activity
20022026
most citedFirst results from the LUX dark matter experiment at the Sanford Underground Research Facility

1.6k citations · 8.8k across the 101 of their papers we have counts for

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hep-ex2026

Search for dark matter particle interactions in an extended nuclear recoil energy window with the LUX-ZEPLIN (LZ) experiment

D. S. Akerib, A. K. Al Musalhi, B. J. Almquist +214

We report on a search for dark matter particles interacting with xenon nuclei in an exposure of 2.84 tonne-years with the LUX-ZEPLIN (LZ) experiment. An extended nuclear recoil ene…

hep-ex2026

Low-Energy Radon Backgrounds from Electrode Grids in Dual-Phase Xenon TPCs

D. S. Akerib, A. K. Al Musalhi, F. Alder +264

The dual-phase xenon time projection chamber (TPC) is a powerful technology to detect rare interactions such as scatters of dark matter particles on nuclei. In particular, the buil…

hep-ex2026

Measurement of the Muon Flux at the Sanford Underground Research Facility with the LUX-ZEPLIN Dark Matter Detector

D. S. Akerib, A. K. Al Musalhi, F. Alder +209

High-energy cosmic-ray muons reaching deep underground laboratories can cause events in detectors that mimic signals expected from dark matter particles, neutrinos, or rare decays.…

hep-ex2024★ 21 cited

The XLZD Design Book: Towards the Next-Generation Liquid Xenon Observatory for Dark Matter and Neutrino Physics

XLZD Collaboration, J. Aalbers, K. Abe +441

This report describes the experimental strategy and technologies for XLZD, the next-generation xenon observatory sensitive to dark matter and neutrino physics. In the baseline desi…

hep-ex2024★ 4 cited

Probing the Scalar WIMP-Pion Coupling with the first LUX-ZEPLIN data

J. Aalbers, D. S. Akerib, A. K. Al Musalhi +200

Weakly interacting massive particles (WIMPs) may interact with a virtual pion that is exchanged between nucleons. This interaction channel is important to consider in models where…

hep-ex2024★ 3 cited

Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection

J. Schueler, H. M. Araújo, S. N. Balashov +40

Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable…