Code and Dataset for Deep-learning-Enabled Resolution-Adaptive Nuclear Spectroscopy with Synchrotron Mössbauer Sources

Nuclear resonant spectroscopies at synchrotron Mössbauer sources are fundamentally constrained by a trade-off between spectral resolution and photon intensity, which limits access to weak signals and extreme sample environments. Here, we show that a deep learning–based inverse model can effectively relax this trade-off by recovering high-resolution nuclear response functions from broadened and noisy synchrotron Mössbauer spectra. A resolution-adaptive neural network is trained entirely on physics-based synthetic data spanning a broad range of instrumental linewidths and noise conditions, and learns to reconstruct the intrinsic hyperfine spectra without explicit knowledge of the instrumental function. Applied to ⁵⁷Fe synchrotron Mössbauer measurements on metallic iron, the method retrieves the transition spectrum in quantitative agreement with high-resolution reference data while operating at up to 17-fold higher count rates. Its effectiveness is further confirmed on complex oxides. The approach remains robust across mixed noise statistics and uncalibrated linewidths, and can be combined with learned denoising networks for very low signal-to-noise regimes. Our results demonstrate that data-driven inverse modeling can transform the effective performance of nuclear resonant spectrometers and provide a general framework for overcoming resolution–throughput constraints in photon-counting spectroscopies.基于同步辐射穆斯堡尔源（synchrotron Mössbauer sources）的核共振谱学（Nuclear resonant spectroscopies），本质上受限于光谱分辨率与光子强度之间的权衡，这一制约因素限制了对弱信号与极端样品环境的探测能力。本研究表明，基于深度学习的逆模型（deep learning–based inverse model）可通过从展宽且带噪声的同步辐射穆斯堡尔光谱中复原高分辨率核响应函数，有效缓解这一权衡难题。我们构建的分辨率自适应神经网络（resolution-adaptive neural network）完全基于覆盖宽泛仪器线宽与噪声条件的物理驱动合成数据（physics-based synthetic data）进行训练，无需显式掌握仪器函数（instrumental function）即可学习复原本征超精细光谱（hyperfine spectra）。将该方法应用于金属铁的铁-57（⁵⁷Fe）同步辐射穆斯堡尔测量时，其复原的跃迁光谱与高分辨率参考数据定量吻合，同时可实现最高达17倍的计数率（count rates）提升。该方法的有效性在复杂氧化物体系中得到了进一步验证。该方法在混合噪声统计特性与未校准仪器线宽的场景下仍保持鲁棒性，还可与已训练的去噪网络（denoising networks）结合，适配极低信噪比的测量环境。本研究结果证实，数据驱动的逆建模（data-driven inverse modeling）可大幅提升核共振光谱仪（nuclear resonant spectrometers）的实际性能，并为解决光子计数谱学（photon-counting spectroscopies）中的分辨率-通量约束（resolution–throughput constraints）问题提供了通用框架。