Enhancement of terahertz radiation from GaP emitters by subwavelength antireflective micropyramid structures

Subwavelength antireflective micropyramid structures, designed by rigorous coupled-wave analysis and fabricated by precision micromachining, are used to enhance the terahertz (THz) radiation output of optical rectification in GaP crystal-based emitters. An average 16% increase in the THz radiation power emitted by a 3 mm GaP crystal is experimentally demonstrated using an antireflective micropyramid grating with a period of 60 μm and a base angle of 55.5°. Optimized pyramidal-frustum gratings are shown to operate as highly efficient antireflective structures within an ultrabroadband range of 0.5-5 THz

Media 1: Blu-ray disk lens as the objective of a miniaturized two-photon fluorescence microscope

Originally published in Optics Express on 16 December 2013 (oe-21-25-31604

Laser in Pediatric Dentistry

Laser technology has different applications in dentistry, and, particularly, in Paediatric Dentistry. Depending on laser wavelengths and the physical properties of the tissue which is to be targeted; it is possible obtain different results in three main dental fields: Diagnosis, Prevention and Operative Therapy. Conventional treatments can sometimes be replaced by laser treatments and better results may be achieved. Laser treatments offer new treatment opportunities in the dental field that were unknown in the past. This chapter aims to outline the clinical protocols and possible applications of different laser systems in Paediatric Dentistry

Improved calorimetric particle identification in NA62 using machine learning techniques

International audienceMeasurement of the ultra-rare ${K}^{+}\to {\pi}^{+}\nu \overline{\nu}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10$^{−5}$ for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10$^{−5}$

Improved calorimetric particle identification in NA62 using machine learning techniques

International audienceMeasurement of the ultra-rare ${K}^{+}\to {\pi}^{+}\nu \overline{\nu}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10$^{−5}$ for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10$^{−5}$

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification efficiency of 75% in the momentum range of 15$-$40 GeV/$c$. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of $10^{-5}$.Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/$c$. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of $10^{-5}$