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The deep ocean is the largest and least known ecosystem on Earth. It hosts numerous pelagic organisms, most of which are able to emit light. Here we present a unique data set consisting of a 2.5-year long record of light emission by deep-sea pelagic organisms, measured from December 2007 to June 2010 at the ANTARES underwater neutrino telescope in the deep NW Mediterranean Sea, jointly with synchronous hydrological records. This is the longest continuous time-series of deep-sea bioluminescence ever recorded. Our record reveals several weeks long, seasonal bioluminescence blooms with light intensity up to two orders of magnitude higher than background values, which correlate to changes in the properties of deep waters. Such changes are triggered by the winter cooling and evaporation experienced by the upper ocean layer in the Gulf of Lion that leads to the formation and subsequent sinking of dense water through a process known as \"open-sea convection\". It episodically renews the deep water of the study area and conveys fresh organic matter that fuels the deep ecosystems. Luminous bacteria most likely are the main contributors to the observed deep-sea bioluminescence blooms. Our observations demonstrate a consistent and rapid connection between deep open-sea convection and bathypelagic biological activity, as expressed by bioluminescence. In a setting where dense water formation events are likely to decline under global warming scenarios enhancing ocean stratification, in situ observatories become essential as environmental sentinels for the monitoring and understanding of deep-sea ecosystem shifts.

The KamLAND experiment uses reactor antineutrinos to study the solar neutrino oscillation parameters. KamLAND recently updated the reactor neutrino measurement, with an almost fourfold increase of the exposure, an improved analysis technique and better understanding of the backgrounds and systematic uncertainties. Extending the analysis down to the inverse beta decay energy threshold gives a best-fit at Δm221 7.58+0.14-0.13(stat)+0.15-0.15(syst) × 10−5 eV2 and tan2 θ12 0.56+0.10-0.07(stat)+0.10-0.06(syst). Local Δχ2-minima at higher and lower Δm221 are now disfavored at >4σ. When combined with solar neutrino data, we obtain Δm221 7.59+0.21-0.21 × 10−5 eV2 and tan2 θ12 0.47+0.06-0.05. KamLAND is presently purifying the detector to measure solar 7Be neutrinos in the near future.

GridPix is a gas-filled detector with an aluminium mesh stretched 50 μm above the Timepix CMOS pixel chip. This defines a high electric field where gas amplification occurs. A feasibility study is ongoing at Nikhef for the application of the GridPix technology as a charge sensitive device in a dual phase noble gas Time Projection Chamber (TPC), within the framework of the DARWIN design study for next generation dark matter experiments. The smallness of the device and well defined materials allow for high radio-purity and low outgassing. The high granularity of a pixel readout and the high detection efficiency of single electrons of GridPix bring benefits especially in terms of energy resolution for small energy deposits. This feature is interesting also for the measurement of the scintillation yield and the ionisation yield of noble liquids. The accurate measurements of such quantities have a direct impact on the data interpretation of dark matter experiments. The application in dual phase argon or xenon TPCs implies several technological challenges, such as the survival of the device at cryogenic temperature as well as the operation in a pure noble gas atmosphere without discharges. We describe here the recent developments of the project.

This topical report of the 2021 US Community Study on the Future of Particle Physics (Snowmass 2021) summarizes the underground facilities needs for upcoming and next generation neutrino experiments. The underground facilities needs are discussed in the context of two broad categories: accelerator neutrinos, in particular with respect to the Deep Underground Neutrino Experiment (DUNE); and non-accelerator neutrinos, focusing on neutrinos from natural sources and on searches for neutrinoless double-beta decay.

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 ± 0.6 % and 84.1 ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/\\(c\\) charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1\\(\\pm0.6\\)% and 84.1\\(\\pm0.6\\)%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.