Modulation of ion escape by ultra-low frequency waves at Venus and Mars in a global hybrid simulation

<p>We investigate the effect of foreshock ultra-low frequency (ULF) waves on the solar wind induced heavy ion escape from Venus and Mars in a global hybrid model. The foreshock ULF waves are excited by backstreaming ion populations scattered at the quasi-parallel bow shock, and convect downstream with the solar wind. In the model, the waves affect magnetic and electric fields in the Venusian and Martian plasma environments causing fluctuations in the heavy ion acceleration processes such as the solar wind ion pickup. This leads to significant modulations in global escape rates of ionized planetary volatiles at the ULF wave frequency. We study this process in a global hybrid model, where ions are treated as particle clouds moving under the Lorentz force and electrons are a charge-neutralizing fluid. The analyzed simulation runs use more than 200 simulation particle clouds per cell on average to allow enough velocity space resolution for resolving foreshock, wave phenomena and ion escape processes self-consistently. We find that at Venus the global ion escape is modulated by the ULF waves even under nominal solar wind and IMF upstream conditions, while at Mars the modulation becomes significant under a strongly radial IMF orientation.</p>

On the Motion of Single and Twin Oblique Particle Clouds in Stagnant Water

The evolution of single and twin oblique particle clouds in stagnant water was investigated using a series of laboratory experiments and the effects of controlling parameters such as sand mass and nozzle spacing were studied. The time variations of particle cloud properties such as frontal position, horizontal and vertical centroids, cloud width, and frontal velocity were measured using image analysis and Particle Image Velocimetry (PIV) techniques. The entrainment coefficients were extracted from the measurements. It was found that the main vortex motion of the frontal heads altered after the collision and a new integrated frontal head was formed. The effects of release angle and particle interactions were studied by comparing the time histories of maximum centerline velocities. It was found that the centerline velocity of twin oblique particle clouds in comparison with twin vertical particle clouds increased with increasing nozzle spacing. The time history of the ratio of horizontal to vertical centroids in oblique particle clouds determined the potential location of sand particles and a practical model was developed to determine the size and location of particle clouds with time. The time histories of normalized cloud width indicated a significant change after the frontal head collision. The particle interactions due to frontal head collision in twin oblique particle clouds significantly increased the cloud width until particle clouds reached the swarm phase. The time at which twin oblique particle clouds reached the swarm phase was recorded and a linear model was proposed to link the time to reach the swarm phase with the cloud aspect ratio and nozzle spacing.

Thin cirrus clouds that extinct supercooled water clouds in the Arctics

<p><span><span>We show that thin cirrus clouds, whose particle radius is greater than 50 μm and number concentration is less than 10 /L, extinct supercooled water clouds (SC) by use of the data of the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the space-borne 94-GHz cloud profiling radar (CPR). We call the cirrus Large-and-Sparse-particle Clouds (LSC). </span></span></p><p><span><span>The space-borne imagers, such as Moderate Resolution Imaging Spectroradiometer (MODIS), cannot measure LSC; hence, LSC had been difficult to be found by satellites. CALIOP is less sensitive to LSC than CPR though CALIOP is usually more sensitive to clouds than CPR because of the cloud particle size distribution of LSC.</span></span></p><p><span><span>The most significant feature of LSC is that LSC extinct SC and cloud particles of SC are changed into pristine ice particles. This is because (1) SC and LSC do not tend to coexist while horizontally oriented ice particle clouds (2D) and LSC tend to coexist, (2) the cloud top height of LSC is higher than that of SC, and (3) the terminal velocity of LSC particles is about 1 km/h.</span></span></p><p><span><span>Because 10-20% of clouds in the Arctic are LSC, LSC would indirectly impact on radiative forcing in the Arctics.</span></span></p>

Casting a graph net to catch dark showers

Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons. Distinguishing such semi-visible jets from large QCD backgrounds is difficult and constitutes an exciting challenge for jet classification. In this article we explore the potential of supervised deep neural networks to identify semi-visible jets. We show that dynamic graph convolutional neural networks operating on so-called particle clouds outperform convolutional neural networks analysing jet images as well as other neural networks based on Lorentz vectors. We investigate how the performance depends on the properties of the dark shower and discuss training on mixed samples as a strategy to reduce model dependence. By modifying an existing mono-jet analysis we show that LHC sensitivity to dark sectors can be enhanced by more than an order of magnitude by using the dynamic graph network as a dark shower tagger.