Related papers: Neuromorphic Readout for Hadron Calorimeters
A digital hadronic calorimeter using MICROMEGAS as active elements is a very promising choice for particle physics experiments at future lepton colliders. These experiments will be optimized for application of the particle flow algorithm…
To address the challenges of providing high performance calorimetry in future hadron collider experiments under conditions of high luminosity and high radiation (FCChh environments), we are conducting R&D on advanced calorimetry techniques…
Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In…
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the…
The prototype of the hadron calorimeter module consisting of 66 scintillator/lead layers with the 15x15 cm^2 cross section and 5 nuclear interaction lengths has been designed and produced for the zero degree calorimeter of the BM@N…
Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines…
An improved weighting algorithm applied to hadron showers has been developed for a fine grained LAr calorimeter. The new method uses tabulated weights which depend on the density of energy deposited in individual cells and in a surrounding…
Low-loss nanophotonic resonators have been widely used in fundamental science and applications thanks to their ability to concentrate optical energy. Key for resonator engineering, the total intrinsic loss is easily determined by…
Computational hardware designed to mimic biological neural networks holds the promise to resolve the drastically growing global energy demand of artificial intelligence. A wide variety of hardware concepts have been proposed, and among…
Self-assembled Plasmonic Nanoparticle Networks (PNN) composed of chains of 12-nm diameter crystalline gold nanoparticles exhibit a longitudinally coupled plasmon mode cen- tered at 700 nm. We have exploited this longitudinal absorption band…
Photonic technologies offer great prospects for novel ultrafast, energy-efficient and hardware-friendly neuromorphic (brain-like) computing platforms. Moreover, neuromorphic photonic approaches based upon ubiquitous, technology-mature and…
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of…
Neuromorphic computing is poised to further the success of software-based neural networks by utilizing improved customized hardware. However, the translation of neuromorphic algorithms to hardware specifications is a problem that has been…
Temperature is one of the most relevant parameters for the regulation of intracellular processes. Measuring localized subcellular temperature gradients is fundamental for a deeper understanding of cell function, such as the genesis of…
While software implementations of neural networks have driven significant advances in computation, the von Neumann architecture imposes fundamental limitations on speed and energy efficiency. Neuromorphic networks, with structures inspired…
We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector's configuration. This may open new…
Topological plasmonics offers new ways to manipulate light by combining concepts from topology and plasmonics, similar to topological edge states in photonics. However, designing such topological states remains challenging due to the…
Understanding the interplay between illumination and the electron distribution in metallic nanostructures is a crucial step towards developing applications such as plasmonic photo-catalysis for green fuels, nano-scale photo-detection and…
The purpose of the MIPP experiment is to study the inclusive production of photons, pions, kaons and nucleons in pi, K and p interactions on various targets using beams from the Main Injector at Fermilab. The function of the calorimeters is…
Neuromorphic computing using spike-based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. In this…