A Deep-Learning Based Waveform Region-of-interest Finder for the Liquid Argon Time Projection Chamber

mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of $$\sim$$0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.