Hans Kraus

Abstract:

We report results from searches for new physics models through electron recoils using data collected by the LUX-ZEPLIN experiment during its first two science runs, with a total exposure of $4.2\mathrm{tonne}\text{−}\mathrm{years}$ . The observed data are consistent with a background-only hypothesis. Constraints are derived for electromagnetic interactions of solar neutrinos, solar axionlike particles (ALPs), mirror dark matter, and the absorption of bosonic dark matter candidates. The inverse Primakoff process for ${}^{57}\mathrm{Fe}$ deexcitation solar ALPs is considered for the first time. These results represent the most stringent constraints to date on keV-scale Primakoff and ${}^{57}\mathrm{Fe}$ solar ALPs, bosonic dark matter, mirror dark matter, and neutrino millicharge, while remaining competitive for the other signal models investigated.

Abstract:

We present searches for light dark matter (DM) with masses $3–9\mathrm{GeV}/c^2$ in the presence of coherent elastic neutrino-nucleus scattering ( $\mathrm{CE}\nu \mathrm{NS}$ ) from ${}^8\mathrm{B}$ solar neutrinos with the LUX-ZEPLIN experiment. This analysis uses a 5.7 tonne-yr exposure with data collected between March 2023 and April 2025. In an energy range spanning 1–6 keV, we report no significant excess of events attributable to dark matter nuclear recoils, but we observe a significant signal from ${}^8\mathrm{B}$ $\mathrm{CE}\nu \mathrm{NS}$ interactions that is consistent with expectation. We set world-leading limits on spin-independent and spin-dependent-neutron DM-nucleon interactions for masses down to $5\mathrm{GeV}/c^2$ . In the no-dark-matter scenario, we observe a signal consistent with ${}^8\mathrm{B}$ $\mathrm{CE}\nu \mathrm{NS}$ events, corresponding to a $4.5\sigma$ statistical significance. This is the most significant evidence of ${}^8\mathrm{B}$ $\mathrm{CE}\nu \mathrm{NS}$ interactions and is enabled by robust background modeling and mitigation techniques. This demonstrates LZ’s ability to detect rare signals at keV-scale energies.

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.