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arXiv 2108.07972astro-ph.COhep-phhep-th
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卷积神经网络探测21 cm断层超轻轴子暗物质

Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Cristiano G. Sabiu, Kenji Kadota, Jacobo Asorey, Inkyu Park

备注：
18 pages, 11 figures, accepted in JCAP
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摘要：

我们目前的预测超轻类轴子粒子（UNOPS）的可探测性从未来的21厘米射电观测周围的时代的再电离（EoR）。我们表明，轴子作为占主导地位的暗物质组分有显着的影响，由于在早期宇宙的小尺度密度扰动的抑制再电离的历史。这种行为强烈地依赖于轴子粒子的质量。通过对大红移范围内中性氢亮温场的数值模拟，我们构造了一组训练数据。该数据用于训练卷积神经网络，该网络可以直接在亮度温度场的空间结构和输入轴子质量之间建立联系。我们构建了未来平方公里阵列调查SKA 1-Low的模拟观测，发现即使存在现实的噪声和分辨率限制，网络仍然能够预测输入的轴子质量。我们发现，轴子质量可以在很宽的质量范围内恢复，精度约为20%，并且作为整个DM贡献，如果轴子质量为$M_X<1.86 \times10^{-20}$ eV，使用SKA 1-Low可以在68%处检测到轴子，尽管如果我们放松我们对天体物理建模的假设，将这些天体物理参数视为讨厌的参数，这可以降低到$M_X<5.25 \times10^{-21}$ eV。

英文摘要：

We present forecasts on the detectability of Ultra-light axion-like particles (ULAP) from future 21cm radio observations around the epoch of reionization (EoR). We show that the axion as the dominant dark matter component has a significant impact on the reionization history due to the suppression of small scale density perturbations in the early universe. This behavior depends strongly on the mass of the axion particle. Using numerical simulations of the brightness temperature field of neutral hydrogen over a large redshift range, we construct a suite of training data. This data is used to train a convolutional neural network that can build a connection between the spatial structures of the brightness temperature field and the input axion mass directly. We construct mock observations of the future Square Kilometer Array survey, SKA1-Low, and find that even in the presence of realistic noise and resolution constraints, the network is still able to predict the input axion mass. We find that the axion mass can be recovered over a wide mass range with a precision of approximately 20\%, and as the whole DM contribution, the axion can be detected using SKA1-Low at 68\% if the axion mass is $M_X<1.86 \times10^{-20}$eV although this can decrease to $M_X<5.25 \times10^{-21}$eV if we relax our assumptions on the astrophysical modeling by treating those astrophysical parameters as nuisance parameters.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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