Atal Agrawal | Searcharxiv

arXiv2026

PDRS : A Linear $\mathcal{O}(N)$ Algorithm for Segmentation of High-Activity Regions in Irregularly Sampled Time Series

Identifying transient high-activity episodes in astronomical time series requires partitioning data into regions of distinct statistical behavior. A widely adopted approach combines Bayesian Blocks with a hill-climbing procedure to isolate high-activity regions, but carries $\mathcal{O}(N^2)$ complexity -- a scalability challenge for wide-field surveys like ZTF and the upcoming Rubin Observatory (LSST), where light curves routinely contain thousands of irregularly sampled observations. We present Peak-Driven Region Segmentation (PDRS), a linear-time $\mathcal{O}(N)$ algorithm for rapid extraction of high-activity regions in irregularly sampled data. PDRS seeds candidate regions at statistically significant local maxima and expands them via a gradient-aware multi-source breadth-first search. Saddle-point merging and a median-based filter suppress spurious detections. Functioning as a computationally efficient pre-processing stage, PDRS isolates candidate transient events for downstream analysis. We demonstrate its efficacy on quasar light curves from SDSS Stripe~82 and AGN light curves from ZTF DR23, showing that PDRS identifies candidate high-activity regions comparable to those from Bayesian Blocks at substantially reduced cost. Its domain-agnostic formulation and physically interpretable parameters make PDRS broadly applicable beyond astronomy, including biomedical signals, seismic recordings, and industrial sensor monitoring.

astro-ph.IM↗

arXiv2026

A Statistical-AI Framework for Detecting Transient Flares in SDSS Stripe 82 Quasar Light Curves

Quasars exhibit stochastic variability across wavelengths, typically well described by a Damped Random Walk (DRW). Occasionally, however, they undergo extreme luminosity changes--known as flares--that represent significant departures from this baseline behavior and provide valuable probes of accretion disc dynamics and the physics of supermassive black hole fueling. Although modern transient surveys have spurred growing interest in flare detection, no systematic search has yet been conducted within the legacy SDSS Stripe 82 dataset, which contains 9,258 spectroscopically confirmed quasars observed over a ~10-year baseline. The principal statistical challenge is distinguishing these rare events from the ever-present stochastic variability. To address this, we present FLARE (Flare detection via physics-informed Learning, Anomaly scoring, and Recognition Engine), a modular three-stage framework for detecting transient flares in quasar light curves. FLARE models baseline DRW behavior, applies statistical anomaly scoring to flag candidate events, and employs a recognition engine to verify detections. For the Stripe 82 implementation, we deploy two complementary baselines--a physics-informed probabilistic Gated Recurrent Unit (GRU) trained on simulated DRW light curves, and an iterative Ornstein-Uhlenbeck (OU) process fitted directly to observed data with outlier masking--followed by Extreme Value Theory (EVT) for anomaly scoring. We benchmark twelve open-weight and proprietary Vision Language Models (VLMs) as recognition engines for final candidate verification. Detection is performed on r-band light curves, with candidates cross-checked against g-band data to rule out instrumental artifacts. Applying this framework, we identify 51 quasars exhibiting distinct flaring activity.

astro-ph.IM↗