=Paper=
{{Paper
|id=Vol-2790/paper26
|storemode=property
|title=
Variable Star Classification with Help of Machine Learning Algorithms (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2790/paper26.pdf
|volume=Vol-2790
|authors=Kirill Naydenkin,Konstantin Malanchev,Maria Pruzhinskaya
|dblpUrl=https://dblp.org/rec/conf/rcdl/NaydenkinMP20
}}
==
Variable Star Classification with Help of Machine Learning Algorithms (short paper)
==
https://ceur-ws.org/Vol-2790/paper26.pdf
Variable Stars Classification with the Help of
Machine Learning
Kirill Naydenkin1, Konstantin Malanchev2,3, and Maria Pruzhinskaya2
1
Physics Faculty, Lomonosov Moscow State University, Leninskii Gori 1, 119234
Russia, rtut654@gmail.com
2
Lomonosov Moscow State University, Sternberg Astronomical Institute,
Universitetsky pr. 13, Moscow, 119234, Russia
3
National Research University Higher School of Economics, 21/4 Staraya
Basmannaya Ulitsa, Moscow, 105066, Russia
Abstract. With the appearance of modern technologies such as CCD-
matrices, large telescopes and computer networks the precision of our
observations increased immensely. On the other hand, such accurate and
complex data formed TBs large data bases which are very fragile and
unattainable for the treatment by classical methods. The scales of this
problem can be seen especially in variable star sky surveys. For many
terabytes of data one has to classify all the stars in catalog to find stars
of particular type of variability. This problem is known as very impor-
tant since almost every part of modern astrophysics is interested in new
objects to study. In some fields like cosmology, this question is very vi-
tal due to high demand for new data of model-anchors like Cepheids
or supernova stars. To facilitate this task many machine learning based
algorithms were proposed (Richards et al., 2011 [2]). In this study we
perform a way to classify the Zwicky Transient Facility Public Data
Release 1 catalog onto variable stars of different types. As the priority
classes we set Cepheids, RR Lyrae and δScuti. “One vs all” classification
technique revealed highly accurate results on validation data, concretely
0.90–0.95 with ROC-AUC metrics.
1 Introduction
The Zwicky Transient Facility (ZTF) is a 48-inch Schmidt telescope
with a 47 sq. deg. field of view at the Palomar Observatory in Cali-
fornia. This large field of view ensures that the ZTF survey can scan
the entire northern sky every night. The ZTF survey started on 2018
March 17. During the planned three years survey, ZTF is expected to
acquire ∼ 450 observational epochs for 1.8 billion objects. Its main
scientific goals are the physics of transient objects, stellar variabil-
ity, and solar system science (Graham et al. 2019 [3]; Mahabal et al.
2019 [5]).
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
296
In this paper we made an attempt to classify the first data release
of ZTF survey (DR1) which contains data acquired between March
and December 2018, thus covering a timespan of around 290 days.
The first data release includes more than 800 thousand light curves
observed in both zr and zg passbands.
The further steps of classification ZTF DR1 imply highly accu-
rate data treatment in the beginning. To have more clear awareness
about the stage of data treatment it is important to divide it onto
small parts. First of all, in demand of supervised machine learning al-
gorithms it is important to prepare precise labeled data which in our
case consist of star-catalog with coordinates and types of 55 thou-
sands variable stars of the General Catalog of Variable Stars (GCVS,
Samus et al. 2017 [4]). Even though this number is relatively small
compared to the size of ZTF data release, there are some ways to
generate the data on the basis of accurate initial frames.
1.1 Data Preparation
Since ZTF DR1 marks the same objects observed in different pass-
bands and/or different sky fields with different identifiers (IDs),
cross-match can yield more than one ZTF DR1 ID for given GCVS
object. Taking into account that ZTF ranged in ≈ 12m − 21m , it is
reasonable to remove those stars which do not belong to that range
(with slight offset due to star magnitude fluctuations with time).
After this filtering, 43 thousands of total 55 thousands GCVS stars
remained.
Fig. 1 represents the distribution of number of cross-matched ZTF
DR1 objects. To find the real range of difference in coordinates be-
tween catalogs we measured distances for the case when the only ID
was found for a given GCVS object and for the case of two IDs sep-
arately in both filters. As the result, we received that only relatively
small amount of objects has more than 0.1” difference. This coordi-
nates distinction can be explained by catalogs’ positional inaccuracy,
which for GCVS is 0.1”.
To this point we had matched labeled GCVS with objects from
ZTF. As we mentioned before, ZTF DR1 contains photometry in
two passbands (zg, zr) which we can be used either separately or in
combination. We have to note that regardless shrinking the search
radius to 1.5” we still can receive multiple results — from a light
curve in only one filter up to a few series in both (Fig. 2). Taking
297
Fig. 1. A number of cross-matched ZTF DR1 IDs distributed by the angular separation
from GCVS objects.
Fig. 2. Distribution of ID-numbers among all responses.
298
into account such asymmetry we made up three homogeneous sets:
19k objects in g band, 14k in r band, and 13k in combination (setting
up threshold for minimal amount of observations in a pair).
2 Objects of Interest
In our study we took some particular classes of interest among the
variable stars: Cepheids, RR Lyrae and δ Scuti. It is important to
note that we do not use multi-class classification for chosen types,
every type goes through one-vs-all approach.
First of all, let’s describe the physical nature of chosen types.
Cepheids are pulsating stars, which radius and brightness (as well as
the temperature) change with time. Cepheids stars are well known
for the dependency between luminosity and pulsating period, this
property makes them important indicators of cosmic distances. RR Lyrae
stars can also be used as standard candles for distance measurements,
though they do not follow a strict period-luminosity relationship at
visual wavelengths. δ Scuti as well as Cepheids are important stan-
dard candles and have been used to establish the distance to many
large clusters around the center of our galaxy.
3 Analysis
The next step toward the data classification is to engineer features
out of light curves. To start we created three most valuable sources of
information (Richards et al., 2011 [2]): magnitude amplitude range,
the main peak period and power of Lomb–Scarge periodogram (Lomb
1976 [7]; Scargle 1982 [8]). For this purpose we used astropy [13]
library-based LombScargle() function which allows us to define
both positional coordinates of Lomb–Scargle periodograms peak.
One possible way to select a set of variable stars out of ZTF DR1
is to use the Lomb–Scarge periodogram. Such attempts were made
recently and yielded to the strong results (Chen et al 2020 [6]).
On the one hand, one could possibly observe the importance of
different features for different types of variable stars from (Richards
et al., 2011 [2]). On the other hand, ZTF light curves can be slightly
different from the data studied in the article and this could possible
change the picture.
After choosing metric for result evaluation we worked with vali-
dation set to find an optimal list of features for the classification task
299
in ZTF DR1. Richards et al., 2011 [2] presented a table of pairwise
random forest feature importance for all basic variable types. As
one can see coordinates of first peaks of Lomb–Scarge periodogram
and their ration (ratio of periodogram’s frequencies) have a signifi-
cant influence on majority of classes. Basic characteristics of a signal
such as amplitude, std, skew, median absolute deviation also have
a strong correlation with correct classification of all classes. For our
one-vs-all approach we took 17 the most strongest features out of
Richards et al., 2011 [2] — coordinates of first four periodogram’s
peaks, frequencies ratio, mentioned basic signal properties and few
additional such as trend angle. To implement these features we used
python libraries — astropy and statistical analysis from scipy. It
is obvious, that application of this approach to one-vs-all classifica-
tion leads to imbalance in labeled data because even the largest types
consist out of less then 15% of the train set. To deal with imbalanced
data few possible options are available. First of all, we can use one
of the ways to generate rare sample, for example random sampling
from a chosen distribution. On the other hand, we can divide the
more abundant class into N distinct clusters and train each of N
classifiers on one of the distinct clusters and on all of the data from
the rare type. After that ensemble the result from all models. As far
as we address the imbalanced problem with significantly low num-
ber of minor class examples, it is more efficient to use over-sampling
techniques instead of under-sampling. For this purpose we took Syn-
thetic Minority Oversampling Technique, or SMOTE. SMOTE first
selects a minority class instance A at random and finds its k-nearest
minority class neighbors. The synthetic instance is then created by
choosing one of the k-nearest neighbors B at random and connecting
A and B to form a line segment in the feature space. The synthetic
instances are generated as a convex combination of the two chosen
instances A and B. Using SMOTE from imblearn python library we
updated the minority class by oversampling to have 10 percent the
number of examples of the majority class, then used random under-
sampling to reduce the number of examples in the majority class to
have 50 per cent more than the minority class.
For the first trial of binary classification we used Cepheids. The
performance is shown in Table 3.
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Table 1. Different metrics for Cepheid stars on validation set.
Passband Accuracy Precision ROC AUC F1
zg 0.869 0.921 0.873 0.857
zr 0.823 0.891 0.811 0.788
Overall 0.814 0.855 0.778 0.734
4 Discussion
4.1 Comparison with the Previous Studies
Comparing our approach with recent study of Chen et al 2020 [6],
which performed a large number of new RR Lyrae, Cepheid and
δ Scuti we used machine learning classification instead of directly
measured distances in parameter space. Machine learning way of
data classification can perform better results because it relies on both
successes and failures to estimate the reliability of certain candidate.
Moreover, applying hierarchical classification we have an ability to
track mistakes of our model on different levels — end up with highest
possible accuracy on large types which generally have some physical
difference like binaries, pulsating, eruptive or rotating stars. On the
next step large types break into sub-types.
4.2 Future Plans
As the future structure of the research we can mark the following
steps. First of all, to obtain some initial results we have to com-
plete the process of validation of our model on GCVS data for our
classes of interest. After that we can start to classify ZTF data ei-
ther after filtrating with Lomb-Scarge periodograms or making fea-
tures directly for each light curve. As the next step we will use more
public available labeled catalogs such as ASAS-SN [9], ATLAS [10],
Catalina catalog of periodic variable stars [11], the Gaia catalog of
RR Lyrae and Cepheids [12]. At this point, we will also introduce
data generation which can significantly improve the accuracy of the
results.
At the final step we will take different classification tools of ML:
classical Random Forest and XGBoost models with the main hyper
parameters to choose and imbalanced learning to make hierarchical
classification.
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5 Conclusions
In this work we consider the machine learning technique as a perspec-
tive approach for the classification task. We use ZTF data release 1
that contains zg and zr photometry of the variable objects. Our pre-
processing procedure includes several steps. First, we have to prepare
the datasets with labels to use them later for training the model and
testing the accuracy of the method. For this purpose the General
Catalogue of Variable Stars is used. After cross-matching the cata-
log with ZTF DR1 we found 19k common objects in zg-band, 14k
in zr-band, and 13k in combination of passbands. Then, we have to
choose the appropriate features to describe the light curves. As a
starting point, we tried the magnitude amplitude range, the main
peak period and power of Lomb—Scarge periodogram.
There are many types of variable stars differ by the underlying
physical processes or their observational appearance. As the objects
of interest we chose RR Lyrae, Cepheid and δ Scuti. We applied
binary classification technique to Cepheid stars and found quite good
results on the validation dataset. The work done is a preparatory step
towards the further thorough machine learning classification of the
variable stars in ZTF data.
Acknowledgments. K. Malanchev and M. Pruzhinskaya are sup-
ported by RBFR grant 20-02-00779. The authors acknowledge the
support by the Interdisciplinary Scientific and Educational School of
Moscow University “Fundamental and Applied Space Research”.
302
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