论文标题
重力波和t t themation:有限温度QFT的有效且易于收敛
Gravitational waves and tadpole resummation: Efficient and easy convergence of finite temperature QFT
论文作者
论文摘要
我们通过分析和数值证明“优化的部分敷料”(OPD)热质量重新召集,它使用插入t的间隙方程溶液,有效地驯服了有效的热势的有限扰动理论计算,而无需使用高效率近似。 OPD重新调整,标准PARWANI重新召集(Daisy重新召集)和尺寸减小的量表依赖性的分析估计值表明,OPD具有与尺寸减小相似的比例依赖性,从而极大地改善了Parwani的重新点火。我们还阐明了如何构建和求解差距方程以进行实际的数值计算,并证明了OPD提高了玩具标量模型的精度。当高温近似分解时,OPD的提高精度在物理上是最显着的,使尺寸减小不可用,而Parwani重新召集高度不准确,而后者低估了与OPD相比,该模型的最大引力波振幅低估了。我们的工作强调了即使分析模型的广泛特征,也需要控制理论上的不确定性。鉴于OPD的简单性与两循环尺寸的降低相比,并且该方案的容易性处理与高温扩展的不同之处,我们认为该方案在分析超出标准模型的实际参数空间方面具有巨大的潜力。
We demonstrate analytically and numerically that "optimized partial dressing" (OPD) thermal mass resummation, which uses gap equation solutions inserted into the tadpole, efficiently tames finite-temperature perturbation theory calculations of the effective thermal potential, without necessitating use of the high-temperature approximation. An analytical estimate of the scale dependence for OPD resummation, standard Parwani resummation (Daisy resummation), and dimensional reduction shows that OPD has similar scale dependence to dimensional reduction, greatly improving Parwani resummation. We also elucidate how to construct and solve the gap equation for realistic numerical calculations, and demonstrate OPD's improved accuracy for a toy scalar model. OPD's improved accuracy is most physically significant when the high-temperature approximation breaks down, rendering dimensional reduction unusable and Parwani resummation highly inaccurate, with the latter underestimating the maximal gravitational wave amplitude for the model by 2 orders of magnitude compared to OPD. Our work highlights the need to bring theoretical uncertainties under control even when analyzing broad features of a model. Given the simplicity of the OPD compared to two-loop dimensional reduction, as well as the ease with which this scheme handles departures from the high-temperature expansion, we argue this scheme has great potential in analyzing the parameter space of realistic beyond the Standard Model models.