activity
20192026
most citedSearch for Coherent Elastic Scattering of Solar B Neutrinos in the XENON1T Dark Matter Experiment

114 citations · 186 across the 14 of their papers we have counts for

collaborators
Showing 2024Show all

12 papers · 1 filter

physics.ins-det2024

The neutron veto of the XENONnT experiment: Results with demineralized water

XENON Collaboration, E. Aprile, J. Aalbers +167

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particl…

physics.ins-det2024

Low-Energy Nuclear Recoil Calibration of XENONnT with a YBe Photoneutron Source

XENON Collaboration, E. Aprile, J. Aalbers +169

Characterizing low-energy, keV-range nuclear recoils near the detector threshold is one of the major challenges for large direct dark matter detectors. To that end, we have success…

hep-ex2024

Search for Light Dark Matter in Low-Energy Ionization Signals from XENONnT

E. Aprile, J. Aalbers, K. Abe +165

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physi…

physics.ins-det2024

Neutrinoless Double Beta Decay Sensitivity of the XLZD Rare Event Observatory

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

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter…

hep-ex2024

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…

physics.ins-det2024

Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline

J. Aalbers, K. Abe, M. Adrover +231

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-t…