Amy Cottle

Abstract:

The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algorithms can predict pixel-wise intensities, they typically treat all regions equally rather than prioritizing overlap regions where attribution is most ambiguous. Recent advances in instance segmentation show that weighting regions of pixel overlap in training can improve segmentation boundary predictions in regions of overlap, but this idea has not yet been extended to segmentation regression. We address this with Overlap-Aware Segmentation Of ImageS (OASIS): a new segmentation-regression framework with a weighted loss function designed to prioritize regions of object-overlap during training, enabling extraction of pixel intensities and topological features from heavily obscured objects. We demonstrate OASIS in the context of the MIGDAL experiment, which aims to directly image the Migdal effect–a rare process where electron emission is induced by nuclear scattering–in a low-pressure optical time projection chamber. This setting poses an extreme test case, as the target for reconstruction is a faint electron recoil track which is often heavily-buried within the order(s)-of-magnitude brighter nuclear recoil track. Compared to unweighted segmentation regression, we demonstrate OASIS’s novel overlap region-targeted loss function weight to be the single most important training weight for improving intensity and topological reconstructions of the low-energy electron tracks that tend to be most dominated by pixel overlap. Averaging over eight training campaigns, we further show the addition of overlap-targeted weights to improve median intensity reconstruction errors from −41.1% to −13.3% for these low-energy electrons. These performance gains demonstrate OASIS as a generalizable methodology for recovering obscured signals in overlap-dominated regions. All code is openly available to facilitate cross-domain adoption.

Abstract:

Measuring radon emanation from detector materials is a key method for controlling radon, a significant background in rare-event physics experiments. Methods for measuring radon emanation are well-established but have predominantly focused on the²²²Rn isotope, the dominant radon isotope for these backgrounds. However, measurements of²²⁰Rn (thoron), the second most abundant radon isotope, remain relatively unexplored.²²⁰Rn emanation measurements are challenging because the²²⁰Rn must be transferred from the emanation chamber to the active detector within its short 55 s half-life. In this study, a direct in-chamber approach for measuring²²⁰Rn emanation is presented in which the sample is placed directly within the active detector chamber, thereby minimising losses during transfer. The method was demonstrated with a DURRIDGE RAD8 electrostatic radon detector, which measured²²⁰Rn emanation from low-activity thoriated rods with an activity of 76 ± 20 mBq. Compared with a conventional flowthrough²²⁰Rn emanation setup, the in-chamber method increased sensitivity by a factor of 3. Using helium as the carrier gas provided a further sensitivity increase, giving an overall sensitivity gain of ∼ 5. These results indicate that in-chamber²²⁰Rn emanation measurements provide an effective tool for low-background experiments and have the potential to accelerate radon studies by exploiting the shorter half-life of²²⁰Rn.