{"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/dm-power-an-algorithm-for-high-precision","title":"DM-power: an algorithm for high precision dispersion measure with application to fast radio bursts","arxiv_id":"2208.13677","date":"2022-08-29","proceeding":null,"authors":["Hsiu-Hsien Lin","Robert Main","Ue-Li Pen","Robert Wharton","Marlon Luis Bause","Suryarao Bethapudi","Dongzi Li","Fang Xi Lin","Visweshwar Ram Marthi","Laura G Spitler"],"abstract":"We present DM-power, a new method for precisely determining the dispersion measure (DM) of radio bursts, and apply it to the Fast Radio Burst (FRB) source FRB~20180916B. Motivated by the complex structure on multiple time scales seen in FRBs, DM-power optimizes the DM by combining measurements at multiple Fourier frequencies in the power spectrum of the burst. By optimally weighting the measurements at each Fourier frequency, DM-power finds a burst DM that effectively incorporates information on many different burst timescales. We validate this technique on simulated Gaussian pulse profiles with a precision down to $\\sigma_{\\rm DM} \\sim 0.001~{\\rm pc~cm}^{-3}$, and then apply it to bursts from pulsar B0329+54 and FRB~20180916B. The precision of these DM measurements are sufficient to measure a statistically significant variation in DM over a $\\approx 2$ hr span. While this variation could be the result of electron density variations along the line of sight, it is more like that the observed variation is the result of intrinsic frequency-dependent burst structure that can mimic a dispersive delay.","url_abs":"https://arxiv.org/abs/2208.13677v2","url_pdf":"https://arxiv.org/pdf/2208.13677v2.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":"dm-power-an-algorithm-for-high-precision","repo_url":"https://github.com/hsiuhsil/dm-power","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}