{"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/litepig-a-lite-parameter-inference-system-for","title":"LitePIG: A Lite Parameter Inference system for the Gravitational wave in the millihertz band","arxiv_id":"2208.13351","date":"2022-08-29","proceeding":null,"authors":["Renjie Wang","Bin Hu"],"abstract":"We present a python based parameter inference system for the gravitational wave (GW) measured in the millihertz band. This system includes the following features: the GW waveform originated from the massive black hole binaries (MBHB), the stationary instrumental gaussian noise, the higher-order harmonic modes, the full response function from the time delay interferometry (TDI) and the gaussian likelihood function with the dynamic nested parameter sampler. In particular, we highlight the role of higher-order modes. By including these modes, the luminosity distance estimation precision can be improved roughly by a factor of 50, compared with the case with only the leading order ($\\ell=2,|m|=2$) mode. This is due to the response function of different harmonic modes on the inclination angle are different. Hence, it can help to break the distance-inclination degeneracy. Furthermore, we show the robustness of testing general relativity (GR) by using the higher-order harmonics. Our results show that the GW from MBHB can simultaneously constrain four of the higher harmonic amplitudes (deviation from GR) with a precision of $c_{21}=0.54^{+0.61}_{-0.82}$, $c_{32}=-0.65^{+0.22}_{-0.08}$, $c_{33}=0.56^{+0.60}_{-0.76}$ and $c_{44}=1.57^{+2.34}_{-1.90}$, respectively.","url_abs":"https://arxiv.org/abs/2208.13351v1","url_pdf":"https://arxiv.org/pdf/2208.13351v1.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":"litepig-a-lite-parameter-inference-system-for","repo_url":"https://github.com/renjiewang888/litepig","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}