Samuel Carter | Search arXiv

arXiv2026

A new methodology for inferring the plasma conditions in solar flare energetic electron source regions from in situ electron energy spectra

The conditions within solar flares that lead to efficient electron acceleration are not well constrained. It is not clear whether the populations accelerated out into the heliosphere and inward into the chromosphere originate in the same regions. By analysing the energy distributions of heliospheric populations, modelling suggests that it should be possible to see evidence of their originating region(s), including the presence of hot, dense flare plasma. By creating and utilising a novel in situ spectral analysis package called INSPEX we have performed this analysis for flare electrons observed in situ on 09/10/2021, constructing both peak flux and fluence spectra from combined Solar Orbiter in situ electron measurements. We compare how differing methodologies for combining the datasets influence the spectral shapes and the retrieved parameters over an energy range of 0.5-80 keV. We fit different functions to the multi-component form of the energy spectra, testing combinations of thermal and/or power law components, comparing the fit statistics. We find that the spectra can be fitted with two distinct thermal curves at energies below 20 keV, corresponding to typical corona/active region and flaring material temperatures, varying between 1.4 - 4.1 MK and 12.5 - 23.1 MK depending on the rebinning window and peak flux extraction method. This study showcases how INSPEX can provide a novel and user-friendly methodology for studying electron spectra with different instrumentation, allowing investigation of multiple spectral types and signatures of acceleration and transport. This first application provides a benchmark case for the analysis of similar flares.

astro-ph.SR↗

arXiv2024

Scene Exploration by Vision-Language Models

Active perception enables robots to dynamically gather information by adjusting their viewpoints, a crucial capability for interacting with complex, partially observable environments. In this paper, we present AP-VLM, a novel framework that combines active perception with a Vision-Language Model (VLM) to guide robotic exploration and answer semantic queries. Using a 3D virtual grid overlaid on the scene and orientation adjustments, AP-VLM allows a robotic manipulator to intelligently select optimal viewpoints and orientations to resolve challenging tasks, such as identifying objects in occluded or inclined positions. We evaluate our system on two robotic platforms: a 7-DOF Franka Panda and a 6-DOF UR5, across various scenes with differing object configurations. Our results demonstrate that AP-VLM significantly outperforms passive perception methods and baseline models, including Toward Grounded Common Sense Reasoning (TGCSR), particularly in scenarios where fixed camera views are inadequate. The adaptability of AP-VLM in real-world settings shows promise for enhancing robotic systems' understanding of complex environments, bridging the gap between high-level semantic reasoning and low-level control.

cs.RO↗