SearcharxivSearch

arXiv subjects

L. Velasco

Publications and source records attributed to L. Velasco.

5 recordsLinked to original sources

BOCOSUR: An all sky network for fireball detection in Uruguay

Over the past couple of decades, several networks for the automatic detection of fireballs have been deployed. Their primary scientific goal is to facilitate the rapid recovery of meteorites, determine their pre-atmospheric orbits, and look for possible dynamic links with parent bodies. The Bocosur network is a contribution to the global deployment of automated fireball networks and to the increase of the number of recoverable meteorite falls. It is located in Uruguay, South America (Lat: -30$^{\circ}$ to -35$^{\circ}$). Its main scientific goal is the detection of fireballs of asteroidal origin, massive enough to produce meteorites, and also to inspire secondary-level students and teachers through their involvement in this citizen-science oriented project. The deployment of this network started in 2019, and was completed in March, 2023, when we installed 20 stations separated $\sim 120$ km, covering an area of $\sim 180,000$ km$^2$. During this period of time, one major technological upgrade was made when we migrated from a well-known camera to a higher-resolution, more sensitive system. We were able to build a completely autonomous system at an affordable cost that can be replicated in all the stations. A comparison between the astrometric and photometric performance of these two detection systems is reported. Also, a photometric methodology for estimating the brightness of very bright fireballs is presented and validated against the known magnitudes of Jupiter and the full Moon. We obtain mean residuals of the astrometric reduction of $\sim$5', and the discrepancy between the obtained brightness of Jupiter and the Moon average to 0.18 and 1.2 magnitudes, respectively. Results on the processing of a very bright (M$_{peak}\sim$-9.0 mag) fireball detected in four stations are also presented.

astro-ph.EP

Machine learning-based event generator for electron-proton scattering

We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof-of-concept to mitigate theory bias in inferring vertex-level event distributions needed to reconstruct physical observables.

hep-ph

Demonstration of latency-aware 5G network slicing on optical metro networks

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semidisaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of European Telecommunications Standards Institute (ETSI) network function virtualization network services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip time over an 80 km link is under 800s and the service deployment time is under 180s.

cs.NI

The dichotomy of distributed and centralized control: METRO-HAUL, when control planes collide for 5G networks

Automating the provisioning of 5G services, deployed over a heterogeneous infrastructure (in terms of domains, technologies, and management platforms), remains a complex task, yet driven by the constant need to provide end-to-end connections at network slices at reducing costs and service deployment time. At the same time, such services are increasingly conceived around interconnected functions and require allocation of computing, storage, and networking resources. The METRO-HAUL 5G research initiative acknowledges the need for automation and strives to develop an orchestration platform for services and resources that extends, integrates, and builds on top of existing approaches, macroscopically adopting Transport Software Defined Networking principles, and leveraging the programmability and open control of Transport SDN.

eess.SP