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Abstract The European spallation source (ESS) will be the world’s brightest neutron source and will open a new intensity frontier in particle physics. The HIBEAM collaboration aims to exploit the unique potential of the ESS with a dedicated ESS instrument for particle physics which offers world-leading capability in a number of areas. The HIBEAM program includes the first search in thirty years for free neutrons converting to antineutrons and searches for sterile neutrons, ultralight axion dark matter and nonzero neutron electric charge. This paper outlines the capabilities, design, infrastructure, and scientific potential of the HIBEAM program, including its dedicated beamline, neutron optical system, magnetic shielding and control, and detectors for neutrons and antineutrons. Additionally, we discuss the long-term scientific exploitation of HIBEAM, which may include measurements of the neutron electric dipole moment and precision studies of neutron decays. 

Trees are an important carbon sink as they accumulate biomass through photosynthesis1 . Identifying tree species that grow fast is therefore commonly considered to be essential for efective climate change mitigation through forest planting. Although species characteristics are key information for plantation design and forest management, feld studies often fail to detect clear relationships between species functional traits and tree growth2 . Here, by consolidating four independent datasets and classifying the acquisitive and conservative species based on their functional trait values, we show that acquisitive tree species, which are supposedly fast-growing species, generally grow slowly in feld conditions. This discrepancy between the current paradigm and feld observations is explained by the interactions with environmental conditions that infuence growth. Acquisitive species require moist mild climates and fertile soils, conditions that are generally not met in the feld. By contrast, conservative species, which are supposedly slow-growing species, show generally higher realized growth due to their ability to tolerate unfavourable environmental conditions. In general, conservative tree species grow more steadily than acquisitive tree species in non-tropical forests. We recommend planting acquisitive tree species in areas where they can realize their fast-growing potential. In other regions, where environmental stress is higher, conservative tree species have a larger potential to fx carbon in their biomass. 

Gene tree discordance is expected in phylogenomic trees and biological processes are often invoked to explain it. However, heterogeneous levels of phylogenetic signal among individuals within datasets may cause artifactual sources of topological discordance. We examined how the information content in tips and subclades impacts topological discordance in the parrots (Order: Psittaciformes), a diverse and highly threatened clade of nearly 400 species. Using ultraconserved elements from 96% of the clade's species-level diversity, we estimated concatenated and species trees for 382 ingroup taxa. We found that discordance among tree topologies was most common at nodes dating between the late Miocene and Pliocene, and often at the taxonomic level of genus. Accordingly, we used two metrics to characterize information content in tips and assess the degree to which conflict between trees was being driven by lower quality samples. Most instances of topological conflict and non-monophyletic genera in the species tree could be objectively identified using these metrics. For subclades still discordant after tip-based filtering, we used a machine learning approach to determine whether phylogenetic signal or noise was the more important predictor of metrics supporting the alternative topologies. We found that when signal favored one of the topologies, noise was the most important variable in poorly performing models that favored the alternative topology. In sum, we show that artifactual sources of gene tree discordance, which are likely a common phenomenon in many datasets, can be distinguished from biological sources by quantifying the information content in each tip and modeling which factors support each topology.