A detection metric designed for O’Connell effect eclipsing binaries

Akaho, S.: A kernel method for canonical correlation analysis. arXiv preprint (2006). arXiv:cs/0609071 Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992) Angeloni, R., Contreras Ramos, R., Catelan, M., et al.: The VVV templates project towards an automated classification of VVV light-curves. I. building a database of stellar variability in the near-infrared. Astron. Astrophys. 567, A100 (2014). https://doi.org/10.1051/0004-6361/201423904 Armstrong, D.J., Kirk, J., Lam, K.W.F., et al.: K2 variable catalogue—II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4. Mon. Not. R. Astron. Soc. 456, 2260–2272 (2016). https://doi.org/10.1093/mnras/stv2836 Barclay, T., Ramsay, G., Hakala, P., et al.: Stellar variability on time-scales of minutes: results from the first 5 yr of the rapid temporal survey. Mon. Not. R. Astron. Soc. 413, 2696–2708 (2011). https://doi.org/10.1111/j.1365-2966.2011.18345.x Bellet, A., Habrard, A., Sebban, M.: Metric learning. Synth. Lect. Artif. Intell. Mach. Learn. 9(1), 1–151 (2015) Blomme, J., Sarro, L.M., O’Donovan, F.T., et al.: Improved methodology for the automated classification of periodic variable stars. Mon. Not. R. Astron. Soc. 418, 96–106 (2011). https://doi.org/10.1111/j.1365-2966.2011.19466.x Cassisi, C., Montalto, P., Aliotta, M., et al.: Similarity measures and dimensionality reduction techniques for time series data mining. In: Advances in Data Mining Knowledge Discovery and Applications. InTech (2012) Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15 (2009) Chen, Y., Keogh, E., Hu, B., et al.: The UCR Time Series Classification Archive (2015) Dalitz, C.: Reject options and confidence measures for knn classifiers. Schriftenreihe des Fachbereichs Elektrotechnik und Informatik Hochschule Niederrhein 8, 16–38 (2009) Deb, S., Singh, H.P.: Light curve analysis of variable stars using Fourier decomposition and principal component analysis. Astron. Astrophys. 507, 1729–1737 (2009). https://doi.org/10.1051/0004-6361/200912851 Debosscher, J.: Automated classification of variable stars: application to the OGLE and CoRoT databases. PhD thesis, Institute of Astronomy, Katholieke Universiteit Leuven, Belgium (2009) Ding, S., Dennis Cook, R.: Matrix variate regressions and envelope models. J. R. Stat. Soc., Ser. B, Stat. Methodol. 80(2), 387–408 (2018) Dubath, P., Rimoldini, L., Süveges, M., et al.: Random forest automated supervised classification of Hipparcos periodic variable stars. Mon. Not. R. Astron. Soc. 414, 2602–2617 (2011). https://doi.org/10.1111/j.1365-2966.2011.18575.x Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012) Eyer, L., Blake, C.: Automated classification of variable stars for all-sky automated survey 1–2 data. Mon. Not. R. Astron. Soc. 358, 30–38 (2005). https://doi.org/10.1111/j.1365-2966.2005.08651.x Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006) Friedman, J.H.: A variable span smoother. Technical report, Stanford Univ CA lab for computational statistics (1984) Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011) Fulcher, B.D., Little, M.A., Jones, N.S.: Highly comparative time-series analysis: the empirical structure of time series and their methods. J. R. Soc. Interface 10(83), 20130048 (2013) Gagniuc, P.A.: Markov Chains: From Theory to Implementation and Experimentation. Wiley, New York (2017) Grabocka, J., Nanopoulos, A., Schmidt-Thieme, L.: Invariant time-series classification. In: Machine Learning and Knowledge Discovery in Databases, pp. 725–740. Springer, Berlin (2012) Graham, M.J., Djorgovski, S.G., Mahabal, A.A., Donalek, C., Drake, A.J.: Machine-assisted discovery of relationships in astronomy. Mon. Not. R. Astron. Soc. 431, 2371–2384 (2013). https://doi.org/10.1093/mnras/stt329 Graham, M.J., Drake, A.J., Djorgovski, S.G., et al.: A comparison of period finding algorithms. Mon. Not. R. Astron. Soc. 434, 3423–3444 (2013). https://doi.org/10.1093/mnras/stt1264 Haber, R., Rangarajan, A., Peter, A.M.: Discriminative interpolation for classification of functional data. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 20–36. Springer (2015) Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, vol. 2. Springer, Berlin (2009) Helfer, E., Smith, B., Haber, R., Peter, A.: Statistical analysis of functional data. Technical report, Florida Institute of Technology, (2015) Hinners, T.A., Tat, K., Thorp, R.: Machine learning techniques for stellar light curve classification. Astron. J. 156, 7 (2018). https://doi.org/10.3847/1538-3881/aac16d Iverson, K.E.: A programming language. In: Proceedings of the May 1–3, 1962, Spring Joint Computer Conference, pp. 345–351. ACM (1962) Johnston, K.B., Caballero-Nieves, S.M., Peter, A.M., Petit, V., Haber, R.: JVarStar: variable star analysis library. Astrophysics Source Code Library (2019) Johnston, K.B., Haber, R.: OCD: O’Connell Effect Detector using push-pull learning. Astrophysics Source Code Library (2019) Johnston, K.B., Oluseyi, H.M.: Generation of a supervised classification algorithm for time-series variable stars with an application to the LINEAR dataset. New Astron. 52, 35–47 (2017). https://doi.org/10.1016/j.newast.2016.10.004 Johnston, K.B., Peter, A.M.: Variable star signature classification using slotted symbolic Markov modeling. New Astron. 50, 1–11 (2017). https://doi.org/10.1016/j.newast.2016.06.001 Kirk, B., Conroy, K., Prša, A., et al.: Kepler eclipsing binary stars. VII. The catalog of eclipsing binaries found in the entire Kepler data set. Astron. J. 151, 68 (2016). https://doi.org/10.3847/0004-6256/151/3/68 Knote, M.F., Kaitchuck, R.H., Berrington, R.C.: Observations and preliminary modeling of the light curves of eclipsing binary systems NSVS 7322420 and NSVS 5726288. J. Am. Assoc. Var. Star Obs. 47 (2019) Lomb, N.R.: Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462 (1976). https://doi.org/10.1007/BF00648343 Mahabal, A., Sheth, K., Gieseke, F., et al.: Deep-learnt classification of light curves. In: Computational Intelligence (SSCI), 2017 IEEE Symposium Series on, pp. 1–8. IEEE (2017) Masci, F.J., Hoffman, D.I., Grillmair, C.J., Cutri, R.M.: Automated classification of periodic variable stars detected by the wide-field infrared survey explorer. Astron. J. 148, 21 (2014). https://doi.org/10.1088/0004-6256/148/1/21 McCartney, S.A.: A 2-D model for the O’Connell effect in W Ursae Majoris systems. PhD thesis, The University Of Oklahoma (1999) McWhirter, P.R., Steele, I.A., Al-Jumeily, D., Hussain, A., Vellasco, M.M.B.R.: The classification of periodic light curves from non-survey optimized observational data through automated extraction of phase-based visual features. In: Neural Networks (IJCNN), 2017 International Joint Conference on, pp. 3058–3065. IEEE (2017) Miller, A.A., Bloom, J.S., Richards, J.W., et al.: A machine-learning method to infer fundamental stellar parameters from photometric light curves. Astrophys. J. 798, 122 (2015). https://doi.org/10.1088/0004-637X/798/2/122 Modak, S., Chattopadhyay, T., Chattopadhyay, A.K.: Unsupervised classification of eclipsing binary light curves through k-medoids clustering. arXiv e-prints (2018). arxiv:1801.09406 Naul, B., Bloom, J.S., Pérez, F., van der Walt, S.: A recurrent neural network for classification of unevenly sampled variable stars. Nat. Astron. 2, 151–155 (2018). https://doi.org/10.1038/s41550-017-0321-z Ngeow, C., Lucchini, S., Kanbur, S., et al.: Preliminary analysis of ULPC light curves using Fourier decomposition technique. In: Space Science and Communication (IconSpace), 2013 IEEE International Conference on, pp. 7–12. IEEE (2013) Nun, I., Protopapas, P., Sim, B., Zhu, M., et al.: FATS: feature analysis for time series. arXiv e-prints (2015). arxiv:1506.00010 O’Connell, D.J.K.: The so-called periastron effect in eclipsing binaries (summary). Mon. Not. R. Astron. Soc. 111, 642 (1951). https://doi.org/10.1093/mnras/111.6.642 Palaversa, L., Ivezić, Ž., Eyer, L., et al.: Exploring the variable sky with LINEAR. III. Classification of periodic light curves. Astron. J. 146, 101 (2013). https://doi.org/10.1088/0004-6256/146/4/101 Park, H., Jeon, M., Rosen, J.B.: Lower dimensional representation of text data based on centroids and least squares. BIT Numer. Math. 43(2), 427–448 (2003) Park, M.J., Cho, S.S.: Functional data classification of variable stars. Commun. Stat. Appl. Meth. 20(4), 271–281 (2013) Pearson, K.A., Palafox, L., Griffith, C.A.: Searching for exoplanets using artificial intelligence. Mon. Not. R. Astron. Soc. 474(1), 478–491 (2017) Pichara, K., Protopapas, P.: Automatic classification of variable stars in catalogs with missing data, Astrophys. J. 777, 83 (2013). https://doi.org/10.1088/0004-637X/777/2/83 Pichara, K., Protopapas, P., Kim, D.-W., et al.: An improved quasar detection method in EROS-2 and MACHO LMC data sets. Mon. Not. R. Astron. Soc. 427, 1284–1297 (2012). https://doi.org/10.1111/j.1365-2966.2012.22061.x Prša, A., Zwitter, T.: A computational guide to physics of eclipsing binaries. I. Demonstrations and perspectives. Astrophys. J. 628, 426–438 (2005). https://doi.org/10.1086/430591 Richards, J.W., Starr, D.L., Butler, N.R., et al.: On machine-learned classification of variable stars with sparse and noisy time-series data. Astrophys. J. 733, 10 (2011). https://doi.org/10.1088/0004-637X/733/1/10 Richards, J.W., Starr, D.L., Miller, A.A., et al.: Construction of a calibrated probabilistic classification catalog: application to 50k variable sources in the all-sky automated survey. Astrophys. J. Suppl. Ser. 203, 32 (2012). https://doi.org/10.1088/0067-0049/203/2/32 Samus’, N.N., Kazarovets, E.V., Durlevich, O.V., et al.: General catalogue of variable stars: version GCVS 5.1. Astron. Rep. 61, 80–88 (2017). https://doi.org/10.1134/S1063772917010085 Scargle, J.D.: Studies in astronomical time series analysis. II—statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1982). https://doi.org/10.1086/160554 Sesar, B., Stuart, J.S., Ivezić, Ž., et al.: Exploring the variable sky with LINEAR. I. Photometric recalibration with the sloan digital sky survey. Astron. J. 142, 190 (2011). https://doi.org/10.1088/0004-6256/142/6/190 Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 1910–1917. IEEE (2012) Stokes, G.H., Evans, J.B., Viggh, H.E.M., Shelly, F.C., Pearce, E.C.: Lincoln near-Earth asteroid program (LINEAR). Icarus 148, 21–28 (2000). https://doi.org/10.1006/icar.2000.6493 Templeton, M.: Time-series analysis of variable star data. J. Am. Assoc. Var. Star Obs. 32, 41–54 (2004) Thompson, S.E., Mullally, F., Coughlin, J., Christiansen, J.L., Henze, C.E., Haas, M.R., Burke, C.J.: A machine learning technique to identify transit shaped signals. Astrophys. J. 812(1), 46 (2015) Valenzuela, L., Pichara, K.: Unsupervised classification of variable stars. Mon. Not. R. Astron. Soc. 474, 3259–3272 (2018). https://doi.org/10.1093/mnras/stx2913 VanderPlas, J.T., Ivezić, Ž.: Periodograms for multiband astronomical time series. Astrophys. J. 812(1), 18 (2015). https://doi.org/10.1088/0004-637X/812/1/18 Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009) http://dl.acm.org/citation.cfm?id=1577069.1577078. ISSN 1532-4435 Wilsey, N.J., Beaky, M.M.: Revisiting the O’Connell effect in eclipsing binary systems. Soc. Astron. Sci. Ann. Symp. 28, 107 (2009) Wilson, R.E., Devinney, E.J.: Realization of accurate close-binary light curves: application to MR cygni. Astrophys. J. 166, 605 (1971). https://doi.org/10.1086/150986 Zboril, M., Djurasevic, G.: Progress report on the monitoring active late-type stars in 2005/2006 and the analysis of V523 cas. Serb. Astron. J. 173, 89 (2006). https://doi.org/10.2298/SAJ0673089Z