# RoboPol: AGN polarimetric monitoring data

D. Blinov1,2,3, S. Kiehlmann1,2, V. Pavlidou1,2, G. V. Panopoulou4, R.
Skalidis1,2, E. Angelakis5, C. Casadio1,2,6, E. N. Einoder4, T. Hovatta7,8, K.
Kokolakis9,2, A. Kougentakis1, A. Kus10, N. Kylafis2,1, E. Kyritsis2,1, A.
Lalakos11, I. Liodakis7, S. Maharana12, E. Makrydopoulou2, N. Mandarakas1,2,
G. M. Maragkakis13,2, I. Myserlis6, I. Papadakis1,2, G. Paterakis1, T. J.
Pearson4, A. N. Ramaprakash12,1,4, A. C. S. Readhead4, P. Reig2,1, A.
Słowikowska10, K. Tassis1,2, K. Xexakis2, M. Żejmo14, J. A. Zensus6
1Institute of Astrophysics, Foundation for Research and Technology-Hellas,
Voutes, 71110 Heraklion, Greece
2Department of Physics, University of Crete, 71003, Heraklion, Greece
3Astronomical Institute, St. Petersburg State University, Universitetsky pr.
28, Petrodvoretz, 198504 St. Petersburg, Russia
4Cahill Center for Astronomy and Astrophysics, California Institute of
Technology, 1200 E California Blvd, MC 350-17,
Pasadena CA, 91125, USA
5 Section of Astrophysics, Astronomy & Mechanics, Department of Physics,
National and Kapodistrian University of Athens,
Panepistimiopolis Zografos 15784, Greece
6Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn,
Germany
7Finnish Centre for Astronomy with ESO, FINCA, University of Turku, Quantum,
Vesilinnantie 5, FI-20014, Finland
8Aalto University Metsähovi Radio Observatory, Metsähovintie 114, FI-02540
Kylmälä, Finland
9Geodesy and Geomatics Engineering Laboratory, Technical University of Crete,
GR-73100 Chania, Greece
10Institute of Astronomy, Faculty of Physics, Astronomy and Informatics,
Nicolaus Copernicus University in Toruń, Grudziadzka 5,
PL-87-100 Toruń, Poland
11Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA),
Northwestern University, Evanston, IL 602, USA
12Inter-University Centre for Astronomy and Astrophysics, Post Bag 4,
Ganeshkhind, Pune - 411 007, India
13Institute of Electronic Structure and Laser, Foundation for Research and
Technology-Hellas, Voutes, 71110 Heraklion, Greece
14Janusz Gil Institute of Astronomy, University of Zielona Góra, Prof.
Szafrana 2, PL-65-516 Zielona Góra, Poland
E-mail<EMAIL_ADDRESS>Fellow

(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present uniformly reprocessed and re-calibrated data from the RoboPol
programme of optopolarimetric monitoring of active galactic nuclei (AGN),
covering observations between 2013, when the instrument was commissioned, and
2017. In total, the dataset presented in this paper includes $5068$
observations of $222$ AGN with Dec > $-$25°. We describe the current version
of the RoboPol pipeline that was used to process and calibrate the entire
dataset, and we make the data publicly available for use by the astronomical
community. Average quantities summarising optopolarimetric behaviour (average
degree of polarization, polarization variability index) are also provided for
each source we have observed and for the time interval we have followed it.

###### keywords:

galaxies: active – galaxies: jets – galaxies: nuclei – polarization

††pubyear: 2016††pagerange: RoboPol: AGN polarimetric monitoring data–B

##  1 Introduction

The RoboPol Collaboration111http://robopol.org monitored the optical linear
polarization and brightness of a large sample of active galactic nuclei (AGN)
from 2013 to 2017, using the RoboPol polarimeter (Ramaprakash et al., 2019),
which was developed for this project, and which is installed at the 1.3m
telescope of the Skinakas Observatory in Crete, Greece. The main science goal
of the RoboPol project was to explore a possible link between optical
polarization behaviour, particularly the electric vector position angle (EVPA)
rotations, and flares in the $\gamma$-ray emission of blazars. The main
project was run between 2013 May and 2015 November. During this period the
monitoring was focused on two well-defined samples: the main sample,
consisting of $\gamma$-ray loud AGN detected in the Fermi–LAT Second Source
Catalogue (2FGL, Nolan et al., 2012); and the control sample, consisting of
otherwise similar AGN, which however had not been detected by Fermi–LAT. The
sample selection is discussed in §2 and in greater detail in Pavlidou et al.
(2014). In addition to the main monitoring programme, we observed additional
sources for other projects, also described in §2. Major results based on the
data set presented in this paper were published in Pavlidou et al. (2014);
Blinov et al. (2015, 2016a, 2016b, 2018); Angelakis et al. (2016); Hovatta et
al. (2016); Kiehlmann et al. (2017); Liodakis et al. (2017).

In this paper, we present a complete and uniform reprocessing of all
observations in the 5-year AGN monitoring dataset, using the latest version of
the RoboPol pipeline. The samples of sources that have been included in our
monitoring during the programme are summarised in §2. Information on the
RoboPol polarimeter and the telescope, where it is mounted, is given in §3.
The current version of the RoboPol pipeline that has been used to process all
data presented in this paper is described in §4. Details on the standard stars
used for calibration are given in §5. The reader is alerted to caveats that
have arisen in the processing of specific sources in §6. The data we release
for each source, including individual observations and summary statistics
quantifying the optopolarimetric behaviour of each source are described in §7.
This data are made publicly available for use by the astronomical community.
Our data policy is discussed in §8. The values of EVPA in this work are
measured from North to East following the IAU convention (IAU Commission 40,
1974). All monitoring data made available in this paper are not corrected for
positive bias of the polarization fraction (Serkowski, 1958).

With this paper we publish $5068$ polarimetric measurements of $222$ AGN
located at Dec >$-$25°obtained during the 2013 – 2017 observing seasons.

##  2 Observing samples

We provide data for sources belonging to several observing samples as
indicated by the ’sample’ flag in the data Table A. Here, we briefly describe
these samples, and we refer the reader to the corresponding results-and-
analysis papers for more details.

During, and immediately after, the RoboPol polarimeter commissioning, we
performed a month-long single-epoch survey of two unbiased samples of 89
$\gamma$-ray–loud (Sample Identifier SID=1, see Table A) and 15
$\gamma$-ray–quiet (SID=2) sources. The details of the sample selection are
described in Pavlidou et al. (2014).

During the main monitoring programme, we regularly observed sources belonging
to two samples: the ’main’ $\gamma$-ray–loud sample (SID=3), consisting of 62
sources selected by placing a photon-flux cut on sources from the Second Fermi
LAT Catalogue (2FGL, Nolan et al., 2012) and an additional optical magnitude
cut, as well as constraints on source visibility and separation from other
field sources; and the ’control’ $\gamma$-ray–quiet sample (SID=4), of 15
sources selected based on their radio variability properties and absence from
2FGL. Control sample sources were selected from non-2FGL sources from CGRaBS
(Candidate Gamma-Ray Blazar Survey, Healey et al., 2008) placing a 15 GHz flux
density cut, and identical optical magnitude, visibility, and field-quality
cuts as for the $\gamma$-ray–loud sample. Among all sources satisfying these
criteria, we selected the ones that were most variable in radio, as quantified
by their 15 GHz modulation index (Richards et al., 2011). Slight changes to
the initial ’control’ sample were made in Blinov et al. (2016a, b, 2018)
because two of the $\gamma$-ray–quiet sources appeared in the Third Fermi LAT
Catalogue (3FGL Acero et al., 2015). For this reason we added two more sources
satisfying our selection criteria that were not present in any of the Fermi
LAT catalogues.

In addition to the ’main’ and ’control’ samples, during 2013 – 2014 we
monitored a sample of 24 individually-selected sources of high interest.
Moreover, 44 other AGN were observed for different projects along the project
execution. These sources that have not been selected with uniform criteria are
assigned SID=5. In 2014 we also conducted a distinct polarization monitoring
programme focused on intermediate- and high-synchrotron peaked BL Lac objects.
This programme included 29 TeV-detected sources and 19 non-TeV sources,
collectively described in Table A with SID=6. Details of the selection
criteria for these samples are described in Hovatta et al. (2016).

##  3 Telescope and polarimeter

Optical polarimetric observations were performed using the 1.3-m telescope at
the Skinakas Observatory222http://skinakas.physics.uoc.gr in Crete (1750
m.a.s.l., 35°12′43″N, 24°53′57″E). The telescope is equipped with the RoboPol
imaging polarimeter (Ramaprakash et al., 2019), which was designed
specifically for this monitoring programme. RoboPol is comprised of two
adjacent half-wave retarders with fast-axes rotated by $67.5^{\circ}$ with
respect to each other. They are followed by two Wollaston prisms with
orthogonal fast-axes. This configuration splits every incident ray into two
pairs of spots on the CCD that carry information about Q/I and U/I normalised
Stokes parameters in the instrument reference frame (see Eq. 1 in King et al.,
2014). In the case when a photometric standard with known magnitude is in the
field, the total intensity or Stokes I parameter can be obtained together with
Q/I and U/I with a single exposure. Unfortunately, only a small fraction of
sources in our samples has accurately measured robust photometric standards in
their fields. Therefore, here we present only normalised Stokes parameters
without the total flux data. Since the instrument has no moving parts other
than the filter wheel, it is relieved of random and systematic errors due to
sky changes between measurements, imperfect alignment and non-uniformity of
rotating optical elements. In order to increase the SNR (signal-to-noise
ratio) for the central target measurements, a mask of a special shape was
introduced in the centre of the focal plane. An example of an image obtained
with RoboPol is shown in Fig. 1.

Figure 1: An example of a RoboPol image. Each point in the sky is mapped to
four spots on the CCD. A focal plane mask, held in place by four support legs,
reduces the sky background level for the central target.

The polarimeter is equipped with a $2048\times 2048$ pixels ANDOR DW436 CCD
with a 13.5 $\mu$m pixel size. It provides a scale of 0.435 arcsec pixel-1 and
a field of view (FoV) of $13\times 13$ arcmin2. All observations described in
this paper were made with a Johnson-Cousins R-band filter.

##  4 RoboPol pipeline

The RoboPol pipeline was planned and implemented to be capable of working with
no user intervention. It is able to derive the magnitude and linear
polarization of every unobscured source in the field (i.e. every source that
is not blocked by the focal plane mask and its holders). The pipeline is
written in Python, with some procedures written in Cython to improve
performance. A detailed description of the pipeline is presented by King et
al. (2014). Upgrades in the pipeline designed to optimize performance for
field sources are discussed in Panopoulou et al. (2015). In the following
sections we describe only recent upgrades in the pipeline that have not been
described elsewhere.

###  4.1 Upgrades of the pipeline

####  4.1.1 Mask location and background estimates

The main science targets are positioned within the central, masked area of the
RoboPol image shown in Fig. 2. The sky background for each source spot is
estimated within the corresponding masked areas (the blue squares in Fig. 2).
The four squares pattern is automatically identified by the pipeline. First
versions of the pipeline considered this pattern as a whole. However, it was
found that the relative positions of the shadows are variable within $\sim 10$
pix. Therefore, the latest version of the pipeline locates each square
separately. This provides more accurate histograms of the background counts,
preventing biasing of the background estimate towards brighter values.

Figure 2: Masked central area of the RoboPol image with the central science
target. The red circles are the photometry apertures. The blue squares are the
automatically identified background estimation areas for the central science
target.

The default background estimation method used in the pipeline has been changed
to a procedure similar to the one used in SExtractor (Bertin & Arnouts, 1996).
The procedure consists of the following steps. (1) The histogram of pixel
values within the masked square is iteratively sigma-clipped, with $2.7\sigma$
as the limit. (2) If the sigma-clipped $\sigma$ changed more than 20 per cent
from its initial value, the histogram is considered to be skewed, and the
background value is estimated using ${\rm Mode}=2.5\times{\rm
Median}-1.5\times{\rm Mean}$. Otherwise, the mean of the clipped histogram is
taken as the value for the background.

The background histograms and calculated background values for all
measurements in this paper have been visually inspected. In the cases where
the default method provided unsatisfactory results, the images were
reprocessed using one of the other algorithms implemented in the pipeline.
These include the following: (a) Mean value of the background pixels; (b) Mode
of the background pixels; (c) The original background estimation procedure
described in King et al. (2014), which searches for the centroid in the sigma-
clipped and smoothed histogram of background pixels values.

####  4.1.2 Aperture photometry

The full width at half maximum (FWHM) of the vertical pair of spots is
different from that of the horizontal pair (see Fig. 2), due to the instrument
design. The optimal size of the photometry aperture, which minimizes the
uncertainties, depends on the FWHM. Therefore, the current version of the
pipeline measures and treats two pairs of spots independently.

For AGN with prominent host galaxies, measured polarization values are reduced
by unpolarized emission of the host inside the aperture. This depolarization
effect varies with seeing: fractional polarization is lower for shots with
larger FWHM (Sosa et al., 2017). In order to minimize this effect for sources
with bright hosts we used fixed apertures 3 arcsec to 6 arcsec in diameter
depending on the source. The corresponding value for each source is given in
the ’aperture’ column of Table A. All other unresolved objects or sources with
negligible host galaxy contribution were measured with aperture dependent on
FWHM that optimised SNR. We processed each source with 8 different apertures
with sizes in the range 1 – 4.5 $\times$ FWHM. Then we visually verified that
the fractional polarization and EVPA stabilised within this range of
apertures. Finally, we selected the aperture size that provided the highest
SNR in fractional polarization in the stability range. This value was 5.1
arcsec on median.

####  4.1.3 Centroids of the main target spots

Figure 3: The pattern of 4 spots at each position $(x,y)$ on the CCD and 6
parameters describing it. $\Delta_{x}(x,y)$ is the distance between the
horizontal spots, $\delta_{x}(x,y)$ is the distance from the right-spot to the
central point, and $\phi_{x}(x,y)$ is the angle between the CCD x-axis and the
line connecting horizontal points. $\Delta_{y}(x,y)$, $\delta_{y}(x,y)$ and
$\phi_{y}(x,y)$ denote similar quantities for the vertical pair of spots.

The pipeline uses SExtractor (Bertin & Arnouts, 1996) for identification of
sources in the image. It provides a windowed centroid position for each source
in the frame. In a small fraction of images with a strong gradient of the sky
background at the edges of the masked regions, the centroid calculation
procedure fails. This causes inaccurate polarization measurements of the
central target. In order to avoid this problem, we have added a second
procedure for centroid computation, based on the
PyGuide333http://staff.washington.edu/rowen/PyGuide/Manual.html library. After
the identification of the central target spots in the SExtractor output
catalogue, the pipeline finds parameters $\Delta_{x}$, $\delta_{x}$,
$\phi_{x}$, $\Delta_{y}$, $\delta_{y}$ and $\phi_{y}$ of the spatial
distortions model, whose meaning is demonstrated in Fig. 3 (see also §2.2.1 in
King et al. (2014)). If any of these parameters deviates from the model
prediction more than the maximum residual in the model fit (see § 4.2 and Fig.
8, 9), then the spots centroids calculation is considered to have failed. In
this case the second PyGuide based centroid computation algorithm is used. The
combination of both algorithms has proven sufficient in practice. We have not
identified any image in our observations where both methods fail.

####  4.1.4 Additional astrometry procedure

The RoboPol pipeline uses the Astrometry.net (Lang et al., 2010) library to
perform astrometry. Accurate coordinates of the field sources are needed for
the pointing procedure during observations and for the differential photometry
during the data processing. In the case of a bright sky or very sparse stellar
field (e.g. in some of the fields studied in Skalidis et al., 2018, or AGN at
high galactic latitudes) Astrometry.net is unable to find a solution for the
World Coordinate System. For this reason we have added a second astrometry
routine which is based on the Alipy444https://obswww.unige.ch/~tewes/alipy/
library. This routine starts only if Astrometry.net fails. It finds a
geometrical transformation between the current frame and a preliminary stored
source catalogue, which has a World Coordinate System (WCS) solution. Then the
calculated transformation in the CCD frame is converted to WCS transformation
and, thereby, the new WCS of the current frame is defined. This routine
requires either a previous RoboPol image of the same field solved by
Astrometry.net or the Digitized Sky Survey fits image with a WCS solution in
the header.

####  4.1.5 Polarization measurements statistics

Values of PD and its uncertainty were calculated following Eq. 5 in King et
al. (2014) under the assumption of Gaussianity of the Stokes parameters. Any
linear polarization measurement is subject to bias towards higher polarization
degree (PD) values (Serkowski, 1958). The PD follows the Rice (1945)
distribution, which deviates significantly from the normal distribution at low
SNR. Since there is a variety of methods suggested for correction of this bias
(e.g. Simmons & Stewart, 1985; Vaillancourt, 2006; Plaszczynski et al., 2014)
we did not include any bias correction of the fractional polarization into the
pipeline. The monitoring data discussed in this paper is uncorrected for bias.

EVPA measurements are also non-Gaussian and defined by the following
probability density (Naghizadeh-Khouei & Clarke, 1993):

$\begin{multlined}G({\rm EVPA,EVPA_{0},PD_{0}})=\\\
=\frac{1}{\sqrt{\pi}}\left\\{\frac{1}{\sqrt{\pi}}+\eta_{0}\exp\left(\eta_{0}^{2}\right)[1+\operatorname{erf}(\eta_{0})]\right\\}\exp\left(-\frac{\rm
PD_{0}^{2}}{2\sigma_{\rm PD}^{2}}\right),\end{multlined}G({\rm
EVPA,EVPA_{0},PD_{0}})=\\\
=\frac{1}{\sqrt{\pi}}\left\\{\frac{1}{\sqrt{\pi}}+\eta_{0}\exp\left(\eta_{0}^{2}\right)[1+\operatorname{erf}(\eta_{0})]\right\\}\exp\left(-\frac{\rm
PD_{0}^{2}}{2\sigma_{\rm PD}^{2}}\right),$ (1)

where $\eta_{0}={\rm PD_{0}}\cos{2({\rm EVPA}-{\rm EVPA_{0}})}/(\sigma_{\rm
PD}\sqrt{2})$, $\operatorname{erf}$ is the Gaussian error function, ${\rm
PD_{0}}$ and ${\rm EVPA_{0}}$ are the true values of PD and EVPA and
$\sigma_{\rm PD}$ is the uncertainty of PD.

In the latest version of the pipeline we determine the EVPA uncertainty
$\sigma_{\rm EVPA}$ numerically solving the following integral:

$\int_{-1\sigma_{\rm EVPA}}^{1\sigma_{\rm EVPA}}G({\rm EVPA,PD_{0}}){\rm
dEVPA}=0.6827.$ (2)

The true PD value in this procedure is estimated using the Modified ASymptotic
(MAS) estimator proposed by Plaszczynski et al. (2014) as follows:

${\rm PD_{0}}={\rm PD}-\sigma_{\rm PD}^{2}\frac{1-\exp(-{\rm
PD}^{2}/\sigma_{\rm PD}^{2})}{\rm 2PD}$ (3)

For high SNR values ${\rm PD}/\sigma_{\rm PD}\geq 10$ the uncertainty of EVPA
is approximated as $\sigma_{\rm EVPA}=0.5\sigma_{\rm PD}/{\rm PD_{0}}$.

Figure 4: New EVPA uncertainty estimates calculated according to § 4.1.5
plotted against the former estimates calculated according to King et al.
(2014). The solid line is y=x. The colour coding shows the signal-to-noise-
ratio of the polarization fraction.

Figure 4 shows the new EVPA uncertainty estimates plotted against the former
estimates calculated with the previous pipeline as described in King et al.
(2014). The uncertainties tend to have been underestimated in moderately low
SNR ($1\lesssim{\rm SNR}\lesssim 10$) and to have been overestimated in very
low ${\rm SNR}<1$.

###  4.2 Instrument model

Instrumental polarization, vignetting and spatial distortions of the four-
spots-pattern are corrected by the instrument model. The instrument model
consists of two independent parts. The first part approximates the
dependencies of the six parameters in Fig. 3 on the (x,y)-position on the CCD.
The second part is a spatial function that describes multipliers for photon
counts in each of the four spots as a function of (x,y). These multipliers
compensate for the global variations of the instrumental polarization in the
field and vignetting. The instrument model coefficients are obtained by
fitting the model to measurements from a raster scan of a zero-polarized star
in the field. The complete description of the RoboPol instrument model was
presented by King et al. (2014).

The instrument response can vary with time, telescope position, flexure,
temperature etc. For this reason, we performed a raster scan of a zero-
polarized star several times per season and after every removal of the RoboPol
polarimeter from the telescope. During the 2013 – 2017 period we obtained 11
model scans using 7 different zero-polarized standards in different positions
of the telescope. Comparing results of the model fit to these data, we do not
find any significant systematic difference between these models.

Combining multiple model raster scans of zero-polarized stars that were
observed at different epochs, we are able to reduce the random errors of the
model fits. Therefore, for the data discussed in this paper we used the
combined model. It was created using 1624 measurements of unpolarized stars in
all 11 raster scans obtained in 2013 – 2017 and shown in Fig. 7 of Appendix B.
Figures 8 – 13 of the same appendix show the fits of model parameters and
their residuals. This model gives the best approximation of the instrumental
polarization and is less noise-dominated than any single raster scan.

##  5 Calibration using standard stars

The instrument model accounts for the variation of the polarization response
of the instrument globally across the FoV, while all AGN were measured in the
narrow central masked area. Since the model can deviate locally from the real
instrumental polarization value we have also been monitoring polarimetric
standard stars in the mask. The list of stars observed during the project
execution period and their catalogued polarization values together with
corresponding references are given in Table 1.

Table 1: Polarization parameters of standard stars monitored by RoboPol, as reported in the literature. Source | Band | PD (%) | EVPA (°) | Reference
---|---|---|---|---
polarized
BD$+$57.2615 | R | 2.02 $\pm$ 0.05 | 41.0 $\pm$ 1.0 | Whittet et al. (1992)
BD$+$59.389 | R | 6.430 $\pm$ 0.022 | 98.14 $\pm$ 0.10 | Schmidt et al. (1992)
BD$+$64.106 | R | 5.150 $\pm$ 0.098 | 96.74 $\pm$ 0.54 | Schmidt et al. (1992)
CMaR1 24 | R | 3.18 $\pm$ 0.09 | 86.0 $\pm$ 1.0 | Whittet et al. (1992)
CygOB2 14 | R | 3.13 $\pm$ 0.05 | 86.0 $\pm$ 1.0 | Whittet et al. (1992)
HD147283 $\left\\{\begin{array}[]{l}{}\\\ {}\end{array}\right.$ | R | 1.59 $\pm$ 0.03 | 174.0 $\pm$ 1.0 | Whittet et al. (1992)
R | 1.81 | 176.0 | Carrasco et al. (1973)
HD147343 | R | 0.43 $\pm$ 0.05 | 151.0 $\pm$ 3.0 | Whittet et al. (1992)
HD150193 | R | 5.19 $\pm$ 0.05 | 56.0 $\pm$ 1.0 | Whittet et al. (1992)
HD154445 $\left\\{\begin{array}[]{l}{}\\\ {}\end{array}\right.$ | R | 3.683 $\pm$ 0.072 | 88.91 $\pm$ 0.56 | Schmidt et al. (1992)
R | 3.63 $\pm$ 0.01 | 90.0 $\pm$ 0.1 | Hsu & Breger (1982)
HD155197 | R | 4.274 $\pm$ 0.027 | 102.88 $\pm$ 0.18 | Schmidt et al. (1992)
HD161056 | R | 4.012 $\pm$ 0.032 | 67.33 $\pm$ 0.23 | Schmidt et al. (1992)
HD183143b $\left\\{\begin{array}[]{l}{}\\\ {}\end{array}\right.$ | R | 5.90 $\pm$ 0.05 | 179.2 $\pm$ 0.2 | Hsu & Breger (1982)
R | 5.7 $\pm$ 0.04 | 178.0 $\pm$ 1.0 | Bailey & Hough (1982)
HD204827b $\left\\{\begin{array}[]{l}{}\\\ {}\\\ {}\end{array}\right.$ | R | 4.893 $\pm$ 0.029 | 59.10 $\pm$ 0.17 | Schmidt et al. (1992)
R | 4.86 $\pm$ 0.05 | 60.0 $\pm$ 1.0 | Bailey & Hough (1982)
R | 4.99 $\pm$ 0.05 | 59.9 $\pm$ 0.1 | Hsu & Breger (1982)
HD215806 | R | 1.83 $\pm$ 0.04 | 66.0 $\pm$ 1.0 | Whittet et al. (1992)
HD236633 | R | 5.376 $\pm$ 0.028 | 93.04 $\pm$ 0.15 | Schmidt et al. (1992)
Hiltner960a | R | 5.210 $\pm$ 0.029 | 54.54 $\pm$ 0.16 | Schmidt et al. (1992)
VICyg12b $\left\\{\begin{array}[]{l}{}\\\ {}\\\ {}\end{array}\right.$ | R | 7.97 $\pm$ 0.05 | 117.0 $\pm$ 1.0 | Whittet et al. (1992)
R | 7.893 $\pm$ 0.037 | 116.23 $\pm$ 0.14 | Schmidt et al. (1992)
R | 7.18 $\pm$ 0.04 | 117.0 $\pm$ 1.0 | Hsu & Breger (1982)
unpolarized
BD$+$28.4211 | V | 0.054 $\pm$ 0.027 | 54.22 | Schmidt et al. (1992)
BD$+$32.3739 | V | 0.025 $\pm$ 0.017 | 35.79 | Schmidt et al. (1992)
BD$+$33.2642 | R | 0.20 $\pm$ 0.15 | 78 $\pm$ 20 | Skalidis et al. (2018)
BD$+$40.2704 | ? | 0.07 $\pm$ 0.02 | 57 $\pm$ 9 | Berdyugin & Teerikorpi (2002)
G191B2B | V | 0.061 $\pm$ 0.038 | 147.65 | Schmidt et al. (1992)
HD14069 | V | 0.022 $\pm$ 0.019 | 156.57 | Schmidt et al. (1992)
HD154892 | B | 0.05 $\pm$ 0.03 | - | Turnshek et al. (1990)
HD212311 | V | 0.034 $\pm$ 0.021 | 50.99 | Schmidt et al. (1992)
HD21447 | V | 0.051 $\pm$ 0.020 | 171.49 | Schmidt et al. (1992)
HD94851 | B | 0.057 $\pm$ 0.018 | - | Turnshek et al. (1990)
WD2149+021 | R | 0.050 $\pm$ 0.006 |  $-$63 $\pm$ 3 | Cikota et al. (2017)
a \- possibly variable You et al. (2017);b \- variable in RoboPol data or/and
in Hsu & Breger (1982); Dolan & Tapia (1986);

We note that some stars monitored in the project turned out to be variable,
despite the fact that they are considered as standards in other polarimetric
programmes and literature. For example, we certainly observe variability in HD
204827, HD 183143 and VI Cyg 12, which is in agreement with Hsu & Breger
(1982); Dolan & Tapia (1986). Moreover, some standards appear to be stable but
their polarization significantly deviates from that reported in the literature
(e.g. CMaR1 24 and BD$+$57.2615). Furthermore, the situation is complicated by
multiple inconsistent polarimetric parameters reported in different works for
some standards. In general, the situation with optical polarimetric standards
can be characterised as disheartening. For this reason in 2018 we started an
effort intended to establish a well defined sample of stable polarimetric
standards. Its results will be reported elsewhere.

Figure 5 shows the distribution of relative Stokes parameters of zero-
polarized standards measured along the whole observing period. Measurements of
WD2149+021 systematically deviate from the centroid of all other measurements.
Therefore, this star was excluded from the analysis. The weighted mean
centroid for all other measurements in Fig. 5 is located at ${\rm
Q/I}=-0.0015$, ${\rm U/I}=-0.0004$. This value is the difference between the
model-predicted and the real instrumental polarization at the central mask
region.

Figure 5: Relative Stokes parameters of zero-polarization standards observed
in 2013 – 2017. The weighted mean is shown by the black point.

We used this quantity as an additional instrument polarization correction for
all AGN measurements. The standard deviations of the Stokes parameters in Fig.
5 are $0.0014$ and $0.0011$. These values were considered as uncertainties and
propagated to the AGN measurements uncertainties. It is worth noting that in
this work we use a different approach from the one reported in Ramaprakash et
al. (2019), where polarization standards were processed without the instrument
model correction. There, it was found that the instrumental polarization
varies between observing seasons with an amplitude $\sim$ 0.1 – 0.2 per cent.
This variation can be partly explained by the fact that the mask shadow
location on the CCD changes slightly with time. Unlike Ramaprakash et al.
(2019), in this work we use the instrument model correction of polarization
parameters, which accounts for changing position of a target on the CCD.
Presumably for this reason we do not find any significant variability of the
instrumental polarization among observing seasons. Thus, here we use the
constant instrumental polarization correction value for all measurements. Even
if a residual systematic error is present in such an instrumental polarization
correction, its value is negligible compared to typical photon noise of our
AGN measurements.

In order to find the rotation of the instrumental Q/I - U/I plane with respect
to the standard reference frame we used 331 observations of 12 high-
polarization stars that are considered to be the most stable standards. The
difference between catalogued and averaged RoboPol values of EVPA for these
stars are shown in Fig. 6. The weighted average for 12 stars is ${\rm
EVPA_{rbpl}}-{\rm EVPA_{cat}}=1.1\pm 1.1^{\circ}$. Relative Stokes parameters
Q/I and U/I of AGN were corrected for this EVPA zero point offset and the
uncertainties were propagated.

Figure 6: Differences between weighted average of observed EVPA and
corresponding catalogue values for 12 most reliable highly-polarized
standards. Weighted mean value for all 12 stars is shown by the solid red
line, while their weighted standard deviation is shown by the red dashed
lines.

##  6 Notes on individual sources

In this section we list caveats that arose during the processing of individual
sources in the sample.
J0035+5950: A source of comparable brightness is located 1.58 arcsec from the
blazar. It was initially considered as the lensed image of the blazar, however
it was not detected in radio (Aleksić et al., 2015). Due to the small
separation between the two sources, it could not be resolved in all seeing
conditions in RoboPol images. For this reason we performed the aperture
photometry with a fixed 6 arcsec aperture surrounding both sources. Therefore,
the polarization fraction values are most likely significantly underestimated,
while the EVPA should be reliable.
J0324+3410: For this source we used a fixed 4 arcsec aperture. However, this
Narrow-Line Seyfert 1 (NLSy1) has asymmetric spiral arms resolved in the host
galaxy (Zhou et al., 2007). The spiral arms can potentially give non-zero
polarization of the stellar light. A dedicated study is needed to clarify
whether the starlight of the host has significant polarization.
J0728+5701: A foreground star is located nearby. To avoid depolarization due
to contamination by the star light we used a fixed 4 arcsec aperture.
J0849+5108: This NLSy1 has a prominent spiral host galaxy (Hamilton et al.,
2020). It appears to be asymmetric in images and can potentially contribute to
the polarization. We measured the nucleus with a fixed 4 arcsec aperture.
J1148+5924: NGC 3894 has a very bright host. The galaxy fills the masked areas
entirely. For this reason there is a possibility of deviation of the PD from
its real values due to inaccurate estimate of the sky background. We measured
this source with a fixed 6 arcsec aperture.
J1442+1200: 1ES 1440+122 has a prominent host galaxy. There is also a nearby
source located 2.5 arcsec from the core according to Gaia data (Gaia
Collaboration et al., 2018). It could potentially affect the polarization. We
measured the nucleus with a fixed 4.5 arcsec aperture.
J1505+0326: This NLSy1 has a prominent elliptical host galaxy, however there
is an asymmetric structure 3 arcsec from the core that is probably produced by
an interaction with another galaxy (D’Ammando et al., 2018). Since we measured
the source with a fixed 4 arcsec aperture the emission of this component can
potentially contribute to the polarization.
J1653+3945: Mkn501 has a very bright host. The galaxy fills the masked areas
entirely. For this reason there is a possibility of deviation of the PD from
its real values due to inaccurate estimate of the sky background. We measured
this source with a fixed 6 arcsec aperture.
J1728+0427: PKS 1725+044 has a prominent host galaxy. There is also a nearby
source located 1.6 arcsec from the core according to Gaia data. It could
potentially affect the polarization. We measured the nucleus with a fixed 4
arcsec aperture.
J1733$-$1304: PKS 1730$-$130 has a nearby source located 1.8 arcsec from the
core according to Gaia data. It could potentially affect the polarization. We
measured the nucleus with a fixed 5 arcsec aperture.
J1743+1935: This AGN has a prominent host galaxy. There is also a nearby
source located 3.4 arcsec from the core according to Gaia data. It could
potentially affect the polarization. We measured the nucleus with a fixed 5
arcsec aperture.
J1943+2118: A few measurements for this TeV source have been presented in
(Hovatta et al., 2016). However, reanalysing data for this paper, we
discovered that the source was previously misassociated. The measured
polarization corresponds to a $\sim 17$ mag star, while the TeV AGN is
associated with 2MASS source J1943562+2118233 (Landi et al., 2009) that is
located 5.3 arcsec North-West from this star. Since the AGN is very faint
$R\approx 22.4$ mag, it cannot be measured in our images. For this reason
J1943+2118 is not present in the data table.
J2031+1219: There is a nearby source located 2.9 arcsec from the core
according to Gaia data. It could potentially affect the polarization. We
measured the nucleus with a fixed 3 arcsec aperture.
J2033+2146: This AGN has a prominent host galaxy. There is also a nearby
foreground star located 2.3 arcsec from the core according to Gaia data. It
could potentially affect the polarization. We measured the nucleus with a
fixed 4 arcsec aperture.

##  7 Monitoring data and polarization parameters

The data products that are made publicly available in this work are the
polarimetric monitoring data of the sample sources and their average
polarization parameters. Machine-readable form of the data tables can be
accessed via Harvard dataverse555https://doi.org/10.7910/DVN/IMQKSE and
Vizier. Abridged versions of the average polarization parameters table and
monitoring data table are also presented in the Appendix A.

In Table A of Appendix A we list general information about AGN in the sample
including their equatorial coordinates, redshift with corresponding reference
and their sample ID. We also give the total number of observations of each
source, and the number of seasons during which it has been observed, and the
median time sampling interval. Additionally, we provide the following
statistics to quantify the average and variability of the fractional
polarization and the EVPA. We estimate the intrinsic mean polarization
fraction $p_{0}$ and the intrinsic modulation index $m_{p}$ by modelling the
distribution of the measured polarization fraction as a Beta distribution
following Blinov et al. (2016a). We infer the distribution parameters with
Bayesian modelling using `PyStan`666https://pystan.readthedocs.io/. To
quantify the EVPA variability we take the difference of the 75 per cent and 25
per cent quantiles of the measured EVPA distribution. We subtract this value
from $180^{\circ}$ if the difference exceeds $90^{\circ}$. The result is
divided by $90^{\circ}$. This variability index, $v_{\chi}$, is normalised to
the interval $[0,1]$, where 0 corresponds to a perfectly stable EVPA and 1
corresponds to a variable EVPA with a perfectly uniform distribution, i.e. no
preferred orientation. For sources with $v_{\chi}<0.5$ we show the wrap-
corrected median EVPA as a measure of the preferred orientation.

The entire AGN monitoring data are given in electronic format, while the first
rows of this table are given in Table 3. There for each observation we provide
Julian Date (JD), relative Stokes parameters and their uncertainties before
the instrument polarization correction, relative Stokes parameters after the
instrument polarization and EVPA zero-point correction as well as
corresponding values of PD and EVPA.

##  8 Data Policy

These data are being made available to the public as a service to the
astronomical community. If you use RoboPol data in your research, we request
that you cite the present publication, allowing us to keep track of the impact
of our work, and that you include the following acknowledgement: ’This
research has made use of data from the RoboPol programme, a collaboration
between Caltech, the University of Crete, IA-FORTH, IUCAA, the MPIfR, and the
Nicolaus Copernicus University, which was conducted at Skinakas Observatory in
Crete, Greece.’

## Acknowledgements

We thank A. Steiakaki and E. Paleologou for their invaluable contribution and
technical support. The RoboPol project is a collaboration between Caltech in
the USA, MPIfR in Germany, Toruń Centre for Astronomy in Poland, the
University of Crete/FORTH in Greece, and IUCAA in India. The U. of Crete group
acknowledges support by the “RoboPol” project, which was implemented under the
“Aristeia” Action of the “Operational Programme Education and Lifelong
Learning” and was co-funded by the European Social Fund (ESF) and Greek
National Resources, and by the European Comission Seventh Framework Programme
(FP7) through grants PCIG10-GA-2011-304001 “JetPop” and PIRSES-GA-2012-31578
“EuroCal”. This research was supported in part by NASA grant NNX11A043G and
NSF grant AST-1109911, and by the Polish National Science Centre, grant
numbers 2011/01/B/ST9/04618 and 2017/25/B/ST9/02805. C.C., D.B., N.M., R.S.
and K.T. acknowledge support from the European Research Council (ERC) under
the European Union Horizon 2020 research and innovation programme under the
grant agreement No 771282. K.T. and G.V.P. acknowledge support by the European
Commission Seventh Framework Programme (FP7) through the Marie Curie Career
Integration Grant PCIG-GA-2011-293531 “SFOnset” as well as NASA Hubble
Fellowship grant # HST-HF2-51444.001-A awarded by the Space Telescope Science
Institute, which is operated by the Association of Universities for Research
in Astronomy, Incorporated, under NASA contract NAS5-26555. T.H. was supported
by the Academy of Finland projects 317383, 320085, and 322535

## DATA AVAILABILITY

The data underlying this article are available in Harvard Dataverse, at
https://dx.doi.org/10.7910/DVN/IMQKSE.

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##  Appendix A Sample sources information and monitoring data

Table A lists the monitored sources, additional source information, and
polarization statistics as described in § 7.

Table 2: Information on the sample sources. (1) - J2000 name; (2) -
Alternative source identifier; (3) - Right ascension; (4) - Declination; (5) -
Redshift; (6) - Redshift reference; (7) - Sample identifier; (8) - Aperture;
(9) - Number of measurements; (10) - Number of seasons; (11) - Median time
sampling interval; (12) - Mean intrinsic polarisation fraction; (13) -
Intrinsic modulation index of the polarization fraction; (14) - Variability
index of the EVPA; (15) - Preferred EVPA orientation of less variable sources.
The entire table is available online.

Name | Alt. name | RA (J2000) | DEC (J2000) |  $z$ a |  $z$ ref. |  SIDb |  Aperturec |  $N$ meas. |  $N$ seas. | sampling | $p_{0}$ | $m_{p}$ | $v_{\chi}$ | $\chi_{0}$
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  | h:m:s | °:′:″ |  |  |  | arcsec |  | d | d | % |  |  | deg
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15)
RBPLJ0006$-$0623 |  PKS 0003$-$066 | 00:06:13.9 |  $-$06:23:35.3 | 0.34668 | Jones et al. (2009) | 5 | v | 15 | 2 | 8 | 21.20 | 0.45 | 0.13 |  $-$14.0
RBPLJ0017+8135 | S5 0014+81 | 00:17:08.5 |  $+$81:35:08.1 | 3.366 | Osmer et al. (1994) | 2,4 | v | 30 | 3 | 7 | 0.80 | 0.62 | 0.65 | -
RBPLJ0035+5950 | 1ES 0033+595 | 00:35:52.7 |  $+$59:50:04.2 | 0.086 | Falomo & Kotilainen (1999) | 5 | 6 | 5 | 1 | 8 | - | - | - | -
RBPLJ0045+2127 | - | 00:45:19.3 |  $+$21:27:40.0 | - | - | 3 | v | 64 | 4 | 3 | 4.83 | 0.59 | 0.84 | -
RBPLJ0102+5824 | TXS 0059+581 | 01:02:45.8 |  $+$58:24:11.1 | 0.644 | Sowards-Emmerd et al. (2005) | 5 | v | 18 | 3 | 9 | 13.45 | 0.32 | 0.53 | -
RBPLJ0114+1325 | - | 01:14:52.8 |  $+$13:25:37.5 |  0.583* | Stadnik & Romani (2014) | 3 | v | 59 | 4 | 4 | 7.04 | 0.48 | 0.74 | -
RBPLJ0136+3905 | B3 0133+388 | 01:36:32.5 |  $+$39:05:59.6 | 0.750 | Neronov et al. (2015) | 6 | v | 7 | 2 | 27 | - | - | - | -
RBPLJ0136+4751 | OC 457 | 01:36:58.6 |  $+$47:51:29.0 | 0.859 | Hewitt & Burbidge (1987) | 3 | v | 64 | 4 | 3 | 8.75 | 0.64 | 0.86 | -
RBPLJ0152+0146 | RBS 0248 | 01:52:39.6 |  $+$01:47:17.4 | 0.08 | Laurent-Muehleisen et al. (1998) | 6 | 4 | 9 | 2 | 20 | - | - | - | -
RBPLJ0211+1051 | - | 02:11:13.2 |  $+$10:51:35.0 |  0.2* | Meisner & Romani (2010) | 3 | v | 65 | 4 | 3 | 14.98 | 0.51 | 0.31 |  $-$30.0
RBPLJ0217+0837 | - | 02:17:17.1 |  $+$08:37:03.9 | 0.085 | Shaw et al. (2013) | 3 | v | 62 | 4 | 3 | 6.11 | 0.47 | 0.50 | 76.0
RBPLJ0221+3556 | S4 0218+35 | 02:21:05.5 |  $+$35:56:13.9 | 0.944 | Cohen et al. (2003) | 5 | v | 1 | 1 | - | - | - | - | -
RBPLJ0222+4302 | 3C 66A | 02:22:39.6 |  $+$43:02:07.8 | 0.37 | Furniss et al. (2013) | 5,6 | v | 25 | 2 | 6 | 7.99 | 0.40 | 0.38 | 16.0
RBPLJ0232+2017 | - | 02:32:48.6 |  $+$20:17:17.4 | 0.14 | Schachter et al. (1993) | 6 | 4 | 8 | 2 | 18 | - | - | - | -
RBPLJ0238+1636 | AO 0235+164 | 02:38:38.9 |  $+$16:36:59.0 | 0.94 | Hewitt & Burbidge (1987) | 5 | v | 20 | 3 | 5 | 12.15 | 0.49 | 0.39 | 22.0
RBPLJ0259+0747 | PKS 0256+075 | 02:59:27.1 |  $+$07:47:39.6 | 0.893 | Murphy et al. (1993) | 3 | v | 34 | 3 | 5 | 23.59 | 0.47 | 0.07 | 53.0
RBPLJ0303+4716 | B3 0300+470 | 03:03:35.2 |  $+$47:16:16.3 | 0.475 | Hughes et al. (1992) | 5 | v | 15 | 3 | 10 | 7.25 | 0.45 | 0.40 | 81.0
RBPLJ0303$-$2407 |  PKS 0301$-$243 | 03:03:26.5 |  $-$24:07:11.5 | 0.2657 | Pita et al. (2014) | 3 | v | 12 | 3 | 17 | 6.67 | 0.24 | 0.19 | 54.0
RBPLJ0316+4119 | TXS 0313+411 | 03:16:43.0 |  $+$41:19:29.9 | 0.01894 | Bernardi et al. (2002) | 5 | 4 | 8 | 2 | 19 | - | - | - | -
RBPLJ0319+1845 | RBS 0413 | 03:19:51.8 |  $+$18:45:34.4 | 0.19 | Stocke et al. (1991) | 5,6 | v | 7 | 3 | 12 | - | - | - | -
…

a \- Redshift notes: (*) non spectroscopic redshift, (l) lower limit on
redshift, (m) multiple values are present in literature. b \- Sample IDs: (1)
$\gamma$-ray–loud sub-sample from the ’June survey’ sample, (2)
$\gamma$-ray–quiet sub-sample from the ’June survey’ sample, (3)
$\gamma$-ray–loud sub-sample from the monitoring sample, (4)
$\gamma$-ray–quiet sub-sample from the monitoring sample, (5) additional
sample of 68 hand-picked sources of high interest, (6) ISP/HSP blazar sample.
c \- fixed aperture is given in arcsec, ’v’ denotes variable aperture (for
details see sections 4.1.2 and 6).

Table 3: Monitoring data: (1) - J2000 name; (2) - Julian Date; (3,4) - Q/I, U/I relative Stokes parameters before the correction for the instrumental polarization; (5,6) - Q/I and U/I relative Stokes parameters corrected for the instrumental polarization and EVPA zero-point; (7,8) - Fractional polarization and polarization vector position angle corrected for the instrumental polarization and EVPA zero-point. This table gives only first 3 rows of the entire dataset, which can be accessed at https://doi.org/10.7910/DVN/IMQKSE. AGN ID | JD | ${\rm Q/I}_{\rm inst}$ | ${\rm U/I}_{\rm inst}$ | Q/I | U/I | $\mathrm{P}\mathrm{D}$ | $\mathrm{E}\mathrm{V}\mathrm{P}\mathrm{A}$
---|---|---|---|---|---|---|---
|  |  |  |  |  | $\%$ | $\mathrm{d}\mathrm{e}\mathrm{g}$
(1) | (2) | (3) | (4) | (5) | (6) | $\mathrm{(}\mathrm{7}\mathrm{)}$ | $\mathrm{(}\mathrm{8}\mathrm{)}$
RBPLJ0017+8135 | 2451023.41611 | $0.0131\pm 0.00022$ | $0.0121\pm 0.00012$ | $0.0131\pm 0.00022$ | $0.0121\pm 0.00012$ | $5.2\pm 0.3$ | $20.4\pm 1.2$
RBPLJ0017+8135 | 2459016.01214 | $0.0031\pm 0.00002$ | $0.0048\pm 0.00005$ | $0.0431\pm 0.00022$ | $0.1221\pm 0.00022$ | $12.2\pm 0.1$ | $20.4\pm 1.2$
RBPLJ0017+8135 | 2459086.08916 | $0.0536\pm 0.00003$ | $0.0022\pm 0.00006$ | $0.0321\pm 0.00022$ | $0.0441\pm 0.00043$ | $6.2\pm 0.2$ | $120.4\pm 2.1$
…

##  Appendix B Instrument model

The instrument model accounts for two separate imperfections of the RoboPol
polarimeter: the spatial distortions of the four spots pattern on the CCD, and
the instrumental polarization across the FoV. The functional dependencies used
for approximation of the instrument response are given in King et al. (2014).
Here we present updated plots of the instrument model parameters obtained from
11 series of exposures of 7 standard unpolarized stars observed at different
positions across the FoV in 2013 – 2017. The locations of these standard stars
on the CCD are shown in 7.

Figures 8 \- 10 demonstrate variation of parameters $\Delta_{x}(x,y)$,
$\delta_{x}(x,y)$ and $\phi_{x}(x,y)$ of the spatial model depicted in Fig. 3
across the FoV. The y version of these parameters looks similar. The
corresponding plots were omitted for brevity.

Figures 11 and 12 demonstrate effectiveness of the instrument intensity model
used to correct the measured spot intensities for systematic variation of the
instrumental polarization across the FoV. The distribution of the residual Q/I
and U/I Stokes parameters of the unpolarized standards measured in the field
after the instrumental polarization correction is shown in Fig. 13. For the
detailed description of the instrument model correction we forward readers to
King et al. (2014).

Figure 7: A plot showing the location of unpolarized standard stars used for
the combined 2013 – 2017 instrument model. The individual spots are indicated
by black dots, and the central point by a red cross. Figure 8: The data
(left), best-fit model (centre), and residuals (right) for the quantity
$\Delta_{x}$ in the instrument spatial pattern model. Note the change in
colour scale for the residual plot. Figure 9: The data (left), best-fit model
(centre), and residuals (right) for the quantity $\delta_{x}$ in the
instrument spatial pattern model. Note the change in colour scale for the
residual plot. Figure 10: The data (left), best-fit model (centre), and
residuals (right) for the quantity $\phi_{x}$ in the instrument spatial
pattern model. Note the change in colour scale for the residual plot. Figure
11: The uncorrected (left) and corrected (right) relative Stokes Q/I
parameter, which corresponds to before and after applying the instrument
intensity model to the data, respectively. Figure 12: The uncorrected (left)
and corrected (right) relative Stokes U/I parameter, which corresponds to
before and after applying the instrument intensity model to the data,
respectively. Figure 13: Residuals of relative Stokes parameters after the
instrument model fit.