论文标题

迈向模型比较的一步:通过EFT和贝叶斯统计,将电动尺度可观察到BSM连接到BSM

A Step Toward Model Comparison: Connecting Electroweak-Scale Observables to BSM through EFT and Bayesian Statistics

论文作者

Anisha, Bakshi, Supratim Das, Chakrabortty, Joydeep, Patra, Sunando Kumar

论文摘要

认识到有效的现场理论在相似的基础上对多个BSM场景提出了多个BSM场景的潜力,并有可能比较它们,我们检查了SM的11个单一标量 - 磁性扩展对Electroweak Precision Precision Precision可观测值和HIGGS信号强度数据的效果,并通过系统地将重型乘务和Smefer coeffers和Wilss coeffs coeffs和Wilss coeffs coeffers和wills coeffers组成。注意到多个BSM模型会产生一组退化的WC,然后我们直接在BSM参数和相关的独立WCS集上执行贝叶斯统计推断。使用BSM参数的后代,我们推断出相应的(相关)WC分布,并通过覆盖两个过程中的2-D边缘WC-Posterers来比较模型与模型无关和相关的分析,从而使数据驱动的基础构成了与不同的bsm bsm andive bsm bsm bsm andive bsm bsm bsm andive bsm interty interty interty interty and intern intern intern interny offience and nocy nocy in nable of Inshige and的方法。我们还通过示例模型证明了理论约束的关键作用,以排除大部分BSM参数空间。全部数值结果可在GitHub中获得。

Recognizing the potential of effective field theories to posit multiple BSM scenarios in similar footing, with a possibility to compare them, we inspect the effects of 11 single scalar-multiplet extensions of the SM on the combined set of electroweak precision observables and Higgs signal strength data, by systematically integrating out the heavy multiplets and computing the resulting SMEFT operators and Wilson coefficients (WCs) up to one-loop level. Noting that multiple BSM models give rise to a degenerate set of WCs, we then perform Bayesian statistical inference both directly on the BSM parameters and on the associated set of independent WCs. Using the posteriors of the BSM parameters, we infer the respective (correlated) WC-distributions and compare both the model-independent and dependent analyses by overlaying the 2-D marginal WC-posteriors from both processes, thus laying the ground for a data-driven attempt to compare diverse BSM theories of different origins, and hopefully, a possible way to approach the intractable inverse problem. We also demonstrate, with an example model, the crucial role of theoretical constraints to rule out large chunks of BSM parameter spaces. The entirety of numerical results is available in GitHub.

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