activity
20092026
most citedThe Atacama Cosmology Telescope: Cosmological Parameters from the 2008 Power Spectra

401 citations · 2.4k across the 42 of their papers we have counts for

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7 papers · 1 filter

physics.ins-det2026

Enhancing Neutrinoless Double-Beta Decay Sensitivity of Liquid-Xenon Time Projection Chamber with Augmented Convolutional Neural Network

E. Aprile, J. Aalbers, K. Abe +173

Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its adv…

physics.ins-det2025

Proof-of-concept of a xenon-based cryogenic heat pump demonstrator for future liquid xenon observatories

P. Schulte, D. Wenz, L. Althueser +7

This manuscript details the proof-of-concept of a small-scale cryogenic heat pump demonstrator, a technology designed to enable high-flow xenon distillation systems for the removal…

physics.ins-det2025★ 2 cited

Radon Removal in XENONnT down to the Solar Neutrino Level

E. Aprile, J. Aalbers, K. Abe +169

The XENONnT experiment has achieved an exceptionally low Rn activity concentration within its inner 5.9tonne liquid xenon detector of (0.900.01stat.$\…

physics.ins-det2024★ 6 cited

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★ 11 cited

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…

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…