论文标题
用密集的神经网络对早期的能量注入建模
Modeling early-universe energy injection with Dense Neural Networks
论文作者
论文摘要
我们表明，在公共代码套件Dark Histhistory的背景下，可以使用密集的神经网络来准确地对早期宇宙中高能颗粒的冷却进行建模。 Dark Histomentory自愿计算出奇异能量注射的情况下早期宇宙的温度和电离历史，例如an灭或衰减暗物质。 DarkHistory的原始版本使用大型预计的传输表来在红移步骤中发展光子和电子光谱，这需要大量的内存和存储空间。我们提出了一个淡淡的暗历史版本，该版本利用简单密集的神经网络来存储和插入传输功能，该功能在没有重记忆或存储使用情况的小型计算机上表现良好。该方法可以预期未来的扩展，并在传输函数中具有其他参数依赖性，而无需指数较大的数据表。
We show that Dense Neural Networks can be used to accurately model the cooling of high-energy particles in the early universe, in the context of the public code package DarkHistory. DarkHistory self-consistently computes the temperature and ionization history of the early universe in the presence of exotic energy injections, such as might arise from the annihilation or decay of dark matter. The original version of DarkHistory uses large pre-computed transfer function tables to evolve photon and electron spectra in redshift steps, which require a significant amount of memory and storage space. We present a light version of DarkHistory that makes use of simple Dense Neural Networks to store and interpolate the transfer functions, which performs well on small computers without heavy memory or storage usage. This method anticipates future expansion with additional parametric dependence in the transfer functions without requiring exponentially larger data tables.
