Abstract: Most time-series models assume that the data come from observations that are equally spaced in time. However, this assumption does not hold in many diverse scientific fields, such as astronomy, finance, and climatology, among others. There are some techniques that fit unequally spaced time series, such as the continuous-time autoregressive moving average (CARMA) processes. These models are defined as the solution of a stochastic differential equation. It is not uncommon in astronomical time series, that the time gaps between observations are large. Therefore, an alternative suitable approach to modeling astronomical time series with large gaps between observations should be based on the solution of a difference equation of a discrete process. In this work we propose a novel model to fit irregular time series called the complex irregular autoregressive (CIAR) model that is represented directly as a discrete-time process. We show that the model is weakly stationary and that it can be represented as a state-space system, allowing efficient maximum likelihood estimation based on the Kalman recursions. Furthermore, we show via Monte Carlo simulations that the finite sample performance of the parameter estimation is accurate. The proposed methodology is applied to light curves from periodic variable stars, illustrating how the model can be implemented to detect poor adjustment of the harmonic model. This can occur when the period has not been accurately estimated or when the variable stars are multiperiodic. Last, we show how the CIAR model, through its state space representation, allows unobserved measurements to be forecast.

Abstract: We propose a stochastic methodology for risk assessment of a large earthquake when a long time has elapsed from the last large seismic event. We state an approximate probability distribution for the occurrence time of the next large earthquake, by knowing that the last large seismic event occurred a long time ago. We prove that, under reasonable conditions, such a distribution is exponential with a rate depending on the asymptotic slope of the cumulative intensity function corresponding to a nonhomogeneous Poisson process. As it is not possible to obtain an empirical cumulative distribution function of the waiting time for the next large earthquake, an estimator of its cumulative distribution function based on existing data is derived. We conduct a simulation study for detecting scenario in which the proposed methodology would perform well. Finally, a real-world data analysis is carried out to illustrate its potential applications, including a homogeneity test for the times between earthquakes.

Abstract: The Birnbaum-Saunders (BS) distribution is an asymmetric probability model that is receiving considerable attention. In this article, we propose a methodology based on a new class of BS models generated from the Student-t distribution. We obtain a recurrence relationship for a BS distribution based on a nonlinear skew-t distribution. Model parameters estimators are obtained by means of the maximum likelihood method, which are evaluated by Monte Carlo simulations. We illustrate the obtained results by analyzing two real data sets. These data analyses allow the adequacy of the proposed model to be shown and discussed by applying model selection tools.

Abstract: Context. The transiting exoplanetary system WASP-174 was reported to be composed by a main-sequence F star (V = 11.8 mag) and a giant planet, WASP-174b (orbital period P-orb = 4.23 days). However only an upper limit was placed on the planet mass (<1.3 M-Jup), and a highly uncertain planetary radius (0.7-1.7 R-Jup) was determined.Aims. We aim to better characterise both the star and the planet and precisely measure their orbital and physical parameters.Methods. In order to constrain the mass of the planet, we obtained new measurements of the radial velocity of the star and joined them with those from the discovery paper. Photometric data from the HATSouth survey and new multi-band, high-quality (precision reached up to 0.37 mmag) photometric follow-up observations of transit events were acquired and analysed for getting accurate photometric parameters. We fit the model to all the observations, including data from the TESS space telescope, in two different modes: incorporating the stellar isochrones into the fit, and using an empirical method to get the stellar parameters. The two modes resulted to be consistent with each other to within 2<sigma>.Results. We confirm the grazing nature of the WASP-174b transits with a confidence level greater than 5 sigma, which is also corroborated by simultaneously observing the transit through four optical bands and noting how the transit depth changes due to the limb-darkening effect. We estimate that approximate to 76% of the disk of the planet actually eclipses the parent star at mid-transit of its transit events. We find that WASP-174b is a highly-inflated hot giant planet with a mass of M-p = 0.330 +/- 0.091 M-Jup and a radius of R-p = 1.435 +/- 0.050 R-Jup, and is therefore a good target for transmission-spectroscopy observations. With a density of rho (p) = 0.135 +/- 0.042 g cm(-3), it is amongst the lowest-density planets ever discovered with precisely measured mass and radius.

Abstract: Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.

Abstract: We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with >= 6 g-band or >= 6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online)..

Abstract: The Birnbaum-Saunders (BS) distribution is a model that is receiving considerable attention due to its good properties. We provide some results on moments of a reparameterized version of the BS distribution and a generation method of random numbers from this distribution. In addition, we propose estimation and inference for the mentioned parameterization based on maximum likelihood, moment, modified moment and generalized moment methods. By means of a Monte Carlo simulation study, we evaluate the performance of the proposed estimators. We discuss applications of the reparameterized BS distribution from different scientific fields and analyze two real-world data sets to illustrate our results. The simulated and real data are analyzed by using the R software.

Abstract: Context. Long-period transiting planets provide the opportunity to better understand the formation and evolution of planetary systems. Their atmospheric properties remain largely unaltered by tidal or radiative effects of the host star, and their orbital arrangement reflects a different and less extreme migrational history compared to close-in objects. The sample of long-period exoplanets with well-determined masses and radii is still limited, but a growing number of long-period objects reveal themselves in the Transiting Exoplanet Survey Satellite (TESS) data.
Aims. Our goal is to vet and confirm single-transit planet candidates detected in the TESS space-based photometric data through spectroscopic and photometric follow-up observations with ground-based instruments.
Methods. We used high-resolution spectrographs to confirm the planetary nature of the transiting candidates and measure their masses. We also used the Next Generation Transit Survey (NGTS) to photometrically monitor the candidates in order to observe additional transits. Using a joint modeling of the light curves and radial velocities, we computed the orbital parameters of the system and were able to precisely measure the mass and radius of the transiting planets.
Results. We report the discovery of two massive, warm Jupiter-size planets, one orbiting the F8-type star TOI-5153 and the other orbiting the G1-type star NGTS-20 (=TOI-5152). From our spectroscopic analysis, both stars are metal rich with a metallicity of 0.12 and 0.15, respectively. Only TOI-5153 presents a second transit in the TESS extended mission data, but NGTS observed NGTS-20 as part of its mono-transit follow-up program and detected two additional transits. Follow-up high-resolution spectroscopic observations were carried out with CORALIE, CHIRON, FEROS, and HARPS. TOI-5153 hosts a planet with a period of 20.33 days, a planetary mass of 3.26(-0.17)(+0.18) Jupiter masses (M-j), a radius of 1.06(-0.04)(+0.04)R(J), and an orbital eccentricity of 0.091(-0.02)(6)(+0.024). NGTS-20 b is a 2.98(-)(0.)(15)(+0.16) M-J planet with a radius of 1.07(-0.0)(4)(+0.04) R-J on an eccentric (0.432(-0.023)(+0.023)) orbit with an orbital period of 54.19 days. Both planets are metal enriched and their heavy element content is in line with the previously reported mass-metallicity relation for gas giants.
Conclusions. Both warm Jupiters orbit moderately bright host stars, making these objects valuable targets for follow-up studies of the planetary atmosphere and measurement of the spin-orbit angle of the system.