Related papers: Analyzing X-ray variability by State Space Models
In a number of data-driven applications such as detection of arrhythmia, interferometry or audio compression, observations are acquired indistinctly in the time or frequency domains: temporal observations allow us to study the spectral…
Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the ability of TSAD is limited by two key challenges: (i) the…
We present a series of X-ray variability results from a long XMM-Newton + NuSTAR campaign on the bright, variable AGN NGC 3227. We present an analysis of the lightcurves, showing that the source displays typically softer-when-brighter…
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep…
State-space models effectively model multivariate time series by updating over time a representation of the system state from which predictions are made. The state representation is usually a vector without any explicit structure.…
The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic…
We have pioneered a new method for the measurement of extragalactic distances. This method uses the time-lag between variations in the short wavelength and long wavelength light from an active galactic nucleus (AGN), based on a quantitative…
Some progress in understanding AGN variability is reviewed. Reprocessing of X-ray radiation to produce significant amounts of longer-wavelength continua seems to be ruled out. In some objects where there has been correlated X-ray and…
This paper addresses the prediction of stationary functional time series. Existing contributions to this problem have largely focused on the special case of first-order functional autoregressive processes because of their technical…
Hard X-ray surveys are an important tool for the study of active galactic nuclei (AGN): they provide almost an unbiased view of absorption in the extragalactic population, allow the study of spectral features such as reflection and high…
Environmental research increasingly uses high-dimensional remote sensing and numerical model output to help fill space-time gaps between traditional observations. Such output is often a noisy proxy for the process of interest. Thus one…
We propose an explanation of the variability of AGN based on a cloud model of accretion onto a black hole. These clouds, present at a distance of 10-100 Schwarzschild radii, possibly come from the disrupted innermost disk and they can…
Attenuation bias -- the systematic underestimation of regression coefficients due to measurement errors in input variables -- affects astronomical data-driven models. For linear regression, this problem was solved by treating the true input…
Dynamic heterogeneity has often been modeled by assuming that a single-particle observable, fluctuating at a molecular scale, is influenced by its coupling to environmental variables fluctuating on a second, perhaps slower, time scale.…
We propose here a stochastic binary element whose transition rate depends on its state at a fixed interval in the past. With this delayed stochastic transition this is one of the simplest dynamical models under the influence of ``noise''…
State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the…
We present a study of variability time scales in a large sample of Active Galactic Nuclei at several frequencies between 4.8 and 230 GHz. We investigate the differences of various AGN types and frequencies and correlate the measured time…
Irregular multivariate time series with missing values present significant challenges for predictive modeling in domains such as healthcare. While deep learning approaches often focus on temporal interpolation or complex architectures to…
Learning identifiable representations and models from low-level observations is helpful for an intelligent spacecraft to complete downstream tasks reliably. For temporal observations, to ensure that the data generating process is provably…
We present a method for applying spatially resolved adiabatic and radiative loss processes to synthetic radio emission from hydrodynamic simulations of radio sources from active galactic nuclei (AGN). Lagrangian tracer particles, each…