MG1-1995959: An Eclipsing Binary Star with a Pronounced O’Connell Effect

The eclipsing binary system 2M 1938+4603 consists of a pulsating hot subdwarf B star and a cool M dwarf companion in an effectively circular three-hour orbit. The light curve shows both primary and secondary eclipses, along with a strong reflection effect from the cool companion. Here we present constraints on the component masses and eccentricity derived from the R{\o}mer delay of the secondary eclipse. Using six months of publicly-available Kepler photometry obtained in Short Cadence mode, we fit model profiles to the primary and secondary eclipses to measure their centroid values. We find that the secondary eclipse arrives on average 2.06 +/- 0.12 s after the midpoint between primary eclipses. Under the assumption of a circular orbit, we calculate from this time delay a mass ratio of q = 0.2691 +/- 0.0018 and individual masses of M_sd = 0.372 +/- 0.024 Msun and M_c = 0.1002 +/- 0.0065 Msun for the sdB and M dwarf, respectively. These results differ slightly from those of a previously-published light curve modeling solution; this difference, however, may be reconciled with a very small eccentricity, e cos \omega\ ~ 0.00004. We also report an orbital period decrease of P-dot = (-1.23 +/- 0.07) x 10^-10.

The effectiveness of the joint estimation (JE) outlier detection method as a process control technique for short autocorrelated time series is investigated and compared with exponentially weighted moving average (EWMA). The research goal is to determine the effectiveness of the method for detecting out‐of‐control observations when they are the last observation in a short autocorrelated time series. This is an important problem because detecting an outlier in the period when it occurs, rather than several periods after it occurs, will preclude the production of more defective units. Two cases are investigated: short simulated time series when normality is assumed, and short real time series when the assumption is violated. The results show that JE is effective for short time series, particularly for autoregressive series when normality is violated. Joint estimation is also effective for moving average series under the normality assumption and less effective when the assumption is violated. In all cases, JE is found to be more effective than EWMA.

Variability observed in photometric light curves of late-type stars (on time-scales longer than a day) is a dominant noise source in exoplanet surveys and results predominantly from surface manifestations of stellar magnetic activity, namely faculae and spots. The implementation of faculae in light-curve models is an open problem, with scaling typically based on spectra equivalent to hot stellar atmospheres or assuming a solar-derived facular contrast. We modelled rotational (single period) light curves of active G2, K0, M0, and M2 stars, with Sun-like surface distributions and realistic limb-dependent contrasts for faculae and spots. The sensitivity of light-curve variability to changes in model parameters such as stellar inclination, feature area coverage, spot temperature, facular region magnetic flux density, and active band latitudes is explored. For our light-curve modelling approach we used actress, a geometrically accurate model for stellar variability. actress generates two-sphere maps representing stellar surfaces and populates them with user-prescribed spot and facular region distributions. From this, light curves can be calculated at any inclination. Quiet star limb darkening and limb-dependent facular contrasts were derived from MURaM 3D magnetoconvection simulations using ATLAS9. 1D stellar atmosphere models were used for the spot contrasts. We applied actress in Monte Carlo simulations, calculating light-curve variability amplitudes in the Kepler band. We found that, for a given spectral type and stellar inclination, spot temperature and spot area coverage have the largest effect on variability of all simulation parameters. For a spot coverage of $1{{\ \rm per\ cent}}$, the typical variability of a solar-type star is around 2 parts per thousand. The presence of faculae clearly affects the mean brightness and light-curve shape, but has relatively little influence on the variability.

It is known that the light curve of a prompt emission of cosmic gamma-ray bursts consists of separate pulses. Moreover, in a real light curve these pulses often overlap so that it becomes impossible to study separately the pulses in each light curve. However, spectral analysis, in particular, the power spectral density (PSD) of light curves, bears the imprints of both the pulse shape and the distribution of pulses in a particular light curve. This paper is devoted to solving the problem of finding the distribution of the parameters of these pulses by comparing the PSD of model light curves and the PSD of an ensemble of real light curves. The problem is solved by modeling light curves composed of a set of pulses whose parameters have some distributions. Several analytic forms of pulses are considered: bilateral exponent, log-normal form, and a function known in the literature as Fast Rise Exponential Decay (FRED). The light curves are generated using a single type of pulses, assuming that there is some distribution for each pulse parameter. The influence of the analytic pulse shape and the distribution of pulse parameters on the PSD of the ensemble of modeled light curves is investigated. It is shown that only the duration and symmetry parameters affect the appearance of the PSD of the ensemble of light curves composed of these pulses. The distributions of the pulse parameters that produce the power spectral density of the ensemble of modeled light curves coinciding with the power spectral density of the ensemble of real light curves consisting of these pulses have been found. It is shown that for this purpose it is necessary to introduce a dependency between the parameters of duration and symmetry of the pulse.

BackgroundInferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.ResultsHere we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.ConclusionsOur study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

In this work, the optical radiation of the young pre-cataclysmic variable (PV) SDSS J162256 was investigated. Spectroscopic observations were carried out at BTA SAO RAS, photometric observations were carried out at RTT-150 telescope. Numerical modeling of theoretical light curves and spectra was done. The measured sets of radial velocities were analyzed taking into account the possible influence of reflection effects. The fundamental parameters of SDSS J162256 were determined based on a comprehensive analysis of observational data. It was shown that the previously proposed method for determining the masses of the PV components by modeling of the radial-velocity curves is effective only for systems with significant reflection effects.

Absolute parameters of eclipsing binaries in Southern Hemisphere sky – II: QY Tel

We analyze the dynamic quality of the R– R interbeat intervals of electrocardiographic signals from healthy people and from patients with premature ventricular contractions (PVCs) by applying different measure algorithms to standardised public domain data sets of heart rate variability. Our aim is to assess the utility of these algorithms for the above mentioned purposes. Long and short time series, 24 and 0.50 h respectively, of interbeat intervals of healthy and PVC subjects were compared with the aim of developing a fast method to investigate their temporal organization. Two different methods were used: power spectral analysis and the integral correlation method. Power spectral analysis has proven to be a powerful tool for detecting long-range correlations. If it is applied in a short time series, power spectra of healthy and PVC subjects show a similar behavior, which disqualifies power spectral analysis as a fast method to distinguish healthy from PVC subjects. The integral correlation method allows us to study the fractal properties of interbeat intervals of electrocardiographic signals. The cardiac activity of healthy and PVC people stems from dynamics of chaotic nature characterized by correlation dimensions d f equal to 3.40±0.50 and 5.00±0.80 for healthy and PVC subjects respectively. The methodology presented in this article bridges the gap between theoretical and experimental studies of non-linear phenomena. From our results we conclude that the minimum number of coupled differential equations to describe cardiac activity must be six and seven for healthy and PVC individuals respectively. From the present analysis we conclude that the correlation integral method is particularly suitable, in comparison with the power spectral analysis, for the early detection of arrhythmias on short time (0.5 h) series.

We present model light curves for accreting black hole candidates (BHCs) based on a recently proposed model for their spectro-temporal properties. According to this model, the observed light curves and aperiodic variability of BHCs are due to a series of soft photon injections at random (Poisson) intervals near the compact object and their reprocessing into hard radiation in an extended but nonuniform hot plasma corona surrounding the compact object. We argue that the majority of the timing characteristics of these light curves are due to the stochastic nature of the Comptonization process in the extended corona, whose properties, most notably its radial density dependence, are imprinted in them. We compute the corresponding power spectral densities (PSD), autocorrelation functions, time skewness of the light curves, and time lags between the light curves of the sources at different photon energies and compare our results to observation. Our model light curves compare well with observations, providing good fits to their overall morphology, as manifest by the autocorrelation and skewness functions. The lags and PSDs of the model light curves are also in good agreement with those observed (the model can even accommodate the presence of quasi-periodic oscillations). Finally, while most of the variability power resides at timescales approximately a few seconds, at the same time, the model allows also for shots of a few milliseconds in duration, in accordance with observation. We suggest that refinements of this type of model along with spectral and phase lag information can be used to probe the structure of this class of high-energy sources.

Divergence in perpendicular recurrence plot; quantification of dynamical divergence from short chaotic time series

This work utilizes established models of synchrotron-powered light curves for core-collapse supernovae in dense circumstellar environments, namely, type IIn and Ibn, to demonstrate the potential for detecting millimeter emission from these events. The progenitor types of these supernovae are still an open question, but using the synchrotron light curves as probes for the circumstellar environments could shed light on the mass-loss histories of the progenitors and discern between different theories. Observations in millimeter bands are particularly fruitful, as they probe regions at smaller radii and higher ambient densities, where centimeter emission tends to be self-absorbed. In our application of these light curves, we explore a diversity of progenitor types and mass-loss profiles to understand their effects on the light-curve shapes. Additionally, we fit model parameters to the 8 GHz light curve of type IIn supernova 2006jd and then create millimeter light curves using these parameters to show the possibility of detecting an early millimeter peak from such an event. We predict that next generation millimeter surveys will possess the capability to detect nearby and extreme events. However, there is a pressing need for millimeter follow-up of optically discovered interacting supernovae to more completely sample the true population.

Photometric analysis of the contact binary star V829 Hercules using light curves on three consecutive years

We propose a criterion to detect determinism in short time series. This criterion is based on the estimation of the parameter E2 defined by the averaged false neighbors method for analyzing time series [Cao, Physica D 110, 43 (1997)]. Using surrogate data testing with several chaotic and stochastic simulated time series, we show that the variation coefficient of E2 over a few values of the embedding dimension d defines a suitable statistic to detect determinism in short data sequences. This result holds for a time series generated by a high-dimensional chaotic system such as the Mackey-Glass one. Different decreasing lengths of the time series are included in the numerical experiments for both synthetic and real-world data. We also investigate the robustness of the criterion in the case of deterministic time series corrupted by additive noise.

We present an extension of the BEER model for eccentric binaries – eBEER, approximating the BEaming, Ellipsoidal, and Reflection effects by harmonic series of the Keplerian elements of their orbit. As such, it can be a tool for fast processing of light curves for detecting non-eclipsing eccentric binary systems. To validate the applicability of the eccentric model and its approximations, we applied eBEER to the Kepler light curves, identified a sample of bright non-eclipsing binary candidates, and followed three of them with the Wise observatory eShel spectrograph. After confirming the three systems are indeed radial velocity (RV) binaries, we fitted the light curves and the RV data with PHOEBE, a detailed numerical light curve and RV model, and showed that the PHOEBE derived parameters are similar to those obtained by the eBEER approximation.

Type II supernovae (SNe II) are the most frequently observed outcome of core-collapse explosions and provide a valuable window into the physical mechanisms governing the deaths of massive stars. However, estimates of explosion properties based on optical light-curve modeling often show tension with the predictions of modern neutrino-driven explosion models. In particular, when based on light curves from the explosions of red supergiants (RSGs) tied to specific stellar wind models, many SNe II are found to originate from low-mass progenitors yet exhibit unusually high explosion energies ( E K ), far exceeding theoretical predictions. In this study, we incorporate late-phase (nebular) spectroscopy to estimate the helium core mass of the progenitor ( M He core ), which serves as an additional constraint to break degeneracies in light-curve modeling. This approach is applied to a sample of 32 well-observed SNe II, using a light-curve model grid constructed from RSGs with arbitrarily stripped hydrogen-rich envelopes, rather than assuming a fixed wind model. Examining the resulting correlations among the physical parameters, we find that the tension between the observed M He core – E K and E K – M Ni relations and those predicted by neutrino-driven explosion models has significantly lessened by incorporating nebular spectroscopy in light-curve modeling. This study highlights the crucial role of nebular spectroscopy in interpreting SNe II observations and provides support to the neutrino-driven explosion mechanism as the dominant engine powering these events.