{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gammapy-sylc-a-package-for-simulating-and","title":"gammapy_SyLC: A Package for Simulating and Fitting Variability in High-Energy Light Curves","arxiv_id":"2503.14156","date":"2025-03-18","proceeding":null,"authors":["Claudio Galelli"],"abstract":"Characterizing the temporal variability of astrophysical sources is key to understanding the underlying physical processes driving their emissions. This work introduces a gammapy_SyLC, a Python package that offers tools to simulate and fit time-domain data, with a focus on Active Galactic Nuclei (AGN) variability. The package was developed taking into account possible interactions with gammapy but does not directly depend on it. gammapy_SyLC incorporates optimized implementations of the Timmer & Koenig and Emmanoulopoulos algorithms for light curve simulation, capable of generating synthetic lightcurves from specified PSDs and amplitude distribution models. It also provides functionalities for PSD fitting, histogram-based PDF interpolation, and Monte Carlo-based parameter estimation, making it a full-stack tool for investigating variable phenomena and specifically the long-term behavior of AGNs. To showcase its capabilities, the package was applied to gamma-ray light curves from the Fermi Large Area Telescope repository, reconstructing PSDs and PDFs and constraining variability models for observed sources.","url_abs":"https://arxiv.org/abs/2503.14156v1","url_pdf":"https://arxiv.org/pdf/2503.14156v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"gammapy-sylc-a-package-for-simulating-and","repo_url":"https://github.com/cgalelli/gammapy_SyLC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}