Semi-Visible Dark Photon Phenomenology at the GeV Scale

In rich dark sector models, dark photons heavier than tens of MeV can behave as semi-visible particles: their decays contain both visible and invisible final states. We present models containing multiple dark fermions which allow for such decays and inscribe them in the context of inelastic dark matter and heavy neutral leptons scenarios. Our models represent a generalization of the traditional inelastic dark matter model by means of a charge conjugation symmetry. We revisit constraints on dark photons from $e^+e^-$ colliders and fixed target experiments, including the effect of analysis vetoes on semi-visible decays, $A^\prime \to \psi_i (\psi_j \to \psi_k \ell^+\ell^-)$. We find that in some cases, the BaBar and NA64 experiments no longer exclude large kinetic mixing, $\varepsilon \sim 10^{-2}$, and, specifically, the related explanation of the discrepancy in the muon $(g-2)$. This reopens an interesting window in parameter space for dark photons with exciting discovery prospects. We point out that a modified missing-energy search at NA64 can target short-lived $A^\prime$ decays and directly probe the newly-open parameter space.Comment: 41 pages, 22 figures, version published in PR

Panorama of new-physics explanations to the MiniBooNE excess

The MiniBooNE low-energy excess stands as an unexplained anomaly in short-baseline neutrino oscillation experiments. It has been shown that it can be explained in the context of dark sector models. Here, we provide an overview of the possible new-physics solutions based on electron, photon, and dilepton final states. We systematically discuss the various production mechanisms for dark particles in neutrino-nucleus scattering. Our main result is a comprehensive fit to the MiniBooNE energy spectrum in the parameter space of dark neutrino models, where short-lived heavy neutral leptons are produced in neutrino interactions and decay to e+e- pairs inside the detector. For the first time, other experiments will be able to directly confirm or rule out dark neutrino interpretations of the MiniBooNE low-energy excess

DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering

We introduce DarkNews, a lightweight Python-based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering.Comment: 18 pages, 6 tables, 8 figure

A panorama of new-physics explanations to the MiniBooNE excess

The MiniBooNE low-energy excess stands as an unexplained anomaly in short-baseline neutrino oscillation experiments. It has been shown that it can be explained in the context of dark sector models. Here, we provide an overview of the possible new-physics solutions based on electron, photon, and dilepton final states. We systematically discuss the various production mechanisms for dark particles in neutrino-nucleus scattering. Our main result is a comprehensive fit to the MiniBooNE energy spectrum in the parameter space of dark neutrino models, where short-lived heavy neutral leptons are produced in neutrino interactions and decay to $e^+e^-$ pairs inside the detector. For the first time, other experiments will be able to directly confirm or rule out dark neutrino interpretations of the MiniBooNE low-energy excess.Comment: 35 pages, 21 figure

A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level

Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today's novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to derive such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model. Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R2) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R2 between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat. Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing

CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands

The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence

Dipole-Coupled Neutrissimo Explanations of the MiniBooNE Excess Including Constraints from MINERvA Data

We revisit models of heavy neutral leptons (neutrissimos) with transition magnetic moments as explanations of the $4.8\sigma$ excess of electron-like events at MiniBooNE. We perform a detailed Monte Carlo-based analysis to re-examine the preferred regions in the model parameter space to explain MiniBooNE, considering also potential contributions from oscillations due to an eV-scale sterile neutrino. We then derive robust constraints on the model using neutrino-electron elastic scattering data from MINERvA. We find that MINERvA rules out a large region of parameter space, but allowed solutions exist at the $2\sigma$ confidence level. A dedicated MINERvA analysis would likely be able to probe the entire region of preference of MiniBooNE in this model.Comment: 14 page

Responsible AI for Earth Observation

The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges such as environmental monitoring, disaster response and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the responsible dimensions inherent in its application within these domains. In this paper, we represent a pioneering effort to systematically define the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geo-privacy and privacy-preserving measures, as well as maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the paper explores potential opportunities and emerging trends, providing valuable insights for future research endeavors

Responsible AI for Earth Observation

The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges such as environmental monitoring, disaster response and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the responsible dimensions inherent in its application within these domains. In this paper, we represent a pioneering effort to systematically define the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geo-privacy and privacy-preserving measures, as well as maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the paper explores potential opportunities and emerging trends, providing valuable insights for future research endeavors