The discovery rate of fast radio bursts (FRBs) continues to increase with the advent of new radio facilities and yet extracting their astrophysical parameters such as scattering timescale ($τ$) remains a significant bottleneck. Current $τ$ measurement approaches like fitting analytic template models and scattering aware de-convolution are accurate but slow, sensitive to initialization, limited by low signal to noise and often require manual supervision. These limitations inspired us to explore fast, robust and scalable machine learning methods to estimate the astrophysical parameter value. We present a deep learning approach named Multimodal Transformer Based Generic Mixture Density Network (MT-GMDN) which ingests FRB dynamic spectrum and its corresponding timeseries profile through parallel transformer encoders, fuses their latent representations and predicts the distribution of $τ$ with probabilistic output derived from generic mixture-density formulation. This formulation not only estimates the value of $τ$ but also captures the (zero inflated) nature of FRB populations where a significant fraction of bursts exhibit unresolvable scattering. We trained MT-GMDN on $\sim3500$ FRBs from CHIME/FRB \cattwo while holding out some fraction of FRBs for validation during training and for testing after the training completes. The model achieves a coefficient of determination ($R^2$) value of $94\%$ on the expected value of $τ$ for the events with measurable scattering with an excellent recall value of $90\%$ on the test data set. The model was also able to incorporate heteroskedastic errors enabling us the construction of a confidence interval for the predictions.

翻译：随着新型射电设施的涌现，快速射电暴（FRBs）的发现率持续增长，然而，提取其天体物理参数（如散射时标τ）仍是一个重大瓶颈。当前的τ测量方法，如拟合解析模板模型和散射感知反卷积，虽具准确性，但运行缓慢，对初始值敏感，受低信噪比限制，且常需人工干预。这些局限性促使我们探索快速、鲁棒且可扩展的机器学习方法以估算天体物理参数值。我们提出一种名为"基于多模态Transformer的通用混合密度网络"（MT-GMDN）的深度学习方法，该方法通过并行Transformer编码器输入FRB动态谱及其对应时域轮廓，融合其潜在表示，并利用源自通用混合密度公式的概率输出预测τ的分布。该公式不仅能估算τ值，还可捕捉FRB群体中（零膨胀）特性——即相当一部分暴发呈现不可分辨的散射。我们使用来自CHIME/FRB《第二目录》的大约3500个FRB训练MT-GMDN，同时保留部分FRB用于训练中的验证及训练完成后的测试。模型对可测量散射事件τ期望值的决定系数R²达到94%，在测试数据集上召回率高达90%。该模型还能纳入异方差误差，从而为预测构建置信区间。