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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2511.02612 |
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| _version_ | 1866911705996460032 |
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| author | Yang, Aidi Idegawa, Chikako Huang, Fa Peng |
| author_facet | Yang, Aidi Idegawa, Chikako Huang, Fa Peng |
| contents | We investigate the capability of TianQin and LISA to reconstruct the model parameters in the Lagrangian of new physics scenarios that can generate an electroweak SFOPT. Taking the dimension-six Higgs operator extension of the Standard Model as a representative scenario for a broad class of new physics models, we establish the mapping between the model parameter $Λ$ and the observable spectral features of the stochastic gravitational wave background. We begin by generating simulated data incorporating Time Delay Interferometry channel noise, astrophysical foregrounds, and signals from the dimension-six model. The data are then compressed and optimized, followed by geometric parameter inference using both Fisher matrix analysis and Bayesian nested sampling with PolyChord, which efficiently handles high-dimensional, multimodal posterior distributions. Finally, machine-learning techniques are employed to achieve precise reconstruction of the model parameter $Λ$. For benchmark points producing strong signals, parameter reconstruction with both TianQin and LISA yields relative uncertainties of approximately $20$-$30\%$ in the signal amplitude and sub-percent precision in the model parameter $Λ$. The sub-percent precision reflects the statistical reconstruction capability of the detectors in an idealized setting: it incorporates the machine-learning inference uncertainty and is established at a fixed bubble wall velocity, while theoretical uncertainties in the effective potential calculation are not included. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Parameter Reconstruction of Electroweak Phase Transition with TianQin and LISA: Insights from the Dimension-Six Model Yang, Aidi Idegawa, Chikako Huang, Fa Peng High Energy Physics - Phenomenology We investigate the capability of TianQin and LISA to reconstruct the model parameters in the Lagrangian of new physics scenarios that can generate an electroweak SFOPT. Taking the dimension-six Higgs operator extension of the Standard Model as a representative scenario for a broad class of new physics models, we establish the mapping between the model parameter $Λ$ and the observable spectral features of the stochastic gravitational wave background. We begin by generating simulated data incorporating Time Delay Interferometry channel noise, astrophysical foregrounds, and signals from the dimension-six model. The data are then compressed and optimized, followed by geometric parameter inference using both Fisher matrix analysis and Bayesian nested sampling with PolyChord, which efficiently handles high-dimensional, multimodal posterior distributions. Finally, machine-learning techniques are employed to achieve precise reconstruction of the model parameter $Λ$. For benchmark points producing strong signals, parameter reconstruction with both TianQin and LISA yields relative uncertainties of approximately $20$-$30\%$ in the signal amplitude and sub-percent precision in the model parameter $Λ$. The sub-percent precision reflects the statistical reconstruction capability of the detectors in an idealized setting: it incorporates the machine-learning inference uncertainty and is established at a fixed bubble wall velocity, while theoretical uncertainties in the effective potential calculation are not included. |
| title | Model Parameter Reconstruction of Electroweak Phase Transition with TianQin and LISA: Insights from the Dimension-Six Model |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.02612 |