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@article{Forster_2021,
	doi = {10.3847/1538-3881/abe9bc},
	url = {https://doi.org/10.3847/1538-3881/abe9bc},
	year = 2021,
	month = {apr},
	publisher = {American Astronomical Society},
	volume = {161},
	number = {5},
	pages = {242},
	author = {F. Förster and G. Cabrera-Vives and E. Castillo-Navarrete and P. A. Est{\'{e}}vez and P. S{\'{a}}nchez-S{\'{a}}ez and J. Arredondo and F. E. Bauer and R. Carrasco-Davis and M. Catelan and F. Elorrieta and S. Eyheramendy and P. Huijse and G. Pignata and E. Reyes and I. Reyes and D. Rodr{\'{\i}}guez-Mancini and D. Ruz-Mieres and C. Valenzuela and I. {\'{A}}lvarez-Maldonado and N. Astorga and J. Borissova and A. Clocchiatti and D. De Cicco and C. Donoso-Oliva and L. Hern{\'{a}}ndez-Garc{\'{\i}}a and M. J. Graham and A. Jord{\'{a}}n and R. Kurtev and A. Mahabal and J. C. Maureira and A. Mu{\~{n}}oz-Arancibia and R. Molina-Ferreiro and A. Moya and W. Palma and M. P{\'{e}}rez-Carrasco and P. Protopapas and M. Romero and L. Sabatini-Gacitua and A. S{\'{a}}nchez and J. San Mart{\'{\i}}n and C. Sep{\'{u}}lveda-Cobo and E. Vera and J. R. Vergara},
	title = {The Automatic Learning for the Rapid Classification of Events ({ALeRCE}) Alert Broker},
	journal = {The Astronomical Journal},
	abstract = {We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve–based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see https://alerce.science). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 108 alerts, the stamp classification of 3.4 × 107 objects, the light-curve classification of 1.1 × 106 objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.}
}

@article{Elorrieta_2021,
    author = {Elorrieta, Felipe and Eyheramendy, Susana and Palma, Wilfredo and Ojeda, Cesar},
    title = "{A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {505},
    number = {1},
    pages = {1105-1116},
    year = {2021},
    month = {05},
    abstract = "{In several disciplines, it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one passband. Some examples are Pan-STARRS, ZTF, and also the LSST. However, current commonly used time series models that estimate the time dependence in astronomical light curves consider the information of each band separately (e.g, CIAR, IAR, and CARMA models) disregarding the dependence that might exist between different passbands. In this paper, we propose a novel bivariate model for irregularly sampled time series, called the Bivariate Irregular Autoregressive (BIAR) model. The BIAR model assumes an autoregressive structure on each time series; it is stationary, and it allows to estimate the autocorrelation, the cross-correlation and the contemporary correlation between two unequally spaced time series. We implemented the BIAR model on light curves, in the g and r bands, obtained from the ZTF alerts processed by the ALeRCE broker. We show that if the light curves of the two bands are highly correlated, the model has more accurate forecast and prediction using the bivariate model than a similar method that uses only univariate information. Further, the estimated parameters of the BIAR are useful to characterize long-period variable stars and to distinguish between classes of stochastic objects, providing promising features that can be used for classification purposes.}",
    issn = {0035-8711},
    doi = {10.1093/mnras/stab1216},
    url = {https://doi.org/10.1093/mnras/stab1216},
    eprint = {https://academic.oup.com/mnras/article-pdf/505/1/1105/38391762/stab1216.pdf},
}


@article{Eyheramendy_2018,
    author = {Eyheramendy, Susana and Elorrieta, Felipe and Palma, Wilfredo},
    title = "{An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {481},
    number = {4},
    pages = {4311-4322},
    year = {2018},
    month = {09},
    abstract = "{Time series observations are ubiquitous in astronomy and are generated, for example, to distinguish between different types of supernovae to detect and characterize extrasolar planets and to classify variable stars. These time series are usually modelled using a parametric and/or physical model that assumes independent and homoscedastic errors, but in many cases, these assumptions are not accurate and there remains a temporal dependence structure on the errors. This can occur, for example, when the proposed model cannot explain all the variability of the data or when the parameters of the model are not properly estimated. In this work, we define an autoregressive model for irregular discrete-time series based on the discrete time representation of the continuous autoregressive model of order 1. We show that the model is ergodic and stationary. We further propose a maximum likelihood estimation procedure and assess the finite sample performance by Monte Carlo simulations. We implement the model on real and simulated data from Gaussian as well as other distributions, showing that the model can flexibly adapt to different data distributions. We apply the irregular autoregressive model to the residuals of a transit of an extrasolar planet to illustrate errors that remain with temporal structure. We also apply this model to residuals of an harmonic fit of light curves from variable stars to illustrate how the model can be used to detect incorrect parameter estimation.}",
    issn = {0035-8711},
    doi = {10.1093/mnras/sty2487},
    url = {https://doi.org/10.1093/mnras/sty2487},
    eprint = {https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf},
}



@article{Elorrieta_2019,
	author = {{Elorrieta, F} and {Eyheramendy, S} and {Palma, W}},
	title = {Discrete-time autoregressive model for unequally spaced time-series observations},
	DOI= "10.1051/0004-6361/201935560",
	url= "https://doi.org/10.1051/0004-6361/201935560",
	journal = {A&A},
	year = 2019,
	volume = 627,
	pages = "A120",
}

@article{Jordan_etal13,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {http://adsabs.harvard.edu/abs/2013ApJ...778..184J},
Archiveprefix = {arXiv},
Author = {{Jord{\'a}n}, A. and {Espinoza}, N. and {Rabus}, M. and {Eyheramendy}, S. and {Sing}, D.~K. and {D{\'e}sert}, J.-M. and {Bakos}, G.~{\'A}. and {Fortney}, J.~J. and {L{\'o}pez-Morales}, M. and {Maxted}, P.~F.~L. and {Triaud}, A.~H.~M.~J. and {Szentgyorgyi}, A.},
Doi = {10.1088/0004-637X/778/2/184},
Eid = {184},
Eprint = {1310.6048},
Journal = {The Astrophysical Journal},
Keywords = {planetary systems, planets and satellites: atmospheres, techniques: spectroscopic},
Month = dec,
Pages = {184},
Primaryclass = {astro-ph.EP},
Title = {{A Ground-based Optical Transmission Spectrum of WASP-6b}},
Volume = 778,
Year = 2013}

@article{Lira_2015,
    author = {Lira, P. and Arévalo, P. and Uttley, P. and McHardy, I. M. M. and Videla, L.},
    title = "{Long-term monitoring of the archetype Seyfert galaxy MCG-6-30-15: X-ray, optical and near-IR variability of the corona, disc and torus}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {454},
    number = {1},
    pages = {368-379},
    year = {2015},
    month = {09},
    abstract = "{We present long-term monitoring of MCG-6-30-15 in X-rays, optical and near-IR wavelengths, collected over 5 yr of monitoring. We determine the power spectrum density of all the observed bands and show that after taking into account the host contamination similar power is observed in the optical and near-IR bands. There is evidence for a correlation between the light curves of the X-ray photon flux and the optical B band, but it is not possible to determine a lag with certainty, with the most likely value being around 0 d. Strong correlation is seen between the optical and near-IR bands. Cross-correlation analysis shows some complex probability distributions and lags that range from 10 to 20 d, with the near-IR following the optical variations. Filtering the light curves in frequency space shows that the strongest correlations are those corresponding to the shortest time-scales. We discuss the nature of the X-ray variability and conclude that this is intrinsic and cannot be accounted for by absorption episodes due to material intervening in the line of sight. It is also found that the lags agree with the relation τ ∝ λ4/3, as expected for an optically thick geometrically thin accretion disc, although for a larger disc than that predicted by the estimated black hole mass and accretion rate in MCG-6-30-15. The cross-correlation analysis suggests that the torus is located at ∼20 light-days from the central source and at most at ∼50 light-days from the central region. This implies an active galactic nucleus bolometric luminosity of ∼3 × 1043 erg s−1 cm−2.}",
    issn = {0035-8711},
    doi = {10.1093/mnras/stv1945},
    url = {https://doi.org/10.1093/mnras/stv1945}
}

