Conditional Neural Process for non-parametric modeling of AGN light curve

TL;DR

This paper introduces a Conditional Neural Process model tailored for simulating and predicting the complex, stochastic light curves of active galactic nuclei, demonstrating its effectiveness on real survey data and potential for large-scale applications.

Contribution

The paper develops a specialized CNP algorithm for AGN light curve modeling, trained on survey data, and shows its efficiency and accuracy in handling complex variability.

Findings

CNP accurately predicts AGN flux fluctuations.

The model handles large datasets efficiently.

Preliminary results suggest superiority over traditional methods.

Abstract

The consequences of complex disturbed environments in the vicinity of a supermassive black hole are not well represented by standard statistical models of optical variability in active galactic nuclei (AGN). Thus, developing new methodologies for investigating and modeling AGN light curves is crucial. Conditional Neural Processes (CNPs) are nonlinear function models that forecast stochastic time-series based on a finite amount of known data without the use of any additional parameters or prior knowledge (kernels). We provide a CNP algorithm that is specifically designed for simulating AGN light curves. It was trained using data from the All-Sky Automated Survey for Supernovae, which included 153 AGN. We present CNP modeling performance for a subsample of five AGNs with distinctive difficult-to-model properties. The performance of CNP in predicting temporal flux fluctuation was assessed using a minimizing loss function, and the results demonstrated the algorithm's usefulness. Our preliminary parallelization experiments show that CNP can efficiently handle large amounts of data. These results imply that CNP can be more effective than standard tools in modeling large volumes of AGN data (as anticipated from time-domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time).