Simulazione monte carlo del decadimento beta inverso nel rilevatore Juno

Juno è un esperimento sul neutrino che si propone di trovare una risposta, tra gli altri, al problema ancora aperto della gerarchia di massa. in questa tesi si è elaborato un software per simulare la reazione "beta" inversa dell'antinetrino nel rivelatore, semplificando, laddove possibile, la fisica per ottenere un codice leggero e veloce

Convolutional Neural Network data analysis development for the Large Sized Telescope of CTA and broadband study of the blazar 1ES 1959+650

This thesis representes the summary of the activities I performed in the field of very-high-energy (VHE) gamma-ray astronomy, and it is articulated in two distinct parts. VHE gamma-ray astronomy is the science studying the photons emitted at TeV energies in cataclysmic events of the Universe. When these highly-energetic gamma-rays interact with the high atmosphere of the Earth, they produce cascades of particles that emit flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images than can be analyzed to extract the properties of the primary gamma ray. Dominating background for IACTs is constituted by images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard machine learning technique adopted to separate gamma-rays from hadrons is based on a set of parameters extracted from the images. On the other hand, state-of-the-art Deep Learning techniques such as Convolutional Neural Networks could enhance the analysis, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information irreversibly washed out during the parametrization process. In the first part of this work, I present the development of a novel approach to the analysis of the images produced by a new-generation IACT, the Large Sized Telescope (LST) of CTA. I use Convolutional Neural Networks to separate gamma rays from the dominating background of cosmic hadrons and to reconstruct their properties, showing that this technology performs remarkably better than the standard analysis technique. In the second part, I present the study of the emission of the blazar 1ES 1959+650, that is an ive galaxy emitting two extremely energetic jets of plasma in the outer space. First, I describe the analysis of the VHE gamma-ray emission observed during 2017 by the MAGIC IACTs, then I perform a multiwavelength characterization of the activity exhibited by the source between 2016 and 2020, modeling its broadband emission. The results of this investigation give insights on the mechanisms at work in the jets, preluding to a wider study that will probe the physics of this kind of sources to a deeper degree

Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA

International audienceWhen very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results

Studying the long-term spectral and temporal evolution of 1ES 1959+650

The high-frequency peaked BL Lac type object (HBL) 1ES 1959+650 is one of the brightest blazars in the very-high-energy (VHE, E≳100 GeV) gamma-ray sky. HBLs have been proposed as possible neutrino emitters implying the presence of hadrons in the emission mechanisms. In 2002, AMANDA reported neutrino candidates from this source simultaneously observed with a gamma-ray flaring activity without an X-ray emission enhancement, interpreted as an orphan flare. Standard one-zone synchrotron self-Compton (SSC) emission models cannot explain this behavior. The MAGIC telescopes have been observing 1ES 1959+650 since 2004. An extreme outburst triggered by multiwavelength observations reaching 300% of the Crab nebula flux level above 300 GeV was detected in 2016. Leptonic and hadronic models are equally successful in describing the observed emission. To study the long-term behavior and the characteristics in different emission states of 1ES 1959+650, we have densely monitored it since 2017 for more than 300 hours. Together with the FACT monitoring (more than 2000 hours since 2012), this is the most intense monitoring for any blazar after Mrk421 and Mrk501 in the VHE range. The monitoring shows a decline of the VHE flux with occasional flaring episodes reaching in 2019 a low-state emission corresponding to 10% of the Crab nebula. We present the long-term monitoring study results using multiwavelength data from MAGIC, FACT, Fermi-LAT, Swift, OVRO, and Tuorla. Lastly, we discuss the differences in the broadband spectral energy distributions between the flaring states from 2016 and the low state in 2019.ISSN:1824-803