3 emission line ratios. However, the
populations are easily distinguished using the He ii $\lambda$4686 and He ii
1640 emission lines (see cyan and magenta points on Figures 2, 3, 4).

  * •

The estimated bolometric luminosities using narrow-emission lines ([O iii], C
iii]and H$\beta$) are in the range of $6\times 10^{41}$–$5\times 10^{45}$ ergs
s-1 (see Figure 8). The selected AGN host galaxies have a median stellar mass
of 107.9 M⊙ consistent with the median stellar mass of inactive galaxies of
108.0 M⊙.

  * •

We investigated the distance from the star-forming main sequence,
log(SFR/SFRms). Our AGN candidates have a mode and width of the distribution
of log(SFR/SFRms) of $-0.07\pm 0.10$ and $0.61\pm 0.07$, respectively. The
mode of distribution is consistent with SF galaxies while the width of the
distribution is a factor of two broader than for star-forming galaxies,
consistent with previous results at Cosmic Noon.

  * •

We estimated the contribution of the AGN host galaxies to the UV luminosity
function on the order of $\sim 20$%, with a slight (increasing) dependence on
luminosity (see Figure 10).

## Acknowledgements

We thank Anna Feltre for providing us with the updated Cloudy models for star-
forming and AGN with expanded emission line coverage. We thank Michaela
Hirschmann for the productive discussion to help with the selection based on
UV emission lines. JS, RM, FDE, WB, XJ, TJL acknowledge ERC Advanced Grant
695671 “QUENCH” and support by the Science and Technology Facilities Council
(STFC) and by the UKRI Frontier Research grant RISEandFALL. RM acknowledges
support by the UKRI Frontier Research grant RISEandFALL as well as funding
from a research professorship from the Royal Society. SCa and GV acknowledge
support by European Union’s HE ERC Starting Grant No. 101040227 - WINGS. AJB,
AJC, JC, AS & GCJ acknowledge funding from the ”FirstGalaxies” Advanced Grant
from the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (Grant agreement No. 789056). ECL
acknowledges support of an STFC Webb Fellowship (ST/W001438/1). This research
is supported in part by the Australian Research Council Centre of Excellence
for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number
CE170100013. DJE is supported as a Simons Investigator and by JWST/NIRCam
contract to the University of Arizona, NAS5-02015. Funding for this research
was provided by the Johns Hopkins University, Institute for Data Intensive
Engineering and Science (IDIES). MP acknowledges support from Grant
PID2021-127718NB-I00 funded by the Spanish Ministry of Science and
Innovation/State Agency of Research (MICIN/AEI/ 10.13039/501100011033). BER,
KH, ZJ, JL, MR, FS, and CNAW acknowledge support from the NIRCam Science Team
contract to the University of Arizona, NAS5-02015. WB acknowledges support by
the Science and Technology Facilities Council (STFC). BRP acknowledges support
from the research project PID2021-127718NB-I00 of the Spanish Ministry of
Science and Innovation/State Agency of Research (MICIN/AEI/
10.13039/501100011033). MSS acknowledges support by the Science and Technology
Facilities Council (STFC) grant ST/V506709/1. The research of CCW is supported
by NOIRLab, which is managed by the Association of Universities for Research
in Astronomy (AURA) under a cooperative agreement with the National Science
Foundation. HÜ gratefully acknowledges support by the Isaac Newton Trust and
by the Kavli Foundation through a Newton-Kavli Junior Fellowship. This work
was performed using resources provided by the Cambridge Service for Data
Driven Discovery (CSD3) operated by the University of Cambridge Research
Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using
Tier-2 funding from the Engineering and Physical Sciences Research Council
(capital grant EP/T022159/1), and DiRAC funding from the Science and
Technology Facilities Council (www.dirac.ac.uk). The authors acknowledge use
of the lux supercomputer at UC Santa Cruz, funded by NSF MRI grant AST
1828315.

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##  Appendix A Stacked spectra

We show the stacked rest-frame UV and optical spectra in Figures 12 and 13 for
the AGN and star forming galaxies.

Figure 12: Resulting spectrum from the stacking analysis of AGN host galaxies.
The blue line shows the continuum subtracted data, while the red dashed line
indicates the best fit to the stacked spectrum. Top row: Stacked spectrum of
H$\alpha$, [N ii] and [S ii] (left panel) and He ii $\lambda$4686, H$\beta$
and [O iii] $\lambda$5008 (right panel). Bottom row: Stacked spectrum of C iv
(left panel), He ii $\lambda$1640 and [O iii] $\lambda$1666 (middle panel),
and C iii] (right panel).

Figure 13: Resulting spectrum from the stacking analysis of star forming
galaxies. The blue line shows the continuum subtracted data, while the red
dashed line indicates the best fit to the stacked spectrum. Top row: Stacked
spectrum of H$\alpha$, [N ii] and [S ii] (left panel) and He ii $\lambda$4686,
H$\beta$ and [O iii] $\lambda$5008 (right panel). Bottom row: Stacked spectrum
of C iv (left panel), He ii $\lambda$1640 and [O iii] $\lambda$1666 (middle
panel), and C iii] (right panel).

##  Appendix B UV fits

We fit the [Ne iv] $\lambda$2424, [Ne v] $\lambda$3427 and NV$\lambda$1240
emission lines in the § 2.2.1 and we show the spectra for the objects with
detected [Ne iv] $\lambda$2424 in Figure 14. Furthermore, we report the fluxes
of the detected lines in Table 5.

Table 5: List of AGN and their detected fluxes of high ionisation lines: [Ne iv] $\lambda$2422, [Ne v] $\lambda$3420 and N V $\lambda$1240. ID | field | Flux [NeIV]$\lambda$2424 | Flux [NeV]$\lambda$3427 | Flux NV$\lambda$1240
---|---|---|---|---
|  | 10-19ergs s-1 cm-2 | 10-19ergs s-1 cm-2 | 10-19ergs s-1 cm-2
4902 | 1210 | - | - | -
7099 | 1210 | - | - | -
7762 | 1210 | 2.38$\pm 0.67$ | - | -
8083 | 1210 | 7.50$\pm 1.17$ | - | -
8456 | 1210 | - | - | -
8880 | 1210 | - | - | -
9422 | 1210 | - | - | -
9452 | 1210 | - | - | -
10073 | 1210 | - | - | -
16745 | 1210 | - | - | -
17072 | 1210 | - | - | -
17670 | 1210 | - | - | -
21842 | 1210 | - | - | 3.48$\pm 0.17$
22251 | 1210 | - | - | -
10000626 | 1210 | 5.91$\pm 1.07$ | - | -
10008071 | 1210 | - | - | -
10011849 | 1210 | - | - | -
10012477 | 1210 | - | - | -
10012511 | 1210 | - | - | -
10013597 | 1210 | - | - | -
10013609 | 1210 | - | 3.37$\pm 0.75$ | -
10013905 | 1210 | - | - | -
10015338 | 1210 | - | - | -
10035295 | 1210 | - | - | -
10036017 | 1210 | - | - | -
10040620 | 1210 | - | - | -
10056849 | 1210 | 16.75$\pm 1.70$ | - | -
10058975 | 1210 | 1.88$\pm 0.48$ | - | -
95256 | 3215 | - | - | -
99671 | 3215 | - | - | -
104075 | 3215 | - | - | -
108487 | 3215 | - | - | -
111091 | 3215 | - | - | -
111511 | 3215 | - | - | -
114573 | 3215 | - | - | -
132213 | 3215 | - | - | -
143403 | 3215 | - | - | -
201127 | 3215 | - | - | -
202208 | 3215 | - | - | -
208643 | 3215 | - | - | -
209979 | 3215 | - | - | -

Figure 14: Summary of the detection of [Ne iv] $\lambda$2424, [Ne v]
$\lambda$3420 and NV$\lambda$1240\. The blue lines show the continuum
subtracted observed spectrum, black dotted lines indicate the uncertainties on
the flux and the red dashed lines show the best fit to the data.

##  Appendix C Emission lines used in this work

In Table 6 we summarise the emission lines used in this work, their wavelength
and ionisation potential.

Table 6: List of emission lines used in this work, their wavelengths and ionisation potential Emission line |  $\lambda_{\rm rest}$(Å) | Ionisation potential (eV)
---|---|---
[S ii] | 6718,32 | 10.7
[N ii] | 6584,48 | 14.5
H$\alpha$ | 6563 | 13.6
[O iii] | 5008,4961 | 35.1
H$\beta$ | 4861 | 13.6
He ii | 4686 | 54.4
[O ii] | 3727,29 | 13.6
[Ne iii] | 3689 | 41.0
[Ne v] | 3427 | 97.11
[Ne iv] | 2422,24 | 77.2
C iii] | 1907,09 | 24.4
He ii | 1640 | 54.4
C iv | 1548,1550 | 47.9
[N V] | 1239,42 | 77