Brandon Perez | Searcharxiv

arXiv2026

The Ultraviolet Spectrograph on ESA's Jupiter Icy Moons Explorer Mission (JUICE-UVS)

The Jupiter Icy Moons Explorer (JUICE) mission, led by ESA, has an Ultraviolet Spectrograph (JUICE-UVS) contributed by NASA and built at Southwest Research Institute. JUICE-UVS is designed to provide a diversity of measurements to further our understanding of the potential habitability of icy ocean worlds at Jupiter and to study Jupiter and the Jovian system as an archetype for gas giants. JUICE-UVS observes photons in the 50-204 nm wavelength range at moderate spectral and spatial resolution along a 7.5 deg slit composed of 7.3 deg x 0.1 deg and 0.2 deg x 0.2 deg contiguous sections. JUICE-UVS performs a comprehensive study of icy satellite atmospheres, plumes, surfaces, and local space environments; Jupiter's atmosphere and aurora; Io and its Io Plasma Torus; and other Jupiter system targets (rings, small moons, etc.) as available. The variety of observational techniques employed include: nadir push-broom imaging, disk scans, limb stares, stellar and solar occultations, Jupiter transit observations, and neutral cloud/plasma torus stares and scans. This paper describes the UVS investigation's science plans, instrument details, concept of operations, and data formats in the context of the JUICE mission's habitability and Jupiter system goals.

astro-ph.EP↗

arXiv2019

FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

physics.data-an↗