11institutetext:  IGAM, Institute of Physics, University of Graz,
Universitätsplatz 5/II, 8010 Graz, Austria 22institutetext: School of Physics,
Astronomy & Mathematics, University of Hertfordshire, Hatfield AL10 9AB, UK
33institutetext: Department of Physics & Astronomy, University College London,
Gower Street, London WC1E 6BT, UK 44institutetext: Institut d’Estudis
Espacials de Catalunya, Universitat de Barcelona (ICC-UB), Martí i Franquès 1,
E-08028 Barcelona, Spain 55institutetext: Universitat Politècnica de
Catalunya, Departament de Física, c/Esteve Terrades 5, 08860 Castelldefels,
Spain 66institutetext: Isaac Newton Group, Apartado de correos 321, E-38700
Santa Cruz de La Palma, Canary Islands, Spain 77institutetext: Bay Area
Environmental Research Institute, P.O. Box 25, Moffett Field, CA 94035, USA
88institutetext: Thüringer Landessternwarte, Sternwarte 5, 07778 Tautenburg,
Germany 99institutetext: Instituto de Astrofísica de Canarias, E-38205 La
Laguna, Tenerife, Spain 1010institutetext: Astrophysics Group, School of
Physics, University of Bristol, Tyndall Av, Bristol, BS8 1TL, UK
1111institutetext: School of Physics, University of Exeter, Exeter EX4 4QL, UK
1212institutetext: Instituto de Astronomía, Universidad Nacional Autónoma de
México (UNAM), AP 106, Ensenada 22800, BC, México 1313institutetext:
Astrophysics Group, Keele University, Keele, ST5 5BG, UK 1414institutetext:
Department of Astrophysics/IMAPP, Radboud University, P.O. Box 9010, 6500 GL
Nijmegen, The Netherlands 1515institutetext: Department of Astronomy,
University of Cape Town, Private Bag X3, Rondebosch, 7701, South Africa
1616institutetext: South African Astronomical Observatory, P.O. Box 9,
Observatory, 7935, South Africa 1717institutetext: The Inter-University
Institute for Data Intensive Astronomy, University of Cape Town, Private Bag
X3, Rondebosch, 7701, South Africa 1818institutetext: Institute of Astronomy,
University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK
1919institutetext: Centro Astronómico Hispano-Alemán, Observatorio de Calar
Alto, Sierra de los Filabres s/n, E-04550 Gérgal, Almeria, Spain
2020institutetext: The University of Hong Kong, Department of Physics, Hong
Kong SAR, China 2121institutetext: The Laboratory for Space Research,
Cyberport 4, Hong Kong SAR, China 2222institutetext: Department of Physics,
University of Durham, South Road, Durham, DH1 3LE 2323institutetext: Jodrell
Bank Centre for Astrophysics, Alan Turing Building, Manchester M13 9PL, UK

# High resolution H$\alpha$ imaging of the Northern Galactic Plane, and the
IGAPS images database

R. Greimel High resolution H$\alpha$ imaging of the Northern Galactic Plane,
and the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images database J. E. Drew High resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images database M. Monguió High
resolution H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS
images databaseHigh resolution H$\alpha$ imaging of the Northern Galactic
Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images database R. P. Ashley High resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database G. Barentsen High resolution H$\alpha$ imaging of
the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images database J. Eislöffel High resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database A. Mampaso High resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
database R. A. H. Morris High resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images database T. Naylor High
resolution H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS
images databaseHigh resolution H$\alpha$ imaging of the Northern Galactic
Plane, and the IGAPS images database C. Roe High resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
database L. Sabin High resolution H$\alpha$ imaging of the Northern Galactic
Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images database B. Stecklum High
resolution H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS
images databaseHigh resolution H$\alpha$ imaging of the Northern Galactic
Plane, and the IGAPS images database N. J. Wright High resolution H$\alpha$
imaging of the Northern Galactic Plane, and the IGAPS images databaseHigh
resolution H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS
images database P. J. Groot High resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database M. J. Irwin High resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
database M. J. Barlow High resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images database C. Fariña High
resolution H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS
images databaseHigh resolution H$\alpha$ imaging of the Northern Galactic
Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
database A. Fernández-Martín High resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database Q. A. Parker High resolution H$\alpha$ imaging of
the Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images databaseHigh resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images database S. Phillipps High resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database S. Scaringi High resolution H$\alpha$ imaging of the
Northern Galactic Plane, and the IGAPS images databaseHigh resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
database A. A. Zijlstra High resolution H$\alpha$ imaging of the Northern
Galactic Plane, and the IGAPS images databaseHigh resolution H$\alpha$ imaging
of the Northern Galactic Plane, and the IGAPS images database High resolution
H$\alpha$ imaging of the Northern Galactic Plane, and the IGAPS images
databaseHigh resolution H$\alpha$ imaging of the Northern Galactic Plane, and
the IGAPS images database

(Received March 31, 2021; accepted July 13, 2021)

The INT Galactic Plane Survey (IGAPS) is the merger of the optical photometric
surveys, IPHAS and UVEX, based on data from the Isaac Newton Telescope (INT)
obtained between 2003 and 2018. These capture the entire northern Galactic
plane within the Galactic coordinate range, $|b|<5^{\circ}$ and
$30^{\circ}<\ell<215^{\circ}$. From the beginning, the incorporation of
narrowband H$\alpha$ imaging has been a unique and distinctive feature of this
effort. Alongside a focused discussion of the nature and application of the
H$\alpha$ data, we present the IGAPS world-accessible database of images for
all 5 survey filters, $i$, $r$, $g$, $U_{\rm RGO}$ and narrowband $H\alpha$,
observed on a pixel scale of 0.33 arcsec and at an effective (median) angular
resolution of 1.1–1.3 arcsec. The background, noise, and sensitivity
characteristics of the narrowband $H\alpha$ filter images are outlined.
Typical noise levels in this band correspond to a surface brightness at full
$\sim$1 arcsec resolution of around $2\times 10^{-16}$ erg cm-2 s-1 arcsec-2.
Illustrative applications of the $H\alpha$ data to planetary nebulae and
Herbig-Haro objects are outlined and, as part of a discussion of mosaicking
technique, we present a very large background-subtracted narrowband mosaic of
the supernova remnant, Simeis 147. Finally we lay out a method that exploits
the database via an automated selection of bright ionized diffuse interstellar
emission targets for the coming generation of wide-field massive-multiplex
spectrographs. Two examples of the diffuse H$\alpha$ maps output from this
selection process are presented and compared with previously published data.

###### Key Words.:

Surveys – Astronomical databases: miscellaneous – ISM: general – (ISM:) HII
regions – (ISM:) planetary nebulae: general – ISM: supernova remnants

##  1 Introduction

The stellar and diffuse gaseous content of the Galactic Plane continues to be
a vitally important object of study as it offers the best available angular
resolution for exploring how galactic disc environments are built, and how
they operate and evolve over time. For studies of the diffuse interstellar
medium (ISM), the optical offers H$\alpha$ – emission in this strong
transition is the pre-eminent tracer of ionized gas. By definition, the ISM is
an extended object that must involve investigation by means of imaging data.
Our purpose here is to present and describe a newly-complete resource that
enables this style of astronomy, specifically within the gas- and dust-rich
northern Galactic plane.

There is a history, stretching back over the last century, of comprehensive
surveying of the optical night sky. Until around 1990, much of the wide-area
effort depended on photographic emulsions on glass as detectors (e.g. the
Palomar, ESO and UK-Schmidt sky surveys, described by Lund & Dixon, 1973;
West, 1974; Morgan et al., 1992, respectively). The last thirty years has seen
a switch to digital detectors that has brought with it the benefits of
linearity and increased dynamic range, paving the way for increasingly precise
photometric calibration. Thanks to this change and the advance of data
science, the community now has access to a number of wide-area broad band
surveys offering images at $\sim$ arcsecond angular resolution and point-
source catalogues (e.g. SDSS, Pan-STARRS, DECaPS, Skymapper: see Alam et al.,
2015; Chambers et al., 2016; Schlafly et al., 2018; Wolf et al., 2018).

Wide-area narrow band H$\alpha$ imaging, our main focus here, has generally
been pursued separately from broad band work (most notably VTSS, SHASSA and
WHAM, see Dennison et al., 1998; Gaustad et al., 2001; Haffner et al., 2003) .
Finkbeiner (2003) merged these surveys into a single map covering much of the
sky, albeit at an angular resolution limited to 6 arcmin. Inevitably, most
H$\alpha$ nebulosity is concentrated in the Galactic plane, along with most of
the Galaxy’s gas, dust and stars. Within the plane, the angular resolution
needs to be better than this to begin to resolve individual clusters and HII
regions that show structure on the sub-arcminute scale. The UK Schmidt
H$\alpha$ Survey (SHS Parker et al., 2005), based on photographic film, has
met this challenge in the southern Galactic plane with imaging data of a
resolution approaching 1 arcsec. Before the imaging presented here began to be
collected, the same could not be said for the plane in the northern
hemisphere.

The focus of this paper is on full coverage of the plane of the northern Milky
Way, via digital narrowband H$\alpha$ imaging at $\sim$1 arcsec angular
resolution obtained from 2003 up to 2018 using the Wide Field Camera (WFC) on
the Isaac Newton Telescope (INT) in La Palma. We showcase the properties of
the H$\alpha$ images and point out different modes of exploitation, past,
present and future. Along side this, we also present the IGAPS 5-filter image
database, where IGAPS is the acronym for ”The INT Galactic Plane Surveys”.
IGAPS is the cross-calibrated merger of the two Galactic Plane surveys, IPHAS
(The INT Photometric $H\alpha$ survey of the northern Galactic Plane, Drew et
al., 2005), and UVEX (The UV-Excess survey of the northern Galactic Plane,
Groot et al., 2009)111In concept, these two surveys are the older siblings to
VPHAS+, the survey covering the southern Galactic Plane and Bulge (Drew et
al., 2014).. Together these surveys have offered a new mix of narrow-band
H$\alpha$ alongside four broad bands spanning the optical. The IPHAS filters
were $r$/$i$/$H\alpha$, while the UVEX survey incorporated a repeat in $r$ and
$g$/$U_{\rm RGO}$ observations. The total IGAPS footprint is a 1850 sq.deg.
strip along the Galactic Plane defined on the sky in Galactic coordinates by:-
$-5^{\circ}<b<+5^{\circ}$ and $30^{\circ}<\ell<215^{\circ}$.

Monguió et al. (2020) have recently presented the merged and calibrated IGAPS
point-source catalogue of aperture photometry derived from the IPHAS and UVEX
surveys. The focus of this study was on the extraction of stellar photometric
data. It did not take on characterisation of the extended ionised emission
traced by the H$\alpha$ images. This is the partner paper in which this
missing piece is put in place.

We will outline the world-accessible database of IGAPS images we have set up
to hold the H$\alpha$ (and broad-band) data. It is reached via a website that
also provides access to the Monguió et al. (2020) point source catalogues. The
imagery we have archived incorporates all IPHAS and UVEX observations
including a minority that did not meet all the desired survey quality
criteria. The majority of the images included benefit from the uniform
photometric zero points computed by Monguió et al. (2020) in building the
IGAPS point-source catalogue.

The structure of the paper is as follows. In section 2, we summarise the
relevant features of IGAPS data and our methods and summarise the contents of
the image database held within http://www.igapsimages.org/. This is followed
up by an outline of the search tool available for querying the database
(section 3). Attention then switches to our main focus: the properties and
application of the narrowband H$\alpha+[NII]$ imagery. Section 4 sets the ball
rolling with an overview of the sensitivity and the nature of the background
captured in the narrowband filter. We then go on, in section 5 to highlight
two contrasting examples of its exploitation for the science of diffuse
nebulae (planetary nebulae and Herbig-Haro objects). A discussion of
techniques for mosaicking the H$\alpha$ data then follows (section 6). Looking
to the near future of massively-multiplexed optical spectrographs, we present
a method for automated searching of the IGAPS images database for high
H$\alpha$ surface brightness non-stellar diffuse-ISM spectroscopic targets
(section 7). To round off, we show two contrasting examples of the output from
the automated search in section 7.4.

##  2 Description of the image database

Filter | ING/WFC name | $N$ | exposure | PSF FWHM | sky count | ZP | 5$\sigma$ depth | moon phase
---|---|---|---|---|---|---|---|---
|  |  | (sec) | (arcsec) |  | (mag.) | (mag.) |
$i$ | WFCSloanI | 83652 | 10 (83%), and 20 (17%) | 1.06 | 92 | 26.42 | 20.28 | 0.68
$H\alpha$ | WFCH6568 | 83652 | 120 | 1.20 | 57 | 26.57 | 20.40 | ”
$r_{I}$ | WFCSloanR | 83652 | 30 (85%), and 10 (15%) | 1.16 | 164 | 28.19 | 21.37 | ”
$r_{U}$ | ” | 67896 | 30 | 1.18 | 126 | 28.20 | 21.67 | 0.21
$g$ | WFCSloanG | 67895 | 30 | 1.26 | 61 | 28.71 | 22.38 | ”
$U_{\rm RGO}$ | WFCRGOU | 67892 | 120 | 1.48 | 43 | 27.85 | 21.47 | ”
Table 1:  Properties of the repository contents by filter for the better-
quality image sets graded A to C. The number $N$ specifies the number of CCD
images available in the A to C grade range for each filter (D-grade images
with identified problems, are not included in the count). Median values are
listed for the full width half maximum of the point spread function (PSF
FWHM), the sky background count, zeropoint (ZP), 5$\sigma$ detection limit in
the Vega system, and moon phase. The PSF FWHM and the background count are
pipeline measures, while the zeropoint and limiting magnitude are based on the
uniform calibration (Monguió et al., 2020). Moon phase is given as a fraction,
such that 0 corresponds to new moon and 1 to full moon. The $r_{I}$ exposure
time started out at 10 sec, but was raised to 30 sec from 2004 on, while the
$i$ exposure time was increased to 20 sec starting in October 2010.

A series of papers (Drew et al., 2005; González-Solares et al., 2008; Groot et
al., 2009; Barentsen et al., 2014; Monguió et al., 2020) has already described
the survey data acquisition and pipelining. So here it is only necessary to
repeat pertinent details.

The camera used, the INT’s Wide Field Camera (WFC), is a 4-CCD mosaic arranged
in an L shape with a pixel size of 0.33 arcsec/pixel. Each CCD images a sky
region of roughly $11\times 22$ sq.arcmin, giving a total combined field per
exposure of approximately 0.22 sq.deg. The five filters used – $U_{\rm RGO}$,
$g$, $r$, $i$, and a narrow-band $H\alpha$ – have central wavelengths of
364.0, 484.6, 624.0, 774.3, and 656.8 nm respectively. Despite the filter’s
different naming, the $U_{\rm RGO}$ transmission curve quite closely resembles
that of Sloan $u$ (Doi et al., 2010). We shall refer to the narrow band filter
as the $H\alpha$ filter throughout this work, but we note for completeness
that the 95 Å bandpass also captures the [NII] 654.8, 658.4 nm forbidden lines
typical of HII-region emission. The numbers of CCD images per filter in the
repository are listed along with other performance parameters in Table 1. UVEX
and IPHAS $r$ band observations are distinguished by labelling them $r_{U}$
and $r_{I}$ respectively.

IPHAS and UVEX shared the same footprint and set of pointings, such that the
northern Galactic plane was covered via 7635 WFC pointings, tessellating the
survey area with, typically, a small overlap. In addition, each field was
observed again, and usually around 5 minutes later, at an offset of +5 arcmin
in RA and +5 arcmin in Dec in order to fill in the gaps between the CCDs and
also to minimize the effects of bad pixels and cosmic rays. As a result,
almost all locations in the northern plane have been imaged twice in either
survey. The presence of the $r$ band in both surveys means that there will
usually be at least 4 CCD images, uniquely in this band, covering any given
position within the footprint. The only exception to mention is that there is
a triangular patch of sky towards $\ell\sim 215^{\circ}$ where there are no
UVEX $U_{\rm RGO}$, $g$, $r_{U}$ images.

A distinction between the two surveys is that the blue UVEX data were obtained
during dark time, while IPHAS observations were generally made with the moon
above the horizon. This difference means that background sky counts are
typically higher in the red IPHAS images than they are in UVEX images. A
practical consequence of this is that UVEX $r_{U}$ data go deeper than IPHAS
$r_{I}$ by 0.3 magnitudes, on average (see Table 1). Another impact is that
the background levels in the brighter-time observations in the H$\alpha$
filter can exhibit marked variation, depending on how far away and full the
moon is and on cloud cover (see section 4). These variations, especially when
mixed with H$\alpha$ nebulosity, represent a challenge when mosaicking IPHAS
data to build large-area images. How this challenge can and has been met is
the subject of section 6.

A part of the pipelining procedure for all survey data was to apply a flux
calibration based on nightly standard-star observations. The zero points, in
the Vega system, obtained on this basis remain associated with all images in
the repository and are stored in every image header as the keyword MAGZPT. The
meaning of MAGZPT is that it is the magnitude in the Vega system of an object
giving 1 count per second (it does not fold in the exposure time). For details
of how it is derived see equation 4 in González-Solares et al. (2008).
Experience showed that broadly consistent and reliable results were obtained
for the $U_{\rm RGO}$ and H$\alpha$ filters if their zero points were fixed at
a constant offset relative to (respectively) their partner $g$ and $r_{I}$
frames. For $U_{\rm RGO}$ this is the only calibration presently available.
For $g$, $r_{U}$, $r_{I}$, $i$ and $H\alpha$, a uniform recalibration of the
photometry was undertaken in preparing the point source catalogue (Monguió et
al., 2020). The result of this is that around 2/3 of the images in the
repository now carry a revised zero point (PHOTZP header keyword) that rests
on a comparison with PanSTARRS $g$, $r$ and $i$ photometry. This zero point
has incorporated the image exposure time, which means it is the magnitude of a
source giving 1 count integrated over the exposure. Hence, the Vega magnitude
of any imaged source can be computed from:

$m=-2.5\log({\rm enclosed\,\,counts})+PHOTZP$ (1)

where, for a point source, the enclosed counts would be the total counts
within a user-specified aperture, after subtraction of an estimate for the
enclosed underlying background.

The $H\alpha$ filter magnitude scale is not one conventionally defined within
the Vega system. This means we need to define the flux corresponding to the
zero of the magnitude scale appropriate to it. We determine that the zero-
magnitude flux entering the top of the Earth’s atmosphere within the WFC
$H\alpha$ filter transmission profile is

$F[m(H\alpha)=0]=1.57\times 10^{-7}{\rm ergs\,\,cm}^{-2}{\rm s}^{-1}.$ (2)

In the AB magnitude system this is equivalent to a magnitude of 0.328.

Expressed in point-source magnitudes the bright limit of the images is in the
region of 11–12 in $i$ and $H\alpha$, rising to 12–13 in the more sensitive
$r$ and $g$ bands. There were specific observation periods in which WFC
electronic issues meant that saturation was reached at appreciably lower count
levels than the norm. For example, there was a particular problem affecting
CCD 2 in November 2006 that would push the bright limit fainter by up to a
magnitude. Users of the data can track such problems in the images via the
count level recorded against the header keyword, SATURATION.

As part of creating the point source catalogue images were graded from best to
worst as A++, A+, and A to D. For a definition of the grades see appendix A in
Monguió et al. (2020). A last point to make about the images is to note that,
while there is minimal vignetting of CCDs 1, 2 and 4, there is persistent
vignetting of the corner of CCD 3 furthest from the optical axis of the WFC
(which passes through CCD 4). In Appendix A.2 a typical confidence map is
shown to illustrate this. In Appendix A.3, we briefly outline common artefacts
in the image data.

##  3 Accessing the images

All the reduced images from both surveys can be accessed via the website,
http://www.igapsimages.org/, hosted by University College London. Altogether
the repository contains 527736 CCD frames, of which 314923 (or 60%) carry the
best-quality A grades and 73097 (14%) are minimum-quality D graded. These
grade assignments were made at the level of the basic unit of observation in
the two constituent surveys – the consecutive trio of exposures obtained at
each sky position. The details of the grading system at work in evaluating
data for the IGAPS catalogue can be found in Appendix A of the catalogue paper
(Monguió et al., 2020). A consequence of this approach, also applied to the
earlier IPHAS DR2 release (Barentsen et al., 2014), is that the grade
assignment, referred to individual CCD frames or even filters, can be
pessimistic, as it takes just one substandard exposure in a set of three to
pull down the grade for all of them. In view of this, and the occasional
scientific value of maximising the number of images to examine, the decision
was taken to retain all D graded data in the repository, alongside the A to C
graded exposures.

The images access page within the website offers a search tool that enables
users to search for and download images that either overlap a single specified
position or occupy a square box of size up to $1\times 1$ sq.deg. on the sky.
The user can choose the filters of interest and decide whether to omit grade D
data. In response to a query, the tool returns a table listing the images
meeting requirement, along with key metadata (grade, seeing, depth, whether
calibrated) that can inform the user’s final choice of images for download.
For convenience, there is a column of tick boxes in the table, that allows the
user to deselect some of the listed images before initiating the download of a
gzipped or tarred collection of Rice-compressed (.fz) images.

It is generally the case that a contemporaneous $r_{I}$ image accompanied an
H$\alpha$ and $i$ image of the same pointing. Similarly the UVEX $r_{U}$, $g$
and $U_{\rm RGO}$ images were observed as consecutive triplets. Given that
stars are sometimes subject to variability, users of the repository may need
to bear this in mind when deciding how to select images for scientific
exploitation.

The website also provides a link to a large table of metadata that previews
the header information provided with the full set of image profiles.

Figure 1: Map of the uniformly-calibrated IGAPS field centres (grey). A darker
grey colour is seen where the database contains more than one uniformly-
calibrated exposure. The orange overplotted points mark the 339 exposure sets
(1356 CCD frames), without alternatives in the database, where the $H\alpha$
sky background exceeds 210 counts ($>95^{{\rm th}}$ percentile).

##  4 Properties of the $H\alpha$ images: backgrounds and sensitivity

At the outset, the IPHAS survey was allocated time on the expectation the
programme could cope with a moonlit sky. After the first few seasons and some
experience had accumulated, the brightest nights were increasingly avoided.
Indeed, in the late stages when the acquisition of the blue UVEX filters took
priority, dark and grey nights became the norm. The net result is that the
median background level among all the uniformly-calibrated $H\alpha$ images is
closer to grey, than bright. Table 2 provides some numerical detail
illustrating the strongly skewed distribution finally achieved. For comparison
with the magnitudes in the table, we mention that the ING exposure time
calculator uses 20.6, 19.7 and 18.3 mag arcsec-2 to represent dark, grey and
bright sky in the $R$ photometric band – values closely resembling the
tabulated 5th, 50th and 95th percentiles.

Around a third of the images were not passed through uniform calibration: of
these (generally inferior) data, just under a third have background levels in
$H\alpha$ exceeding the 210 counts marking the 95th percentile of the
uniformly-calibrated set of images. Most of them were obtained early on in the
survey, and most have been repeated, resulting in calibrated alternatives.
Just 339 calibrated $H\alpha$ exposures (1356 CCD frames, or 2 percent of the
total) are left without alternatives in the database, where the background
count exceeds the 95th percentile of 210 counts. Where these sit in the survey
footprint is shown in Figure 1.

Quantity | Percentiles
---|---
| 5 | 50 | 95
Counts | 27 | 52 | 210
Vega magnitude |  |  |
(mag arcsec-2) | 20.6 | 19.9 | 18.3
Surface brightness |  |  |
($10^{-15}$ erg cm-2 s-1 arcsec-2) | 0.91 | 1.7 | 7.4
Table 2:  Percentiles of the distribution of narrowband H$\alpha$+[NII]
pipeline-computed background levels in uniformly-calibrated images. The sample
used here numbers 63956 CCD frames (or 15989 WFC exposures). Not included are
the 36808 CCD frames (9202 WFC exposures) for which only the pipeline
photometric calibration is available.  Figure 2: Density plot of background
surface brightness in the $H\alpha$ band as a function of measured mean sky
level in counts. The colour scale is logarithmic, with blue representing the
highest density of points. The images used to build this diagram are the IGAPS
uniformly-calibrated set, which enables validated conversion of the sky counts
to surface brightness via each image’s zeropoint. The black dashed line has a
gradient of $3.32\times 10^{-17}$ ergs cm-2 s-1 arcsec-2 per count.  Figure 3:
Density plot of the pipeline-measured sky noise in the $H\alpha$ filter as a
function of sky level. Both are in units of counts per pixel. The coloured
data shown, and plotted on a logarithmic density scale, are drawn from the
IGAPS uniformly-calibrated set. The solid black line is the fit discussed in
the text, given as equation 4, that shows the sky noise increases mainly as
the square root of the sky level. The lighter grey data (underneath) are from
images with zeropoints that were not passed through uniform photometric
calibration.

Figure 2 shows how the recorded sky background count levels convert into
narrow-band $H\alpha$ surface brightness, via the set of zeropoints, PHOTZP.
The plot includes only data that have passed through the uniform photometric
calibration. Most of the data conform reasonably well to a linear trend such
that 1 count per pixel corresponds to $3.32\times 10^{-17}$ ergs cm-2 s-1
arcsec-2 (or 5.9 Rayleighs)222At H$\alpha$, 1 Rayleigh is equivalent to
$5.67\times 10^{-18}$ ergs cm-2 s-1 arcsec-2. The absolute minimum sky
brightness measured in any calibrated frame is $6.6\times 10^{-16}$ ergs cm-2
s-1 arcsec-2 ($\sim 120$ Rayleighs), but it is commonly more than twice this.
Sky transparency necessarily influences the behaviour. At times of reduced
transparency the sensitivity suffers, driving data points from affected nights
onto steeper linear trends in Figure 2.

Figure 3 is a plot of the pipeline measurement of sky noise versus the
estimate of sky background, both in counts per pixel. For around a half of the
uniformly calibrated frames, the noise is limited to under 6 counts per pixel
(or $\sim 2\times 10^{-16}$ ergs cm-2 s-1 arcsec-2), and it is extremely rare
that the sky noise is more than $\sim$12 counts per pixel. A simple
expectation for the form of the relation between sky noise and level is

$N_{bg}=(N_{RN}+\sqrt{C_{bg}})/\sqrt{n_{pe}}$ (3)

where $N_{bg}$ and $C_{bg}$ are respectively the sky noise and sky level,
$N_{RN}$ is the CCD read noise, also in counts per pixel, and $n_{pe}$
represents the effective number of pixels over which the sky statistics are
measured. Basically, we expect the noise to be the sum of a constant and a
Poisson component. In practice, the sky level is determined numerically by the
pipeline using two-dimensional non-linear background tracking across the full
CCD at a superpixel level. After the background fit has been subtracted from
an image, the sky noise is calculated iteratively from the clipped median
absolute deviation (MAD) of the residuals.

As the readnoise for the WFC is known to be $N_{RN}=2.37$ counts, we only need
to fit one parameter of this function. Before the fitting, outliers above the
dashed line in Figure 3 were removed. The fit was performed using an iterative
3-$\sigma$ clipped Levenberg-Marquardt algorithm using the median and MAD in
place of mean and $\sigma$. The result of the fit is

$N_{bg}=(2.37+\sqrt{C_{bg}})/\sqrt{2.88}=1.4+0.589\sqrt{C_{bg}}$ (4)

Allowing an additional fit parameter, namely a multiplicative factor for the
$\sqrt{C_{bg}}$ term, does not lead to a statistically significant improvement
in the fit, and the factor is found to be very close to 1. Hence we are
confident that equation 3 accurately describes the distribution.

In presenting the Schmidt H$\alpha$ Survey, Parker et al. (2005), compared its
sensitivity with IPHAS and other available H$\alpha$ wide-area surveys. In
their Table 1 it was estimated that the depth reached by the narrowband
(IPHAS) data presented here is $\sim$3 Rayleighs. This is a surface brightness
of $1.7\times 10^{-17}$ ergs cm-2 s-1 arcsec-2 or, as required by the mean
calibration illustrated in Figure 2, the equivalent of around a half count per
pixel in a 120 sec exposure at the typical 1 to 1.5 arcsec seeing. The on-sky
solid angle that the Parker et al. (2005) estimate refers to was not made
explicit – clearly it cannot be 1 arcsec2. If we assume $C_{bg}=0.5$ counts
pixel-1 at 3 $\sigma$, $N_{bg}=0.5/3$, we can use equation 3 to calculate the
pixel area needed to achieve a sensitivity of 3 Rayleighs. We obtain
$18.5\times 18.5$ pixels or $6.1\times 6.1$ arcsec.

Used at full seeing-limited resolution, the $H\alpha$ data provide safe
detection of raised surface brightness due to diffuse-ISM emission at the
level of a few times $10^{-16}$ ergs cm-2 s-1 arcsec-2, or 50–100 Rayleighs.
That this is so will be demonstrated by different means in section 7.

##  5 Exploitation of the $H\alpha$ images

The search for and characterization of extended nebulae was an original goal
of the IPHAS component of the merged IGAPS survey. Initially this mostly
concentrated on planetary nebulae (PNe). Discoveries of examples of those
alternative products of end-state stellar evolution – supernova remnants –
have also been found and recorded (Sabin et al., 2013).

Below we present brief discussions of how nebulae can be found and studied,
taking the contrasting examples of new PNe and – from the more obscured first
phase of the stellar life cycle – a Herbig Haro object.

###  5.1 Planetary nebulae

Planetary nebulae (PNe) are the end-products of low and intermediate mass
stars ($\sim$1–8M⊙) where a hot central star fully ionises its surrounding
shell leading to a glowing nebula. These objects are ideal tools to study the
later stages of stellar evolution as most stars in our Galaxy will go through
this phase. We can access information related to their physical
characteristics via plasma diagnostics (density, temperature, velocity and so
on) and the chemical composition allows the measurement of their impact on the
chemical enrichment of any given galaxy. However most of the known PNe are
bright and/or nearby as the faint ones have been ignored or are out of reach
due to observational constraints. An aim of IPHAS was to perform a near
complete census of the PNe in the northern Galactic Plane, where a higher
concentration of ionised sources co-exists with high extinction. Progress
towards this goal has been described by Sabin (2008) and Sabin et al. (2014).

####  5.1.1 Detection Methods

Depending on the size of the sources two methods were adopted. On the one
hand, compact or point-source PNe have been selected based on H$\alpha$ excess
as measured and recorded in photometric catalogues, and cross-checked against
IR photometry (e.g. 2MASS, see Viironen et al., 2009). On the other hand, for
the case of extended PNe, a mosaicking process was developed using 2∘ $\times$
2∘ $H\alpha-r$ mosaics with 5$\times$5 pixels and 15$\times$15 pixels binning
factors (corresponding to $\sim$1.7 and $\sim$5 arcsec effective pixel sizes
respectively, see Sabin, 2008). The coarser binning was mainly used to detect
large and low-surface brightness nebulae, while the lower binning was aimed at
detecting smaller nebulae hidden in crowded stellar fields. In the latter case
we also used the technique adopted in the southern MASH survey (Parker et al.,
2006; Miszalski et al., 2008) where RGB composite imagery (H$\alpha$, r’ and
i’ filters) is used to distinguish stars from diffuse ionised nebulae.333With
advances in the use of machine learning, the classifications of objects in the
HASH database can now be automated (Awang Iskandar et al., 2020) Always, new
objects were confirmed by independent eyeballing of the data by several team
members.

This task was found to be ideal for undergraduate projects and two example
discoveries are shown below.

####  5.1.2 New Discoveries

Hundreds of new PN candidates were found as a result of the visual search with
the mosaics and a first large spectroscopic follow-up involving no less than
nine world wide telescopes/instruments ranging from 1.5m (ALBIREO spectrograph
at the Observatory of Sierra Nevada, Spain) to 10.4 m (GTC-OSIRIS spectrograph
at the Observatorio del Roque de los Muchachos, Spain) was conducted (Sabin et
al., 2014). We identified 159 objects as True (113), Likely (26) and Possible
(20) PNe and unveiled a large range of shapes (mostly elliptical, bipolar and
round PNe) and sizes (up to $\sim$8 arcminutes).

Among all the newly detected PNe, some caught our attention first due to their
outstanding morphology and then due to their interesting characteristics
revealed by subsequent deep analyses. Notable examples include: the knotty
bipolar IPHASX J194359.5+170901, now known as the Necklace Nebula (Corradi et
al., 2011); the quadrupolar IPHASX J012507.9+635652 (alternately named
Príncipes de Asturias by Mampaso et al., 2006); IPHASX J052531.19+281945.1 at
large galactocentric distance (Viironen et al., 2011); and finally, the PN
discovered around the nova V458 Vul (Wesson et al., 2008). IPHAS, and now
IGAPS, also allows us to target the particular group of PNe interacting with
the interstellar medium (ISM). Those objects which are slowly diluting in the
ISM are particularly difficult to detect most of all in their late phase. In
this case IPHAS imaging data can reveal such faint material (Sabin et al.,
2010).

We show in Figures 4 and 5, two of the many new PNe that have been found and
have not yet appeared in the refereed literature. Most of the new finds are
published in Sabin et al. (2014). The first object IPHASXJ015624.9+652830 (PNG
129.6+03.4), shown in Figure 4, came from parallel searches of the $15\times
15$ pixel binned $H\alpha-r$ (5 arcsec per pixel) mosaics, carried out by team
members and undergraduate students supervised by them in 2007. This object can
be seen in the H$\alpha$ images but is easily missed, yet it stands out very
clearly in the binned difference images as the only object in an otherwise
blank frame. This demonstrates the usefulness of this method for the discovery
of new objects, especially those that, like this one, are only detectable in
optical wavebands.

Figure 4: IPHASXJ015624.9+652830: this is a cutout from one of the 2 by 2
degree binned H$\alpha$-r mosaics used for searching for new PN.  Figure 5:
PNG 95.3+0.2: this is a cutout from one of two CCD frames downloaded from the
IGAPS image archive. The alternative available image of the PN (not shown)
happens to sit adjacent to a ’bad line’ in the CCD. It is precisely to
mitigate against this kind of problem that the survey strategy was to collect
data from paired offset pointings. The object IPHAS J213508.15+521128.0,
discussed in the text, is the lower left of the three bright stars in the
central region of the nebula.

The second nebula, PNG 95.3+0.2, shown in Figure 5, was discovered using the
same search method by Fernández-Martín (2007). Subsequent high resolution (0.6
arcsec seeing) imaging with the NOT telescope on the night of 4th September
2007, through H$\alpha$, [OIII] 5007Å, and [NII] 6583Å filters further
clarified an intricate morphology with a bright, roughly elliptical central
shell, and a pair of fainter twisted protrusions that show up especially well
in the [NII]-only image. Since the IPHAS H$\alpha$ filter bandwidth also
incorporates this line, Figure 5 is a composite image, showing both the
[NII]-dominated outer filamentary structure and the H$\alpha$ dominated inner
ellipse.

PNG 95.3+0.2 is now listed in the HASH Catalogue of Parker et al. (2016). It
is associated with an infrared (WISE, and 90$\mu$m AKARI), and 1420 MHz radio
source (Taylor et al., 2017). According to Anders et al. (2019), the $G\simeq
17.1$ mag star near the geometrical centre, IPHAS J213508.15+521128.0, or Gaia
EDR3 2171830374492778880, is a distant, reddened, and apparently relatively
cold star (D = 5.5$\pm$ 0.9 kpc, $A_{V}=3.9\pm 0.2$ mag,
$\mathrm{T_{eff}}=4800\pm 260$ K). However, our evaluation of the IGAPS broad-
band photometry (Monguió et al., 2020) is that the available magnitudes are
also consistent with this object being a much hotter, even more extinguished
star. The other two, brighter stars embedded in the nebula are located in the
foreground at much more secure parallax-based distances of 0.9 and 1.6 kpc: in
the Anders et al. (2019) database, they too are assigned low
$\mathrm{T_{eff}}$ values, incompatible with those of a hot PN central star.
Vioque et al. (2020) combine IPHAS, 2MASS and WISE data in a search for new
Herbig Ae/Be stars and list IPHAS J213508.15+521128.0 as a non-Herbig AeBe,
non-pre Main Sequence, and non-classical Be star – nor do they confirm an
association with a PN (their FPN flag is empty). The WISE source, detected in
the four bands and centered at 1.1 arcsec from IPHAS J213508.15+521128.0,
shows red IR colours like known PNe, while the spectral energy distribution of
the star, built from Pan-STARRS, 2MASS, ALLWISE, and AKARI data, is typical of
a reddened star up to the WISE W3 (12$\mu$m) band. Beyond that, a strong IR
excess appears up to 90$\mu$m, and points to a physical association of IPHAS
J213508.15+521128.0 with the nebula. If that is the case, the apparent nebular
size of around 30 arcsec would imply a rather large, evolved nebula, 0.8 pc in
diameter, and also suggest the existence of a hidden hot star (a binary?) in
the surroundings. PNG 95.3+0.2 is an appealing example of an IPHAS extended
object with plenty of online, publicly available information, that
nevertheless deserves further dedicated observations including careful
quantitative spectroscopy to pin down the central star.

Basic confirmation spectra exist for both the above nebulae. The objects have
also been independently discovered more recently by amateur astronomers. The
first object, IPHASXJ015624.9+652830, has also come to be known as Ferrero 6,
Fe6, PN G129.6+03.4, while the second, PNG 95.3+0.2, is also known as StDr
Objet 1. Full details on both can be found in the HASH database (Parker et
al., 2016).

####  5.1.3 Previously known PN

The survey is also useful for the re-analysis of already known PNe. IPHAS
images can unveil new faint structures associated with known PNe that have
hitherto evaded detection. A clear example of this is the detection of the
extended tail of the known Sh 2-188 by Wareing et al. (2006) which enabled the
reevaluation of its full extent. A different application has recently been
presented by Dharmawardena et al. (2021): with a view to appraising different
methods of determining PN distances, they have collected IPHAS H$\alpha$+[N
ii] aperture photometry fluxes for 151 previously known nebulae as well as for
46 confirmed or possible PNe that had been discovered by the IPHAS survey.

###  5.2 HH-objects

During their growth, young stellar objects (YSOs) eject a fraction of the in-
falling matter at high speed via bipolar jets and outflows. Their shock
fronts, delineated by line emission, particularly in H$\alpha$, [SII], and
[OI], are called Herbig-Haro objects (HHOs, Herbig, 1950; Haro, 1952). HHOs
not only trace the presence of young stars, but can also serve as a record of
their accretion history. The kinematics of HHOs, derived from proper motions
(PMs) and radial velocities (RVs) allow a kinematic dating of the ejection
event. Moreover, such data provide information on the inclination $i$ of the
circumstellar accretion disk. Constraining the latter is crucial for the
analysis of YSO spectral energy distributions using radiative transfer
modelling.

Figure 6: Top: RGB image (epoch 2007) of the dark cloud Dobashi 3782, based on
$I,H\alpha$, and $[SII]$ frames taken with the Tautenburg Schmidt telescope.
It hosts the YSO IRAS 01166+6635, marked by the circle, which drives a jet
that excites a compact HHO south of it (centre). It appears greenish-blue in
this RGB representation because of its strong H$\alpha$ emission. Bottom:
IGAPS RGB image (epoch 2013), based on $i$, $H\alpha$, and $r_{I}$ frames. The
YSO, as well as the HHO, appears green because it is not clearly detected in
either $r_{I}$ or $i$. The HHO is just resolved at no more than 2 arcsec
across.

The potential of IGAPS for such studies is illustrated by the example of a
hitherto unknown HHO, driven by IRAS 01166+6635. This low-mass YSO (Connelley
et al., 2008) is emerging from the small dark cloud, Dobashi 3782, situated at
a kinematic distance of 240 pc (Wouterloot & Brand, 1989). Narrow-band imaging
performed in 2007 with the Tautenburg Schmidt telescope revealed a compact HHO
south of the YSO within $\sim$1 arcsec of the position RA 01:20:02.9, DEC
+66:51:00 (J2000) (Fig. 6). The estimated extinction out to the distance of
this cloud is about $A_{V}\sim 2.5$, averaged across a few arcminutes (Sale et
al., 2014). The extinction towards the optically-faint YSO is without doubt
much more. At the position of this YSO there are eight entries in the IGAPS
catalogue (Monguió et al., 2020) within a radius of 5 arcsec. Six of the eight
are H$\alpha$ only sources.

Blinking with the POSS1 red image showed evidence for proper motion within
$\sim$50 years. Thus, in order to establish its kinematics, $H\alpha$ and
$R$-band frames have been secured for as many epochs as possible. Four
$H\alpha$ frames with grade A quality (r367494 and r367497 obtained 2003,
r1018994 and r1018997 obtained 2013) were retrieved from the IGAPS image
server. These are supplemented by archival Tautenburg $H\alpha$ images (2007,
2012) and two frames taken in 2020 with the new TAUKAM instrument (Stecklum et
al., 2016). The POSS 1 and 2 $R$-band images (1954, 1991) were added as well,
using both plate digitisations from STSci and SuperCosmos. Before deriving the
HHO positions by fitting its image profile, all frames were tied to the Gaia
EDR3 astrometric reference system (Gaia Collaboration, 2020).

Figure 7: HHO position displacement with regard to the ALLWISE position (Cutri
& et al., 2014) over time (red - DEC, blue - RA). The respective regression
lines are shown as well.

The coordinate offsets of the HHO relative to the driving source for the
various epochs are shown (Fig. 7), along with their respective linear fits.
For the distance given above, the DEC slope (red) corresponds to a velocity of
$-91.0\pm 3.5$ km ${\rm s^{-1}}$. Assuming constant speed, this implies a
kinematic age of $\sim$430 yr. It is likely a major accretion event happened
around that time which induced jet strengthening. Accounting for the position
angle of the HHO movement of $190^{\circ}$ (measured from N through E), a
total PM speed of $92.4\pm 3.5$ km ${\rm s^{-1}}$ can be derived. With the
help of an RV estimate of $-74.8\pm 4.6$ km ${\rm s^{-1}}$, obtained by low-
resolution spectroscopy of the HHO using the Nasmyth spectrograph at the
Tautenburg telescope, the inclination of the velocity vector follows as
$51^{\circ}\pm 2^{\circ}$. This intermediate inclination is consistent with
the cometary appearance of the YSO in the optical.

This example shows that the IGAPS $H\alpha$ line emission images provide an
excellent means for the detection of HHOs. In this instance, neither the
driving YSO nor the HHO are detected even in $i$-band, and yet the detection
of the HHO in $H\alpha$ is clear, at around 30 counts above background
(depending on the seeing). Moreover, the $1$-arcsec resolution of IGAPS images
allows for good position measurements of these small (but extended) nebulous
objects. Together with the availability of repeated IGAPS observations, with
several years of epoch difference, precise proper motion measurements are
possible, from which information about the physics of the HH flows, as well as
their driving sources, can be obtained.

##  6 Image mosaics

Figure 8: Supernova remnant, S147. This is a full resolution background-
corrected mosaic made from the $H\alpha$-filter data (no $r$ subtraction).
Approximate image dimensions are $4.2\times 3.6$ sq.deg. North is up and east
is to the left. The greyscale is negative such that the brightest emission is
darkest. Compared to the earlier mosaic appearing as Figure 19 in Drew et al.
(2005), based on $H\alpha-r$ data, with background fitting and removal, there
are fewer artefacts thanks in large part to the incorporation of better re-
observations. Much of the faint structure left, such as the ragged diffuse
emission in and below the centre of the remnant,is real.

The background of astronomical images would ideally be flat and dark. In
reality the background in images from ground-based telescopes varies due to
the interplay of different sources (e.g., airglow, moonlight) contributing
varying levels of unwanted light.

To begin to tackle this securely, when working with the standard image data
reduction available in the database, we recommend using the available
confidence maps to reject pixels with confidence levels less than 90-95%
typically. But we note that the level can vary across the sky and between
filters, usually requiring a lower confidence level threshold for the $g$
filter and especially $U_{\rm RGO}$.

The Montage software444Available from http://montage.ipac.caltech.edu/  can be
used to effectively mosaic images in a given filter, re-projecting images as
needed to a single-projection algorithm and direction, adjusting the
background levels in overlapping pairs of images to produce a smooth mosaic
over a large area.

In addition to uniform background light sources, the $i$-band can sometimes be
beset by fringing that originates from the airglow OH lines interfering via
internal reflections in the CCD chips. This wave-like structure contributes
$\sim$2% of the total counts in some images (Irwin & Lewis, 2001). It is
almost entirely removed in the CASU pipeline (who use a library of $i$-band
fringe frames from other INT WFC observing runs), although some will remain at
the $\sim$0.2% level due to night-to-night variations. When $i$-band data is
mosaicked, overlapping fringing is occasionally exaggerated and can remain
visible in some mosaics.

As noted in Section 4, IPHAS observations were at first carried out at any
level of moon brightness throughout the Galactic plane season. Observations
during bright time were soon found to exhibit varying levels of background
counts in the form of a small but noticeable gradient across each CCD (leading
in later seasons to tighter moon phase and distance requirements). Ultimately,
$\sim$8% of all IPHAS images were taken under such conditions.

Moonlight affects IPHAS images through both scattered light across the night
sky and a component that reflects off the inside of the telescope dome and
across the CCD array. The resulting illumination is therefore not necessarily
uniform across all four CCDs, and requires a CCD-by-CCD solution. Its
character is also influenced by the phase of the moon, its altitude above the
horizon, angular separation from the pointing of the telescope, and the extent
(and position) of cloud cover across the sky. These relatively small gradients
can be exacerbated by mosaicking CCD images over large areas of the sky (many
degrees), becoming a significant issue in the production of large mosaics.

The recommended solution to removing the moonlight and achieving a flat and
dark background is to model and fit the background gradient for each CCD.
Since the $r$ and $H\alpha$ band images contain nebulosity that could affect
the fit, we recommend fitting the background gradient to $r-H\alpha$ images
(after scaling the images to correct for their different exposure times),
since both filters contain the H$\alpha$ and forbidden [Nii] lines that
typically dominate diffuse astronomical emission. Binning the image into
$100\times 100$ pixel bins and taking the median pixel value in each bin
provides a simple method to measure the background level in that bin. A two-
dimensional gradient of the form $Z=Ax+By+C$ can then be fit to the data,
where $A$, $B$ and $C$ are free parameters, using, for example a Markov Chain
Monte Carlo simulation and the Python code emcee (Foreman-Mackey et al.,
2013). More complex models have been tested (including Fourier transform
techniques), but none were found to provide a significant improvement. In
short, the two-dimensional gradient method will prove effective in the
majority of cases.

Some care should also be applied when very bright stars fall on (or near) one
of the CCDs, as saturation, atmospheric and lens effects can heavily affect an
image and the model fit to it. Identifying and excluding a magnitude-dependent
radius around such stars using the Tycho-2 catalogue of bright stars (Høg et
al., 2000) proved effective for overcoming these problems.

As an example of the potential of IGAPS image mosaicking, we present a large,
$4.2\times 3.6$ degree $H\alpha$ mosaic of the supernova remnant Simeis 147,
produced using the techniques described above. Simeis 147 (hereafter S147) is
otherwise known as SNR G180.0-01.7, Shajn 147, or Sharpless 2-240. It is a
large, faint, late-stage remnant located just below the Galactic Anticentre.
It was discovered in 1952 and lies at a distance of 0.8–1.6 kpc (Gvaramadze,
2006), on the near side of the Perseus spiral arm. The SNR consists of
numerous filaments embedded in large-scale diffuse emission. The east and west
edges of S147 show signs of blow-outs, the southern edge shows a sharp
boundary, with a less regular one in the north. The undistorted appearance of
the SNR may partly be due to it expanding into a region of space already
partly cleared by a previous supernova (Gvaramadze, 2006). Regardless,
examples of large and pristine SNRs are rare and observations of them can be
important for constraining hydrodynamic simulations of their expansion and
structure.

Figure 8 shows the full mosaic constructed as outlined above. The challenge of
this object is its great size, allied with very intricate and sometimes very
faint small scale detail. The number of individual CCD frames included is in
the region of 250. The full-resolution S147 mosaic (199 MB) itself is provided
as a fits-formatted file attached to this paper as supplementary material.

##  7 Nebular target selection for massive-multiplex spectroscopy

The next decade will see an increase in large, multi-object digital
spectroscopic surveys on 4m class telescopes. In drawing up target lists,
these surveys will make use of the data that has been acquired by wide-area
digital photometric surveys, like IGAPS. Two examples due to start soon are
the WEAVE survey on the 4-metre William Herschel Telescope (WHT) of the Roque
de los Muchachos Observatory in La Palma (Dalton et al., 2020) and the 4MOST
survey on the 4-metre VISTA telescope operated by ESO at Paranal (de Jong et
al., 2019). Both facilities will collect on the order of 1000 targets per
pointing, and a major science driver for both is Milky Way science in the Gaia
era.

Massive-multiplex spectroscopy requires informed target selection. The IPHAS
$H\alpha$ images, in particular, can characterise the diffuse sky for studies
of the ionised ISM. Here, we outline a software method – named HaGrid – aimed
at doing this through the interrogation of H$\alpha$ images. The positions
generated by HaGrid will be used in constructing the SCIP (’Stellar,
Circumstellar and Interstellar Physics’) northern Galactic plane programme – a
strand within the overall WEAVE 5-year survey. The software is also being
deployed to find targets for the southern plane (based on VPHAS$+$ data), to
enable similar observations via 4MOST. In the text below, the acronym WEAVE
appearing on its own will stand both for the instrument and for the WEAVE/SCIP
survey strand, according to context.

###  7.1 Building lists of science targets with HaGrid

A WEAVE pointing is defined by the coordinates of its centre and a field of
view of radius of 1 degree, projecting a circle on the sky covering $\pi$
sq.deg. Within such a field, the aim is to identify several hundred positions
that coincide with regions of (locally) maximum H$\alpha$ brightness.

In outline, the steps taken are as follows:

  * •

Find all $H\alpha$ CCD images from the IGAPS repository in the area of
interest.

  * •

Mask out stars, CCD borders, bad pixels and vignetted areas from the data.

  * •

Divide the image into superpixels.

  * •

Select the superpixels with the highest counts as candidate source positions.

The application of the algorithm, confronted with real data, is necessarily
more complicated, particularly as it must deal as far as possible with all the
artefacts that mimic real nebulosity. So we now itemize the steps involved in
more detail:

  * •

Collect the CCD images from the IGAPS repository in the area of interest. For
one WEAVE field of radius 1 degree, this will be a list of over 100 H$\alpha$
CCD frames and their associated $r_{I}$ exposures. Since frames with poor data
quality often lead to false detections of H$\alpha$ emission, we excluded,
where possible, grade C and D frames and favoured uniformly-calibrated over
pipeline-calibrated data.

  * •

Create a star mask for each CCD image from both the IGAPS point-source
database and a bright star catalogue to identify stars needing larger
exclusion zones. The mask radius of IGAPS sources is set as a function of
$r_{I}$ magnitude, H$\alpha$ seeing and ellipticity. Also masked are
diffraction spikes of bright stars and a visible halo for very bright stars (¡
4.5 mag, see right panel of figure 9). The halo position relative to the star
depends on the angular separation of the star from the optical axis of the
telescope.

  * •

Other artefacts like CCD borders, pixels that fall below a specified threshold
in the linked confidence map (see A.2), hot or cold pixels and artificial
linear structures (satellite trails, noise bands, gain-change strips, bright
star reflections, …) found by visual inspection, are also masked.

  * •

Create superpixels and rank them by H$\alpha$ brightness. Each masked CCD
frame is divided into superpixels, which are squares of $n\times n$ native
pixels, where $n$ is an adjustable input parameter. For WEAVE, $n=25$ giving
$8.25\times 8.25$ arcsec2 superpixels. This choice tensions between good-
enough angular resolution and the typical loss of area and statistics
inflicted by the masking. Superpixels that are more than 50% masked are
rejected. To avoid particle hits being mistaken for astronomical signal, the
data are median filtered using 3$\times$3 pixel (about 1 arcsec2) binning. The
superpixels are then ranked by mean count determined from the unmasked pixels.

Figure 9: Left: Ghost on image r372056, CCD#4 due to a bright star. Notice the
stray-light image even picks up details of protruding cabling at prime focus
of the INT. Part of a much fainter ghost can be seen top right. Right: Example
of a halo around a bright star fooling the HaGrid selection from r541554
CCD#3. Source positions selected by HaGrid are shown as red numbered circles.
They crowd into the faint extended halo that is a little offset from the star
position. A larger stellar mask for bright stars and the rejection of ’star-
like’ selections as described in Section 7.2 helps eliminate these. The blue
circles mark identified counts minima that are stored as potential sky fibre
positions.

  * •

Estimate the H$\alpha$ sky background. A sound determination of the local sky
value is very important, especially for determining the correct H$\alpha$
surface brightness. The algorithm measures the sum of sky and any significant
astronomical background from the H$\alpha$ frames. In predefined areas of
extensive and intense nebulosity we also derive an estimate of the H$\alpha$
sky-only background value from the $r_{I}$ sky value. This uses a linear fit
to a global plot of the $H\alpha$ against the $r_{I}$ sky level, exploiting
the fact that most of the Galactic plane is free from nebular emission. If the
sky value inferred from $r_{I}$ is lower than the $H\alpha$ sky value, then we
adopt the average of the two. Taking the average was precautionary against
problems with the $r_{I}$ sky prediction due to changing moonlight reflections
into the telescope from clouds and other structures such as the dome.

  * •

Select H$\alpha$-excess source positions from the ranked superpixel list for
every CCD frame. The superpixel list is searched starting at the highest mean
count. A superpixel is rejected if: it is closer than a distance limit (1
arcmin for WEAVE) to an already selected superpixel; the difference between
the superpixel mean and the frame H$\alpha$ sky is less than the sky noise
(one sigma). Finally, the maximum 3$\times$3 pixel mean-filtered count within
the superpixel is located and its position is adopted as the candidate target
position. If the difference between this more localised mean and the H$\alpha$
sky is $>10$ ADU, the superpixel goes forward into a merged overview table.

  * •

The location, count, surface brightness and other data for each selected
high-H$\alpha$ candidate position is appended to the overview table covering a
large user-defined sky area, ready for further checking and analysis.

As it searches for positions of bright H$\alpha$ emission, HaGrid also
identifies suitable low-count sky positions and gathers statistics on sky
noise. The distribution of sky noise versus sky background it finds closely
resembles the distribution found by the pipeline shown in Figure 3. The HaGrid
found sky noise is lower as it does not involve a fit over the whole CCD, and
so avoids contributions from fainter stars.

Figure 10: Excess $H\alpha$-band counts compared with the excess counts in the
$r_{I}$ band. Both quantities are the difference between the measured peak
count and the estimated background level returned by HaGrid. Note that the
HaGrid -output list is restricted to excess narrowband counts $>10$. The data
are plotted as a density map and before any second-stage cleaning. The
selection shown is from the sky region: ($30^{\circ}<\ell<95^{\circ}$,
$|b|<4^{\circ}$). Working from top to bottom the red lines are: equality
between counts (shown dashed), a line of slope 0.77 (solid) representative of
nebula-dominated positions, and a line of slope 1/13 (solid) characteristic of
star-like positions.

The algorithm can be applied to areas of arbitrary size. As each CCD is
independent of the others, the code parallelizes very effectively. The choices
of minimum distance between accepted source positions is driven mainly by the
design of the destination wide-field spectrograph, and the anticipated
observing strategy. For WEAVE, applying the 1 arcmin minimum distance between
fibre placements leads to at most $\sim$200 source positions selected per WFC
CCD (with a much lower median of 5).

###  7.2 Final processing: list cleaning and reduction

Figure 11: Final distribution of selected diffuse ISM sky positions for the
WEAVE footprint as a function of the logarithm of excess $H\alpha$ counts. It
is highly skewed to low excess counts. A rough translation into surface
brightness is that 10 excess counts, the minimum accepted, corresponds to
$\sim 3\times 10^{-16}$ ergs cm-2 s-1 arcsec-2.

Figure 12: Top: Map of HaGrid selected positions in the Cygnus-X region of the
northern Galactic plane. Every selected position has been coloured according
to the logarithm of the estimated surface brightness (in ergs cm-2 s-1
arcsec-2). White areas will have log(surface brightness) $<-15.5$. The region
shown spans 50 square degrees or 3.6% of the total area processed and contains
20% of the identified candidate target positions. It is the most line
emission-rich part of the northern plane. Bottom: Same area in the Finkbeiner
(2003) H$\alpha$ map, with 6 arcmin pixels. The data available in this region
is a combination of images from VTSS (circular footprint) and WHAM (making up
the much coarser resolution background).

The list of potential target positions generated as described above is long
and needs further cleaning and reduction. Not everything that appears bright
in H$\alpha$ and is passed through by HaGrid has an astronomical origin: for
example, some satellite trails, ghosts and unrecognised haloes around brighter
stars can remain (Figure 9). There are a number of further test-and-eliminate
steps that can be taken to reduce the list to a high-confidence core. One such
step is to favour repeat selection of the same emission structure and to
reject isolated points (typically due to cosmic ray strikes that slip
through). Generally speaking, we expect any given high surface brightness
structure to be picked up twice or more, given that most sky locations are
covered by a minimum of two images.

An important piece of empirically-driven post-analysis is illustrated by
Figure 10 that compares the excess H$\alpha$ counts above the estimated
background level with the excess obtained in the $r$ band, after scaling the
latter to correct for the shorter exposure time. Two main trends, drawn as
solid lines, are apparent. The uppermost of the two runs a bit below the
equality line. In the ideal case where H$\alpha$ and [NII] 654.8, 658.4 nm
nebular line emission dominate the total counts measured in the $r$ band, the
expectation would be that the measured count excesses in both the narrow and
the broad band would be the same (given that the peak transmission in the
narrow-band filter corrected for CCD response is closely comparable to the
mean of the same quantity for the $r$ band). This ideal does not apply,
because of other nebular lines within the $r$ band, not captured by the narrow
band (e.g. the [SII] 671.6, 673.1 nm doublet that can be strengthened by shock
excitation, and potentially some [OI] 630.0, 636.2 nm emission). The
approximate regression line shown in Figure 10 has a slope less than 1, for
this reason. The objects of the search are indeed the candidate positions
clustered around this empirical trend and, as such, they are the ones to keep.

In contrast, the second much lower gradient trend apparent in Figure 10,
running close to the horizontal axis, is created by sky locations where the
spectrum is continuum-dominated, i.e. star-like. These locations can be
stellar haloes where HaGrid picks up a seeming H$\alpha$ excess thanks to the
typically wider seeing profile in the longer and unguided narrow band
exposures, or ghosts (see Figure 9 for examples). In the case of a typical
$0.5\lesssim r-i\lesssim 1$ stellar continuum across the $r$ band, the
expectation would be that the narrow-band excess counts would be approximately
1/13 of the $r$ counts – this is the last of the three lines superimposed in
Figure 10. Candidate positions of this type need to be removed.

To make an accept/reject decision for every candidate position in the list,
the distances to the expected nebular and stellar trend lines are calculated.
These distances, $N$, are then expressed scaled to $\sigma$, the relevant
Poisson-like error on the computed distance (subscript $n$ for nebular, $s$
for star-like). This is followed by cuts applied in the $N_{n},N_{s}$ plane to
select the most credible nebular targets. Inevitably, at low count levels, the
confidence in assigning a candidate to the ‘nebular’ and ‘star-like’ classes
weakens greatly. The minimum excess count of 10 imposed by HaGrid helps deal
with this, but a minimum cut on $N_{s}$ is also needed. Where it is placed has
to be tested empirically: for WEAVE we required $N_{s}>3.5$.

The selection can also be trimmed down to surface densities appropriate to the
instrument used and the survey observing strategy (eg. number of visits,
required science sampling). In the case of WEAVE this meant a 2 arcmin grid
was placed over the relevant sky area and only two positions with the highest
flux are kept in each grid cell. Taking all the steps together for the WEAVE
example, the original list of about 1.3 million target positions reduced to
under 200 000 potential targets, of which we expect around $1/4$ to be
selected for observing.

###  7.3 Testing the down-size against known Herbig-Haro objects

We have performed a retrospective test that compares the character of the long
list with that of the final down-size by cross-matching them both with a list
of known Herbig-Haro objects. The latter has been established by a CDS
criteria query using the term otype=’HH’&ra>0. The coordinate condition was
necessary to dismiss about 800 entries without coordinate information. The
resulting table comprises 2622 positions: just 388 of them lie in the sky
region defined by the Galactic coordinate ranges,
$30^{\circ}<\ell<210^{\circ}$, $|b|<4^{\circ}$ (roughly the footprint HaGrid
has been applied to). When a limit of 10 arcsec is set on the angular
separation, there are 70 and 40 successful cross-matches with the HaGrid long
and short lists, respectively. These numbers drop to 45 and 27 if the limit on
angular separation is reduced to 5 arcsec.

That no more than 20 percent, at best, of the listed HHO in the region are
recovered is attributable to HHO position uncertainties, their high proper
motion and the relatively low surface brightness of many. Another occasional
factor at work will be the presence of substantial scattered starlight
lowering the contrast between the $H\alpha$ and $r$ images to below an
acceptance threshold (emission in the vicinity of V645~Cyg is subject to
this). The most relevant point is that the down-sized list of candidate
emission line positions captures more than half the number matching with the
long list, despite the fact it contains only $\sim$0.15 as many positions.
Proportionately, the shorter list is doing appreciably better, indicating that
the downsizing has the side benefit of raising list quality.

Figure 13: S147, the supernova remnant, as a test of HaGrid position
selection. The upper panel is a comparison between the pattern of final HaGrid
positions, in red, and the VTSS (Dennison et al., 1998) arbitrarily-scaled
continuum-subtracted H$\alpha$ surface brightness map shown in greyscale. A
good correspondence is achieved. The lower panel is a zoom into a $0.27\times
1.00$ sq.deg. area within the remnant superposing HaGrid positions on the
IGAPS $H\alpha$ mosaic at full $\sim$1 arcsec resolution (shown in full in
Figure 8). This shows how the fine filaments making up the remnant are
accurately traced by the HaGrid selections (green dots): those with surface
brightness exceeding $10^{-15}$ erg cm-2 s-1 arcsec-2 are encircled in red.
Both panels use Galactic coordinates such that latitude increases upwards and
longitude towards the left.

###  7.4 Results of selection

The distribution of diffuse ionized emission in the northern plane is heavily
weighted to low surface brightness and its presence along the plane is
extremely uneven. These are the outstanding features of the out turn from the
application of HaGrid to the IGAPS images database. The extent to which low
surface brightness is favoured is illustrated by the histogram of excess
$H\alpha$ counts, presented as Figure 11. The mean of the distribution is 32.5
excess counts, while the more informative median is just 21.6 (translating to
a surface brightness of $\sim 7.2\times 10^{-16}$ ergs cm-2 s-1 arcsec-2 or
close on 130 Rayleighs).

The upper panel of Figure 12 is a cut-out of 50 square degrees in the Cygnus-X
region of the plane, a region that is very rich in ionized gas: around 20
percent of all the selections are contained in just this 3.6 percent of the
processed sky area. Every data point is a candidate target position and is
coloured according to the logarithm of H$\alpha$ narrowband surface
brightness. The figure amounts to a ’pointilliste’ rendition of an $H\alpha$
image of the region emphasising the brightest locations. Comparison with the
much coarser angular resolution H$\alpha$ map presented by Finkbeiner (2003),
in the lower panel of Figure 12, shows that the larger scale structure in
H$\alpha$ surface brightness is shared, with the important difference that the
HaGrid output offers much more better-resolved, brighter detail that vanishes
when smoothed to several arcminutes. The bright North America Nebula is
located towards bottom left in both panels. The hole in the emission in the
top right corner of the upper panel is a product of needing to leave a zone
clear around the 2nd magnitude star, $\gamma$ Cyg. The larger hole below it is
real in the sense that it is a dark cloud free of line emission.

A further example of the candidate positions output by HaGrid and the
subsequent reduction is presented in Figure 13 for the case of S147 (already
discussed and illustrated in Section 6). Its appearance is very filamentary,
somewhat resembling a collection of soap bubbles, with the filaments at the
interfaces. The overall distribution of emission is captured very well by the
selection, as shown by the comparison with the excerpt from the Virginia Tech
Spectral-line Survey (VTSS, Dennison et al., 1998), in the upper panel of
Figure 13. However the 1.6-arcmin pixel scale of VTSS does not entirely
resolve the structure present. The filaments, with typical widths of under 1
arcmin, emerge more clearly in the IGAPS narrowband imagery, thanks to its
native $\sim$1 arcsec angular resolution. This is picked up faithfully by the
HaGrid selection. The surface brightness of the selections ranges from
$3\times 10^{-16}$ to (very infrequently) $3\times 10^{-15}$ ergs cm-2 s-1
arcsec-2.

##  8 Closing remarks

A goal of this paper has been to present and describe the new IGAPS image
database, formed from merging the data from both the IPHAS (Drew et al., 2005)
and UVEX (Groot et al., 2009) surveys of the northern Galactic Plane. Around
two-thirds of the database carries photometric zeropoints from the uniform
calibration described previously by Monguió et al. (2020). The collection is
complete in that it contains all images of all qualities obtained over the
course of the two long-running survey programmes. This creates options to
compare different epochs, given that many fields were observed more than once.

The main focus of this paper has been on the $H\alpha$ narrow band: the only
one of the IGAPS set expressly targeting line emission. Before summarising
what we have presented on $H\alpha$, we recall that the Sloan $g$ band
contains within its range the sometimes extremely bright [O iii] 495.9, 500.7
nm doublet. Accordingly, images taken using this filter can be used to compare
and contrast the appearance of prominent nebulous regions in low ionization
lines (H$\alpha$ and the [N ii] 654.8, 658.4 nm doublet) and the much higher
ionization [O iii]. This will work especially well for lower extinction
sightlines, where the [O iii] lines are not disadvantaged by dust obscuration
on top of the 1:4 exposure time ratio. An outstanding example of such a
comparison, for the Dumbbell Nebula, is shown as Figure 14. This is a large
and bright planetary nebula in which it can be seen that the [O iii] emission
($g$-band image, upper panel) is less clumpy than the H$\alpha$ \+ [N ii]
emission ($H\alpha$ image, lower panel), while the extent of the main nebula
is nearly the same in both. Furthermore, even in the $g$ band 30-second
exposure, fainter more extended structure is also apparent beyond the main
nebula rim (seen to the west/right in the figure): its existence was first
noticed in the lower resolution narrow-band images presented by Papamastorakis
et al. (1993). The central star also stands out at this shorter wavelength.
The price paid for this kind of direct exploitation is, necessarily the strong
pick-up of star light in the field because the $g$ band is broad. Some
improvement on this might be achieved by constructing $g-r$ difference images.

Returning to our main aim – we have provided a characterisation of the
distinctive narrow-band $H\alpha$ data, whilst also showcasing some new
illustrative applications. Thanks to the Monguió et al. (2020) zero points
calibration, it is now more certain what the noise levels and sensitivities
are: at full $\sim$1 arcsec angular resolution it is possible to distinguish
nebulosity of surface brightness down to $\sim 2\times 10^{-16}$ ergs cm-2 s-1
arcsec-2 (the typical noise level). We have described here, in detail, how
this may be exploited on the large scale to build target lists for diffuse-ISM
spectroscopy using the coming generation of massive multiplex wide-field
spectrographs (section 7 on the HaGrid algorithm). On rebinning the native
0.333 arcsec pixels, the sensitivity increases as expected: Sabin et al.
(2014) already claimed a typical sensitivity of $\sim 10^{-17}$ ergs cm-2 s-1
arcsec-2 for 5 $\times$ 5 arcsec2 binning. We endorse this, with the necessary
qualification that, in reality, there will always be a range in sensitivity,
linked directly to the prevailing background level (see Figure 3).

Figure 14: Dumbbell Nebula: as imaged in the $g$ and $H\alpha$ bands (upper
and lower panels respectively). The $g$ image includes the strong [O iii]
495.9, 500.7 nm lines. The cutouts are taken from the same corner of CCD #2 in
runs r-477762 and r-1241402, picked for having well-matched seeing
(respectively 1.31 and 1.25 arcsecs). The colour scales used are capped at
1250 counts for $g$ and 5000 counts for $H\alpha$ to match the ratio of their
exposure times. They run from black at background level up through
green/magenta/red to yellow at the bright end. This PN has a diameter of
approximately 6 arcmin. North up, east to the left.

So far, most science exploitation of IGAPS image data has been directed
towards planetary nebulae and, to a lesser extent, supernova remnants. We
anticipate this will continue into upcoming programmes using WEAVE and other
new generation wide field spectroscopic instruments. But there is also the
opportunity to use especially the $H\alpha$ images to support science of the
diffuse ISM in star-forming regions. In particular the software tool, HaGrid
is permitting dense sampling across a wide area of many of the HII regions of
the northern plane (as illustrated by Figure 12). Target lists of this kind
for multi-object fibre spectroscopy will be included in the WEAVE survey and
should lead to new insights into the detailed chemistry and kinematics of the
diffuse environment in and around young star clusters. A programme of a
similar kind is already underway using LAMOST (Wu et al., 2020): this samples
the northern plane from a catalogue built also with the assistance of IPHAS
images, now contained within the database presented here.

The full collection of IGAPS images is available to the community via the
website http://www.igapsimages.org. This website provides an interface that
facilitates downloads of selected images, served up as individual CCD frames.
It also provides a number of related resources, including the HaGrid
-generated list of bright diffuse H$\alpha$ northern-plane positions presented
here.

###### Acknowledgements.

This work is based on observations made with the Isaac Newton Telescope
operated on the island of La Palma by the Isaac Newton Group of Telescopes in
the Spanish Observatorio del Roque de los Muchachos of the Instituto de
Astrofísica de Canarias. This research has made use of the University of
Hertfordshire high-performance computing facility (https://uhhpc.herts.ac.uk/)
located at the University of Hertfordshire (supported by STFC grants including
ST/P000096/1). This study has used part of an image obtained by the Virginia
Tech Spectral-Line Survey, which is supported by the National Science
Foundation.
We thank the following Bristol University students: Greg Mould, William Howie,
Luke Davies, Heidi Naumann, Will Summers, Alex Townshend, Paul May, Matina
Mitchell, Finn Hoolahan, Tom Burgess, Ashley Akerman, James Jordan, Simon
Palmer, Anna Kovacevic, Jai Tailor, Olivia Smedley and Daniel Huggins for
their work searching through difference images looking for PN candidates as
part of their final year undergraduate projects.
RG benefitted from support via STFC grant ST/M001334/1 as a visitor to UCL.
JED & MM acknowledge the support of research grants funded by the Science,
Technology and Facilities Council of the UK (STFC, grants ST/M001008/1 and
ST/J001333/1). MM was partially supported by the MINECO (Spanish Ministry of
Economy) through grant ESP2016-80079-C2-1-R and RTI2018-095076-B-C21
(MINECO/FEDER, UE), and MDM-2014-0369 of ICCUB (Unidad de Excelencia ’María de
Maeztu’). AM acknowledges support from the State Research Agency (AEI) of the
Spanish Ministry of Science, Innovation and Universities (MCIU) and the
European Regional Development Fund (FEDER) under grant AYA2017-83383-P. PJG is
partially supported by NRF-SARChI grant 111692 and acknowledges support from
the Netherlands Organisation for Scientific Research (NWO), in contributing to
the Isaac Newton Group of Telescopes and through grant 614.000.601.
Aspects of the analysis presented have been carried out via TopCat and stilts
(Taylor, 2006). This research has made use of both the SIMBAD database and the
”Aladin sky atlas”, respectively operated and developed at CDS, Strasbourg,
France. This research has also made use of the image manipulation software,
Montage. It is funded by the National Science Foundation under Grant Number
ACI-1440620, and was previously funded by the National Aeronautics and Space
Administration’s Earth Science Technology Office, Computation Technologies
Project, under Cooperative Agreement Number NCC5-626 between NASA and the
California Institute of Technology.

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##  Appendix A On image properties and common artefacts

Here we comment briefly on the known $U_{\rm RGO}$ and $g$ PSF variations and
present an example of a confidence map. We then describe artefacts that one
might come across in the survey imagery, especially in lower graded images.
These are not unique to the WFC, as they can be found on most imaging
instruments. We include this additional material to place it on record for the
benefit of future users who may be less familiar with these oddities. The
survey data acquisition and pipelining has been described in previous papers
(Drew et al., 2005; González-Solares et al., 2008; Groot et al., 2009;
Barentsen et al., 2014; Monguió et al., 2020), with relevant aspects
summarized here in section 2.

###  A.1 $U_{\rm RGO}$ and $g$ PSF variations

Figure 15: Dependence of pipeline measured source ellipticity on the distance
to the instrument rotator center. The data is from IGAPS field 6395o, showing
the sources from the $g$ image r584231 on the left, and from the $U_{\rm RGO}$
image r584230 on the right. On the left the data of all four CCDs is shown
together with a running median plotted as a black line. On the right the data
from each CCD is shown with different colours and symbols: CCD#1 as red filled
dots, CCD#2 as blue plus signs, CCD#3 as green triangles and CCD#4 as orange
squares.

The $U_{\rm RGO}$ filter stands out among the filters used by the IGAPS survey
in being a liquid filter: between 1mm UG2 and UBK7 glass plates lies a 5mm
thick $CuSO_{4}$ solution.

The liquid nature of the filter leads to different image properties of the
$U_{\rm RGO}$ data. Figure 15 shows a typical pipeline measured ellipticity
distribution for the $g$ filter on the left, as an example representing the 4
normal glass filters used by IGAPS. The pixel distance to the instrument
rotator center, which should be close to the optical axis, is shown on the
x-axis. The data for all 4 WFC CCDs is combined in this plot. A running median
is plotted as the thick black line. It can be seen that the ellipticity
depends on the radial distance from the optical axis. The right panel of
figure 15 shows the same information for the liquid $U_{\rm RGO}$ filter. A
single radial trend does not exist. This leads to a more erratic point spread
function (see the right-hand panel of figure 16). It is also the reason why an
additional 5th order term is sometimes necessary for the astrometric solution
(Monguió et al., 2020). It can also be seen in this example that, despite
being a low extinction region, the number of sources visible in the 120 second
$U_{\rm RGO}$ exposure is clearly less than in the 30 second $g$ exposure. The
change in point source image morphology across the WFC array also has an
effect on the morphological classification of sources, which are less likely
to be classified as stellar the further they are located from the optical
axis, independent of filter (cf. section 2.2 in Farnhill et al., 2016).

As the seal around the edges of the $U_{\rm RGO}$ filter is not completely
tight, the contained solution slowly evaporates over time and hence needs to
be topped up whenever an air bubble becomes apparent. Occasionally
observations were made with a bubble visible in the filter, which always will
drift to the filter edge in zenith direction. At minimum the red leak of the
$U_{\rm RGO}$ filter would be increased for stars observed in the bubble area.
Accordingly, bright patches of stars near the edge of frames should be viewed
with caution.

During the creation of the IGAPS catalogue it was discovered that there is
also an optical blemish on the surface of the $g$ filter, that has an effect
on the image quality. The location of this blemish changed over time,
depending on the orientation in which the filter was reinserted into the
filter holder after cleaning. For more information see section 6.1 and
appendix B in Monguió et al. (2020).

Figure 16: Cutout from CCD#3 of images r584231 ($g$ filter, left) and r584230
($U_{\rm RGO}$, right), showing the difference in PSF appearance between these
filters.

###  A.2 Confidence maps

Figure 17: Confidence map for the H$\alpha$ filter from November 2012. Areas
shown in black have a confidence value $<50$. The layout of the CCDs has #2 on
top, and #3, #4 and #1 from the left at the bottom.

The confidence maps produced in pipeline processing are used for the masking
of bad pixels and vignetted areas on the CCDs. The pipeline produces the
confidence maps per filter from the observations of flatfields taken during an
observing run. Hence each IGAPS observing run has its own set of confidence
maps associated with it. The confidence map is referenced in the FITS header
item CONFMAP (see appendix B). To define bad pixels, often a limit of
confidence $<90$ is used. Figure 17 shows an example of a confidence map. The
vignetting of the image area is clearly visible. The use of a round filter in
the WFC has its biggest impact on CCD#3, but also affects corners of CCD#2 and
CCD#1.

The worst column defects are also visible in the figure. Many thinner column
defects and small bad pixel areas are not visible at this image resolution.
For specific purposes, such as the selection of diffuse-ISM targets for wide
field spectrographs as described in section 7, the automatic selection of bad
pixels via the confidence maps can benefit from the addition of further hot
and cold pixels identified by the user.

###  A.3 Artefacts

A number of artefacts can be found on the IGAPS WFC images, just as they are
in data from a range of astronomical cameras. The cause of these are either
optical reflections, external particle or light sources or electronic
components.

####  A.3.1 Bright stars

For bright stars even the faintest parts of the point spread function become
visible. The right panel of Figure 9 shows a bright star and its associated
halo, with a radius of $\sim$1100 pixels. It can be seen that the halo is
offset from the star. This offset depends on the distance to the optical axis
of the WFC, which hints at the reflection being caused by a curved optical
surface. As shown by this example, the halo of a bright star can affect a
considerable part of a CCD.

Apart from the direct image and halo, the light of a star also gets reflected
on different optical surfaces of the instrument and forms several large
reflections, that can appear more than a degree from the star (’ghosts’). In
the case that a star is very bright, these reflections will become visible in
the images. One example is shown in the left panel of figure 9. The reflection
nearly covers a full CCD, and a lot of fine detail from the telescope entrance
pupil can be made out – including the cabling of the WFC at the prime focus
that protrudes beyond the central obstruction. Also visible is part of a
fainter large reflection in the top right corner of the CCD. The seeming small
bipolar nebula at the bottom of that fainter reflection is not real either.
That it is just reflected light can be checked by inspecting the offset
partner image of the same sky location.

Figure 18: Reflections of bright stars outside the CCD. Left: image r430532,
CCD#1, $H\alpha$ filter. The reflection in the center is up to 25% of the
background. The one near the top reaches more than 300% of the background
counts. Right: image r764550, CCD#3, $i$ filter. The large and small
reflection have 6% and 21% more counts than the background, respectively. The
visible fringing is at about 1.5% of background. The HaGrid superpixel map is
shown to enhance low level detail. Black areas correspond to rejected
superpixels due to high levels of masking. The color scale goes from red (low
values) to blue (high values).

Reflections do not always have to appear complete or have a circular shape.
Figure 18 shows the superpixel map created by HaGrid, as described in section
7.1, as this makes low level detail more readily visible. The left panel of
that figure shows two odd reflections. One with a square appearance, which is
actually just a cutout from a much larger circular reflection. The other one
visible in the top right corner of the CCD is quite different in appearance
from the usual circular reflections. The right panel shows part of a circular
reflection from a star outside the CCD. On top of it is a smaller, more
elliptical reflection. Also visible in this superpixel map is low level
fringing outside of the areas covered by the reflections. And the effect of
vignetting on CCD#3 is also clearly discernible.

Figure 19: Reflection of a bright star on image r414326, CCD#2. On the left
side the H$\alpha$ image is shown. The pixel mask is shown on the right. Black
corresponds to masked pixels. The bright star is located just outside the top
edge of the CCD. This creates a cometary like tail that covers about half the
CCD length and a square reflection near the CCD top. The usual circular
reflection is also visible at the top. As the masked pixels are generated from
the catalogued sources it can be seen that a lot of faint spurious sources are
detected due to the reflections.

Figure 19 shows the effect of a bright star located at or near the edge of a
CCD. This leads to the starlight being reflected on to the CCD at an oblique
angle. The left panel shows that this creates a cometary tail like structure,
that can extend quite a distance from the star. Also an almost-square
reflection is visible near the top edge, along with a fainter circular
reflection. The mask created by HaGrid (cf. section 7.1) is shown in the right
panel. It can be seen that this reflection creates a lot of faint spurious
sources, picked up erroneously by the IGAPS catalogue, which are arranged in
the circular pattern of the reflection. Further spurious sources can be seen
extending along the cometary reflection. A further faint part of a circular
reflection must exist at the centre of the right CCD edge, as another circular
structure of spurious sources can be seen there.

####  A.3.2 Cosmic Rays and satellite trails

Cosmic ray impacts are a well known nuisance in astronomical images (Smith et
al., 2002). Despite their name, not all impacting particles actually are of
direct cosmic origin. Figure 20 shows a few prominent examples of particle
impacts visible in the 120s H$\alpha$ exposures. The long streaks visible in
the right hand panels are caused by muon impacts. The track in the lower left
panel shows a kink in the track, probably due to a collision with a particle
in the CCD. The top left panel shows a so called ”worm”, caused by multiply-
scattered low energy electrons.

Figure 20: Examples of prominent cosmic ray impacts. Top left: A worm caused
by multiply scattered low energy electrons. The other three panels show muon
impacts. The images are: top left, r372612 CCD#4; top right, r418359 CCD#2;
bottom left, r431220 CCD#2; bottom right, r570439 CCD#3. Each cutout is 180
pixels squared.

Another nuisance in astronomical images are satellite tracks. With the current
and future planned mega satellite constellations in low earth orbit, this
problem is very much on the increase. Satellite tracks are mostly straight
lines running through the image at an angle. Sometimes flares can be seen,
where the brightness increases for a short time due to the alignment of
reflecting satellite surfaces with the observing direction. Rather rare is the
observation of fine structure in the satellite track. One such example can be
seen in figure 21. The cause of these high frequency ”wiggles” may either be
due to telescope-tracking glitches in declination or to satellite spin
bringing different structures into illumination.

Figure 21: Details of the satellite track on the H$\alpha$ image r541904,
CCD#3. Two excursions away from linear are shown. Telescope tracking or
satellite structure might have caused them.

####  A.3.3 Pickup noise and gain changes

Occasionally the WFC images suffer from electronic noise, either from external
sources (pick up noise) or from readout electronic problems (gain changes).
Over time, with ageing electronics, especially the gain changes became more
common. The occurrence of gain changes seems to be random, and subsequent
images usually are read out correctly. In most cases there is only one gain
change during the readout. As certain observing runs had an increased
occurrence rate of gain changes, not all of the affected fields could be re-
observed at a later date. Hence the data reduction pipeline was modified to
deal with the gain changes and still produce a useful object catalogue. An
extreme example of many gain changes during the readout is shown in figure 22
in the right panel. These extreme cases could not be salvaged by the data
reduction.

The left panel of figure 22 shows an image with typical pick up noise.

Figure 22: Left: Pickup noise on image r1018959, CCD#4. The variations here
are small, and represent minor degradation. Right: Gain changes on image
r1166073, CCD#4. The changes are both spread across the whole and large,
rendering this frame unusable.

####  A.3.4 Cross talk

Cross talk during readout is a well known phenomenon for CCD arrays
(Freyhammer et al., 2001). With the WFC, cross talk only becomes visible when
a very bright source falls on one of the CCDs. An example is shown in figure
23. The bright star in CCD#1 creates a negative cross talk in CCD#2 at a level
of about -10 ADU. CCD#4 shows a positive cross talk at about 10 ADU, and CCD#3
only shows a very small positive cross talk signal in this case.

Figure 23: Cross talk on image r1023036. CCD#1 is shown top left, #2 top
right, #3 bottom left and #4 bottom right. The same pixel section is shown for
each CCD. The bright star on CCD#1 is seen as a negative imprint on #2 for
pixels that are saturated, as a positive imprint on #4 and a faint positive
imprint on #3.

####  A.3.5 Multiple images

Very rarely, multiple images of each source are found on an IGAPS exposure.
Normally these appear as double images or streaks. The latter effect is caused
by the telescope not being settled on the observing position by the time of
exposure start. The former is caused by jumps in the telescope tracking or, in
the case of the INT, the oscillation of the main mirror support system (see
figure 24). The mirror support system at the INT consists of 36 pneumatic
pads, which are controlled together in three $120\deg$ segments. Oscillations
of the servo loop were audible in the control room and could be stopped by the
observer by moving the telescope to a different position. Note that due to
differences in the time spent at the end points of the oscillation the double
images are of different brightness.

Figure 24: Double images on frame r367586, CCD#4. Bright stars clearly show
the sign of INT mirror support oscillation, forming dumbell like images.

####  A.3.6 Other

Very occasionally images suffer rare, sometimes unexplained artifacts. Two
such examples are shown in figure 25. The left hand panel shows reduced counts
near the left and right edge of the CCD. This effect is visible in CCDs 2 and
4, but not in CCDs 1 and 3.

The right hand panel of figure 25 shows the effect of a drop of liquid (water
or oil) on the filter. This happened during the two nights of October 10 and
11 2006. The extent and form of the feature changed over time during these
nights.

Figure 25: Left: image r418779, CCD#4. The left and right edge show 5%
difference in background counts. Right: image r531376, CCD#4. The feature, top
right, causes up to 10% difference relative to the background. The HaGrid
superpixel map is shown to enhance low level detail. Black areas correspond to
rejected superpixels due to high levels of masking. The color scale goes from
red (low values) to blue (high values).

##  Appendix B Header parameters

The table below provides an example of the header content associated with each
CCD image file. Much of this information is also captured in the metadata
table available for download in compressed form as
igapsimages.org/data/images/igaps-images.fits.gz.

Parameter | Example value | Explanation
---|---|---
BITPIX | 8 | Data type of original image
NAXIS | 2 | Dimension of original image
NAXIS1 | 2048 | Length of original image axis
NAXIS2 | 4096 | Length of original image axis
PCOUNT | 0 | Size of special data area
GCOUNT | 1 | One data group (required keyword)
RUN | 677729 | Run number
OBSERVAT | LAPALMA | Name of observatory (IRAF style)
OBJECT | intphas_5023 r | Title of observation
LATITUDE | 28.761907 | Telescope latitude (degrees)
LONGITUD | -17.877559 | Telescope longitude (degrees)
HEIGHT | 2348 | [m] Height above sea level.
SLATEL | LPO2.5 | Telescope name known to SLALIB
TELESCOP | INT | 2.5m Isaac Newton Telescope
MJD-OBS | 55015.0452356 | Modified Julian Date at start of observation
JD | 2455015.5452356 | Julian Date at start of observation
PLATESCA | 6.856013 | [d/m] Platescale ( 24.68arcsec/mm)
TELFOCUS | 0.043825 | Telescope focus (metres)
AIRMASS | 1.021475 | Effective mean airmass
TEMPTUBE | 13.508864 | Truss Temperature (degrees Celsius)
INSTRUME | WFC | INT wide-field camera is in use.
WFFPOS | 5 | Position-number of deployed filter
WFFBAND | r | Waveband of filter
WFFID | 214 | Unique identifier of filter
SECPPIX | 0.333 | Arcseconds per pixel
DETECTOR | WFC | Formal name of camera
CCDSPEED | FAST | Readout speed
CCDXBIN | 1 | Binning factor in x axis
CCDYBIN | 1 | Binning factor in y axis
CCDSUM | 1 1 | Binning factors (IRAF style)
CCDTEMP | 151.959 | [K] Cryostat temperature
NWINDOWS | 0 | Number of readout windows
DATE-OBS | 2009-07-03T01:05:10.8 | Start time of the exposure [UTC]
INHERIT | T | Extension inherits primary HDU.
EXTNAME | extension1 | Extension name
EXTVER | 1 | Extension version number
IMAGEID | 1 | Image identification
DASCHAN | 1 | Number of readout channel
WINNO | 0 | Number of readout window
CHIPNAME | A5506-4 | Name of detector chip.
CCDNAME | A5506-4 | Name of detector chip.
CCDCHIP | A5506-4 | Name of detector chip.
CCDTYPE | EEV42-80 | Type of detector chip.
CCDXPIXE | 0.00001350 | [m] Size of pixels in x.
CCDYPIXE | 0.00001350 | [m] Size of pixels in y.
AMPNAME | LH | Name of output amplifier.
GAIN | 2.80000000 | Nominal Photo-electrons per ADU.
READNOIS | 6.40000000 | Nominal Readout noise in electrons.
SATURATE | 64276.0 | Highest value that is unsaturated
BIASSEC | [11:50,3:4098] | Bias pixels.
TRIMSEC | [51:2098,3:4098] | Illuminated pixels.
RTDATSEC | [2062:4215,13:4212] | Location in d-space for RTD.
RADESYS | ICRS | WCS calibrated against Gaia-DR2
EQUINOX | 2000.0 | Equinox of the astrometry
CTYPE1 | RA ZPN | Algorithm type for axis 1
CTYPE2 | DECZPN | Algorithm type for axis 2
CRUNIT1 | deg | Unit of right ascension coordinates
CRUNIT2 | deg | Unit of declination coordinates
PV2_1 | 1.0 | Coefficient for r term
PV2_2 | 0.0 | Coefficient for r**2 term
PV2_3 | 213.741679 | Coefficient for r**3 term
PV2_5 | 0.0 | Coefficient for r**5 term
CRVAL1 | 292.931917 | [deg] Right ascension at the reference pixel
CRVAL2 | 28.6651568 | [deg] Declination at the reference pixel
CRPIX1 | -329.738223 | [pixel] Reference pixel along axis 1
CRPIX2 | 2945.36999 | [pixel] Reference pixel along axis 2
CD1_1 | -1.3972E-06 | Transformation matrix element
CD1_2 | -9.2449E-05 | Transformation matrix element
CD2_1 | -9.2444E-05 | Transformation matrix element
CD2_2 | 1.3945E-06 | Transformation matrix element
STDCRMS | 0.02526049569007405 | Astrometric fit error (arcsec)
MOONDIST | 81.0 | Distance to the moon in degrees
MOONALT | 18.29999923706055 | Altitude of the moon above the horizon
MOONPHAS | 83.40000152587891 | Phase of the moon
SKYLEVEL | 252.99 | Sky level
SKYNOISE | 10.96000003814697 | Sky noise
PERCORR | -0.005 | Sky calibration correction (mags)
MAGZPT | 24.47 | Uncorrected nightly ZP (per second)
MAGZRR | 0.02 | Photometric ZP error (mags)
EXTINCT | 0.09 | Extinction coefficient (mags)
PHOTZP | 28.2187 | mag(Vega) = -2.5*log(pixel value) + PHOTZP
PHOTZPER | 0.03 | Default 1-sigma PHOTZP uncertainty in IGAPS
PHOTSYS | Vega | Photometric system
FLUXCAL | IGAPS-UNIFORM | Identifies the origin of PHOTZP
SEEING | 0.753579 | Average FWHM (arcsec)
ELLIPTIC | 0.1319999992847443 | Average ellipticity
EXPTIME | 30.07 | [sec] Exposure time adopted
CONFMAP | iphas_jul2009 | r_conf.fits Confidence map
CHECKSUM | ZfA6ad53VdA3Zd53 | HDU checksum updated 2020-02-11T11:35:56
DATASUM | 1159687462 | data unit checksum updated 2020-02-11T11:35:56