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The astrophysical origin of over 90 compact binary mergers discovered by the LIGO and Virgo gravitational wave observatories is an open question. While the unusual mass and spin of some of the discovered objects constrain progenitor scenarios, the observed mergers are consistent with multiple interpretations. A promising approach to solve this question is to consider the observed distributions of binary properties and compare them to expectations from different origin scenarios. Here we describe a new hierarchical population analysis framework to assess the relative contribution of different formation channels simultaneously. For this study we considered binary formation in active galactic nucleus (AGN) disks along with phenomenological models, but the same framework can be extended to other models. We find that high-mass and high-mass-ratio binaries appear more likely to have an AGN origin compared to having the same origin as lower-mass events. Future observations of high-mass black hole mergers could further disentangle the AGN component from other channels.

 

We present linear polarimetry for seven hydrogen-poor superluminous supernovae (SLSNe-I) of which only one has previously published polarimetric data. The best-studied event is SN 2017gci, for which we present two epochs of spectropolarimetry at +3 d and +29 d post-peak in rest frame, accompanied by four epochs of imaging polarimetry up to +108 d. The spectropolarimetry at +3 d shows increasing polarisation degree P towards the redder wavelengths and exhibits signs of axial symmetry, but at +29 d, P  ∼ 0 throughout the spectrum, implying that the photosphere of SN 2017gci evolved from a slightly aspherical configuration to a more spherical one in the first month post-peak. However, an increase of P to ∼0.5% at ∼ + 55 d accompanied by a different orientation of the axial symmetry compared to +3 d implies the presence of additional sources of polarisation at this phase. The increase in polarisation is possibly caused by interaction with circumstellar matter (CSM), as already suggested by a knee in the light curve and a possible detection of broad H α emission at the same phase. We also analysed the sample of all 16 SLSNe-I with polarimetric measurements to date. The data taken during the early spectroscopic phase show consistently low polarisation, indicating at least nearly spherical photospheres. No clear relation between the polarimetry and spectral phase was seen when the spectra resemble Type Ic SNe during the photospheric and nebular phases. The light-curve decline rate, which spans a factor of eight, also shows no clear relation with the polarisation properties. While only slow-evolving SLSNe-I have shown non-zero polarisation, the fast-evolving ones have not been observed at sufficiently late times to conclude that none of them exhibit changing P . However, the four SLSNe-I with increasing polarisation degree also have irregular light-curve declines. For up to half of them, the photometric, spectroscopic, and polarimetric properties are affected by CSM interaction. As such, CSM interaction clearly plays an important role in understanding the polarimetric evolution of SLSNe-I. 

Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them occlusion-free while keeping visual linkings legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers the current and predicted future states of objects and labels, such as positions and velocities, as well as the user’s viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. Our experiments on two real-world datasets show that RL-LABEL effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on AR labels within dynamic scenes. 

In turbulence, non-linear terms drive energy transfer from large-scale eddies into small scales through the so-called energy cascade. Turbulence often relaxes toward states that minimize energy; typically these states are considered globally. However, turbulence can also relax toward local quasi-equilibrium states, creating patches or cells where the magnitude of non-linearity is reduced and the energy cascade is impaired. We show, using data from the Magnetospheric Multiscale (MMS) mission, and for the first time, compelling observational evidence that this ‘cellularization’ of turbulence can occur due to local relaxation in a strongly turbulent natural environment such as the Earth’s magnetosheath.

 

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.