Acceleration of cosmic rays and gamma-ray emission from supernova remnants in the Galaxy

Galactic cosmic rays are believed to be accelerated at supernova remnant shocks. Though very popular and robust, this conjecture still needs a conclusive proof. The strongest support to this idea is probably the fact that supernova remnants are observed in gamma-rays, which are indeed expected as the result of the hadronic interactions between the cosmic rays accelerated at the shock and the ambient gas. However, also leptonic processes can, in most cases, explain the observed gamma-ray emission. This implies that the detections in gamma rays do not necessarily mean that supernova remnants accelerate cosmic ray protons. To overcome this degeneracy, the multi-wavelength emission (from radio to gamma rays) from individual supernova remnants has been studied and in a few cases it has been possible to ascribe the gamma-ray emission to one of the two processes (hadronic or leptonic). Here we adopt a different approach and, instead of a case-by-case study we aim for a population study and we compute the number of supernova remnants which are expected to be seen in TeV gamma rays above a given flux under the assumption that these objects indeed are the sources of cosmic rays. The predictions found here match well with current observational results, thus providing a novel consistency check for the supernova remnant paradigm for the origin of galactic cosmic rays. Moreover, hints are presented for the fact that particle spectra significantly steeper than E^-2 are produced at supernova remnants. Finally, we expect that several of the supernova remnants detected by H.E.S.S. in the survey of the galactic plane should exhibit a gamma-ray emission dominated by hadronic processes (i.e. neutral pion decay). The fraction of the detected remnants for which the leptonic emission dominates over the hadronic one depends on the assumed values of the physical parameters and can be as high as roughly a half.Comment: 14 pages, 4 figures, 4 tables, submitted to MNRA

The low rate of Galactic pevatrons

Although supernova remnants remain the main suspects as sources of Galactic cosmic rays up to the knee, the supernova paradigm still has many loose ends. The weakest point in this construction is the possibility that individual supernova remnants can accelerate particles to the rigidity of the knee, $\sim 10^{6}$ GV. This scenario heavily relies upon the possibility to excite current driven non-resonant hybrid modes while the shock is still at the beginning of the Sedov phase. These modes can enhance the rate of particle scattering thereby leading to potentially very-high maximum energies. Here we calculate the spectrum of particles released into the interstellar medium from the remnants of different types of supernovae. We find that only the remnants of very powerful, rare core-collapse supernova explosions can accelerate light elements such as hydrogen and helium nuclei, to the knee rigidity, and that the local spectrum of cosmic rays directly constrains the rate of such events, if they are also source of PeV cosmic rays. On the other hand, for remnants of typical core-collapse supernova explosions, the Sedov phase is reached at late times, when the maximum energy is too low and the spectrum at very-high energies is very steep, being mostly produced during the ejecta dominated phase. For typical thermonuclear explosions, resulting in type Ia supernovae, we confirm previous findings that these objects can only produce cosmic rays up to $\lesssim 10^{5}$ GeV. The implications for the overall cosmic ray spectrum observed at the Earth and for the detection of PeVatrons by future gamma-ray observatories are discussed.Comment: 17 pages, 6 Figures, accepted to Astroparticle Physic

Machine Learning-Based Classification to Disentangle EEG Responses to TMS and Auditory Input

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) offers an unparalleled opportunity to study cortical physiology by characterizing brain electrical responses to external perturbation, called transcranial-evoked potentials (TEPs). Although these reflect cortical post-synaptic potentials, they can be contaminated by auditory evoked potentials (AEPs) due to the TMS click, which partly show a similar spatial and temporal scalp distribution. Therefore, TEPs and AEPs can be difficult to disentangle by common statistical methods, especially in conditions of suboptimal AEP suppression. In this work, we explored the ability of machine learning algorithms to distinguish TEPs recorded with masking of the TMS click, AEPs and non-masked TEPs in a sample of healthy subjects. Overall, our classifier provided reliable results at the single subject level, even for signals where differences were not shown in previous works. Classification accuracy (CA) was lower at the group level, when different subjects were used for training and test phases, and when three stimulation conditions instead of two were compared. Lastly, CA was higher when average, rather than single-trial TEPs, were used. In conclusion, this proof-of-concept study proposes machine learning as a promising tool to separate pure TEPs from those contaminated by sensory input

Machine Learning-Based Classification to Disentangle EEG Responses to TMS and Auditory Input

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) offers an unparalleled opportunity to study cortical physiology by characterizing brain electrical responses to external perturbation, called transcranial-evoked potentials (TEPs). Although these reflect cortical post-synaptic potentials, they can be contaminated by auditory evoked potentials (AEPs) due to the TMS click, which partly show a similar spatial and temporal scalp distribution. Therefore, TEPs and AEPs can be difficult to disentangle by common statistical methods, especially in conditions of suboptimal AEP suppression. In this work, we explored the ability of machine learning algorithms to distinguish TEPs recorded with masking of the TMS click, AEPs and non-masked TEPs in a sample of healthy subjects. Overall, our classifier provided reliable results at the single-subject level, even for signals where differences were not shown in previous works. Classification accuracy (CA) was lower at the group level, when different subjects were used for training and test phases, and when three stimulation conditions instead of two were compared. Lastly, CA was higher when average, rather than single-trial TEPs, were used. In conclusion, this proof-of-concept study proposes machine learning as a promising tool to separate pure TEPs from those contaminated by sensory input

Constraining atmospheric parameters and surface magnetic fields with ZeeTurbo\texttt{ZeeTurbo}: an application to SPIRou spectra

We report first results on a method aimed at simultaneously characterising atmospheric parameters and magnetic properties of M dwarfs from high-resolution nIR spectra recorded with SPIRou in the framework of the SPIRou Legacy Survey. Our analysis relies on fitting synthetic spectra computed from MARCS model atmospheres to selected spectral lines, both sensitive and insensitive to magnetic fields. We introduce a new code, $\texttt{ZeeTurbo}$, obtained by including the Zeeman effect and polarised radiative transfer capabilities to $\texttt{Turbospectrum}$. We compute a grid of synthetic spectra with $\texttt{ZeeTurbo}$ for different magnetic field strengths and develop a process to simultaneously constrain $T_{\rm eff}$, $\log{g}$, [M/H], [$\alpha$/Fe] and the average surface magnetic flux. In this paper, we present our approach and assess its performance using simulations, before applying it to six targets observed in the context of the SPIRou Legacy Survey (SLS), namely AU Mic, EV Lac, AD Leo, CN Leo, PM J18482+0741, and DS Leo. Our method allows us to retrieve atmospheric parameters in good agreement with the literature, and simultaneously yields surface magnetic fluxes in the range 2-4 kG with a typical precision of 0.05 kG, in agreement with literature estimates, and consistent with the saturated dynamo regime in which most of these stars are.Comment: 17 pages plus supplementary material. Accepted for publication in MNRA

solar building systems for the mediterranean region research outputs between italy and france

This paper comes from previous investigations carried out by the authors, in France and Italy, and from a cross border cooperation projects based on the joint collaboration between the University of Corsica Pascal Paoli and the University of Genoa. The authors focus on the enhancement of passive solar systems and thermal solar systems, with particular attention to their operation/efficiency and their architectural integration. The exchange between Italian and French experiences, especially between regions with similar climate, can enhance solar building strategies, in accordance with the new European energy standards as well as the Mediterranean climate, the traditional construction technologies and users' needs

Development of advanced Thomson spectrometers for nuclear fusion experiments initiated by laser

Thomson Spectrometers are devices capable to separate the several particle species (with distinct charge-to-mass ratio and energy) produced by the different regimes of laser-matter experiments. In this work we describe the development of advanced spectrometers for low and medium energy particles. In particular, they are suitable for protons in the 5 keV–2 MeV and 100 keV–10 MeV energy ranges, respectively. The new prototypes of spectrometers have been designed and built to have a high sensitivity and be adaptable to many experimental situations and configurations, and are tailored to the characterization of charged particles and products of nuclear fusion reactions initiated by high energy and intensity lasers. Details on the realized prototypes, on their characterization and testing, together with the first experimental results are discussed

Time-of-flight methodologies with large-area diamond detectors for the effectively characterization of tens MeV protons

A novel detector based on a polycrystalline diamond sensor is here employed in an advanced time-of-flight scheme for the characterization of energetic ions accelerated during laser-matter interactions. The optimization of the detector and of the advanced TOF methodology allow to obtain signals characterized by high signal-to-noise ratio and high dynamic range even in the most challenging experimental environments, where the interaction of high-intensity laser pulses with matter leads to effective ion acceleration, but also to the generation of strong Electromagnetic Pulses (EMPs) with intensities up to the MV/m order. These are known to be a serious threat for the fielded diagnostic systems. In this paper we report on the measurement performed with the PW-class laser system Vega 3 at CLPU (30 J energy, 1021 W/cm2 intensity, 30 fs pulses) irradiating solid targets, where both tens of MeV ions and intense EMP fields were generated. The data were analyzed to retrieve a calibrated proton spectrum and in particular we focus on the analysis of the most energetic portion (E > 5.8 MeV) of the spectrum showing a procedure to deal with the intrinsic lower sensitivity of the detector in the mentioned spectral-range