Physical Properties of 29 sdB+dM Eclipsing Binaries in Zwicky Transient Facility

The development of large-scale time-domain surveys provides an opportunity to study the physical properties as well as the evolutionary scenario of B-type subdwarfs (sdB) and M-type dwarfs (dM). Here, we obtained 33 sdB+dM eclipsing binaries based on the Zwicky Transient Facility (ZTF) light curves and {\sl Gaia} early data release 3 (EDR3) parallaxes. By using the PHOEBE code for light curve analysis, we obtain probability distributions for parameters of 29 sdB+dM. $R_1$, $R_2$, and $i$ are well determined, and the average uncertainty of mass ratio $q$ is 0.08. Our parameters are in good agreement with previous works if a typical mass of sdB is assumed. Based on parameters of 29 sdB+dM, we find that both the mass ratio $q$ and the companion's radius $R_2$ decrease with the shortening of the orbital period. For the three sdB+dMs with orbital periods less than 0.075 days, their companions are all brown dwarfs. The masses and radii of the companions satisfy the mass--radius relation for low-mass stars and brown dwarfs. Companions with radii between $0.12R_\odot$ and $0.15R_\odot$ seem to be missing in the observations. As more short-period sdB+dM eclipsing binaries are discovered and classified in the future with ZTF and {\sl Gaia}, we will have more information to constrain the evolutionary ending of sdB+dM.

Light curve analysis of the eclipsing binary system V781 Tau

V781 Tau is one of W UMa eclipsing binary systems whose orbital period is 0.34 days. The 0.7-meter telescope with CCD photometric system in B and V filters was conducted at the Regional Observatory for the Public, Chachoengsao, Thailand during December 2018, UT. The Wilson-Devinney Technique was used for calculating the physical properties of V781 Tau. The results showed the inclination of their orbital is 66.140°±0.14. The effective temperature of the primary and secondary star is 6,060 and 5,881 K, respectively and the degree of contact is 4.38 %

On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAEt) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAEt). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAEt embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the F1 score in the task of classifying exoplanets. Moreover, the S-VRAEt model increases all the advantages of VRAEt, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.

Cosmological models of generalized ghost pilgrim dark energy (GGPDE) in the gravitation theory of Saez–Ballester

In this paper, we study the mechanism of the cosmic model in the presence of generalized ghost pilgrim dark energy (GGPDE) and matter in locally rotationally symmetric (LRS) Bianchi type-I space-time by the utilization of new holographic DE in Saez–Ballester theory. Here, we discuss all the data for three scenarios, the first is supernovae type-Ia union data, the second is SN Ia data in combination with baryon acoustic oscillation and cosmic microwave background observations and the third is a combination with observational Hubble data and joint light-curve analysis observations. From this, we get a model of our universe, where transit state exists from deceleration to acceleration phase. Here, we have observed that the results yielded by cosmological parameters like [Formula: see text] (energy density), equation of state [Formula: see text], squared speed of sound [Formula: see text] and [Formula: see text]–[Formula: see text] are compatible with the recent observations. The [Formula: see text]–[Formula: see text] trajectories lie in both thawing and freezing regions and the correspondence of the quintessence field with GGPDE is also discussed. Some physical aspects of the GGPDE models are mainly highlighted.

Artificial neural network spectral light curve template for type Ia supernovae and its cosmological constraints

The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate parameter. After training and testing the neural network, the SED sequence could give both the spectrum with wavelength range from 3000 Åto 8000 Åand the light curve with phase from 20 days before to 50 days after the maximum luminosity for the supernovae with different colors and decline rates. Therefore, we call this the Artificial Neural Network Spectral Light Curve Template (ANNSLCT) model. We retrain the Joint Light-curve Analysis (JLA) supernova sample by using the ANNSLCT model and obtain the parameters for each supernova to make a constraint on the cosmological [Formula: see text]CDM model. We find that the best fitting values of these parameters are very close to those from the JLA sample trained with the Spectral Adaptive Lightcurve Template 2 (SALT2) model. It is expectable that the ANNSLCT model has potential to analyze more SN Ia multi-color light curves measured in future observation projects.

The models of transit cosmology along with observational constriction in f(Q,T) gravity

Xu et al. (Eur. Phys. J. C 79 (2019) 708) have anticipated the theory of Gravity. The modified study of [Formula: see text] is elucidated here as Cosmological model. In it the action holds a role as a capricious arbitrary function [Formula: see text]. At this juncture [Formula: see text] functions as non-metricity and for matter fluid, [Formula: see text] outlines as energy-momentum tensor. The function [Formula: see text] quadratic in [Formula: see text] and linear in [Formula: see text] as [Formula: see text] has been taken as our research in which [Formula: see text], [Formula: see text] and [Formula: see text] stand as model parameters, induced by [Formula: see text] gravity. A range of cosmological parameters have been attained by us such as in Universe viz. Hubble parameter [Formula: see text], Friedmann–Lemaitre–Robertson–Walker (FLRW), deceleration parameter [Formula: see text], etc. in terms of scale-factor and in terms of redshift [Formula: see text] by confining to the law of energy-conservation. The fittest values of the model parameters have been acquired by us as the observational constrictions on the model, by utilizing the accessible data sets like Hubble data sets [Formula: see text], union 2.1 compilation of SNe Ia data sets and Joint Light Curve Analysis (JLA) data sets. We have applied [Formula: see text]-test formula. The values of various observational parameters have been premeditated by us viz. [Formula: see text], [Formula: see text], [Formula: see text] and state finder parameters [Formula: see text]. They are absolutely very close to the standard cosmological models. It has also been observed by us that the deceleration parameter [Formula: see text] exhibits signature-flipping (transition) point within the range [Formula: see text]. It is observed that it changes its phase from decelerated to accelerated expanding universe with equation of state (EoS) [Formula: see text] for [Formula: see text].