D. Edgeman | Searcharxiv

arXiv2026

Optical effects in Gaseous Electron Multipliers (GEMs)

Optical time projection chambers (OTPCs) are well suited for applications that require the highest spatial resolution for particle track reconstruction. The MIGDAL experiment uses a glass GEM-based OTPC and observes a systematic excess in both the intensity and width of particle tracks in its optical readout, when compared with charge readout simulations. One hypothesis is that scintillation light produced inside a GEM hole during the avalanche propagates through the GEM substrate and exits neighboring holes. We present lab measurements testing this hypothesized optical broadening effect in three types of GEM substrates: glass, ceramic, and FR4. Our observations quantify this optical broadening and demonstrate it to be strongest in glass GEMs. Additionally, we use Geant4 simulations to both reproduce our observations and quantify optical broadening effects in realistic charge avalanches. Applying our glass GEM effects to simulated particle tracks yields increases of track intensity and widths by up to around 26% and 31%, respectively. This may explain the larger than expected intensity and track widths observed in the MIGDAL OTPC and is expected to be an observed effect in all GEM-based OTPCs.

physics.ins-det↗

arXiv2025

Overlap-aware segmentation for topological reconstruction of obscured objects

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.

hep-ex↗