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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.02843 |
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| _version_ | 1866913087362170880 |
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| author | Agrawal, Atal |
| author_facet | Agrawal, Atal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02843 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PDRS : A Linear $\mathcal{O}(N)$ Algorithm for Segmentation of High-Activity Regions in Irregularly Sampled Time Series Agrawal, Atal Instrumentation and Methods for Astrophysics Astrophysics of Galaxies 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. |
| title | PDRS : A Linear $\mathcal{O}(N)$ Algorithm for Segmentation of High-Activity Regions in Irregularly Sampled Time Series |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2605.02843 |