Related papers: Analyzing X-ray variability by Linear State Space …
We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly…
The Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double…
Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…
The analysis of photometric time series in the context of transiting planet surveys suffers from the presence of stellar signals, often dubbed "stellar noise". These signals, caused by stellar oscillations and granulation, can usually be…
We present a sample of SRG/eROSITA X-ray sources located in the eastern Galactic hemisphere (0<l<180 deg), with significant proper motions according to GAIA eDR3 measurements and whose extragalactic nature has been confirmed. The catalog…
We develop a framework for self-consistently extracting cosmological information from the clustering of tracers in redshift space, $\textit{without}$ relying on model-dependent templates to describe the baryon acoustic oscillation (BAO)…
We present results of recurrence analysis of 46 active galactic nuclei (AGN) using light curves from the 157-month catalog of the Swift Burst Alert Telescope (BAT) in the 14-150 keV band. We generate recurrence plots and compute recurrence…
We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density…
The paper introduces a novel topological method for prediction and modeling for a nonlinear time--series that exhibit recurring patterns. According to the model, global manifold of the reconstructed state--space can be approximated by a few…
Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dynamical relationships between two sets of attributes, for…
We integrate machine learning approaches with nonlinear time series analysis, specifically utilizing recurrence measures to classify various dynamical states emerging from time series. We implement three machine learning algorithms Logistic…
We describe photometric recalibration of data obtained by the asteroid survey LINEAR. Although LINEAR was designed for astrometric discovery of moving objects, the dataset described here contains over 5 billion photometric measurements for…
We discuss the impact of special relativistic effects on the observed light curves and variability duty cycles of AGNs. We model the properties of AGN light curves at radio wavelengths using a simulated shot noise process in which the…
X-ray observations of star-planet systems are important to grow our understanding of exoplanets; these observation allow for studies of photoevaporation of the exoplanetary atmosphere, and in some cases even estimations of the size of the…
We consider the problem of threshold estimation for autoregressive time series with a "space switching" in the situation, when the regression is nonlinear and the innovations have a smooth, possibly non Gaussian, probability density.…
Active Galactic Nuclei (AGN) are perfect laboratories to check General Relativity (GR) effects by using Broad Line Region (BLR) clouds eclipses to probe the innermost regions of the accretion disk. A new relativistic X-ray spectral model…
This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small…
We study the variability mechanism of active galactic nuclei (AGN) within the framework of the flare model. To this end we examine the case of Seyfert/LINER galaxy NGC 4258, which is observed at high inclination angle and exhibits rapid…
Bayesian methods are increasingly being applied to parameterize mechanistic process models used in environmental prediction and forecasting. In particular, models describing ecosystem dynamics with multiple states that are linear and…
In modeling spatial processes, a second-order stationarity assumption is often made. However, for spatial data observed on a vast domain, the covariance function often varies over space, leading to a heterogeneous spatial dependence…