论文标题
带有ACTPOL和BICEP3数据的小型野外模型 - 可能性分析
Small Field models with ACTPol and BICEP3 data -- Likelihood analysis
论文作者
论文摘要
我们使用Planck'18，ActPol和Bicep3生产的最新数据集，对通货膨胀的小型通货膨胀模型进行贝叶斯分析。我们采用人工神经网络（ANN）用模型系数进行分析，而不是其代理慢倾参数。 ANN将模型与他们的投影标量指数$ n_s $和运行$α$的索引相连接，以代替较不准确的lyth-riotto表达式。我们恢复了第六级多项式通货膨胀潜力的最可能的系数，这产生了张量与尺度比率$ r \ lyssim 0.03 $。对于联合Planck和ACTPOL数据集，我们这样做，并且仅针对每个数据集。 Bicep3数据都包含在所有三个分析中。我们表明这些模型可能是可能的，其系数调整为约$δ\ gtrsim 1/60 $。奇怪的是，我们还发现了ACTPOL和PLANCK数据集之间的显着张力，我们试图解释这一点。
We perform a Bayesian analysis for small field models of inflation, using the most recent datasets produced by Planck`18, ACTPol, and BICEP3. We employ Artificial Neural Networks (ANN) to perform analyses with model coefficients, instead of their proxy slow-roll parameters. The ANN connects the models with their projected scalar index $n_s$ and index running $α$, in lieu of the less accurate Lyth-Riotto expressions. We recover the most likely coefficients for a sixth degree polynomial inflationary potential, which yields a tensor-to-scalar ratio $r\lesssim 0.03$. We do so for the case of joint Planck and ACTPol datasets, and for each dataset alone. The BICEP3 data is included in all three analyses. We show that these models are likely, with coefficients that are tuned to about $Δ\gtrsim 1/60$. Curiously, we also find a significant tension between ACTPol and Planck datasets, which we try to account for.
