=Paper= {{Paper |id=Vol-2523/paper21 |storemode=property |title= Cross-Matching of Objects in Large Sky Surveys |pdfUrl=https://ceur-ws.org/Vol-2523/paper21.pdf |volume=Vol-2523 |authors=Oleg Malkov,Sergey Karpov,Dana Kovaleva,Jayant Murthy,Sergey Sichevsky,Nikolay Skvortsov,Sergey Stupnikov,Gang Zhao,Aleksandr Zhukov |dblpUrl=https://dblp.org/rec/conf/rcdl/MalkovKKMSSSZZ19 }} == Cross-Matching of Objects in Large Sky Surveys == https://ceur-ws.org/Vol-2523/paper21.pdf

Cross-Matching of Objects in Large Sky Surveys

Oleg Malkov1,2 , Sergey Karpov3,4,5 , Dana Kovaleva1 , Jayant Murthy6 , Sergey
Sichevsky1 , Nikolay Skvortsov7 , Sergey Stupnikov7 , Gang Zhao2 , and
Aleksandr Zhukov1,8,9
1
Institute of Astronomy, Moscow 119017, Russia,
malkov@inasan.ru,
WWW home page: http://www.inasan.ru/~malkov
2
National Astronomical Observatories, Beijing 100012, China
3
Institute of Physics, Czech Academy of Sciences, 182 21 Prague 8, Czech Republic
4
Special Astrophysical Observatory, Nizhnij Arkhyz 36916, Russia
5
Kazan Federal University, Kazan 420008, Russia
6
Indian Institute of Astrophysics, Bengaluru 560034, India
7
Institute of Informatics Problems, Federal Research Center “Computer Science and
Control” of the Russian Academy of Sciences, Moscow 119333, Russia
8
Sternberg Astronomical Institute, Moscow 119234, Russia
9
Russian Technological University (MIREA), Moscow 119454, Russia

Abstract. The study of the stellar physical properties as well as the
spatial distribution of interstellar extinction, is important for many in-
vestigations of galactic and extragalactic objects. We have developed a
method for determination of stellar parameters and interstellar extinc-
tions from multicolor photometry. This method was applied to objects
drawn from modern large photometric surveys and, in this work, we give
a review of the surveys and discuss problems of cross-identification.

Keywords: Cross-matching · Sky surveys · Photometry · Interstellar
extinction

1 Introduction
An outstanding problem of astrophysics is the study of the stellar physical prop-
erties. Because the stars are observed through interstellar dust, their light is
dimmed and reddened, complicating their parameterization and classification.
The parameters of a given star, as well as the interstellar reddening, may be
obtained from its spectrum but one must either use a large telescope or only
observe bright objects in order to get spectral energy distributions with good
resolution and sufficient accuracy. On the other hand, recently constructed large
photometric surveys with new tools for cross-matching objects provide us with
the possibility of getting multicolor photometric data for hundreds of millions of
objects. From these, we may not only parameterize objects but also determine
the 3-dimensional interstellar extinction in the Galaxy.
We have developed a method for the determination of stellar parameters and
interstellar extinction values from multicolor photometry. The application of this

Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).

217
method to a set of stars in a small area in the sky allows us to determine an in-
crease of interstellar extinction with distance in that direction and, consequently,
to construct a 3-d extinction map of the Milky Way Galaxy.
Published interstellar extinction maps are described in Section 2. Section 3
contains description of our procedure for parameterization of stars. In Section 4
we give a review of sky surveys, and present principles of their cross-matching.
Our future plans are discussed in Section 6 with the conclusions in Section 7.

2 Interstellar Extinction Maps

Three-dimensional (3D) extinction models have been constructed using spec-
tral and photometric stellar data, open cluster data, star counts, Galactic dust
distribution models.
The standard approach to construct a 3D extinction model has been to parcel
out the sky in angular cells, each defined by boundaries in Galactic coordinates
(l, b). The visual extinction (AV ) in each cell may then be obtained as a function
of distance (d): AV (l, b, d) from the stars in the cells. The angular size of the
cells has varied from study to study, although each cell was generally chosen to
be large enough to contain a statistically significant number of calibration stars
at different distances.
Published 3D models, using spectral and photometric data, were based on
104 -105 stars, or were constructed for a very limited area in the sky (see, e.g.,
[36], [16], [21], the earlier studies were reviewed in [28]). Modern large surveys
contain photometric (3 to 5 bands) data for 107 − 109 stars. However, to make
those data (obtained at different wavelengths and with different observational
techniques) useful for a 3D extinction model construction, one needs to run a
correct cross-identification of objects between surveys. Such cross-identification
was laborious and time consuming, but using Virtual Observatory (VO) data
access and cross-correlation technologies, a search for counterparts in a subset of
different catalogues can now be carried out in a few minutes. It is now feasible
to obtain information on interstellar extinction from modern large photometric
surveys.
To properly obtain astrophysical parameters from catalogued photometry one
needs to study the possibility and sphere of application of the parameterization
method. We indicate areas in the parameter space [effective temperature log Teff ,
gravity log g, metallicity [F e/H], visual extinction AV , total-to-selective extinc-
tion ratio RV ], where observational photometry precision, achieved in modern
large multi-color surveys, allows us to obtain astrophysical parameters with ac-
ceptable accuracy [40].

218
3 Multicolor Photometry and Parameterization of Stars
3.1 Parameterization Procedure
We studied a problem of classification and parameterization of stars from mul-
ticolor photometry in detail (see, e.g., [41], [42]). In particular, a problem of
binary stars parameterization was studied in [30] and [29].
We have developed a method, which allows us to construct AV (l, b, d) rela-
tions from multicolor photometry. Varying (i) the spectral type of the star (SpT),
(ii) its distance (d), and (iii) interstellar extinction value (AV ), we simulate the
observational brightness, m, with the distance modulus equations
m = Mi (SpT) + 5 log d − 5 + Ai (AV ) (1)
for every photometric band, and, based on the quality of the simulation process,
choose the most appropriate SpT-d-AV set. A calibration relation Mi (SpT) and
interstellar extinction law Ai (AV ) should be available for each of the i photo-
metric bands included in the original surveys.
We have to remove all non-stellar objects, unresolved photometric binaries,
variable stars and other contaminating objects, based on flags included in the
original surveys with flags from our simulation techniques.
This method of simulation/parameterization, as described above, allows one
to plot parameterized objects in the distance-extinction (d-AV ) plane, approx-
imate them (by the cosecant law or more complicated function) and estimate
interstellar extinction parameters in a given direction on the sky.
Note that for high galactic latitude areas (|b| > 15o or so) the interstellar
extinction is thought to be (roughly) uniformly distributed and to satisfy the so-
called cosecant (barometric) law, suggested by Parenago in [32]. That function
should be modified (complicated) for lower latitudes, as dust clouds concentrated
in the Galactic plane, will have to be taken into account.

3.2 Modifications of the Procedure
Our procedure may be modified to use the astrometric and spectral information
on the studied objects as input parameters. In particular, our procedure can be
modified to determine stellar parameters and interstellar extinction values from
not only multicolor photometry but also using additional information such as
precise parallaxes and spectral classification, where available, thus reducing the
number of unknowns in Eq. 1.
One notable improvement has come with the recent release of the Gaia DR2
(see Table 1) set of parallaxes, which allows us to use distance as an input (rather
than as a free) parameter. It should significantly increase the accuracy of our
results, especially when we can substitute the more precise parallaxes from Gaia
DR3 for the DR2 data we currently use.
Our procedure can also be modified for stars with spectral classification avail-
able from LAMOST [23], the largest source of spectral classification of objects
in the northern sky. LAMOST Data Release 4 contains data on 7.6 × 106 objects
and is available through VizieR database (V/153).

219
4 Sky Surveys and Cross-matching

4.1 Sky Surveys Selection

The following sky surveys are selected for our study:

– The DENIS database [12];
– 2MASS All-Sky Catalog of Point Sources [9];
– The SDSS Photometric Catalogue, Release 12 [1];
– GALEX-DR5 (GR5) sources from AIS and MIS [4], [5];
– UKIDSS-DR9 LAS, GCS and DXS Surveys [22];
– AllWISE Data Release [10];
– IPHAS DR2 Source Catalogue [3];
– The Pan-STARRS release 1 (PS1) Survey - DR1 [7];
– Gaia DR2 [15], [2].

Some information on the surveys is given in Table 1, their photometric systems
response curves are shown in Fig. 1 (the mid-IR AllWISE photometric bands
are located in the 26,000 – 280,000 Å area and are not shown here).

Table 1. Large photometric surveys

Survey Number of Sky Photometric Limiting
objects, 106 coverage bands magnitude
DENIS 355 Southern Gunn-i, J, KS 18.5, 16.5, 14.0
hemisphere
2MASS 471 All sky J, H, KS 15.8, 15.1, 14.3
SDSS 12 325 25% u, g, r, i, z g,r=22.2
GALEX DR5 78 90% FUV, NUV ∼25
(AIS+MIS)
UKIDSS DR9 83 15% Z, Y, J, H, K K=18.3
LAS
AllWISE 748 All sky W1, W2, W3, W4 16.6, 16.0, 10.8, 6.7
IPHAS DR2 219 Northern r, i, Hα r=21-22
Galactic
plane
Pan-STARRS 1919 All sky but g, r, i, z, y i∼20
PS1 - DR1 southern cap
GAIA DR2 1693 All sky G, BP, RP G=20

The selected surveys satisfy the following criteria:

– the number of objects exceeds 10 × 106 ;
– the survey covers a large area in the sky (the only exception is IPHAS, which
covers a relatively small but important area in the sky);
– the photometric accuracy is better than about 0.05 mag;

220
– the depth of the survey exceeds V∼20 mag.

For every survey the following information should be available: absolute mag-
nitude – spectral type (Mλ – SpT) calibration tables and Aλ (AV ) relations for
every photometric band λ. If these information is not available in literature,
we construct it using response curves of photometric bands and spectral energy
distribution (SED) for every spectral type, as well as the interstellar extinction
law ([14], [6], [13]). Besides, relations between spectral type and atmospheric
parameters (effective temperature log Teff and surface gravity log g) for stars of
different luminosity classes should be available.
To model observational photometry one needs to know spectral energy dis-
tribution, and a number of spectrophotometric atlases are designed to meet that
requirement (e.g, [34], [46]). We have made a comparative analysis of the most
known semi-empirical and empirical spectral atlases. The results show that the
standard error of synthesized stellar magnitudes calculated with SEDs from best
spectral atlases reaches 0.02 mag. It has been also found that some modern spec-
tral atlases are burdened with significant systematic errors [19].

DENIS
0.5

0.5 2MASS

0.5 SDSS

0
GALEX
0.5

0.5 UKIDSS

0.5 IPHAS

0.5 Pan-STARRS

0
Gaia DR2
0.5

0
5000 10000 15000 20000 25000
Wavelength, A

Fig. 1. Response curves of the photometric surveys

221
A preliminary analysis of applicability of SDSS and 2MASS photometry for
determining the properties of stars and interstellar extinction was made by in
[39].

4.2 Cross-matching of Surveys

The number of surveys available at any wavelength is large enough to construct
detailed Spectral Energy Distributions (SEDs) for any kind of astrophysical ob-
ject. However, different surveys/instruments have different positional accuracy
and resolution. In addition, the depth of each survey is different and, depend-
ing on sources brightness and their SED, a given source might or might not be
detected at a certain wavelength. All this makes the pairing of sources among
catalogues not trivial, especially in crowded fields.
We have implemented an algorithm of fast positional matching of large astro-
nomical catalogs in small (up to one degree) areas with filtering of false identifi-
cation [25]. In particular, for each area and each pair we estimated the matching
radius. As a result, we drew in a number 0.1-degree radius areas samples of
point-like objects counterparts from the DENIS, 2MASS, SDSS, GALEX, and
UKIDSS surveys, and performed a cross-identification within these surveys [18],
[24]. We have compiled the corresponding subcatalogues in the VOTable [31]
format. The tool developed as a result of this work can be used to cross-identify
objects in arbitrary sky areas for the further classification and determination
of stellar parameters, including the measurement of the amount of interstellar
extinction.
In some surveys (e.g., GALEX, SDSS, UKIDSS) more than one observation
per object was made and, consequently, more than one entry per object is present
in the catalogue. In such cases we use weighted average values for the photometry.
In the cross-identification process (and later for the parameterization) we
use all positional information and all photometry available in surveys. To select
objects for further study we also pay attention to various flags, presented in the
surveys. The flags can indicate quality of observations and provide information
on a nature of object (duplicity, variability, extended shape). As it was mentioned
above, on this stage we do not use trigonometric parallax as an input parameter.
Response curves of photometric bands of the surveys are shown in Fig.1. It
can be seen that some bands in different surveys are the same or similar (e.g.
KS -band in DENIS and KS -band in 2MASS). The comparison of brightness of
objects in such pairs of bands provides us an additional filter to discard objects
irrelevant for the parameterization: a large magnitude difference may indicate
variability, a rare evolutionary stage, or non-stellar nature of the object. Too
bright and too faint objects for this particular survey (i.e., overexposed and
underexposed, respectively) can also be spotted and omitted at this stage.

222
4.3 Selection of Sky Areas

To test our procedure, we have to select sky areas which are interesting from
various astrophysical points of view and where our results can be compared with
independent studies.
It is instructive and useful to apply the model to estimate interstellar extinc-
tion for several areas of the sky where individual estimates were made by [38],
and used to calculate extinction for SNs in the Universe accelerating expansion
study [33].
Among other interesting objects, RR Lyr-type variable stars (variables) were
selected for the study. RR Lyr-type pulsating variables satisfy a period-luminosity
relation (PLR) that simplifies estimation of their distances (and, consequently,
distances to stellar systems they reside). However, PLR is not yet well calibrated,
and our study of dust distribution in the RR Lyr-type variables directions is in-
tended to improve the situation. Several hundreds of RR Lyr-type variables with
available spectral classification were selected for our study from the General Cat-
alogue of Variable Stars [37].
Another interesting direction in the sky to study is the solar apex, i.e., the
direction that the Sun travels with respect to the mean motion of material in
the Solar neighborhood. The solar apex is in the constellation of Hercules, the
approximate galactic coordinates are l=56o .24, b=22o .54. There is a practical
interest in the study of dust distribution in the Galaxy in that direction. The
movement of the Solar system through the clots of interstellar gas could lead
to the direct invasion of a dense mixture of gas and dust into the Solar system.
That has such potential consequences as global glaciation and reducing the size
of the heliosphere (up to the Earth’s orbit) which protects us from cosmic rays.

5 Results and Discussion

In our pilot study [26] we applied this method to construct AV (l, b, d) relations
for selected areas at high galactic latitudes. We have cross-matched objects in
2MASS, SDSS, GALEX and UKIDSS surveys in selected areas in the sky, using
Virtual Observatory facilities. As a result of the cross-matching, we find multi-
wavelength (i = 9 to 13 bands) photometric data for each object.
We have compared our results with LAMOST [23] data and extinction val-
ues to distant SNs (based on IRAS and DIRBE microwave data), available in
the literature. The comparison exhibits a good agreement (see [26] for details).
A comparison of our results with recently released Gaia DR2 data also demon-
strates a good agreement for stars as faint as 19m .6 gSDSS , and shows that our
method allows us to determine spectral type, distance and interstellar extinction
of objects out to 4.5 kpc [27]. It indicates that the proposed algorithm (after some
modifications, required for low galactic latitudes) can be used for construction
of a 3D map of interstellar extinction in the Milky Way Galaxy.

223
6 Future Plans
6.1 Coming Photometric Surveys
Our experience is thought to be a practical guide to issues that will be particular
important as soon as the new surveys will become available. In particular, the
following surveys can be mentioned here.

LSST. Large Synoptic Survey Telescope (LSST) is the most ambitious survey
currently planned in the optical [17]. LSST will be a large, wide-field ground-
based system designed to obtain repeated images covering the sky visible from
northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror,
a 9.6 deg2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering
the wavelength range 320-1050 nm. The project is in the construction phase
and will begin regular survey operations by 2022. A 18,000 deg2 region will be
uniformly observed during the anticipated 10 yr of operations and will yield a
co-added map to r ∼ 27.5. These data will result in databases including about
32 trillion observations of 20 billion galaxies and a similar number of stars, and
they will serve the majority of the primary science programs.

SAGE. Stellar Abundance and Galactic Evolution (SAGE) project aims to study
the stellar atmospheric parameters of ∼0.5 ×109 stars in the ∼12.000 deg2 of
the northern sky, with declination δ > −5o , excluding the bright Galactic disk
(|b| < 10o ) and the sky area of 12 < R.A. < 18 hr [48]. The survey uses a self-
designed SAGE photometric system, which is composed of eight photometric
bands Stromgren-u, SAGE-v, SDSS g,r,i, Hα wide, Hα narrow, and DDO-51.

UVIT. The UVIT instrument on-board the Indian space observatory ASTROSAT
consists of two 38-cm telescopes — one for the FUV and the other for the NUV
and visible bands. It has a circular field of view ≈ 280 in diameter. It collects data
in three channels simultaneously, in FUV, NUV and Visible bands correspond-
ing to λ = 1300-1800 A, 2000-3000 A and 3200-5500 A, respectively. Full details
of the instrument and calibration results can be found in [45]. UVIT does not
provide data for large number of objects, however, its data will be used as the
UV spectral range is very important for the study of the interstellar extinction.

Another aspect which we can tackle is how the accuracy of the results depend
on missing data (in fact the larger the number of the surveys cross-matched, the
larger should be the fraction of missing data). According to our preliminary
results [26], the presence or absence of 2MASS data in the set (subject to the
availability of SDSS, GALEX and UKIDSS data) does not significantly change
the result, but this issue needs a further study.

6.2 Use of Spectral Surveys in Parameterization
Ongoing (LAMOST [23], APOGEE (all-sky, ∼450,000 objects) [35], SEGUE
(northern sky, ∼350,000 objects) [47], RAVE (southern sky, ∼460,000 objects)

224
[20] and upcoming (4MOST [11], MOONS [8], WEAVE [44]) spectroscopic sur-
veys can serve as an exceptional sources not only of stellar parameter values,
but also of the nature of interstellar dust and its distribution in the Milky Way.
Atmospheric parameters (Teff , log g) and/or spectral classifications — obtained
from spectroscopy combined with observational photometry — allow us to de-
termine distances and interstellar extinctions for stars with high accuracy and
thereby to construct a 3D map of interstellar extinction.

7 Conclusion

The parameterization of stars is a well known problem and used for various pur-
poses in astronomy (e.g., while solving the problem of searching for well defined
stars to be used for secondary photometric standards [43]). We have shown that
multicolor photometric data from large modern surveys can be used for param-
eterization of stars. A comparison of our results with independent data shows a
good agreement. We prove that with sufficiently good quality photometry, one
may compute a 3D extinction map by comparing catalogued multicolor photom-
etry with photometry derived from the secondary estimators such as the distance
modulus and the interstellar extinction law with suitable calibration tables for
absolute magnitudes with reasonable spectral types, extinctions and distances.
With the advent of large, existing and coming, photometric surveys and
the evolution of computing power and data analysis techniques (in particular,
Virtual Observatory tools for cross-matching), interstellar extinction can now be
computed for hundreds of millions of stars in a reasonable amount of time, and
a 3D interstellar extinction map can be constructed.

Acknowledgement. We thank our reviewers whose comments greatly helped
us to improve the paper. OM thanks the CAS President’s International Fellow-
ship Initiative (PIFI). The work was partly supported by the Russian Foundation
for Basic Researches (project 17-52-45076) and by DST grant INT/RUS/RFBR/P-
265 to JM. This research has made use of NASA’s Astrophysics Data System,
and use of the VizieR catalogue access tool, CDS, Strasbourg, France.

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