Machine-learning approaches to dispersion measure estimation for fast radio bursts

TL;DR

This paper explores deep learning models, including CNN, ResNet-50, and CNN-LSTM, for automated and efficient dispersion measure estimation of fast radio bursts, demonstrating promising accuracy on synthetic data.

Contribution

It introduces and benchmarks three novel deep-learning architectures for DM estimation, highlighting the hybrid CNN-LSTM's superior performance and potential for real-time application.

Findings

Hybrid CNN-LSTM achieves highest accuracy and stability.

Models trained on synthetic data can be fine-tuned for real observations.

Deep learning offers scalable, real-time DM estimation for large FRB surveys.

Abstract

Fast radio bursts (FRBs) are bright, mostly millisecond-duration transients of extragalactic origin whose emission mechanisms remain unknown. As FRB signals propagate through ionized media, they experience frequency-dependent delays quantified by the dispersion measure (DM), a key parameter for inferring source distances and local plasma conditions. Accurate DM estimation is therefore essential for characterizing FRB sources and testing physical models, yet current dedispersion methods can be computationally intensive and prone to human bias. In this proof-of-concept study, we develop and benchmark three deep-learning architectures, a conventional convolutional neural network (CNN), a fine-tuned ResNet-50, and a hybrid CNN-LSTM model, for automated DM estimation. All models are trained and validated on a large set of synthetic FRB dynamic spectra generated using CHIME/FRB-like specifications. The hybrid CNN-LSTM achieves the highest accuracy and stability while maintaining low computational cost across the investigated DM range. Although trained on simulated data, these models can be fine-tuned on real CHIME/FRB observations and extended to future facilities, offering a scalable pathway toward real-time, data-driven DM estimation in large FRB surveys.