D. Dias | Searcharxiv

arXiv2026

Average universal shower profile reconstruction using radio interferometry

Radio detection of extensive air showers enables near-continuous observation and precise measurements of the shower geometry and the depth of shower maximum, $X_{\rm max}$. Beyond $X_{\rm max}$, the longitudinal shower development follows a Universal Shower Profile (USP), whose shape parameters contain information on primary mass composition and hadronic interaction models. While previous studies have focused on event-by-event reconstruction of profile parameters, in this work we investigate the reconstruction of the average USP using radio interferometric techniques. Using Monte Carlo simulations, we reconstruct the average longitudinal profile directly from radio data and extract the Gaisser-Hillas shape parameters $(R,L)$. We find that the radio-derived average profiles provide enhanced separation between primary masses and hadronic interaction models compared to that obtained from fluorescence-equivalent longitudinal profiles. These results demonstrate that radio interferometry can access higher-order information on the longitudinal shower development and that the use of the average USP significantly improves the sensitivity to composition and hadronic interaction studies at ultra-high energies.

astro-ph.HE↗

arXiv2022

OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.

cs.RO↗