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Register a new userWawHelioGlow: a model of the heliospheric backscatter glow. I. Model definition
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The helioglow is a fluorescence of interstellar atoms inside the heliosphere, where they are excited by the solar EUV emission. So far, the helioglow of interstellar H and He have been detected. The helioglow features a characteristic distribution in the sky, which can be used to derive both the properties of interstellar neutral gas and those of the solar wind. This requires a simulation model capable of catching with a sufficient realism the essential coupling relations between the solar factors and interstellar. The solar factors include the solar wind flux and its variation with time and heliolatitude, as well as the heliolatitude and time variation of the solar EUV output. The ISN gas inside the heliosphere features a complex distribution function, which varies with time and location. The paper presents the first version of a WawHelioGlow simulation model for the helioglow flux using an optically thin, single scattering approximation. The helioglow computations are based on a sophisticated kinetic treatment of the distribution functions of interstellar H and He provided by the (n)WTPM model. The model takes into account heliolatitudinal and spectral variations of the solar EUV output from observations. We present a formulation of the model and the treatment of the solar spectral flux. The accompanying Paper II illustrates details of the line of sight evolution of the elements of the model and a brief comparison of results of the WawHelioGlow code with selected sky maps of the hydrogen helioglow, obtained by the SWAN instrument onboard the SOHO mission.
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The helioglow is a fluorescence of interstellar atoms inside the heliosphere, where they are excited by the solar EUV. Because the mean free path between collisions for the interstellar gas is comparable to the size of the heliosphere, the distribution function of this gas inside the heliosphere strongly varies in space and with time and is non-Maxwellian. Coupling between realistically modeled solar factors and the distribution function of interstellar neutral gas is accounted for in a helioglow model that we have developed. WawHelioGlow is presented in the accompanying Paper I. Here, we present the evolution of the gas density, solar illumination, helioglow source function, and other relevant parameters building up the helioglow signal for selected lines of sight observed at 1 au. We compare these elements for various phases of the solar cycle and we present the sensitivity of the results to heliolatitudinal anisotropy of the solar EUV output. We assume a realistic latitudinal anisotropy of the solar wind flux using results from analysis of interplanetary scintillations. We compare the simulated helioglow with with selected maps observed by the SOHO/SWAN instrument. We demonstrate that WawHelioGlow is able to reproduce fundamental features of the sky distribution of the helioglow. For some phases of the solar cycle, the model with an anisotropy of the solar EUV output better reproduces the observations, while for other phases no EUV anisotropy is needed. In all simulated cases, the solar wind anisotropy following insight from interplanetary scintillation measurements is present.
The Drag-based Model (DBM) is a 2D analytical model for heliospheric propagation of Coronal Mass Ejections (CMEs) in ecliptic plane predicting the CME arrival time and speed at Earth or any other given target in the solar system. It is based on the equation of motion and depends on initial CME parameters, background solar wind speed, $w$ and the drag parameter $gamma$. A very short computational time of DBM ($<$ 0.01s) allowed us to develop the Drag-Based Ensemble Model (DBEM) that takes into account the variability of model input parameters by making an ensemble of n different input parameters to calculate the distribution and significance of the DBM results. Thus the DBEM is able to calculate the most likely CME arrival times and speeds, quantify the prediction uncertainties and determine the confidence intervals. A new DBEMv3 version is described in detail and evaluated for the first time determing the DBEMv3 performance and errors by using various CME-ICME lists as well as it is compared with previous DB
In this study, we present a new method for forecasting arrival times and speeds of coronal mass ejections (CMEs) at any location in the inner heliosphere. This new approach enables the adoption of a highly flexible geometrical shape for the CME front with an adjustable CME angular width and an adjustable radius of curvature of its leading edge, i.e. the assumed geometry is elliptical. Using, as input, STEREO heliospheric imager (HI) observations, a new elliptic conversion (ElCon) method is introduced and combined with the use of drag-based model (DBM) fitting to quantify the deceleration or acceleration experienced by CMEs during propagation. The result is then used as input for the Ellipse Evolution Model (ElEvo). Together, ElCon, DBM fitting, and ElEvo form the novel ElEvoHI forecasting utility. To demonstrate the applicability of ElEvoHI, we forecast the arrival times and speeds of 21 CMEs remotely observed from STEREO/HI and compare them to in situ arrival times and speeds at 1 AU. Compared to the commonly used STEREO/HI fitting techniques (Fixed-$Phi$, Harmonic Mean, and Self-similar Expansion fitting), ElEvoHI improves the arrival time forecast by about 2 hours to $pm 6.5$ hours and the arrival speed forecast by $approx 250$ km s$^{-1}$ to $pm 53$ km s$^{-1}$, depending on the ellipse aspect ratio assumed. In particular, the remarkable improvement of the arrival speed prediction is potentially beneficial for predicting geomagnetic storm strength at Earth.
Aims: We develop a method for estimating the properties of stellar winds for low-mass main-sequence stars between masses of 0.4 and 1.1 solar masses at a range of distances from the star. Methods: We use 1D thermal pressure driven hydrodynamic wind models run using the Versatile Advection Code. Using in situ measurements of the solar wind, we produce models for the slow and fast components of the solar wind. We consider two radically different methods for scaling the base temperature of the wind to other stars: in Model A, we assume that wind temperatures are fundamentally linked to coronal temperatures, and in Model B, we assume that the sound speed at the base of the wind is a fixed fraction of the escape velocity. In Paper II of this series, we use observationally constrained rotational evolution models to derive wind mass loss rates. Results: Our model for the solar wind provides an excellent description of the real solar wind far from the solar surface, but is unrealistic within the solar corona. We run a grid of 1200 wind models to derive relations for the wind properties as a function of stellar mass, radius, and wind temperature. Using these results, we explore how wind properties depend on stellar mass and rotation. Conclusions: Based on our two assumptions about the scaling of the wind temperature, we argue that there is still significant uncertainty in how these properties should be determined. Resolution of this uncertainty will probably require both the application of solar wind physics to other stars and detailed observational constraints on the properties of stellar winds. In the final section of this paper, we give step by step instructions for how to apply our results to calculate the stellar wind conditions far from the stellar surface.
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack to generalize onto new unseen scenarios. To address this issue, we introduce a new challenging task to generate HD maps. In this work, we explore several autoregressive models using different data representations, including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency.
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