Modeling Multi-band Active Galactic Nuclei Lightcurves Using an Self-supervised Attentive Neural Process Network
Abstract
With the the Legacy Survey of Space and Time (LSST), observations of tens of millions of quasars are expected. We present a machine-learning framework based on an attentive neural process (ANP) that models stochastic quasar variability and jointly performs two tasks: (i) probabilistic reconstruction of quasar light curves in all six LSST bands, and (ii) inference of physical parameters of the AGN system --- black hole mass, Eddington ratio, and intrinsic variability timescale --- with joint, full-covariance posteriors. The model processes all six bands within a single masked attention mechanism, so that observations in any band constrain the shared driving process, and natively handles irregularly and asynchronously sampled light curves with realistic heteroscedastic noise. We train and evaluate on full ten-year light curves simulated with a relativistic accretion-disk reprocessing model, observed through the fielded Rubin baseline cadence with per-visit photometric noise. The light-curve model is trained with self-supervised, learning from the sparse noisy observations alone and never accessing the underlying noiseless light curves. On a held-out test set, we show that our ANP model reach comparable performances to that of the near-optimal baseline Gaussian process model, while exceeding it in power spectrum reconstruction and damped-random walk timescale estimation. The compact learned latent representation supports future downstream applications, and the fully observation-driven training objective enables direct fine-tuning on real LSST light curves.
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BibTeX
@misc{shen2026modeling,
title = {{Modeling Multi-band Active Galactic Nuclei Lightcurves Using an Self-supervised Attentive Neural Process Network}},
author = {Tzuken Shen and Chengcheng Xin and Joshua Fagin and Ashley Villar and Matthew J. Graham and K. E. Saavik Ford},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24195},
eprint = {2610.24195},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24195},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Modeling Multi-band Active Galactic Nuclei Lightcurves Using an Self-supervised Attentive Neural Process Network
AU - Tzuken Shen
AU - Chengcheng Xin
AU - Joshua Fagin
AU - Ashley Villar
AU - Matthew J. Graham
AU - K. E. Saavik Ford
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.24195
UR - https://arxiv.org/abs/2610.24195
AB - With the the Legacy Survey of Space and Time (LSST), observations of tens of millions of quasars are expected. We present a machine-learning framework based on an attentive neural process (ANP) that models stochastic quasar variability and jointly performs two tasks: (i) probabilistic reconstruction of quasar light curves in all six LSST bands, and (ii) inference of physical parameters of the AGN system --- black hole mass, Eddington ratio, and intrinsic variability timescale --- with joint, full-covariance posteriors. The model processes all six bands within a single masked attention mechanism, so that observations in any band constrain the shared driving process, and natively handles irregularly and asynchronously sampled light curves with realistic heteroscedastic noise. We train and evaluate on full ten-year light curves simulated with a relativistic accretion-disk reprocessing model, observed through the fielded Rubin baseline cadence with per-visit photometric noise. The light-curve model is trained with self-supervised, learning from the sparse noisy observations alone and never accessing the underlying noiseless light curves. On a held-out test set, we show that our ANP model reach comparable performances to that of the near-optimal baseline Gaussian process model, while exceeding it in power spectrum reconstruction and damped-random walk timescale estimation. The compact learned latent representation supports future downstream applications, and the fully observation-driven training objective enables direct fine-tuning on real LSST light curves.
ER -