标题： 带有潜在空间U-Net编码和XGBoost的噪声21-cm森林光谱的有效中性IGM推断 显示英文标题

标题： Efficient neutral-IGM inference from noisy 21-cm forest spectra with latent-space U-Net encoding and XGBoost

摘要： 21厘米森林，由高红移射电明亮类星体的无线电谱上由中间中性氢留下的窄吸收特征组成，为研究再电离时期的中性宇宙介质（IGM）的热状态提供了独特的敏感探测手段。 尽管现在已知超过30个这样的类星体在$z > 5.5$，但实际中该信号仍然难以捉摸，这是由于仪器噪声、吸收特征本身的微弱以及可用背景源的亮度有限。 近期的研究主要集中在一维透射功率谱作为统计可观测量，但这种方法同样需要高信噪比。 在这里，我们对五种推断流程进行了系统比较，以从$z = 6$的模拟21厘米森林光谱中恢复IGM参数，结合了现实的仪器噪声和望远镜特性。 我们表明，基于机器学习的无似然推断显著优于传统的贝叶斯方法。 特别是，我们最有效的方法完全摒弃了功率谱：我们使用卷积U-Net提取输入光谱的潜在空间编码，并使用XGBoost进行参数回归。 这种方法即使只使用一条50小时的uGMRT视线，也能对IGM中性分数和X射线加热效率得出准确的约束，这相对于现有技术在积分时间上有了数量级的改进。 我们公开发布了我们的代码、训练数据和模型。 除了21厘米森林之外，这些结果强调了混合深度学习和梯度提升推断技术在从天体物理学中的低信噪比数据中提取物理信息方面的前景。 显示英文摘要

摘要： The 21-cm forest, comprising narrow absorption features imprinted on the radio spectra of high-redshift radio-loud quasars by intervening neutral hydrogen, offers a uniquely sensitive probe of the thermal state of the neutral intergalactic medium (IGM) during the epoch of reionization. Although over 30 such quasars are now known at $z > 5.5$, the signal remains elusive in practice, owing to instrumental noise, the intrinsic weakness of the absorption features, and the limited brightness of available background sources. Recent studies have focused on the one-dimensional transmission power spectrum as a statistical observable, but this approach also demands high signal-to-noise ratios. Here, we present a systematic comparison of five inference pipelines for recovering IGM parameters from mock 21-cm forest spectra at $z = 6$, incorporating realistic instrumental noise and telescope characteristics. We show that likelihood-free inference based on machine learning substantially outperforms traditional Bayesian methods. In particular, our most effective method dispenses with the power spectrum entirely: we use a convolutional U-Net to extract a latent-space encoding of the input spectrum and perform parameter regression using XGBoost. This approach yields accurate constraints on the IGM neutral fraction and X-ray heating efficiency even with a single 50-hour uGMRT sightline, which is an orders-of-magnitude improvement in integration time relative to existing techniques. We publicly release our code, training data, and models. Beyond the 21-cm forest, these results underscore the promise of hybrid deep learning and gradient-boosted inference techniques for extracting physical information from low-SNR data across astrophysics.