Mutually compensative pseudo solutions of primary energy spectra in the knee region

The problem of the uniqueness of solutions during the evaluation of primary energy spectra in the knee region using an extensive air shower (EAS) data set and the EAS inverse approach is investigated. It is shown that the unfolding of primary energy spectra in the knee region leads to mutually compensative pseudo solutions. These solutions may be the reason for the observed disagreements in the elementary energy spectra of cosmic rays in the 1-100 PeV energy range obtained from different experiments.Comment: Accepted for publication in Astroparticle Physic

The primary cosmic ray composition between 10**15 and 10**16 eV from Extensive Air Showers electromagnetic and TeV muon data

The cosmic ray primary composition in the energy range between 10**15 and 10**16 eV, i.e., around the "knee" of the primary spectrum, has been studied through the combined measurements of the EAS-TOP air shower array (2005 m a.s.l., 10**5 m**2 collecting area) and the MACRO underground detector (963 m a.s.l., 3100 m w.e. of minimum rock overburden, 920 m**2 effective area) at the National Gran Sasso Laboratories. The used observables are the air shower size (Ne) measured by EAS-TOP and the muon number (Nmu) recorded by MACRO. The two detectors are separated on average by 1200 m of rock, and located at a respective zenith angle of about 30 degrees. The energy threshold at the surface for muons reaching the MACRO depth is approximately 1.3 TeV. Such muons are produced in the early stages of the shower development and in a kinematic region quite different from the one relevant for the usual Nmu-Ne studies. The measurement leads to a primary composition becoming heavier at the knee of the primary spectrum, the knee itself resulting from the steepening of the spectrum of a primary light component (p, He). The result confirms the ones reported from the observation of the low energy muons at the surface (typically in the GeV energy range), showing that the conclusions do not depend on the production region kinematics. Thus, the hadronic interaction model used (CORSIKA/QGSJET) provides consistent composition results from data related to secondaries produced in a rapidity region exceeding the central one. Such an evolution of the composition in the knee region supports the "standard" galactic acceleration/propagation models that imply rigidity dependent breaks of the different components, and therefore breaks occurring at lower energies in the spectra of the light nuclei.Comment: Submitted to Astroparticle Physic

A New Measurement of Cosmic Ray Composition at the Knee

The Dual Imaging Cerenkov Experiment (DICE) was designed and operated for making elemental composition measurements of cosmic rays near the knee of the spectrum at several PeV. Here we present the first results using this experiment from the measurement of the average location of the depth of shower maximum, , in the atmosphere as a function of particle energy. The value of near the instrument threshold of ~0.1 PeV is consistent with expectations from previous direct measurements. At higher energies there is little change in composition up to ~5 PeV. Above this energy is deeper than expected for a constant elemental composition implying the overall elemental composition is becoming lighter above the knee region. These results disagree with the idea that cosmic rays should become on average heavier above the knee. Instead they suggest a transition to a qualitatively different population of particles above 5 PeV.Comment: 7 pages, LaTeX, two eps figures, aas2pp4.sty and epsf.sty included, accepted by Ap.J. Let

Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version