Surface Dyakonov–Cherenkov radiation

Recent advances in engineered material technologies (e.g., photonic crystals, metamaterials, plasmonics, etc.) provide valuable tools to control Cherenkov radiation. In all these approaches, however, the particle velocity is a key parameter to affect Cherenkov radiation in the designed material, while the influence of the particle trajectory is generally negligible. Here, we report on surface Dyakonov–Cherenkov radiation, i.e. the emission of directional Dyakonov surface waves from a swift charged particle moving atop a birefringent crystal. This new type of Cherenkov radiation is highly susceptible to both the particle velocity and trajectory, e.g. we observe a sharp radiation enhancement when the particle trajectory falls in the vicinity of a particular direction. Moreover, close to the Cherenkov threshold, such a radiation enhancement can be orders of magnitude higher than that obtained in traditional Cherenkov detectors. These distinct properties allow us to determine simultaneously the magnitude and direction of particle velocities on a compact platform. The surface Dyakonov–Cherenkov radiation studied in this work not only adds a new degree of freedom for particle identification, but also provides an all-dielectric route to construct compact Cherenkov detectors with enhanced sensitivity.

Interaction of excitons with Cherenkov radiation in WSe2 beyond the non-recoil approximation

Cherenkov radiation from electrons propagating in materials with a high refractive index have applications in particle-detection mechanisms and could be used for high-yield coherent electron beam-driven photon sources. However, the theory of the Cherenkov radiation has been treated up to now using the non-recoil approximation, which neglects the effect of electron deceleration in materials. Here, we report on the effect of electron-beam deceleration on the radiated spectrum and exciton-photon interactions in nm-thick 〖WSe〗_2 crystals. The calculation of the Cherenkov radiation is performed by simulating the kinetic energy of an electron propagating in a thick sample using the Monto Carlo method combined with the Lienard-Wiechert retarded potential. Using this approach, we numerically investigate the interaction between the excitons and generated photons (Cherenkov radiation) beyond the non-recoil approximation and are able to reproduce experimental cathodoluminescence spectra. Our findings pave the way for an accurate design of particle scintillators and detectors, based on the strong-coupling phenomenon.

Neutrino interaction physics in neutrino telescopes

Neutrino telescopes can observe neutrino interactions starting at GeV energies by sampling a small fraction of the Cherenkov radiation produced by charged secondary particles. These experiments instrument volumes massive enough to collect substantial samples of neutrinos up to the TeV scale as well as small samples at the PeV scale. This unique ability of neutrino telescopes has been exploited to study the properties of neutrino interactions across energies that cannot be accessed with man-made beams. Here, we present the methods and results obtained by IceCube, the most mature neutrino telescope in operation, and offer a glimpse of what the future holds in this field.

Embedded software developments in KM3NeT phase I

The KM3NeT collaboration has already produced more than one thousand acquisition boards, used for building two deep-sea neutrino detectors at the bottom of the Mediterranean Sea, with the aim of instrumenting a volume of several cubic kilometers with light sensors to detect the Cherenkov radiation produced in neutrino interactions. The so-called digital optical modules, house the PMTs and the acquisition and control electronics of the module, the central logic board, which includes a Xilinx FPGA and embedded soft processor. The present work presents the architecture and functionalities of the software embedded in the soft processor of the central logic board.

Low energy event classification in IceCube using boosted decision trees

The DeepCore sub-array within the IceCube Neutrino Observatory is a densely instrumented region of Antarctic ice designed to observe atmospheric neutrino interactions above 5 GeV via Cherenkov radiation. An essential aspect of any neutrino oscillation analysis is the ability to accurately identify the flavor of neutrino events in the detector. This task is particularly difficult at low energies when very little light is deposited in the detector. Here we discuss the use of machine learning to perform event classification at low energies in IceCube using a boosted decision tree (BDT). A BDT is trained using reconstructed quantities to identify track-like events, which result from muon neutrino charged current interactions. This new method improves the accuracy of particle identification compared to traditional classification methods which rely on univariate straight cuts.