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Understanding whether organisms will be able to adapt to human-induced stressors currently endangering their existence is an urgent priority. Globally, multiple species moult from a dark summer to white winter coat to maintain camouflage against snowy landscapes. Decreasing snow cover duration owing to climate change is increasing mismatch in seasonal camouflage. To directly test for adaptive responses to recent changes in snow cover, we repeated historical (1950s) field studies of moult phenology in mountain hares ( Lepus timidus ) in Scotland. We found little evidence that population moult phenology has shifted to align seasonal coat colour with shorter snow seasons, or that phenotypic plasticity prevented increases in camouflage mismatch. The lack of responses resulted in 35 additional days of mismatch between 1950 and 2016. We emphasize the potential role of weak directional selection pressure and low genetic variability in shaping the scope for adaptive responses to anthropogenic stressors. 

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling. Published by the American Physical Society2025 

Abstract The DarkSide-20k dark matter experiment, currently under construction at LNGS, features a dual-phase time projection chamber (TPC) with a ∼ 50 t argon target from an underground well. At this scale, it is crucial to optimise the argon flow pattern for efficient target purification and for fast distribution of internal gaseous calibration sources with lifetimes of the order of hours. To this end, we have performed computational fluid dynamics simulations and heat transfer calculations. The residence time distribution shows that the detector is well-mixed on time-scales of the turnover time (∼ 40 d). Notably, simulations show that despite a two-order-of-magnitude difference between the turnover time and the half-life of^83mKr of 1.83 h, source atoms have the highest probability to reach the centre of the TPC 13 min after their injection, allowing for a homogeneous distribution before undergoing radioactive decay. We further analyse the thermal aspects of dual-phase operation and define the requirements for the formation of a stable gas pocket on top of the liquid. We find a best-estimate value for the heat transfer rate at the liquid-gas interface of 62 W with an upper limit of 144 W and a minimum gas pocket inlet temperature of 89 K to avoid condensation on the acrylic anode. This study also informs the placement of liquid inlets and outlets in the TPC. The presented techniques are widely applicable to other large-scale, noble-liquid detectors. 

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low-energy ( $<3\mathrm{MeV}$) blips around fiberglass mechanical support struts along the time projection chamber edges with energy spectrum features consistent with the Compton edge of 2.614 MeV ${}^{208}\mathrm{Tl}$decay $\gamma$rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE’s data to few percent precision and to measure the specific activity of ${}^{208}\mathrm{Tl}$in the fiberglass composing these struts, $(11.7\pm 0.2\left(\mathrm{stat}\right)\pm 3.1\left(\mathrm{syst}\right))\mathrm{Bq}/\mathrm{kg}$. Cosmogenically produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated cosmic-ray simulations, suggesting either incorrect modeling of incident cosmic fluxes or particle transport modeling issues in eant4. Published by the American Physical Society2025 

Abstract DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with 50 tonnes of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Trento, Italy) were selected as the photon sensors covering two$$10.5~\text {m}^2$$ $10.5{\text{m}}^2$Optical Planes, one at each end of the TPC, and a total of$$5~\text {m}^2$$ $5{\text{m}}^2$photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK NUV-HD-cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77 K at the wafer level with a custom-designed probe station. As of March 2025, 1314 of the 1400 production wafers (94% of the total) for DarkSide-20k were tested. The wafer yield is$$93.2\pm 2.5$$ $93.2\pm 2.5$%, which exceeds the 80% specification defined in the original DarkSide-20k production plan. 

We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab’s booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to $6.86\times {10}^{20}$protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, 200–400 MeV for muons and 100–200 MeV for pions. Published by the American Physical Society2024 

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses. Published by the American Physical Society2024 

Abstract SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds. 

Abstract A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE’s total dataset. The selection yields a sample with$$60\%$$ $60\%$of selected tracks corresponding to neutron-induced secondary protons. At this purity, the integrated efficiency is 8.4% for neutrons that produce a detectable proton.