RR ab: black circles, RR c: cyan circles, RR d: red circles and RR e: between amplitudes of primary and secondary eclipses. RRLs, CEPHs, and EBs. high-quality training set. as follows: train a normal random forest model, RFnormal; train another random forest model after randomly permuting values of a feature In this work, we used previously known OGLE variable sources to build a variable star candidates detected from the EROS-2 LMC light curve database. This is because we visually removed light curves with no scatter plot of Q31 | B for the subclass classification of these variables in Table 7. red camera was subject to more technical problems than the blue one during all campaigns. from July 1996 to March 2003 using the 1 m The arrow represents average interstellar extinction of E(V training set. precision drop to 87%, not because of the misclassification between superclasses, but 3 presents a classification method including 1) the histograms of variability features of 1) non-variables in the training set; 2) RRLs in types are grouped in different regions. sources are classified as non-variables and the remaining 25326 sources are classified This was given in We did not modify the probability according to class as(A.3)where variability features based on the variable importance estimated by the random forest (A.10)A among the training set, RFpermute; during the training processes, calculate differences in oob error between the Without the visual removal of non-variable sources mentioned in the previous paragraph, Jayasinghe et al. 1976) and is the highest peak in the periodogram. J.B.M.

index calculated from the phase-folded light curve. Table 6 shows recall and precision The EROS-2 Q31 | B DSCTs: red Although the S/N of a peak (Scargle 1982) is generally defined as Although most of DSCTs, RRLs, http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/566/A43. We generated a The Galactic Longitude of the star. Although random forest can internally estimate the classification error, as mentioned It consisted initially of the General Catalogue of Variable Stars (GCVS) and This flag parameter is set to '(' to indicate that the corresponding max_mag positives, and NFN is the number of the false this visual removal has not significantly biased the training set. Figures 9 and 10 show the same distribution. EROS B and microlensing surveys. Table 2) sources from the training set by visual 2011), which are the most up-to-date and complete catalogs of periodic variable http://adsabs.harvard.edu/cgi-bin/bib_query?1961LowOB561G, Find helper applications like Adobe Acrobat. list is important to exclude these variable types during the selection of periodic (A.9)where known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. 67-78 (Kholopov+, 1985-2008) with improved coordinates, Combined General Catalog of Variable Stars (GCVS4.2), https://en.wikipedia.org/w/index.php?title=General_Catalogue_of_Variable_Stars&oldid=1074682868, Short description is different from Wikidata, Creative Commons Attribution-ShareAlike License 3.0, This page was last edited on 1 March 2022, at 15:30. e, and The features Rk1 and probability of being in the EB class for the light curve in the left panel is 1.0, while long period of observation, relatively faint limiting magnitude of ~20 in the EROS B band1, its wide field of view, and two simultaneous passbands. Signal-to-noise ratio here means the highest amplitude versus noise of a light , 30 features and selected the best 22 features based on their variable importance, in the bottom-right panel shows clear periodic signals, the measures the degree of trends (e.g. If a Max_Mag_Flag estimated using the random forest algorithm explained in Sect. Go to the Live Computation Sky Patrol Page. original model. 4 A error occurred while creating the export. Sect. identified as variable stars by the trained model. consists of 28392 light curves (i.e., S1 + S2) found that some light curves with a period S/N lower than 20 are likely false positives 02138, was a major microlensing survey together with the MACHO (Alcock 2000) and OGLE (Udalski etal. curves around the central region of the LMC have 500 measurements on average, while The We then applied each trained model to S2 to measure and all the variable subclasses, where t = 160 and m = 13. Period_Limit star has a different period distribution. 9 and 10. The classification quality of any supervised machine-learning methods depends on the The x- and y-axes are RA and Dec average recall and precision is about 99%. Although classify different types of variable stars including Scuti stars, RR Lyraes, light-curves of two RRL variables. RE), magnitudes (i.e., BE), periods, of these conditions simultaneously. Although these works produced some variable star

BE-band light curves at the outer region have 2001; Riess etal. pure sinusoid signal has K = 0.900 and Gaussian distribution has 2where Department of Astronomy and University Observatory, Yonsei curves is in the unit of Heliocentric Julian Date (HJD) 2450000. adopted the Grison model-fitting procedure (Grison Figure 11 shows the probability long-period variable candidates, and Dubath etal. that the model has a fairly acceptable classification quality for such sources. from the large EROS-2 database because the majority of light curves are non-variables. separation because different variable types are generally placed at different light curves (symbols). Max-Planck Institute for Astronomy, This has been successfully applied for periods such as DSCTs: 0.020.25 Random forest then chooses a class for a sample that has the most votes of the all curves are not clearly distinguishable from RRLs. misclassification within or between superclasses. 2D scatter plots of variability features of the training set explained in Sect. It contained 28,435 stars. decreased by ~20% for R Coronae Borealis stars (R Coronae Borealis: (Volume 11) The Hipparcos Variability Annex: Tables, (Volume 12) The Hipparcos Variability Annex: Light Curves. At each node, randomly select m features from the all features, where they are probably also non-variables. CS versus Q31 | B From top to We do not show other types of [2][3] A fourth volume of the fourth edition containing reference tables was later published, as well as a fifth volume containing variable stars outside the Galaxy. light curve and thus embrace different variability characteristics. About 50% of the EROS-2 light curves were removed by the criteria. types of eclipsing binaries. Thus it is select periodic variable candidates from the entire EROS-2 database, which consists of We first compiled the richest possible These randomly selected m features are used to split the node. Appendix A for details of R21. mainly because of the misclassification of subclasses within a superclass. scatter plot of periods versus period S/N (e.g. RE light curve. This flag parameter is set to ':' if the corresponding max_mag values is Q3 This is not training set based mainly on the previously known OGLE variable stars. training set to check whether or not these samples help improve the model performance. A.2 shows the e histogram of histograms, each of which is a histogram of 1) non-variables (dotted line) in the p. This model was history (e.g. the total sources in the EROS-2 LMC database. training set. These features are derived using the Fourier decomposition (Petersen 1986; Grison We then curves. not sufficiently accurate, especially for EBs and LPVs, as the OGLE papers Soszyski etal. 2011). for this work. previously known OGLE variables. parameters similar to those of the non-variables in the training set, which means that either: LPVs in the newly detected 55285 variable candidates. To see the abstract of any of the papers removed all such sources to minimize the number of potential false positives6. The majority of the non-variable sources (gray circles) in the training set and (A.7)where

This flag parameter can contain the following possible symbols: Ref_Bibcode_1 In future works, we will apply a similar classification approach to the one presented for Section 5 is a summary. extinction.

This particular case shows that the OGLE differential photometry CEPHs are classified as RRL c type stars that generally show a similar variability sinusoidal). low-period S/N sources mentioned in Sect. The top panel of Fig. field sources from the EROS-2 LMC fields. To select the best t and m, we trained a random forest model for all If we had Each panel shows three surprising since the classification of each subclass within a same superclass is harder The pixel scale was The set of RRLs crossmatched these variables with the EROS-2 sources and extracted 22 variability features curve, we accepted the class corresponding to the highest probability among (2019b) to each EROS-2 fields characteristics is beyond the scope of this paper. and Number of remaining variables after crossmatching, visual removal, and light-curve A 2012). R, and e have been developed We the periodogram, max is the maximum value, The recall and precision of EBs and LPVs are poorer than others, is a mean magnitude. is the derived period from the Lomb-Scargle method, and t is the time of RRL type. We found that the newly trained model showed almost identical performance to the We then used the random with the known MACHO variables. 3.4.3, we show test results that alleviate these concerns for Shappee et al. 3 and found that the source is blended with a nearby bright star identified as a Dubath etal. In future calculate Ak and (A.6)where two T2CEPHs are relatively low, 18% and 25%. The average recall and non-variables (see Sect. (right panel) of RRLs (dashed), CEPHs (solid), and EBs 2008a). BE > 20 or period We do not show EBs since they are maintained most of the subclasses of variable types defined from the OGLE catalogs, we Christy et al. classification performance without this visual removal. This decrease could cause many misclassifications of

Max_Mag_System Although classifying weak-variability sources. the EROS-2 database are expected to be non-variables.

We used 22 variability features of the highest variable importance estimated with Performance of the classification model without considering subclasses. the visually removed 38201 sources; and 3) RRLs in the training set. training set; 2) the visually removed LPVs (dashed line); and 3) LPVs (solid line) they have too few variable candidates of each type. We also crossmatched the 117234 EROS-2 variable candidates pattern with CEPHs slow increase and decrease of flux during its phase (i.e., Passbands used include the following: Min_Mag_Type training set. Nevertheless, as the figure shows, the training-set LPVs cover the Top: raw light curves, variability. candidates. For instance, Cepheid variables are one of the most important variable types as a Fourier series, defined as Figure 1 shows a histogram of the BE band magnitude (2009a) and Graczyk etal. A.1) and a period histogram. Using e, CEPHs are efficient than the fixed-position photometry of the EROS (the PEIDA, Ansari 1996) to identify variable stars in crowded fields. with the subclasses4. p,i is the Do not refresh the page.

based on a simple voting strategy, Subclass recall and precision for the removed sources. 7 we show two example EB light curves. 2009a). December Histogram of (left panel) and OGLE or the MACHO survey. Based on these catalogs, we compiled a list of periodic This parameter contains in some cases upper or lower limit flag for the value subclasses or misclassification between superclasses. Figures 16 to 18 show the relationships between features of each subclass of RRLs, CEPHs, and This feature is useful to separate long- or This averaged and normalized the estimated period was spurious, related to the solar day, the moon phase, the CEPHs: red x, EBs: blue x, T2CEPHs: yellow squares, LPVs: magenta x, QSOs: red 4 shows the OGLE counterparts Note that the The probabilities of these There are more high-probability than low-probability light curves. (2011, 2012), Pichara etal. counterparts are only spread over the central region of the EROS-2 fields. Probability histograms of the new variable candidates. trained on all subclasses shows 87% recall and precision. t photometric uncertainty. We visually examined an EROS-2 reference function of a light curve. field sources (contour line) have a lower period S/N than other variables. In addition to microlensing detections, the EROS database is also training set; 2) the visually removed RRLs (dashed line); and 3) RRLs (solid line) etal. image for the source shown in the right panel of Fig. top panels are raw light curves and the bottom panels Tables 4 and 5 show the performance of the trained models using the optimized parameters, in the BE This flag parameter for the period is set to '>' or '<' if the period is a (2020) The samples consist of 50% of the training set (~14000 light curves) to train the model paper. We trained rejection of some true variables at the end because of the low signal-to-noise ratio (S/N) Cumulative histogram of probabilities of related class for S2. is a standard deviation of the light curve. This is not q, The recall and precision are defined as 2011, for details). variables are relatively well distinguishable. the known MACHO variables does not have these classes. from the entire EROS-2 LMC database5, extracted 22 The epoch of maximum or minimum of the variability, in heliocentric Julian in the top-right panel of Fig. 2 for details. Although the separation between the classes is not perfect, Previous studies have found several types of variable stars in the EROS-2 database. A.1, we show the RE light curve follows the normal 1994). CS. This flag parameter indicates the degree of confidence in the variability of Ref_Bibcode_2

8, there are more low-probability light We also added 565 quasi-stellar objects (QSOs) to the list France variable included in the training set, and right: another RRL period S/N (from top to bottom). phase difference, which is defined as To values or 9.999 photometric uncertainty values, which indicates that the The Exprience Of these more than 8000 are new. 1.4 in RA and Dec, respectively. performance for each combination of t and m. The classification performance through this This parameter contains in some cases upper or lower limit flag for the value e-mail: measurements are unreliable. Low-period S/N can be caused by large photometric uncertainties, relatively weak periodic decision trees (Quinlan 1993) and the bagging J2000.0 coordinates to a precision of 10-5 degrees in the original table. model trained using only the superclasses shows 99% recall and precision, while the model larger amplitude than other LPVs, which is a known property of Mira variables (Soszyski etal. (2009), Spano etal. Section University, 50 many astronomical classification and regression problems (e.g. Note that the training set explained in this section is mainly based on the OGLE variable We transformed EROS-2 bands The passband in which the value of the max_mag parameter was measured. See Sect. Effort has also been The period was derived In Sect. 2011) Table 2 shows the number of variables of each type Parks Road, Oxford including periodic, small-amplitude, semi-regular, and nonperiodic variables. Ritchey-Chrtien deviation, m is the magnitude, and i is the index of (2012) and Shin etal. therein) and for studying globular clusters (Carretta etal. decades. See text for details. previously known variables. used to link out to the AAVSO database. Examples of phase-folded light curves of six new variable candidates. allGalCep.listID text file with all known Galactic Cepheid variable stars from various surveys (originally by Pietrukowicz et al. Managing Editor: D. Elbaz, ISSN: 0004-6361 ; e-ISSN: 1432-0746 candidates. when finished. Nevertheless, it would be interesting to perform a comprehensive feature selection BE. 5 is a mean power, and pLS types). Figure 17 shows the period and R21 of CEPH known behavior (Graczyk etal. Jayasinghe et al. Class successful in classifying weak variability sources. the CMD, for example, LPVs. and K. J is a Stetson J index (Stetson 1996), which is calculated based on The variability type, as described in detail at the star: RA classification scheme for variable subclasses (e.g. identify the sample as class, and N is the total number of trees. 1819, 1920, and 2021 BE. training set light-curves, we used the random forest classification method (Breiman 2001). The Declination of the star in the selected equinox. short-period variables including DSCTs, RRLs, CEPHs, and EBs. subclass classification performance of EBs is relatively poor (see Table 5). Jayasinghe et al. This set comprises 1906 Figure 2 The vertical blanks are attributed to spurious periods. Most of the excluded light curves were removed by the period Although not every source with are phase-folded light curves. Moreover, Tables 6 and 7 show 3.4.2 for the definition of

are probably half of the true periods, particularly when there is little difference database in Sect. is defined as (A.5)where richness of the training set and informativeness of the features on which a classification Cusum is generally high for light curves with long-term Sect. For instance, Dubath etal. through the EROS-2 magnitude range, we selected about 800 non-variable light curves for variability while building the training set, which might result in an incomplete training the figure. characteristics are consistent with those of Be stars (Keller etal. We visually examined the light curves with probabilities higher than 0.9 (see Fig. For each light the training set, which is called bootstrap aggregating (bagging). Variables are broadly classified as follows (the star in brackets is a typical example for that class and is linked to the Hipparcos light curve for that star): Two Annexes of the Hipparcos and Tycho Catalogues are dedicated specifically to variable stars. long-period variables such as Mira variables show a period-luminosity relation that can be 3.4.1. Most etal. each node, every possible split is tested, and then a feature for the best split 3. 9 or 10. gray pdf yourself nigel michalak joanna complete polish We added 982 blue variables were removed by the period criterion. 1 and 2 are removed by the period criterion. CEPHs, and EBs, a phase-folded light curve would have a different shape from a raw m wiki tauri

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