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At least 1,243 records · Page 69

First Measurement of $𝜈_𝑒$ and $\bar{𝜈}_𝑒$ Charged-Current Single Charged-Pion Production Differential Cross Sections on Argon Using the MicroBooNE Detector

Understanding electron neutrino interactions is crucial for measurements of neutrino oscillations and searches for new physics in neutrino experiments. We present the first measurement of the flux-averaged $𝜈_𝑒$ + $\bar{𝜈}_𝑒$ charged-current single charged-pion production cross section on argon using the MicroBooNE detector and data from the NuMI neutrino beam. The total cross section is measured to be [0.93 ± 0.13⁢(stat) ± 0.27⁢(syst)] × 10 −39 cm 2 /nucleon at a mean $𝜈_𝑒$ + $\bar{𝜈}_𝑒$ energy of 730 MeV. Differential cross sections are also reported in electron energy, electron and pion angles, and electron-pion opening angle.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Braiding of Majorana zero modes in vortex cores

Here, we demonstrate the successful simulation of $\sqrt{𝑍-}$, $\sqrt{𝑋-}$, and 𝑋-quantum gates using Majorana zero modes (MZMs) that emerge in magnetic vortices located in topological superconductors. We compute the transition probabilities and geometric phase differences accounting for the full many-body dynamics and show that qubit states can be read out by fusing the vortex core MZMs and measuring the resulting charge density. We visualize the gate processes using the time- and energy-dependent nonequilibrium local density of states. Our results demonstrate the feasibility of employing vortex core MZMs for the realization of fault-tolerant topological quantum computing.

Majorana bound states↗

Emulating 2D Materials with Magnons

Spin waves (magnons) in two-dimensional (2D) materials have received increasing interest due to their unique states and potential for tunability. However, many interesting features of these systems, including Dirac points and topological states, occur at high frequencies, where experimental probes are limited. Here, we study a crystal formed by patterning a hexagonal array of holes in a perpendicularly magnetized thin film. Through simulation, we find that the magnonic band structure imitates that of graphene, but additionally has some kagomelike character and includes a few flat bands. Surprisingly, its nature can be understood using a nine-band tight-binding Hamiltonian. This clear analogy to 2D materials enables band-gap engineering in 2D, topological magnons along 1D phase boundaries, and spectrally isolated modes at 0D point defects. Interestingly, the 1D phase boundaries allow access to the valley degree of freedom through a magnonic analog of the quantum valley Hall insulator. These approaches can be extended to other magnonic systems, but are potentially more general due to the simplicity of the model, which resembles existing results from electron, phonon, photon, and cold-atom systems. This finding brings the physics of spin waves in 2D materials to more experimentally accessible scales, augments it, and outlines a few principles for controlling magnonic states.

Ferrimagnets↗

Viscous Gubser flow with conserved charges to benchmark fluid simulations

We present semi-analytical solutions for the evolution of both the temperature and chemical potentials for viscous Gubser flow with conserved charges. Such a solution can be especially useful in testing numerical codes intended to simulate relativistic fluids with large chemical potentials. The freeze-out hypersurface profiles for constant energy density are calculated, along with the corresponding normal vectors, and presented as a new unit test for numerical codes. We also compare the influence of the equation of state on the semi-analytical solutions. We benchmark the newly developed smoothed particle hydrodynamics code ccake that includes both shear viscosity and three conserved charges. Here, the numerical solutions are in excellent agreement with the semi-analytical solution and are also able to accurately reproduce the hypersurface at freeze-out.

Hydrodynamic models↗

Search for an Anomalous Production of Charged-Current 𝜈 𝑒 Interactions without Visible Pions across Multiple Kinematic Observables in MicroBooNE

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of 1.11 × 10 21 protons on target, a 70% increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. The MicroBooNE data show reasonable agreement with the nominal prediction, with 𝑝 values ≥26.7% when the two 𝜈 𝑒 samples are combined, though the prediction exceeds the data in limited regions of phase space. The data are further compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electronlike interpretation of the MiniBooNE excess based on these models at >99% CLs in all kinematic variables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of charged-current muon neutrino-argon interactions without pions in the final state using the MicroBooNE detector

We report a new measurement of flux-integrated differential cross sections for charged-current (CC) muon neutrino interactions with argon nuclei that produce no final-state pions (𝜈 𝜇 ⁢CC⁢0⁢𝜋). These interactions are of particular importance as a topologically defined signal dominated by quasielasticlike interactions. This measurement was performed with the MicroBooNE liquid argon time projection chamber detector located at the Fermilab Booster Neutrino Beam and uses an exposure of 1.3 ×10 21 protons on target collected between 2015 and 2020. The results are presented in terms of single- and double-differential cross sections as a function of the final-state muon momentum and angle. The data are compared with widely used neutrino event generators. We find good agreement with the single-differential measurements, while only a subset of generators are also able to adequately describe the data in double-differential distributions. This work facilitates comparison with Cherenkov detector measurements, including those located at the Booster Neutrino Be

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Validity of a finite temperature expansion for dense nuclear matter

In this work we provide a new, well-controlled expansion of the equation of state of dense matter from zero to finite temperatures (𝑇) while covering a wide range of charge fractions (𝑌 𝑄 ), from pure neutron to isospin symmetric nuclear matter. Our expansion can be used to describe neutron star mergers using the equation of state inferred from neutron star observations. We discuss how knowledge from low-energy nuclear experiments and heavy-ion collisions can be directly incorporated into the expansion. We also suggest new thermodynamic quantities of interest that can be calculated from theoretical models or directly inferred by experimental data that can be used to infer the finite temperature equation of state. With our new method, we can quantify the uncertainty in our finite 𝑇 and 𝑌 𝑄 expansions without making assumptions about the underlying degrees of freedom. We can reproduce results from a microscopic equation of state up to 𝑇 = 100 MeV for baryon chemical potential 𝜇 𝐵 ≳ 1100 MeV [≈(1–2)⁢𝑛 sat ] within 5% error, with even better results for larger 𝜇 𝐵 and/or lower 𝑇. We investigate the sources of numerical and theoretical uncertainty and discuss future directions of study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection

Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.

composite materials↗

Modeling Plant Nutrient Acquisition Strategies Alters Projections of Carbon and Nitrogen Dynamics in Bioenergy Agroecosystems

Plant strategies to acquire nutrients from limited environments help shape ecosystem carbon (C) and nitrogen (N) cycling and response to environmental change. The effects of plant strategies on ecosystem dynamics are largely uncharacterized in bioenergy agroecosystems, where the impacts could determine bioenergy's ability to meet its sustainability goals of storing C and reducing N loss. We used FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms and Protection), a plant–microbe interaction model of coupled plant nutrient uptake and soil organic matter decomposition, to simulate the effects of nutrient acquisition strategies on soil microbial activity and ecosystem nutrient cycling in bioenergy feedstocks miscanthus (Miscanthus × giganteus) and sorghum (Sorghum bicolor (L.) Moench). We examined the model's ability to reproduce the relative effects of belowground nutrient uptake on microbial activity using a reanalysis of empirical data showing that miscanthus root exudation provoked a larger soil microbial response than sorghum. From baseline model simulations, we found that the ability of miscanthus to retranslocate N resulted in higher N uptake at a lower C cost than the sorghum/soybean rotation and that soil C and N pools increased under perennial (miscanthus) and decreased under annual (sorghum/soybean) cultivation. The model also predicted that greater root exudation increased soil C accumulation, highlighting the role of roots in forming stable soil C. Overall, the baseline model was unable to reproduce field observations of miscanthus root exudation stimulating microbial activity more than sorghum. To improve the model, we updated the soil microbial parameters in miscanthus to have faster decomposition, a higher C/N ratio, and greater carbon use efficiency. These changes improved the simulated soil microbial response to miscanthus root exudation, supporting the hypothesis that miscanthus soils foster a microbial community that is more responsive to root exudation than that of sorghum.

09 BIOMASS FUELS↗

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗

Environmental factors have a greater influence on photosynthetic capacity in C 4 plants than biochemical subtypes or growth forms

Our understanding of how photosynthetic capacity varies among C 4 species and across growth and measurement conditions remains limited. We collated 1696 CO 2 response curves of net CO 2 assimilation rate (A/C i curves) from C 4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (V pmaxA ) and CO 2 -saturated net photosynthetic rate (A max ), two key parameters describing photosynthetic capacity. We examined how V pmaxA and A max vary with species-specific traits, growth and measurement conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Constitutive down‐regulation of liguleless alleles in sorghum drives increased productivity and water use efficiency

Plant architecture influences the microenvironment throughout the canopy layer. Plants with a more erect leaf architecture allow for an increase in planting densities and allow more light to reach lower canopy leaves. This is predicted to increase crop carbon assimilation. Frictional resistance to wind reduces air movement in the lower canopy, resulting in higher humidity. By increasing the proportion of canopy photosynthesis in the more humid lower canopy, gains in the efficiency of water use might be expected, although this may be slightly offset by the more open erectophile form canopy. An anatomical feature in members of the Poaceae family that impacts leaf angle is the articulated junction of the sheath and blade, which also bares the ligule and auricles. Mutants, which lack ligules and auricles, show no articulation at this junction, resulting in leaves that are near vertical. In maize, these phenotypes termed liguleless result from null mutations of genes: ZmLG1 (Zm00001eb67740) and ZmLG2 (Zm00001eb147220). In sorghum, SbiRTx430.06G264300 (SbLG1) and SbiRTx430.03G392300 (SbLG2) are annotated as the respective maize homologues. A hair-pin element designed to down-regulate both SbLG1 and SbLG2 was introduced into the grain sorghum genotype RTx430. Derived transgenic events harbouring the hair-pin failed to develop ligules and displayed reduced leaf angles to the vertical, but less vertical than in null mutations. Under field settings, plots sown with these sorghum events having an erect architecture phenotype displayed an increase in photosynthesis in lower canopy levels, which led to increases in above-ground biomass and seed yield, without an increase in water use.

59 BASIC BIOLOGICAL SCIENCES↗

Phytosulfokine downregulates defense‐related WRKY transcription factors and attenuates pathogen‐associated molecular pattern‐triggered immunity

SUMMARY Phytosulfokine (PSK) is a plant growth‐promoting peptide hormone that is perceived by its cell surface receptors PSKR1 and PSKR2 in Arabidopsis. Plants lacking the PSK receptors show phenotypes consistent with PSK signaling repressing some plant defenses. To gain further insight into the PSK signaling mechanism, comprehensive transcriptional profiling of Arabidopsis treated with PSK was performed, and the effects of PSK treatment on plant defense readouts were monitored. Our study indicates that PSK's major effect is to downregulate defense‐related genes; it has a more modest effect on the induction of growth‐related genes. WRKY transcription factors (TFs) emerged as key regulators of PSK‐responsive genes, sharing commonality with a pathogen‐associated molecular pattern (PAMP) responses, flagellin 22 (flg22), but exhibiting opposite regulatory directions. These PSK‐induced transcriptional changes were accompanied by biochemical and physiological changes that reduced PAMP responses, notably mitogen‐activated protein kinase (MPK) phosphorylation (previously implicated in WRKY activation) and the cell wall modification of callose deposition. Comparison with previous studies using other growth stimuli (the sulfated plant peptide containing sulfated tyrosine [PSY] and Pseudomonas simiae strain WCS417) also reveals WRKY TFs' overrepresentations in these pathways, suggesting a possible shared mechanism involving WRKY TFs for plant growth–defense trade‐off.

Liu, Dian [Biochemistry and Molecular Biophysics T↗

Hybrid PDES Simulation of HPC Networks Using Zombie Packets

Although high-fidelity network simulations have proven to be reliable and cost-effective tools to peer into architectural questions for high-performance computing (HPC) networks, they incur a high resource cost. The time spent in simulating a single millisecond of network traffic in the highest detail can take hours, even for static, well-behaved traffic patterns such as uniform random. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should only be used when appropriate. Thus, there is a need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. Here, we present a surrogate model for HPC networks in which: packets bypass the network, while the network state is left untouched, i.e., suspended. To bypass the network, we use historical data to estimate the arrival time at which every packet should be scheduled at; to suspend the network, all in-flight packets are scheduled to arrive at their destinations, and are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. This light-weight surrogate obtained up to 76× speedup. Keeping the zombies in the network showed an increase in the accuracy of the high-fidelity simulation on restart when compared to restarting the network from an empty state.

HPC networks↗

Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe

Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.

ecophysiology↗

The Arctic

The Arctic environment in 2024 continued on a trajectory that has put it in a state far different from that of the twentieth century. Ongoing accumulation of greenhouse gases in the atmosphere continues to quickly warm the Arctic, resulting in rapid changes in the cryosphere that are driving cascading impacts to climate, ecological, and societal systems. Many weather- and climate-related impacts in the Arctic are the result of compounding change, such as increased riverbank erosion, which is proximately due to increased river discharge from higher seasonal precipitation, yet is also exacerbated by thawing permafrost. However, even individual storms occur within very different ocean and ice conditions than were typically present in the late twentieth century. As a result, the impacts, including high winds, excessive precipitation, and coastal inundation, may be quite different nowadays, as exemplified by the October 2024 storm in northwest Alaska that produced severe coastal flooding in several communities. To share some of these impacts with a wider audience, select extreme weather impacts around the greater Arctic have been highlighted through the inclusion of sidebars in recent State of the Climate Arctic chapters (e.g., Benestad et al. 2023; Thoman et al. 2024).

Thoman, Richard L. [Univ. of Alaska, Fairbanks, AK↗

A Concept of a Convection–Cloud Chamber to Study Aerosol–Cloud–Drizzle Interactions

Understanding and quantifying the full chain of processes from aerosol activation to drizzle formation, and the associated feedbacks to the aerosol chemical and physical properties, all within a turbulent cloud are some of the toughest challenges in atmospheric chemistry and physics and are keys to the cloud–precipitation puzzle. This paper describes a concept for a new type of research facility consisting of a cloud chamber plus associated instrumentation and computational models, to explore aerosol–cloud interactions and processing, cloud optical properties, entrainment–cloud interactions, and quantitative assessment of drizzle onset. The envisioned design is for a 3 m × 3 m × 9 m chamber, such that the height is sufficient to achieve long lifetimes for aerosol processing and for significant drizzle growth by collision and coalescence. A suite of computational tools for simulating microphysical properties in the chamber provides a digital twin for designing the chamber and a range of example experiments. Theory and test results from novel remote sensing systems for exploring chemical and physical interactions and evolution of aerosols, cloud droplets, and drizzle within turbulent clouds are described. Testing of technology needed for the operation of a large-volume chamber, including aerosol generation methods and novel materials for water vapor boundary conditions, is described. Simulations suggest that spatially uniform turbulence and microphysical properties can be sustained in a steady state, with reasonable aerosol and water vapor fluxes, and that substantial drizzle can be produced through collision and coalescence of cloud droplets. Remaining challenges for more detailed engineering design and a discussion of possible first-light experiments are described.

54 ENVIRONMENTAL SCIENCES↗