FASER2

Abstract:

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra.

Abstract:

We present numerical simulations used to interpret laser-driven plasma experiments at the GSI Helmholtz Centre for Heavy Ion Research. The mechanisms by which non-thermal particles are accelerated, in astrophysical environments e.g., the solar wind, supernova remnants, and gamma ray bursts, is a topic of intense study. When shocks are present the primary acceleration mechanism is believed to be first-order Fermi, which accelerates particles as they cross a shock. Second-order Fermi acceleration can also contribute, utilizing magnetic mirrors for particle energization. Despite this mechanism being less efficient, the ubiquity of magnetized turbulence in the universe necessitates its consideration. Another acceleration mechanism is the lower-hybrid drift instability, arising from gradients of both density and magnetic field, which produce lower-hybrid waves with an electric field which energizes particles as they cross these waves. With the combination of high-powered laser systems and particle accelerators it is possible to study the mechanisms behind cosmic-ray acceleration in the laboratory. In this work, we combine experimental results and high-fidelity threedimensional simulations to estimate the efficiency of ion acceleration in a weakly magnetized interaction region. We validate the FLASH MHD code with experimental results and use OSIRIS particle-in-cell (PIC) code to verify the initial formation of the interaction region, showing good agreement between codes and experimental results. We find that the plasma conditions in the experiment are conducive to the lower-hybrid drift instability, yielding an increase in energy ∆E of ∼ 264 keV for 242 MeV calcium ions.

Abstract:

High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.02 and 5.44 TeV, respectively, using the ATLAS detector at the LHC. All observables show distinct features in ultracentral collisions, which are explained by a suppression of the geometrical component as the overlap area reaches its maximum. These results demonstrate a new technique to separate geometrical and intrinsic fluctuations, providing constraints on initial conditions and properties of the quark-gluon plasma, such as the speed of sound.