# Radio Continuum and Water Maser Observations of the High-Mass Protostar IRAS
19035+0641 A

Tatiana M. Rodríguez Physics Department, New Mexico Tech, 801 Leroy Pl.,
Socorro, NM 87801, USA.  Student at the National Radio Astronomy Observatory,
1003 Lopezville Road, Socorro, NM 87801, USA.  Emmanuel Momjian  National
Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville Road, Socorro, NM
87801, USA  Peter Hofner  Physics Department, New Mexico Tech, 801 Leroy Pl.,
Socorro, NM 87801, USA.  Adjunct Astronomer at the National Radio Astronomy
Observatory, 1003 Lopezville Road, Socorro, NM 87801, USA.  Anuj P. Sarma
Physics and Astrophysics Department, DePaul University, 2219 N. Kenmore Ave.,
Chicago, IL 60614, USA  Esteban D. Araya Physics Department, Western Illinois
University, 1 University Circle, Macomb, IL 61455, USA.  Physics Department,
New Mexico Tech, 801 Leroy Pl., Socorro, NM 87801, USA.

###### Abstract

We present Very Large Array (VLA) 1.3 cm continuum and 22.2 GHz H2O maser
observations of the high-mass protostellar object IRAS 19035+0641 A. Our
observations unveil an elongated bipolar 1.3 cm continuum structure at scales
$\lesssim 500\,$au which, together with a rising in-band spectral index,
strongly suggests that the radio emission toward IRAS 19035+0641 A arises from
an ionized jet. In addition, eight individual water maser spots well aligned
with the jet axis were identified. The Stokes V spectrum of the brightest H2O
maser line ($\sim 100\,$Jy) shows a possible Zeeman splitting and is well
represented by the derivatives of two Gaussian components fitted to the Stokes
I profile. The measured $B_{\mathrm{los}}$ are $123\,(\pm 27)$ and $156\,(\pm
8)\,$mG, translating to a pre-shock magnetic field of $\approx 7\,$mG.
Subsequent observations to confirm the Zeeman splitting showed intense
variability in all the water maser spots, with the brightest maser completely
disappearing. The observed variability in a one-year time scale could be the
result of an accretion event. These findings strengthen our interpretation of
IRAS 19035+0641 A as a high-mass protostar in an early accretion/outflow
evolutionary phase.

Unified Astronomy Thesaurus concepts: Jets (870); Radio jets (1347); Star
forming regions (1565); Star formation (1569); Astrophysical masers (103);
Star formation (1569); Protostars (1302); Young stellar objects (1834);
Interstellar magnetic fields (845)

##  1 Introduction

The typical formation time-scale of high-mass stars ($M_{*}>8\,M_{\odot}$) is
much shorter than their lower mass counterparts ($\sim 10^{5}$ versus $\sim
10^{7}\,$yr, e.g., André 1994; Sabatini et al. 2021), and they are still
deeply embedded in their parental cloud by the time hydrogen burning in the
core ignites. This makes it extremely challenging to observationally study the
early stages of high-mass star formation. According to the turbulent core
accretion model (e.g., McKee & Tan, 2003), as the young stellar object (YSO)
accretes material, jets of ionized gas are ejected from very near the
protostellar surface. Even though these ionized jets play a key role in the
high-mass star formation process, their nature is still poorly understood,
mainly due to a lack of sub-arcsecond resolution radio continuum data.
Recently, Rosero et al. (2016, 2019) carried out a deep (rms noise $\sim
5\,$$\mu$Jy beam-1), high angular resolution ($\theta\sim
0$$\overset{\prime\prime}{.}$33) continuum survey at 1.3 and 6 cm toward 58
high-mass star forming regions utilizing the Karl G. Jansky Very Large Array
(VLA). They report the detection of 70 individual radio sources, including H
ii regions, ionized jets, and ionized jet candidates. The latter were
classified as such based on their rising spectral index (expected from ionized
jets as per Reynolds 1986) and association with usual jet tracers, such as
molecular outflows, mostly from single-dish data. However, they were
unresolved or slightly resolved in their observations, which made it
impossible to completely discard ionization by a zero age main sequence star
at the center of a hyper-compact (HC) H ii region as the origin of the radio
continuum emission. Nonetheless, their statistical analysis of ionized jets
and jet candidates provided significant further evidence supporting the common
origin scenario for radio jets driven by YSOs of all luminosities (see Figure
8 in Rosero et al., 2019). This, in turn, supports the idea that high- and
low-mass stars are formed in a similar manner, as predicted by the turbulent
core accretion model. Ionized jets from such high-mass YSOs are expected to
present an elongated morphology at scales of a few hundred au from the driving
protostellar object, while H ii regions are more spherical. In order to
investigate the shock ionization origin of the radio continuum sources
classified as jet candidates on these scales, higher angular resolution
observations are essential. Therefore, we carried out a $3\times$ higher
resolution ($\theta\sim 0$$\overset{\prime\prime}{.}$1) VLA survey at 1.3 cm
toward all jet candidates from Rosero et al. (2016, 2019). The angular
resolution of our data translates to linear resolutions between $\sim 150$ and
1200 au (depending on the distance to each source), which will enable us to
clearly discern whether the jet candidates are indeed jets or H ii regions.

As a byproduct of our radio continuum survey, we observed the 22.2 GHz
($6_{1,6}-5_{2,3}$) H2O (water) maser emission toward the jet candidates.
Water masers are collisionally pumped in high density regions ($n\sim
10^{8-10}\,$cm-3, Elitzur 1992) and are usually associated with jets and
outflows from early stage high-mass YSOs (e.g., Moscadelli et al., 2019;
Ladeyschikov et al., 2022). The detection of water masers in close proximity
to the jet candidates would therefore strongly support the jet interpretation
for the radio continuum sources. Moreover, water masers are highly compact and
can be extremely bright, which makes them an ideal probe to study shock
dynamics at sub-arcsecond resolution (e.g., Moscadelli et al., 2020). In
addition, the Zeeman effect detected using H2O masers offers direct
measurements of the line-of-sight magnetic field strength in star forming
regions (e.g., Sarma et al., 2008). However, because the water molecule is
weakly sensitive to the Zeeman effect (the splitting factor is
$0.0021\,\text{Hz }\mu\text{G}^{-1}$, versus $2.8\,\text{Hz }\mu\text{G}^{-1}$
for H i), this kind of study has only been carried out in a handful of regions
hosting the strongest masers (see the review by Crutcher & Kemball, 2019). In
general, magnetic field measurements in outflows, are extremely difficult to
carry out, thus their role in the launching, collimation, and propagation of
ionized jets has been scarcely studied from an observational point of view. In
this sense, strong water masers are excellent tools to not only investigate
the kinematics of the shocked gas, but also to get key information of the
magnetic fields in the post-shock regions.

In this manuscript, we present observations toward one of the sources in our
sample, IRAS 19035$+$0641 A (I19035A hereafter), as a case study that
demonstrates the potential of the full survey. I19035A is located at a
distance of 2.2 kpc (Osterloh et al., 1997) in a chemically rich environment
(e.g., Fuller et al., 2005; Araya et al., 2005, 2007; López-Sepulcre et al.,
2010, 2011; Lu et al., 2014; Taniguchi et al., 2019). Based on HiGal data,
Rosero et al. (2019) report a bolometric luminosity of $6\times 10^{3}\,$L⊙
for this region. The ultra-compact (UC) H II region IRAS 19035+0641 B (Rosero
et al., 2016) is located at about 2′′ to the South-East of I19035A. The goal
of this work is to investigate the jet nature of I19035A, hence we will
exclude IRAS 19035+0641 B from the following analysis and discussion, but
direct the reader to see Section 2.2 from Rosero et al. (2019) for more
information. This paper is organized as follows: in Section 2, the
observations, data reduction, and imaging details are given. Our results are
presented in Section 3, followed by a discussion in Section 4. Our findings
are summarized in Section 5, and complementary figures are given in the
Appendix.

##  2 Observation & Data Reduction

The K-band ($18-26.5\,$GHz) observations were carried out with NRAO’s111The
National Radio Astronomy Observatory is a facility of the National Science
Foundation operated under cooperative agreement by Associated Universities,
Inc. VLA on March 27, 2022 in the A configuration utilizing the 3-bit
samplers. A total of 57 spectral windows (SPWs) with $128\times 1\,$MHz
channels were used for the continuum. A narrow 16 MHz SPW with $1024\times
15.6\,$kHz (i.e., $1024\times 0.21\,$${\text{km s}^{-1}}$) channels was set up
to simultaneously observe the 22235.08 MHz H2O($6_{1,6}-5_{2,3}$) maser
transition222From the Jet Propulsion Laboratory Molecular Spectroscopy Catalog
(Pickett et al., 1998), as referenced in the Splatalogue database
(https://www.cv.nrao.edu/php/splat/).. The target phase center was set to
$\text{RA(J2000)}=19^{\text{h}}06^{\text{m}}01\overset{{\text{s}}}{.}1$,
$\text{Dec(J2000)}=6^{\circ}46^{\prime}35\overset{{\prime\prime}}{.}0$ and the
total time on source was $\sim 11$ minutes. The primary beam and largest
recoverable scale of these observations are $\sim 2^{\prime}$ and
2$\overset{\prime\prime}{.}$4, respectively. Observations of the quasar 3C286
were used for flux density scale and bandpass calibration, while the complex
gain calibration was done with observations of the calibrator source
J1851+0035, with a cycle time of 4 minutes.

The continuum data were processed through NRAO’s Common Astronomy Software
Applications (CASA, McMullin et al. 2007) Calibration Pipeline, version
6.2.1.7. The images were made using the CASA (version 6.5.2) task tclean a
grid weighting approximately intermediate between natural and uniform (ROBUST
= 0.5). The synthesized beam size, PA, and rms noise of the resulting
continuum images are
0$\overset{\prime\prime}{.}$083$\,\times\,$0$\overset{\prime\prime}{.}$071,
1.75∘, and 13.5 $\mu$Jy beam-1, respectively. The central frequency of the
final continuum image is 22.21 GHz, i.e., 1.35 cm (1.3 cm continuum
hereafter).

The line SPW was calibrated using the CASA pipeline version 5.3.0-147,
modified for spectral line observations. Self-calibration for both phase and
amplitude was performed on the strongest water maser spot (maser spot #4, see
below) using NRAO’s Astronomical Image Processing System (AIPS). The self-
calibration solution tables were then applied to the entire line SPW data. We
used the AIPS task IMAGR with a grid weighting intermediate between natural
and uniform (ROBUST = 0) to create the Stokes I water maser image cube for
total intensity analysis. This resulted in a channel width, synthesized beam
size, PA, and typical rms noise of 0.21 ${\text{km s}^{-1}}$,
0$\overset{\prime\prime}{.}$083$\,\times\,$0$\overset{\prime\prime}{.}$076,
8.1∘, and 13.2 mJy beam-1per channel (in line-free channels), respectively.
Finally, for the Zeeman effect analysis, we additionally made Hanning smoothed
Stokes I and V cubes333$I=(RCP+LCP)/2$ and $V=(RCP-LCP)/2$, where RCP and LCP
are the right and left circular polarization data, respectively..

##  3 Results

###  3.1 Radio Continuum Emission

Our high angular resolution observations unveiled two 1.3 cm continuum peaks,
which are indicated as A2 and A3 in the right panel of Figure 1, with a
significance of $>5\sigma$ toward I19035A. The positions of these peaks are
listed in Table 1. Peaks A2 and A3 are aligned in the NE-SW direction,
separated by $\sim$0$\overset{\prime\prime}{.}$12 ($\sim 265$ au), and were
reported as a single radio continuum source at both 1.3 and 6 cm by Rosero et
al. (2016) in their lower angular resolution observations. In addition, our
detailed inspection of the images from the Rosero et al. (2016) observations
revealed a second, weaker 6 cm continuum peak ($>10\sigma$) located $\sim
0$$\overset{\prime\prime}{.}$65 ($\sim 1400\,$au) to the NE of A2, as seen in
the left panel of Figure 1. We will refer to this second, weaker 6 cm peak as
A1. While the lower resolution 1.3 cm continuum emission is dominated by a
rather compact source, as reported by Rosero et al. (2016), it appears to be
elongated toward A1 (see the lowest red contour in the left panel of Figure
1). However, this weak, extended 1.3 cm continuum emission toward A1 was not
recovered in our higher angular resolution observations.

The spectral index ($\alpha$, with $S_{\nu}\propto\nu^{\alpha}$) between the 6
and 1.3 cm for the dominant continuum source, as seen in the lower resolution
images, has a value of 0.9 (Rosero et al., 2016). Through the re-inspection of
their data, as noted above, we find that the spectral index flattens to 0 as
we reach A1. In our high angular resolution 1.3 cm continuum data, the
measured in-band spectral indices are 0.9 and 0.6 for peaks A2 and A3,
respectively.

We fitted 2-D Gaussians to each continuum peak using the highest resolution
images available on them, that is, the 6 cm for A1 and our 1.3 cm for A2 and
A3. The obtained positions, sizes, PA, peak intensities, and flux densities
are presented in Table 1. Peaks A2 and A3 are marginally resolved, with upper
limits to their deconvolved minor axes sizes at full width half maximum (FWHM)
as reported in the third column of the table. The sum of the flux densities of
A2 and A3 is consistent with the value reported by Rosero et al. (2016) for
the 1.3 cm continuum source in their image.

Figure 1: Left: Our 1.3 cm continuum emission toward I19035A is shown in
color, and overlaid in red and cyan contours are the 1.3 and 6 cm continuum
emission from Rosero et al. (2016), respectively. Contour levels are
$[-2,5,10,20,30,50,57]\times\,$7 $\mu$Jy beam-1 and
$[-3,3,5,10,12,20,25]\times 4\,$$\mu$Jy beam-1 for the K- and C-band data,
respectively. The ellipses in the bottom left represent our 1.3 cm synthesized
beam size (filled white, VLA A configuration), as well as the 1.3 (red dashed,
VLA B configuration) and 6 cm (blue dashed, VLA A configuration) ones from
Rosero et al. (2016). The white rectangle shows the region covered by the
right panel. Right: Our 1.3 cm continuum emission toward I19035A in color and
white contours. The latter show $[-3,3,5,10,15]\times 13.5\,$$\mu$Jy beam-1
levels. The filled white ellipse in the bottom right represents the
synthesized beam size ($\sim 0.08$′′). Table 1: Properties of the continuum
peaks

Peak | RAJ2000 | DecJ2000 | $\theta_{maj}$ | $\theta_{min}$ | PA | $I_{\nu}$ | $S_{\nu}$
---|---|---|---|---|---|---|---
| (h m s) | (${}^{\circ}\,{}^{\prime}\,{}^{\prime\prime}$) | (mas) | (au) | (mas) | (au) | (∘) | ($\mu$Jy beam-1) | ($\mu$Jy)
A1 | 19 06 01.624 | 6 46 36.70 | 442 | 972 | 117 | 257 | 13.8 | 49 (5) | 91 (15)
A2 | 19 06 01.605 | 6 46 36.17 | 57 | 125 | $<21$ | $<46$ | 45.6 | 235 (13) | 277 (26)
A3 | 19 06 01.599 | 6 46 36.08 | 55 | 121 | $<11$ | $<24$ | 106.6 | 150 (13) | 165 (25)

††footnotetext: Values in parentheses are the uncertainties in the
measurements. Note that the values for A1 were obtained using the C-band data
from Rosero et al. (2016).

###  3.2 22.235 GHz H2O Masers

We detected 8 water maser spots that seem to be associated with I19035A. The
maser spots are distributed along the same direction as the continuum emission
and form a narrow linear structure in the NE-SW direction, as shown in Figure
2. Maser #1 is the farthest from the radio continuum source, located at about
0.2 pc ($\sim$19′′) to the NE, while the other maser spots are found within
$\sim$2500 au from the continuum peaks. Masers #2 and #3 lay toward peak A1,
while maser #4 is found close to the center of A2. Masers #5 $-$ #8 are
distributed to the SW of I19035A.

We report the peak position and intensity of each maser spot in columns 2, 3,
and 4 of Table 2, respectively. We measured the velocity of the peak, its full
width at zero power (FWZP), and the total velocity extent of the emission
using the peak pixel spectrum for each maser spot. We list these in columns 5,
6, and 7 of Table 2, respectively, while the spectra can be found in the
Appendix. Maser spots #4 and #6 span a wide velocity range. This likely
indicates that these spots have substantial substructure that we are not able
to spatially resolve. The brightest maser spot, maser #4, has a peak intensity
of 102.8 Jy beam-1 at $V_{\mathrm{LSR}}^{\mathrm{{peak}}}=20.1\,$${\text{km
s}^{-1}}$.

Table 2: Properties of the Detected Maser Spots

# | Peak Pos. | $I_{\nu}$ | $V_{\mathrm{LSR}}^{\mathrm{{peak}}}$ | $\Delta V_{\mathrm{FWZP}}^{\mathrm{peak}}$ | $\Delta V$
---|---|---|---|---|---
| RA (J2000) | Dec (J2000) | (Jy beam-1) | (${\text{km s}^{-1}}$) | (${\text{km s}^{-1}}$) | (${\text{km s}^{-1}}$)
1 | 19h06m2.712s | 6d46m45.98s | 0.37 (0.01) | 37.55 | 1.26 | 1.26
2 | 19h06m1.627s | 6d46m36.77s | 0.77 (0.01) | 33.54 | 2.95 | 2.95
3 | 19h06m1.626s | 6d46m36.62s | 0.37 (0.01) | 31.02 | 1.05 | 1.05
4 | 19h06m1.605s | 6d46m36.18s | 102.78 (0.19) | 20.06 | 4.84 | 35.60
5 | 19h06m1.591s | 6d46m35.96s | 0.29 (0.01) | 37.12 | 1.90 | 1.90
6 | 19h06m1.579s | 6d46m35.92s | 3.61 (0.01) | 36.07 | 1.68 | 42.13
7 | 19h06m1.566s | 6d46m35.49s | 0.11 (0.01) | 28.70 | 1.05 | 1.05
8 | 19h06m1.508s | 6d46m35.25s | 2.11 (0.01) | 30.17 | 1.90 | 3.37

††footnotetext: The columns show the maser spot number (#), peak position,
peak brightness ($I_{\nu}$), velocity of the peak brightness
(V${}_{\mathrm{LSR}}^{\mathrm{peak}}$), FWZP of the peak ($\Delta
V_{\mathrm{FWZP}}^{\mathrm{peak}}$), and the total velocity extent of the
maser spot emission ($\Delta V$). Values in parentheses are formal errors from
the 2-D Gaussian fits.

Figure 2: Left: The 6 cm continuum emission in IRAS 19035+0641 is shown in
color and cyan contours (Rosero et al., 2016). The contours are the same as in
the left panel of Figure 1. Right: Enlarged version of the area marked with
the red rectangle in the left-hand panel. The 6 cm continuum emission is shown
in color (note the scale difference between the panels), and our 1.3 cm
continuum emission is shown in white contours. The contours are the same as in
the right panel of Figure 1, and the outlined and filled ellipses in the
bottom left represent the 6 cm ($\sim$0$\overset{\prime\prime}{.}$33) and our
1.3 cm ($\sim$0$\overset{\prime\prime}{.}$08) synthesized beam sizes,
respectively. The white rectangle shows the region covered in the right panel
of Figure 1. In both panels, the red $+$ symbols mark the position of masers
#1 through #8, which are also numbered.

####  3.2.1 Zeeman splitting in maser #4

The Stokes I and V profiles of the strongest maser #4 feature are shown with a
histogram-like line in the top and bottom panels of Figure 3, respectively.
The Stokes I spectrum shows an asymmetric shape, indicative of at least two
Gaussian components. We used the AIPS task XGAUS to fit the Stokes I profile.
The best fit, shown with a red line in the right panels of Figure 3,
corresponds to two Gaussian components, shown separately in the left panels
with a blue and a green line. The S-shape appearance observed in the Stokes V
profile is usually considered a Zeeman effect detection. We derived the line-
of-sight magnetic field $B_{\mathrm{los}}$ by fitting the Stokes V profile
(e.g., Momjian & Sarma, 2017, 2019) to the expression (Troland & Heiles, 1982;
Sault et al., 1990):

$V=aI+\frac{b}{2}\frac{dI}{d\nu}.$ (1)

The first term on the right-hand side of the equation accounts for small
leakage terms and, in this case, the scale factor $a$ was on the order of
$10^{-3}$. The second term is the frequency derivative of the Stokes I profile
multiplied by a fit parameter $b$, which contains the magnetic field
information. Namely, $b=zB_{\mathrm{los}}$, where z is the Zeeman splitting
factor. For the 22.2 GHz water transition, $z=2.1\,\text{Hz mG}^{-1}$
(Nedoluha & Watson, 1992). We used the AIPS task ZEMAN to fit equation 1,
which derives a b value for each Gaussian component by fitting their
derivative to the Stokes V profile. As seen in Figure 3, the Stokes V profile
of maser #4 is well represented by the derivatives of the fitted Gaussians,
and the measured $B_{\mathrm{los}}$ values are $123\,(\pm 27)$ and $156\,(\pm
8)$ mG. These values are at a level of $4\sigma$ and $19\sigma$
(respectively), hence the measured magnetic field is significant in both
cases. We note that, by convention, a positive value for $B_{\mathrm{los}}$
means the line-of-sight magnetic field is pointing away from the observer.

Figure 3: Stokes I (top) and V (bottom) profiles of the brightest peak of
maser spot #4 are shown with a black histogram-like line. The green and blue
curves in the left panel show the two Gaussian components fitted to the Stokes
I profile (top) and their derivatives (bottom). The red curves in the right
panel show the sum of the two components.

####  3.2.2 Follow-up observations: extreme H2O maser variability

The detection of the $\sim 100$ Jy H2O maser peak in I19035A led us to
investigate the Zeeman splitting of this line and the magnetic field in the
post-shock region. However, since the original observations were not designed
for Zeeman effect analysis (e.g., the use of 3-bit samplers), we carried out
follow-up VLA B-configuration observations on March 14, 2023, to confirm the
Zeeman splitting. These observations, which utilized the 8-bit samplers,
resulted in $\sim$ 30 minutes of on-source time. We used a single 8 MHz wide
SPW with $2048\times 3.91\,$kHz channels, which translates to a $52\,\text{m
s}^{-1}$ channel width. The data were manually calibrated and imaged using
CASA. The synthesized beam size and typical rms noise of the final image cube
are $\sim$0$\overset{\prime\prime}{.}$3 and $5.5\,$mJy beam-1 per channel in
line-free channels, respectively.

The new observations recovered most maser spots seen in the 2022 observations,
with varying degrees of variability. In Figure 4, we show a comparison of the
two epochs for maser spot #4. While most peaks observed in the original
spectrum are present, albeit with different intensities, the bright feature
that showed Zeeman splitting was not detected in the new data. Thus, while we
were not able to confirm the Zeeman splitting detection, the complete
disappearance of the $\sim$100 Jy peak in maser #4 indicates extreme H2O maser
variability in I19035A.

Figure 4: Comparison of maser #4 peak spectrum as observed in March 2022
(purple) and March 2023 (orange). The left and right panels show the full and
zoomed in spectra, respectively. All the spectral features show different
degrees of variability. Particularly, the strong $\sim 100\,$Jy peak was not
detected in the March 2023 observations. The vertical dashed line marks the
adopted systemic velocity ($V_{sys}=33.8$ ${\text{km s}^{-1}}$, Araya et al.,
2005).

##  4 Discussion

###  4.1 Nature of the Radio Continuum Emission

Based on the compact nature of our new 1.3 cm continuum data, one might
consider the possibility of peaks A2 and A3 being two individual YSOs in a
tight ($d\approx 265\,$au) protobinary system (e.g., Kraus et al., 2017; Zhang
et al., 2019). In this scenario, the radio continuum emission would be
attributed to early-stage H II regions. If A2 and A3 have a spherical geometry
of diameter equal to their geometric mean (calculated using the sizes reported
in Table 1), then their radii are approximately 40 and 30 au, or about
$1.5\times 10^{-4}$ pc. These sizes are consistent with those expected for HC
H II regions ($<0.03\,$pc, Kurtz, 2005). Using the ionizing photon rate
($N_{Ly}$) equation from Sánchez-Monge et al. (2013), assuming optically thin
emission from a homogeneous H II region and an electron temperature of
$10^{4}$ K, we calculate $N_{Ly}$ values of $1.5\times 10^{44}$ and $9.5\times
10^{43}\,\text{s}^{-1}$ for A2 and A3, respectively, corresponding to B2-B3
Zero Age Main Sequence stars.

Note that the cm spectrum is still rising in K-band, which for the above
calculation was assumed to mark the turnover point between optically thick to
optically thin conditions. A rising spectrum can also be explained by a
stratified electron density distribution, which naturally would occur in an
ionized stellar wind. We can use equation 5 in Felli et al. (1982) to estimate
the stellar wind mass loss rate. Assuming a terminal wind velocity of 600
${\text{km s}^{-1}}$, we obtain mass loss rates of the order of $10^{-7}$ M⊙
yr-1. This number is several magnitudes higher than what is expected from
near-main-sequence B2-B3 type stars (e.g., Cohen et al., 1997). Nonetheless,
for stars in evolutionary stages prior to the main sequence, such high values
are perhaps not unexpected.

While the above analysis shows that our data are consistent with a model where
the radio continuum peaks A2 and A3 are caused by a high-mass binary, there is
much morphological evidence that an ionized jet model is preferable. First,
the observed extended and elongated morphology of the radio continuum emission
at 6 cm can hardly be attributed to two compact objects located at the
position of A2 and A3. Moreover, this elongated structure is very well aligned
with A2 and A3, in the NE-SW direction. Second, the water maser spots #2 $-$
#7 are also very well aligned in the direction of the radio continuum
emission, along a structure much larger than the distance between A2 and A3,
which is inconsistent with a wind-driven shell scenario (e.g., Beltrán et al.,
2007). Ultimately, even though we cannot entirely discard a high mass binary
for the origin of the observed 1.3 cm continuum emission, it appears more
likely to be tracing an ionized jet. We will now discuss this scenario.

Rosero et al. (2019) had classified I19035A as a possible ionized jet based on
its association with a bipolar molecular outflow observed in CO(2$-$1) and
HCO${}^{+}(1-0$) (Beuther et al., 2002a; López-Sepulcre et al., 2010). The
orientation of the outflow at the angular resolution of these studies (11′′
and 29′′, respectively) is SE-NW, i.e., perpendicular to the NE-SW orientation
of the 6 cm continuum. However, the classification as a high-mass ionized jet
was still made based on a number of additional observational results, like the
coincidence of the $6\,$cm continuum with a cone-shaped 2.2$\,\mu$m structure,
presumably tracing scattered light from an outflow cavity, the presence of
SiO(2$-$1) and (3$-$2) in the region (López-Sepulcre et al., 2011), as well as
detections of Class II 6.7 GHz CH3OH masers in the vicinity of I19035A
(Beuther et al., 2002b; Hu et al., 2016; Ouyang et al., 2019). Furthermore,
with a radio luminosity of $S_{5GHz}d^{2}=1.05\,$mJy kpc2, I19035A falls in
the high-mass end of the $S_{\nu}d^{2}-L_{Bol}$ relation shown in figure 8
from Rosero et al. (2019) for ionized jets, and it is well below the radio
luminosity values for UC/HC H ii regions ($\approx 10^{2}\,$mJy kpc2). This
further indicates that the I19035A radio luminosity at 5 GHz is likely due to
shock ionization rather than photoionization from the central YSO. The fact
that the large scale outflow is perpendicular to the jet direction can be
explained by precession of the jet axis, for which there are a number of known
cases discussed in the literature (e.g., Rodríguez et al., 2021). High angular
resolution observations of the bipolar molecular flow are necessary to verify
this conjecture and unveil the true outflow orientation.

Recently, Fedriani et al. (2023), using the Stratospheric Observatory for
Infrared Astronomy (SOFIA) $7.7-37.1\,\mu$m observations together with multi-
wavelength data from the literature, compiled the detailed SED of IRAS
19035+0641 and carried out radiative transfer modeling based on the turbulent
core accretion theory (Zhang & Tan, 2018, and references therein). Their
average model predicts a central protostellar mass of $15\,$M⊙ accreting at a
rate of $2.1\times 10^{-4}\,$M${}_{\odot}\,$yr-1, and a bolometric luminosity
of $4.9\times 10^{4}\,$L⊙. The mass outflow rate of the CO was estimated by
Beuther et al. (2002a) as $0.9\times 10^{-4}\,$M${}_{\odot}\,$yr-1, i.e.,
consistent with the idea that part of the energy released by the accretion
process powers the outflow.

What do our new continuum data add to this picture? The radio luminosity at 5
GHz discussed above was derived from a source consisting of a compact core
with a NE-SW extension (Rosero et al., 2016). The higher angular resolution
$1.3\,$cm observations reported here resolve the source into two peaks (A2 and
A3) aligned in the NE-SW direction. This shows that the elongated bipolar
structure persists on a much smaller scale of $\lesssim 500\,$au, as expected
from an ionized jet. Moreover, the rising in-band spectral indices for both A2
and A3 (0.9 and 0.6, respectively) are in good agreement with the ideal jet
model from Reynolds (1986). In conclusion, our observations strengthen the
interpretation that I19035A is an ionized jet from a high-mass protostar.

###  4.2 The Water Masers

####  4.2.1 Association with the I19035A Jet

Water masers in high-mass star forming regions can be associated with both
jets/winds and accretion disks (e.g., Imai et al., 2006; Sarma et al., 2008;
Moscadelli et al., 2022). It is therefore pertinent to analyze whether the
water masers detected in I19035A probe the ionized jet or a disk. In the
latter case, water maser spots arise in different regions of the disk, and
sometimes show Keplerian velocity gradients and arc-shape structures of a few
au in size that are only resolved using Very Large Baseline Interferometry
(VLBI) techniques (e.g., Torrelles et al., 1998; Franco-Hernández et al.,
2009; Rodríguez et al., 2012). In contrast, our observations reveal a well
defined linear maser spot distribution covering about 2500 au (excluding maser
#1, located at $\sim 40\,000$ au from the continuum source), and the water
maser positions are well aligned with the ionized jet axis. If these were
associated with a disk, a distribution perpendicular to the jet would be
expected. Therefore, the water masers in I19035A appear to be mostly
associated with the ionized jet observed in the radio continuum. However, we
note that maser spot #4, located close to the radio continuum peak A2, shows
several emission peaks of varied intensity at red- and blue-shifted velocities
from the adopted $V_{LSR}$ (see Figure 4), thus it deserves additional
consideration. We carried out 2-D Gaussian fits to each maser #4 spectral
feature to obtain their positions, which we show color-coded by velocity on
top of the 1.3 cm continuum emission in Figure 5. The individual components of
maser spot #4 are located along a N-S line, and there is an apparent velocity
gradient along this structure. If this gradient was associated with a
Keplerian accretion disk, the equation $M=RV^{2}/G$ can be used to estimate
the mass of the central object. Using this expression, the derived mass is
$\sim 95\,$M⊙, which appears too large for an early stage high-mass YSO.
Therefore, we believe it is unlikely that maser spot #4 traces an accretion
disk. More likely, the observed velocity gradient is reflecting the velocities
of individual shocks in the outflowing gas, and might be tracing an expanding
or rotating flow. VLBI observations and a proper motion study are required to
determine the nature of maser spot #4.

In the following, we discuss the association of the maser spots #1 and #8 with
the I19035A jet. Maser #1 is found at $\sim 0.2\,$pc ($\sim 40000\,$au) from
the ionized jet, thus their relation is not immediate. Although Moscadelli et
al. (2020) found that 84% of water masers are found within 1000 au from the
driving protostar, they report detections at distances up to $\lesssim
18000\,$au (0.09 pc), and strong jet bow-shocks where water masers can be
pumped are usually found at pc-scale distances from the high-mass YSOs (e.g.,
HH 80/81 at $\sim$1 pc from IRAS 18162$-$2048, Reipurth & Graham, 1988). As
seen in Figure 2, the PA of maser #1 is consistent with that of the ionized
jet and the other water maser spots. Also the peak velocity of maser #1 is
within 10 ${\text{km s}^{-1}}$ from the systemic velocity of the region, and
it is consistent with the velocity range in which the other masers in the jet
present emission. Moreover, we detected variability in the intensity of maser
#1 in our follow up observations (see Section 3.2.2), which suggests a common
origin for this and the other maser spots in the jet. Finally, we did not
detect a radio continuum source in the vicinity of maser #1 that could be
associated with its emission, although we do not discard the possibility of
this source being outside our field of view or fainter than $\sim 40\,$$\mu$Jy
beam-1 (i.e., our $3\sigma$ limit). Meanwhile, maser #8 is found in close
proximity (projected) to the UC H ii region IRAS 19035+0641 B. The association
of water masers with H ii regions has been investigated in the past.
Observational studies found that water masers can be actively pumped in the
edges of expanding wind-driven, ionized shells (e.g., Beltrán et al., 2007).
It has also been suggested that they might coexist with H ii regions and
eventually disappear while the YSO remains visible (e.g., Sánchez-Monge et
al., 2013). The PA and peak velocity of maser #8, as in the case of maser #1,
are consistent with the other water masers in the jet. We did not detect maser
#8 in our follow-up observations, which might be indicating extreme
variability and a common pumping mechanism with the other masers that arise in
the jet. Additionally, maser spot #8 was the only one detected nearby the UC H
II region, and we do not see an arc- or ring-like distribution, indicative of
an expanding shell, around IRAS 19035+0641 B. This could be due to an
anisotropic shell structure, however, there is not enough information in the
literature about the kinematics of this UC H II region, such as the expanding
shell velocity. In summary, our observations suggest maser #1 is likely
associated with the I19035A jet and are insufficient to unambiguously
determine the origin of maser #8. A subsequent water maser proper motion study
could shed light on the connection between these masers and the jet.
Alternatively, observations of other shock probes and molecular outflow
tracers could be used to determine whether there is a spatial correlation
between masers #1 and #8 and the jet/outflow structure.

Figure 5: Positions obtained from 2-D Gaussian fits to each maser #4 spectral
feature are shown with $+$ symbols overlaid to the 1.3 cm continuum in black
contours. The data points are color-coded by velocity the contours are the
same as in the right panel of Figure 1. The dashed black ellipse in the bottom
left represents the synthesized beam size. We note that the difference in the
positions obtained are much larger than the formal fit errors provided by the
CASA task imfit.

####  4.2.2 Estimation of Energetic Parameters from the Measured Magnetic
Field

We can use the more significant line-of-sight magnetic field component of
$B_{\mathrm{los}}\sim 156$ mG, measured from our possible Zeeman splitting
detection to estimate some interesting jet and shock parameters. Firstly, we
will estimate the number density ($n_{\mathrm{post}},n_{\mathrm{pre}}$) and
magnetic field strength ($B_{\mathrm{post}},B_{\mathrm{pre}}$) in the pre- and
post-shock gas. If we assume that the magnetic field is amplified
proportionally with the density in the shocked region (e.g., Sarma et al.,
2002), then we can relate these values as:

$\frac{B_{\mathrm{post}}}{B_{\mathrm{pre}}}=\frac{n_{\mathrm{post}}}{n_{\mathrm{pre}}}.$
(2)

In addition, Crutcher (1999) showed that, statistically, the total post-shock
magnetic field ($B_{\mathrm{post}}$) can be well approximated by
$B_{\mathrm{post}}=2B_{\mathrm{los}}$. We can also relate the
$B_{\mathrm{pre}}$ and the pre-shock gas density ($\rho_{\mathrm{pre}}$) as:

$B_{\mathrm{pre}}=\beta\rho_{\mathrm{pre}}^{1/2},$ (3)

where $\beta\approx 5.8\times 10^{5}$ in cgs units (calculated empirically by
Sarma et al., 2002). Combining these expressions, and taking
$n_{\mathrm{post}}\sim 10^{9}\,\text{cm}^{-3}$ (Elitzur, 1992), we find that
the density in the pre-shock region is
$\rho_{\mathrm{pre}}=n_{\mathrm{pre}}m\sim 10^{-16}\,$g cm-3, where $m$ is the
mean molecular weight (i.e., $m=2.8m_{p}$, with $m_{p}$ being the proton
mass). This yields a pre-shock gas number density of the order of
$n_{\mathrm{pre}}\sim 2\times 10^{7}\,\text{cm}^{-3}$, which in turn
translates to a pre-shock magnetic field strength of $B_{\mathrm{pre}}\sim
7\,$mG.

We can also investigate what process is more likely to dominate the motion in
the post-shock region by comparing the magnetic energy density ($\mathcal{M}$)
and the kinetic energy density ($\mathcal{K}$). The magnetic energy density is
defined as:

$\mathcal{M}=B_{\mathrm{post}}^{2}/8\pi.$ (4)

Crutcher (1999) showed that, for an ensemble of measurements, a good
approximation for the post-shock magnetic field value squared is given by
$B_{\mathrm{post}}^{2}=3B_{\mathrm{los}}^{2}$. For $B_{\mathrm{los}}=156$ mG,
$\mathcal{M}=2.9\times 10^{-3}\,\text{erg cm}^{-3}$. Meanwhile, the kinetic
energy density can be expressed as:

$\mathcal{K}=(3/2)mn_{\mathrm{post}}\sigma^{2},$ (5)

where $\sigma$ is the velocity dispersion of the FWHM linewidth $\Delta v$,
i.e., $\sigma=\Delta v(8\ln 2)^{-1/2}$. If $\Delta v$ is the FWHM of the
brightest peak in maser #4, which is $1.16\,$${\text{km s}^{-1}}$, then
$\mathcal{K}\sim 1.7\times 10^{-5}\,\text{erg cm}^{-3}$ in the post-shock
region. However, the maser emission is amplified along the path of greatest
velocity coherence, which translates to a narrowing of the maser line.
Therefore, a $\Delta v$ value obtained from a maser line is a lower limit of
the real dispersion of the gas in the region. If instead we use $\Delta
v=7.0\,$${\text{km s}^{-1}}$ from single-dish CH3CN observations (Araya et
al., 2005), the kinetic energy density would be of the order $\mathcal{K}\sim
6.2\times 10^{-4}\,\text{erg cm}^{-3}$. This is still an order of magnitude
smaller than $\mathcal{M}$, indicating that the magnetic energy is likely
dominating the motion in the post-shock region.

In addition, the shock velocity $v_{s}$ can be estimated by equating the
magnetic energy density and the ram pressure (e.g., Sarma et al., 2008):

$B_{\mathrm{post}}^{2}/8\pi=\rho_{\mathrm{pre}}v_{s}^{2}.$ (6)

For $B_{\mathrm{los}}=156\,$mG and the $\rho_{\mathrm{pre}}$ estimated above,
we obtain a shock velocity of 62 ${\text{km s}^{-1}}$. Typically, shocks are
classified according to their velocity as C\- or J-type shocks, with the
former having $v_{s}\lesssim 30\,$${\text{km s}^{-1}}$ and the latter
$v_{s}>30\,$${\text{km s}^{-1}}$. The derived shock velocity values are in
good agreement with maser spot # 4 arising in a J-type shock.

As noted previously, the above calculations make use of the $B_{\mathrm{los}}$
measurement obtained from our Zeeman splitting detection in the brightest peak
from the maser #4 spectrum. Even though we were unable to confirm this
detection, as noted in Section 3.2.2, we briefly discuss the implications of
the calculated physical parameters on the jet. The estimated magnetic field in
the jet is $B_{\mathrm{pre}}\sim 7\,$mG, which is comparable to the higher
values obtained by Curran & Chrysostomou (2007) for a set of high-mass YSOs at
scales of about 0.1 pc ($\mathbf{<0.1\sim 6}\,$mG). We expect the value of the
magnetic field along the jet axis to remain fairly constant at scales of $\sim
0.5\,$pc (e.g., Carrasco-González et al., 2010), therefore it is likely that
the energetic parameters derived above represent the magnetic field in the
I19035A jet. However, when comparing the magnetic energy density and the
kinetic energy density derived from the pre-shock magnetic field
($B_{\mathrm{pre}}\sim 7\,$mG) and density ($n_{\mathrm{pre}}\sim 2\times
10^{7}\,\text{cm}^{-3}$) values, we find that the magnetic energy density
($\mathcal{M_{\mathrm{pre}}}\sim 1.9\times 10^{-6}\,\text{erg cm}^{-3}$) is an
order of magnitude smaller than the kinetic energy density in the pre-shock
region ($\mathcal{K_{\mathrm{pre}}}\sim 1.2\times 10^{-5}\,\text{erg
cm}^{-3}$). Therefore, while the magnetic field appears to dominate the post-
shock gas dynamics, the motions along the jet seem to be dominated by the gas
kinematics.

####  4.2.3 Maser Variability

There has been increasing observational evidence that variations in the
underlying protostellar object could be responsible for maser flares and maser
variability of several different species. Brogan et al. (2018) discuss the
case for water maser variability in an outflow caused by a burst of infrared
radiation due to an accretion event propagating along the flow cavity. In this
scenario, extreme increase or decrease in water maser intensity has a
radiative origin, even though the principal pumping mechanism is collisions
(see also Gray et al. 2016). A similar scenario was invoked by Hirota et al.
(2021) to explain water maser variability in the high-mass protostellar object
S255 NIRS 3. Alternatively, Trinidad et al. (2003) demonstrate that random
Gaussian fluctuations of an unsaturated maser (i.e., exponential
amplification) in the line opacity can account for maser variability in the
span of a few months. The results in Andreev et al. (2017) are also consistent
with changes in the amplification path length.

In I19035A, we detected strong variability within one year in all water maser
spots, although we do not see a global increase or decrease trend in the
intensities. In all cases, some spectral features become brighter and others
dimmer, new lines appear, and others become completely undetected. No position
dependence of the variability along the NE-SW line of H2O masers was found
either. While we find many very small scale intensity variations, the
weakening of a $\sim 100\,$Jy maser to below $16.5\,$mJy ($3\times$ the rms
noise in our follow-up observations) makes a statistical fluctuation unlikely.
A possible scenario that would explain the observed variations is that
accretion events occurred in I19035A. Monitoring of the H2O maser line, VLBI
observations, and observations of other probes (such as the radiatively pumped
Class II CH3OH masers) toward I19035A are needed to explore in detail the
origin of the water maser variability in this high-mass protostellar object.

##  5 Summary & Conclusions

We report VLA A-configuration observations of the 1.3 cm continuum and 22.2
GHz H2O masers toward the high-mass protostar I19035A. Our discoveries are
summarized as follows:

  1. 1.

We resolved the 1.3 cm continuum emission toward I19035A into a bipolar
structure consisting of two peaks aligned in the NE-SW direction. We showed
that the elongated, bipolar structure reported by Rosero et al. (2016, 2019)
persists at scales $\lesssim 500\,$au. The in-band spectral index calculated
is consistent with what is expected from an ionized jet.

  2. 2.

We detected eight 22.2 GHz H2O maser spots well aligned along the ionized jet
axis. The intensity of the brightest peak in each maser spot ranges between
0.11 (maser #7) and 102.78 Jy beam-1 (maser #4).

  3. 3.

We detected possible Zeeman splitting in the brightest line observed. The
Stokes I profile was well fitted by two Gaussian components, and the measured
line-of-sight magnetic field values are 123 and 156 mG. This yields a pre-
shock magnetic field of $\sim 7\,$mG.

  4. 4.

We carried out a second set of observations of the water maser emission in
I19035A spaced one year from the original to confirm our Zeeman splitting
detection. We found intense variability in all maser spots. Particularly, the
bright $\sim 100\,$Jy beam-1 peak was not detected.

In conclusion, our observations of I19035A strengthen the original
classification as an ionized jet from a high mass protostar based on the
bipolar nature of the $1.3\,$cm continuum on scales $\lesssim 500\,$au, the
occurrence of H2O masers along the jet axis, and the presence of extreme
variability which could have been caused by an accretion event.

Acknowledgments. We thank the anonymous reviewer for their excellent comments
and suggestions that improved this work. T.M.R. is supported by the NRAO’s
Grote Reber Doctoral Fellowship. P.H. and E.D.A. acknowledge support from NSF
grants AST–1814011, and AST–1814063, respectively. This work was partially
funded by the NRAO’s Student Observer Support (T.M.R. and P.H.).

## References

  * André (1994) André, P. 1994, in The Cold Universe, ed. T. Montmerle, C. J. Lada, I. F. Mirabel, & J. Tran Thanh Van, 179
  * Andreev et al. (2017) Andreev, N., Araya, E. D., Hoffman, I. M., et al. 2017, ApJS, 232, 29, doi: 10.3847/1538-4365/aa8b15
  * Araya et al. (2007) Araya, E., Hofner, P., Goss, W. M., et al. 2007, ApJS, 170, 152, doi: 10.1086/513702
  * Araya et al. (2005) Araya, E., Hofner, P., Kurtz, S., Bronfman, L., & DeDeo, S. 2005, ApJS, 157, 279, doi: 10.1086/427187
  * Beltrán et al. (2007) Beltrán, M. T., Cesaroni, R., Moscadelli, L., & Codella, C. 2007, A&A, 471, L13, doi: 10.1051/0004-6361:20077974
  * Beuther et al. (2002a) Beuther, H., Schilke, P., Menten, K. M., et al. 2002a, ApJ, 566, 945, doi: 10.1086/338334
  * Beuther et al. (2002b) Beuther, H., Walsh, A., Schilke, P., et al. 2002b, A&A, 390, 289, doi: 10.1051/0004-6361:20020710
  * Brogan et al. (2018) Brogan, C. L., Hunter, T. R., Cyganowski, C. J., et al. 2018, ApJ, 866, 87, doi: 10.3847/1538-4357/aae151
  * Carrasco-González et al. (2010) Carrasco-González, C., Rodríguez, L. F., Anglada, G., et al. 2010, Science, 330, 1209, doi: 10.1126/science.1195589
  * Cohen et al. (1997) Cohen, D. H., Cassinelli, J. P., & MacFarlane, J. J. 1997, ApJ, 487, 867, doi: 10.1086/304636
  * Crutcher (1999) Crutcher, R. M. 1999, ApJ, 520, 706, doi: 10.1086/307483
  * Crutcher & Kemball (2019) Crutcher, R. M., & Kemball, A. J. 2019, Frontiers in Astronomy and Space Sciences, 6, 66, doi: 10.3389/fspas.2019.00066
  * Curran & Chrysostomou (2007) Curran, R. L., & Chrysostomou, A. 2007, MNRAS, 382, 699, doi: 10.1111/j.1365-2966.2007.12399.x
  * Elitzur (1992) Elitzur, M. 1992, ARA&A, 30, 75, doi: 10.1146/annurev.aa.30.090192.000451
  * Fedriani et al. (2023) Fedriani, R., Tan, J. C., Telkamp, Z., et al. 2023, ApJ, 942, 7, doi: 10.3847/1538-4357/aca4cf
  * Felli et al. (1982) Felli, M., Gahm, G. F., Harten, R. H., Liseau, R., & Panagia, N. 1982, A&A, 107, 354
  * Franco-Hernández et al. (2009) Franco-Hernández, R., Moran, J. M., Rodríguez, L. F., & Garay, G. 2009, ApJ, 701, 974, doi: 10.1088/0004-637X/701/2/974
  * Fuller et al. (2005) Fuller, G. A., Williams, S. J., & Sridharan, T. K. 2005, A&A, 442, 949, doi: 10.1051/0004-6361:20042110
  * Gray et al. (2016) Gray, M. D., Baudry, A., Richards, A. M. S., et al. 2016, MNRAS, 456, 374, doi: 10.1093/mnras/stv2437
  * Hirota et al. (2021) Hirota, T., Cesaroni, R., Moscadelli, L., et al. 2021, A&A, 647, A23, doi: 10.1051/0004-6361/202039798
  * Hu et al. (2016) Hu, B., Menten, K. M., Wu, Y., et al. 2016, ApJ, 833, 18, doi: 10.3847/0004-637X/833/1/18
  * Imai et al. (2006) Imai, H., Omodaka, T., Hirota, T., et al. 2006, PASJ, 58, 883, doi: 10.1093/pasj/58.5.883
  * Kraus et al. (2017) Kraus, S., Kluska, J., Kreplin, A., et al. 2017, ApJ, 835, L5, doi: 10.3847/2041-8213/835/1/L5
  * Kurtz (2005) Kurtz, S. 2005, in Massive Star Birth: A Crossroads of Astrophysics, ed. R. Cesaroni, M. Felli, E. Churchwell, & M. Walmsley, Vol. 227, 111–119
  * Ladeyschikov et al. (2022) Ladeyschikov, D. A., Gong, Y., Sobolev, A. M., et al. 2022, ApJS, 261, 14, doi: 10.3847/1538-4365/ac6b43
  * López-Sepulcre et al. (2010) López-Sepulcre, A., Cesaroni, R., & Walmsley, C. M. 2010, A&A, 517, A66, doi: 10.1051/0004-6361/201014252
  * López-Sepulcre et al. (2011) López-Sepulcre, A., Walmsley, C. M., Cesaroni, R., et al. 2011, A&A, 526, L2, doi: 10.1051/0004-6361/201015827
  * Lu et al. (2014) Lu, X., Zhang, Q., Liu, H. B., Wang, J., & Gu, Q. 2014, ApJ, 790, 84, doi: 10.1088/0004-637X/790/2/84
  * McKee & Tan (2003) McKee, C. F., & Tan, J. C. 2003, ApJ, 585, 850, doi: 10.1086/346149
  * McMullin et al. (2007) McMullin, J. P., Waters, B., Schiebel, D., Young, W., & Golap, K. 2007, Astronomical Society of the Pacific Conference Series, Vol. 376, CASA Architecture and Applications, ed. R. A. Shaw, F. Hill, & D. J. Bell, 127
  * Momjian & Sarma (2017) Momjian, E., & Sarma, A. P. 2017, ApJ, 834, 168, doi: 10.3847/1538-4357/834/2/168
  * Momjian & Sarma (2019) —. 2019, ApJ, 872, 12, doi: 10.3847/1538-4357/aafad8
  * Moscadelli et al. (2022) Moscadelli, L., Sanna, A., Beuther, H., Oliva, A., & Kuiper, R. 2022, Nature, 6, 1068, doi: 10.1038/s41550-022-01754-4
  * Moscadelli et al. (2019) Moscadelli, L., Sanna, A., Goddi, C., et al. 2019, A&A, 631, A74, doi: 10.1051/0004-6361/201936436
  * Moscadelli et al. (2020) —. 2020, A&A, 635, A118, doi: 10.1051/0004-6361/202037472
  * Nedoluha & Watson (1992) Nedoluha, G. E., & Watson, W. D. 1992, ApJ, 384, 185, doi: 10.1086/170862
  * Osterloh et al. (1997) Osterloh, M., Henning, T., & Launhardt, R. 1997, ApJS, 110, 71, doi: 10.1086/312990
  * Ouyang et al. (2019) Ouyang, X.-J., Chen, X., Shen, Z.-Q., et al. 2019, ApJS, 245, 12, doi: 10.3847/1538-4365/ab4db2
  * Pickett et al. (1998) Pickett, H. M., Poynter, R. L., Cohen, E. A., et al. 1998, J. Quant. Spec. Radiat. Transf., 60, 883, doi: 10.1016/S0022-4073(98)00091-0
  * Reipurth & Graham (1988) Reipurth, B., & Graham, J. A. 1988, A&A, 202, 219
  * Reynolds (1986) Reynolds, S. P. 1986, ApJ, 304, 713, doi: 10.1086/164209
  * Rodríguez et al. (2012) Rodríguez, T., Trinidad, M. A., & Migenes, V. 2012, ApJ, 755, 100, doi: 10.1088/0004-637X/755/2/100
  * Rodríguez et al. (2021) Rodríguez, T. M., Hofner, P., Araya, E. D., et al. 2021, ApJ, 922, 66, doi: 10.3847/1538-4357/ac2366
  * Rosero et al. (2016) Rosero, V., Hofner, P., Claussen, M., et al. 2016, ApJS, 227, 25, doi: 10.3847/1538-4365/227/2/25
  * Rosero et al. (2019) Rosero, V., Hofner, P., Kurtz, S., et al. 2019, ApJ, 880, 99, doi: 10.3847/1538-4357/ab2595
  * Sabatini et al. (2021) Sabatini, G., Bovino, S., Giannetti, A., et al. 2021, A&A, 652, A71, doi: 10.1051/0004-6361/202140469
  * Sánchez-Monge et al. (2013) Sánchez-Monge, Á., Beltrán, M. T., Cesaroni, R., et al. 2013, A&A, 550, A21, doi: 10.1051/0004-6361/201219890
  * Sarma et al. (2002) Sarma, A. P., Troland, T. H., Crutcher, R. M., & Roberts, D. A. 2002, ApJ, 580, 928, doi: 10.1086/343799
  * Sarma et al. (2008) Sarma, A. P., Troland, T. H., Romney, J. D., & Huynh, T. H. 2008, ApJ, 674, 295, doi: 10.1086/524939
  * Sault et al. (1990) Sault, R. J., Killeen, N. E. B., Zmuidzinas, J., & Loushin, R. 1990, ApJS, 74, 437, doi: 10.1086/191505
  * Taniguchi et al. (2019) Taniguchi, K., Saito, M., Sridharan, T. K., & Minamidani, T. 2019, ApJ, 872, 154, doi: 10.3847/1538-4357/ab001e
  * Torrelles et al. (1998) Torrelles, J. M., Gómez, J. F., Rodríguez, L. F., et al. 1998, ApJ, 505, 756, doi: 10.1086/306205
  * Trinidad et al. (2003) Trinidad, M. A., Rojas, V., Plascencia, J. C., et al. 2003, Rev. Mexicana Astron. Astrofis., 39, 311
  * Troland & Heiles (1982) Troland, T. H., & Heiles, C. 1982, ApJ, 252, 179, doi: 10.1086/159544
  * Zhang & Tan (2018) Zhang, Y., & Tan, J. C. 2018, ApJ, 853, 18, doi: 10.3847/1538-4357/aaa24a
  * Zhang et al. (2019) Zhang, Y., Tan, J. C., Tanaka, K. E. I., et al. 2019, Nature, 3, 517, doi: 10.1038/s41550-019-0718-y

Figure A.1: Peak pixel spectra of all the detected maser spots. The vertical
dashed line marks the adopted systemic velocity ($V_{sys}=33.8$ ${\text{km
s}^{-1}}$, Araya et al., 2005). The solid red and blue lines represent
residual side lobes contamination from masers #4 and #6, respectively.


# RIShield: Enabling Electromagnetic Blackout in Radiation-Sensitive
Environments

G. Encinas-Lago, M. Rossanese, V. Sciancalepore, M Di Renzo, X. Costa-Perez
G. Encinas-Lago1, M. Rossanese1, V. Sciancalepore1, Marco Di Renzo2, Xavier
Costa-Pérez13 The research leading to these results has received funding from
the EU FP for Research and Innovation Horizon 2020 under Grant Agreements No.
101017011 (RISE-6G), 861222 (MINTS) and 956256 (METAWIRELESS). 1NEC
Laboratories Europe GmbH, Heidelberg, Germany  2Université Paris-Saclay, CNRS,
Centrale Supélec, Gif-sur-Yvette, France  3i2CAT Foundation and ICREA,
Barcelona, Spain

###### Abstract

Reconfigurable Intelligent Surfaces (RIS) have emerged as a disruptive
technology with the potential to revolutionize wireless communication systems.
In this paper, we present RIShield, a novel application of RIS technology
specifically designed for radiation-sensitive environments. The aim of
RIShield is to enable electromagnetic blackouts, preventing radiation leakage
from target areas. We propose a comprehensive framework for RIShield
deployment, considering the unique challenges and requirements of radiation-
sensitive environments. By strategically positioning RIS panels, we create an
intelligent shielding mechanism that selectively absorbs and reflects
electromagnetic waves, effectively blocking radiation transmission.

To achieve optimal performance, we model the corresponding channel and design
a dynamic control that adjusts the RIS configuration based on real-time
radiation monitoring. By leveraging the principles of reconfiguration and
intelligent control, RIShield ensures adaptive and efficient protection while
minimizing signal degradation. Through full-wave and ray-tracing simulations,
we demonstrate the effectiveness of RIShield in achieving significant
electromagnetic attenuation. Our results highlight the potential of RIS
technology to address critical concerns in radiation-sensitive environments,
paving the way for safer and more secure operations in industries such as
healthcare, nuclear facilities, and defense.

##  I Introduction

With the increasing need for privacy and security in radiation-sensitive
environments, such as medical facilities, research laboratories, and
governmental institutions, there is a growing demand for innovative
technologies that can effectively isolate specific areas and prevent
electromagnetic leakage. Literature approaches suggest installing metal
barriers provided with electromagnetic absorbers to limit the incoming signal
and guarantee isolation. However, such solutions may prevent flexibility and
versatility in highly dynamic environments, where shields are required for a
short time according to on-demand events.

In this context, Reconfigurable Intelligent Surfaces (RIS) have emerged as a
promising solution, offering unprecedented opportunities for shielding and
protecting sensitive spaces in a dynamic fashion [1]. Such emerging technology
exhibits its peculiar property of altering programmatically the surrounding
propagation environment: impinging signals can be refracted, absorbed, or
reflected upon a specific RIS configuration. This makes RIS the perfect
candidate to gain control of the propagation environment — always conceived as
a black-box — thereby powering a new wireless revolution. Ultimately, RISs are
designed to be low-complex, nearly passive, and inexpensive devices [2]. This
would extremely increase the technology penetration rate, envisioning a
massive diffusion of such novel devices in the next few years.

Figure 1: RIS used to guarantee privacy and isolation in real environments.

Only a few state-of-the-art proposals have looked at the problem. In
particular, [3] mitigates cross-system interference for secure wireless
applications. The authors introduced absorptive RIS (ARIS) to modify both the
phase and modulus of the impinging signal by absorbing a portion of the signal
energy. Also, [4] introduced the concept of Holographic Multiple Input
Multiple Output Surface (HMIMOS) where the RIS is applied to specific
scenarios where EM-Field Absorption is required. However, none of the
available literature works has proposed a practical solution to exploit the
RIS to isolate specific areas/rooms in both outdoor and indoor scenarios.

Motivated by such an innovative and unprecedented characteristic, in this
paper we design and present RIShield, a pioneering application of RIS
technology specifically designed to enable electromagnetic blackouts and
ensure privacy in radiation-sensitive environments. In particular, RIShield
leverages the unique capabilities of RIS to selectively absorb and reflect
electromagnetic waves, effectively creating a dynamic and intelligent shield
that isolates and safeguards specific rooms or areas from unwanted radiation
leakage on-demand, as shown in Fig. 1.

By strategically deploying RIS panels within the environment, RIShield
empowers administrators and operators with the ability to dynamically control
and manage electromagnetic propagation. This level of control not only
guarantees privacy by preventing sensitive information from being intercepted
but also minimizes the risk of interference with external electromagnetic
signals. To achieve optimal performance and adaptability, we propose the
development of an advanced optimization solution that dynamically configures
the RIS system based on real-time monitoring of radiation levels. These
algorithms ensure that RIShield provides effective shielding while minimizing
signal degradation and accommodating changing requirements within the
radiation-sensitive environment. Through a combination of simulation studies
and experimental evaluations, we aim to demonstrate the efficacy of RIShield
in achieving electromagnetic blackout and guaranteeing privacy in radiation-
sensitive environments. The preliminary results of this analysis will provide
valuable insight into the potential of RIS technology as a versatile and
efficient solution for addressing the privacy and security challenges faced in
radiation-sensitive settings. Overall, RIShield opens up new horizons in
shielding technology, enabling the establishment of radiation-secure spaces
and laying the foundation for enhanced privacy and confidentiality in critical
sectors where electromagnetic isolation is of utmost importance.

###  I-A Notation

We denote vectors and matrices using bold-face lower-case and upper-case
letters, respectively. We use italic letters to denote scalars. We use
$\mbox{$\mathbb{C}$}^{n}$ and $\mbox{$\mathbb{C}$}^{n\times m}$ to represent
the sets of $n$-dimensional complex vectors and $m\times n$ complex matrices,
respectively. Vectors are denoted as column vectors. Subscripts represent an
element in a vector and superscripts elements in a sequence. For example,
$\mathbf{x}^{(t)}=[x^{(t)}_{1},\dots,x^{(t)}_{n}]^{\mathrm{T}}$ is a vector
from $\mbox{$\mathbb{C}$}^{n}$ and $x^{(t)}_{i}$ is its $i$th component. In
addition, operation $(\cdot)^{\mathrm{T}}$ represents the transpose operator,
$\otimes$ stands for Kronecker product while $(\cdot)^{\mathrm{H}}$ denotes
the Hermitian transpose operation. Finally, $\|\cdot\|$ and
$\|\cdot\|_{\mathrm{F}}$ denote the L2-norm of a vector and Frobenius norm of
a matrix, respectively.

##  II System Model

For the sake of simplicity, we model Reconfigurable Intelligent Surfaces
(RISs) as reflectarrays composed of multiple antenna elements placed following
a grid configuration where the inter-distance among each pair of elements is
equal to $\lambda/2$ [5]. This prevents us from experiencing the grating lobe
effect or mutual coupling among the elements [6]. Each antenna element can be
only configured to reflect or absorb the impinging signal in a passive way: no
active components are placed on the RIS board.

Let us consider a conventional scenario where a transmitter is equipped with
$\Sigma$ antennas and the set $\mathcal{U}=\\{1,\ldots U\\}$ denotes the
receivers provided with a single antenna. The connection is established using
a set of Reflecting Intelligent Surfaces (RISs) installed on the buildings or
internal walls. Each RIS is equipped with $N$ equivalent antenna elements.

For downlink data transmission, the transmitter (tx) communicates with each
receiver $u$ through a direct link represented by
$\mathbf{h}_{\mathrm{d},k}\in\mbox{$\mathbb{C}$}^{\Sigma\times 1}$. This link
consists of a line-of-sight (LoS) path with a length of $d_{u}$ and an angle
of departure (AoD) $\theta_{u}$, when the latter exists, along with a
multipath non-line-of-sight (NLoS) component. Furthermore, the transmitter can
utilize a combined link to the RIS, denoted by
$\mathbf{G}\in\mbox{$\mathbb{C}$}^{N\times\Sigma}$.

The RIS reflects the incoming signal towards the receiver through the channel
$\mathbf{h}_{u}\in\mbox{$\mathbb{C}$}^{N\times 1}$. The $\mathbf{h}_{u}$
channel can be decomposed into the LoS tx-RIS path with length $d_{1,u}$,
having an AoD from the transmitter and an angle of arrival (AoA) at the RIS
denoted by $\psi_{D}$ and $\psi_{A}$, respectively. This is accompanied by a
set of scattered NLoS paths and the RIS-receiver $u$ link, which comprises a
LoS path with length $d_{2,u}$ and AoD $\psi_{u}$, if available, and a
multipath NLoS component. Lastly, signals that are reflected more than 1 time
by the RIS are neglected to account for high path loss [7] as considered in
the literature [8, 9].

All channels in the system are modeled as quasi-static flat-fading, which
means they remain constant during the transmission time of a codeword.
Additionally, we assume that the transmitter has access to perfect Channel
State Information (CSI), i.e., it knows the channel vectors
${\mathbf{h}_{\mathrm{d},u}}{u=1}^{U}$, $\mathbf{G}$, and
${\mathbf{h}{u}}_{u=1}^{U}$. The transmitter operates using the time division
duplex mode, allowing the uplink and downlink channels to be reciprocal. As a
result, the downlink physical channel can be estimated using the uplink
training from any single receiver via a separate control channel 111Imperfect
channel information requires a channel estimation process, which has been
already explored in [10, 9] and thus is out of the scope of this analysis..

Each receiver $u$ receives the sum of two contributions, such as a direct path
from the transmitter and a reflected path that has been steered by the RIS.
Hence, we can write the received signal at receiver $u$ as the following

$\displaystyle y_{u}$
$\displaystyle=\left(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}}\right)\mathbf{W}\mathbf{s}+n_{u}\in\mbox{$\mathbb{C}$}$
(1)
$\displaystyle=\left(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}}\right)\mathbf{w}_{u}s_{u}$
$\displaystyle\phantom{=}+\sum_{j\neq
u}\left(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}}\right)\mathbf{w}_{j}s_{j}+n_{u},$
(2)

where

$\displaystyle\mathbf{\Phi}$
$\displaystyle=\begin{bmatrix}\alpha_{1}e^{j\phi_{1}}&0&\ldots&0\\\
\vdots&\alpha_{2}e^{j\phi_{2}}&\ldots&\vdots\\\
0&\ldots&\ddots&\alpha_{N}e^{j\phi_{N}}\end{bmatrix}$ (3)

with $\phi_{i}\in[0,2\pi)$ and $|\alpha_{i}|^{2}\leq 1,\,\,\forall i$
represents the phase shifts and amplitude attenuation introduced by the RIS,
$\mathbf{W}=[\mathbf{w}_{1},\ldots,\mathbf{w}_{U}]\in\mbox{$\mathbb{C}$}^{\Sigma\times
U}$ is the transmit precoder at the transmitter,
$\mathbf{s}=[s_{1},\ldots,s_{U}]^{\mathrm{T}}\in\mbox{$\mathbb{C}$}^{U\times
1}$ is the transmit symbol vector with $\mathbb{E}[|s_{u}|^{2}]=1,\,\forall
u$, and $n_{u}$ is the noise term distributed as
$\mathcal{CN}(0,\sigma_{n}^{2})$.

Now, let us assume a single receiver decoding the signal, the system sum rate
can be defined as follows

$\displaystyle
R\\!\triangleq\\!\sum_{u}\log_{2}\bigg{(}\\!1\\!+\\!\frac{|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{u}|^{2}}{\sum_{j\neq
u}|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{j}|^{2}+\sigma_{n}^{2}}\bigg{)}.$
(4)

In our reference scenario, the RIS is used to limit the incoming signal for
specific areas or receivers. Therefore, we formulate the problem aiming at
optimizing the overall system performance of the considered RIS-empowered
network in terms of system sum rate as per Eq. (4). This would allow us to
directly optimize the precoding strategy at the transmitter and the reflection
coefficient at the RIS. However, given the complexity of such an expression,
we aim at optimizing the sum-mean-square-error (SMSE) over all receivers,
known to be related to Eq. (4), as reported in [11].

Hence, we reformulate our problem by looking at SMSE as a means to reduce
signal leakage and improve privacy and security in specific locations.
Considering all the receivers, $u$, placed within the room or area where we
would like to guarantee privacy and EMF isolation, we aim at minimizing the
overall SMSE for all those receivers. The received MSE of receiver $u$ in a
specific location is given by

$\displaystyle\mathrm{MSE}_{u}$ $\displaystyle=\mathbb{E}[|y_{u}-s_{u}|^{2}]$
(5)
$\displaystyle=|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{u}-1|^{2}+$
$\displaystyle\phantom{=}\sum_{j\neq
u}|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{j}|^{2}+\sigma_{n}^{2}$
(6)
$\displaystyle=\sum_{j}|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{j}|^{2}$
$\displaystyle\phantom{=}-2\,\mathrm{Re}\\{(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{u}\\}+1+\sigma_{n}^{2}.$
(7)

whereas the receive SMSE over all receivers ($\forall u$) is expressed as the
following

$\displaystyle\mathrm{SMSE}$ $\displaystyle=\sum_{u}\mathrm{MSE}_{u}$ (8)
$\displaystyle=\sum_{u}\sum_{j}|(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{j}|^{2}$
$\displaystyle\phantom{=}-2\sum_{u}\mathrm{Re}\\{(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{u}\\}\\!+\\!U(1+\sigma_{n}^{2}).$
(9)

For ease of presentation, let us define

$\displaystyle\mathbf{v}=[\alpha_{1}e^{-j\phi_{1}},\ldots,\alpha_{N}e^{-j\phi_{N}},1]^{\mathrm{T}}\in\mbox{$\mathbb{C}$}^{N+1\times
1},$ (10)

and

$\displaystyle\bar{\mathbf{H}}_{u}\triangleq\begin{bmatrix}\mathrm{\mathrm{diag}}(\mathbf{h}_{u}^{\mathrm{H}})\mathbf{G}\\\
\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}}\end{bmatrix}\in\mbox{$\mathbb{C}$}^{N+1\times\Sigma},$
(11)

such that $\mathbf{\Phi}=\mathrm{diag}(\mathbf{v}[1:N]^{\mathrm{H}})$ and

$\displaystyle(\mathbf{h}_{u}^{\mathrm{H}}\mathbf{\Phi}\mathbf{G}+\mathbf{h}_{\mathrm{d},u}^{\mathrm{H}})\mathbf{w}_{j}=\mathbf{v}^{\mathrm{H}}\bar{\mathbf{H}}_{u}\mathbf{w}_{j}\quad\forall
u,j.$ (12)

Therefore, we can formulate our optimization problem as the following

######  Problem 1 (SHIELD)

$\displaystyle\displaystyle\underset{\mathbf{v},\mathbf{W}}{\mathrm{maximize}}$
$\displaystyle\displaystyle\sum_{u}\sum_{j}|\mathbf{v}^{\mathrm{H}}\bar{\mathbf{H}}_{u}\mathbf{w}_{j}|^{2}-2\sum_{u}\mathrm{Re}\\{\mathbf{v}^{\mathrm{H}}\bar{\mathbf{H}}_{u}\mathbf{w}_{u}\\}$
$\displaystyle\mathrm{subject\leavevmode\nobreak\ to}$
$\displaystyle|v_{i}|^{2}\leq 1,\quad i=1,\ldots,N;$ $\displaystyle
v_{N+1}=1;$ $\displaystyle\|\mathbf{W}\|^{2}_{\mathrm{F}}\leq P;$

Figure 2: Conventional 2D indoor scenario (4 rooms) with 1 indoor transmitter.
The signal propagates mostly in the entire considered area through walls.

Figure 3: Conventional 2D indoor scenario (4 rooms) with 1 indoor transmitter
and RIS enabled as a shield. The right-hand side room is isolated.

Figure 4: Conventional 3D indoor scenario (4 rooms) with 1 indoor transmitter.
The signal propagates mostly in the entire considered area through walls.

Figure 5: Conventional 3D indoor scenario (4 rooms) with 1 indoor transmitter
and RIS enabled as a shield. The right-hand side room is isolated.

with $\mathbf{v}$ defined in Eq. (10) and $P$ the available transmit power at
the transmitter. We have introduced the constraint $|v_{i}|^{2}\leq 1$ to
ensure the passive assumption, i.e., the $i$-th RIS element does not amplify
the incoming signal. While this proposed formulation is convex in the two
optimization variables $\mathbf{v}$ and $\mathbf{W}$, separately, we
preliminary fix the $\mathbf{W}$, i.e., the transmit precoder, assuming it as
input of our problem. This results in an easier problem where only the RIS
configuration ($\mathbf{v}$) is treated as an optimization variable.

##  III Numerical Results

When dealing with the RIS configuration $\mathbf{v}$ as described in _Problem
1 (SHIELD)_ , our solution will trigger a specific phase to be applied to each
antenna element. This will turn each RIS element into a reflective or
absorbing cell. Given the convexity property, we solve the problem of
obtaining the optimal configuration that minimizes the SMSE over all receivers
within a specific area. To provide a complete overview of our RIShield
solution, we test our framework using Remcom WirelessInSite [12], a commercial
software that includes a set of valid RF propagation models thereby providing
3D ray-tracing, fast ray-based methods, and empirical models for the analysis
of site-specific radio wave propagation and wireless communication systems.

###  III-A WirelessInSite Simulations

We test a real-environment topology considering an indoor scenario where the
transmitter is assumed to be an access point operating at $2.4$GHz with a
transmit power $P=20$dbm. The scenario shows a $60$ square meters apartment
including $4$ different rooms with $2.7$m height, external walls of $41$cm
thick, and internal walls of $10-15$cm thick. The transmitter is placed in the
bigger room as shown in Fig. 3.

When no RIS is installed in the apartment, the signal propagates through the
entire scenario while being (slightly) attenuated by the internal walls. It
can be noted that rooms have doors that allow the signal to easily get into
the room. In particular, the right-hand side room in Fig. 3 and its
$3$-dimensional correspondence in Fig. 5, is strongly irradiated when the door
is open. However, when a fully-absorbing RIS is placed on the wall, the signal
propagation is strongly attenuated. In Fig. 3 the right-hand side room gets
almost-zero signal due to the RIS placed on the wall (as shown in Fig. 5).

Please note that the room is not completely isolated, as the RIS is only
installed on a specific side of the room. The transmitter signal is still able
to barely reach the (users inside the) room through the door on the right.
Nonetheless, most of the EM Field exposure has been denied with a simple
fully-absorbing RIS on the wall.

While this behavior is straightforward and expected, it unveils a two-fold
message: $i$) RIS size, orientation and placement have a direct impact on the
overall isolation guarantees and, $ii$) a specific RIS configuration (i.e.,
$\mathbf{v}$ in _Problem 1 (SHIELD)_) can influence the signal spreading and,
in turn, the privacy and security application.

Figure 6: RIS configuration to enable full reflection.

Figure 7: RIS configuration to enable half-reflection.

Figure 8: RIS configuration to enable partial reflection.

Figure 9: Radiation diagram for full-reflective RIS.

Figure 10: Radiation diagram for half-reflective RIS.

Figure 11: Radiation diagram for partially-reflective RIS.

###  III-B CST Simulations

We analyze how to properly select a RIS configuration. We use a full-wave ray
tracing software, namely CST [13], to emulate a conventional planar RIS with
$10\times 10$ antenna elements, operating at $5.3$GHz. In our experimental
campaign, we assume a $1$-bit configuration (this would turn the optimization
variable $\mathbf{v}$ in _Problem 1 (SHIELD)_) into a discretized variable
that can be $0$ or $1$): each RIS antenna element can reflect or absorb the
impinging signal.

We evaluate three different configurations: $i$) all antenna elements are set
to fully reflect the signal, as shown in Fig. 8, $ii$) half of the available
antenna elements are set to fully reflect the signal, as shown in Fig. 8 and
$iii$) a small square of antenna elements ($4\times 4$) in the middle of the
RIS is set to absorb the signal, as shown in Fig. 8. For each configuration,
we run our simulations and depict the corresponding radiation diagram.

It shall be noted that only in the second configuration case, the results show
a grating lobes effect (as depicted in Fig. 11. While this may appear a
drawback of our solution, it suggests interesting hidden aspects: random
configuration may result in unwanted RIS reflections and behaviors. However,
if grating lobes are taken into account, the RIShield framework can still
optimally operate and reduce the EM field exposure for specific users and
areas. Additionally, the first and the third case (as depicted in Figs. 11\-
11), show a single main beam (with different amplitude values) suggesting that
they can be deployed to accurately reduce the signal leakage.

##  IV Conclusions

We have presented RIShield, a novel and efficient solution for enabling
electromagnetic blackout in radiation-sensitive environments. By leveraging
RISs strategically placed in such environments, we have demonstrated the
capability to control and manipulate electromagnetic waves effectively.

The primary goal of RIShield was to create a reliable mechanism to mitigate
electromagnetic interference and ensure the safety and proper functioning of
radiation-sensitive equipment and systems. The success of our solution
represents a significant step forward in electromagnetic interference
management and radiation-sensitive equipment protection. Our experimental
results—using two different commercial tools—have shown that RIShield can
significantly reduce the impact of external electromagnetic signals on
sensitive areas creating a protective shield.

## References

  * [1] N. Awarkeh, D.-T. Phan-Huy, and M. D. Renzo, “A novel ris-aided emf exposure aware approach using an angularly equalized virtual propagation channel,” in _2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)_, 2022, pp. 500–505.
  * [2] M. Rossanese, P. Mursia, A. Garcia-Saavedra, V. Sciancalepore, A. Asadi, and X. Costa-Perez, “Designing, building, and characterizing rf switch-based reconfigurable intelligent surfaces,” in _Proceedings of the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization_, 2022, pp. 69–76.
  * [3] F. Wang and A. Lee Swindlehurst, “Applications of Absorptive Reconfigurable Intelligent Surfaces in Interference Mitigation and Physical Layer Security,” 2023. [Online]. Available: https://arxiv.org/abs/2302.01508
  * [4] C. Huang, S. Hu, G. C. Alexandropoulos, A. Zappone, C. Yuen, R. Zhang, M. D. Renzo, and M. Debbah, “Holographic mimo surfaces for 6g wireless networks: Opportunities, challenges, and trends,” _IEEE Wireless Communications_ , vol. 27, no. 5, pp. 118–125, 2020.
  * [5] P. Mursia, S. Phang, V. Sciancalepore, G. Gradoni, and M. D. Renzo, “SARIS: Scattering aware reconfigurable intelligent surface model and optimization for complex propagation channels,” _IEEE Wireless Communications Letters_ , pp. 1–1, 2023.
  * [6] M. Di Renzo, A. Zappone, M. Debbah, M.-S. Alouini, C. Yuen, J. de Rosny, and S. Tretyakov, “Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead,” _IEEE Journal on Selected Areas in Communications_ , vol. 38, no. 11, pp. 2450–2525, 2020.
  * [7] Q. Wu and R. Zhang, “Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design,” _IEEE Transactions on Wireless Communications_ , vol. 18, no. 11, pp. 5394–5409, 2019\.
  * [8] S. Zhang and R. Zhang, “Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication,” 2019. [Online]. Available: http://arxiv.org/abs/1910.01573
  * [9] D. Mishra and H. Johansson, “Channel Estimation and Low-complexity Beamforming Design for Passive Intelligent Surface Assisted MISO Wireless Energy Transfer,” Brighton, UK, May 2019.
  * [10] Z.-Q. He and X. Yuan, “Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO,” Oct. 2019.
  * [11] P. Mursia _et al._ , “RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access,” _IEEE Journal on Selected Areas in Communications_ , vol. 39, no. 4, pp. 1072–1085, 2020.
  * [12] Remcom, “Wireless InSite - 3D Wireless Prediction Software,” 2023. [Online]. Available: https://www.remcom.com/wireless-insite-em-propagation-software
  * [13] Dassault Systèmes, “CST Studio Suite: Electromagnetic field simulation software,” https://www.3ds.com/products-services/simulia/products/cst-studio-suite/, 2022, accessed: 2022-03-01.