In-vacuo-dispersion features for GRB neutrinos and photons

Over the last 15 years there has been considerable interest in the possibility of quantum-gravity-induced in-vacuo dispersion, the possibility that spacetime itself might behave essentially like a dispersive medium for particle propagation. Two very recent studies have exposed what might be in-vacuo dispersion features for GRB (gamma-ray-burst) neutrinos of energy in the range of 100 TeV and for GRB photons with energy in the range of 10 GeV. We here show that these two features are roughly compatible with a description such that the same effects apply over 4 orders of magnitude in energy. We also characterize quantitatively how rare it would be for such features to arise accidentally, as a result of (still unknown) aspects of the mechanisms producing photons at GRBs or as a result of background neutrinos accidentally fitting the profile of a GRB neutrino affected by in-vacuo dispersion.Comment: 12 pages, latex. arXiv admin note: text overlap with arXiv:1609.0398

Statistical Tools for Imaging Atmospheric Cherenkov Telescopes

The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled the sky in the teraelectronvolt regime, initiating the so-called “TeV revolution”, at the beginning of the new millennium. This revolution was also facilitated by the implementation and adaptation of statistical tools for analyzing the shower images collected by these telescopes and inferring the properties of the astrophysical sources that produce such events. Image reconstruction techniques, background discrimination, and signal-detection analyses are just a few of the pioneering studies applied in recent decades in the analysis of IACTs data. This (succinct) review has the intent of summarizing the most common statistical tools that are used for analyzing data collected with IACTs, focusing on their application in the full analysis chain, including references to existing literature for a deeper examination.publishedVersio

Quantum-gravity-induced dual lensing and IceCube neutrinos

Momentum-space curvature, which is expected in some approaches to the quantum-gravity problem, can produce dual redshift, a feature which introduces energy dependence of the travel times of ultrarelativistic particles, and dual lensing, a feature which mainly affects the direction of observation of particles. In our recent arXiv:1605.00496 we explored the possibility that dual redshift might be relevant in the analysis of IceCube neutrinos, obtaining results which are preliminarily encouraging. Here we explore the possibility that also dual lensing might play a role in the analysis of IceCube neutrinos. In doing so we also investigate issues which are of broader interest, such as the possibility of estimating the contribution by background neutrinos and some noteworthy differences between candidate "early neutrinos" and candidate "late neutrinos".Comment: In this version V2 we give a definition of variation probability which could be considered in alternative to the notion of variation probability already introduced in version V1; both notions of variation probability are contemplated in the data analysis. arXiv admin note: text overlap with arXiv:1605.0049

DFT-Based Channel Estimation for Holographic MIMO

Holographic MIMO (hMIMO) systems with a massive number of individually controlled antennas N make minimum mean square error (MMSE) channel estimation particularly challenging, due to its computational complexity that scales as $N^3$ . This paper investigates uniform linear arrays and proposes a low-complexity method based on the discrete Fourier transform (DFT) approximation, which follows from replacing the covariance matrix by a suitable circulant matrix. Numerical results show that, already for arrays with moderate size (in the order of tens of wavelengths), it achieves the same performance of the optimal MMSE, but with a significant lower computational load that scales as $N \log N$. Interestingly, the proposed method provides also increased robustness in case of imperfect knowledge of the covariance matrix.Comment: 5 pages,4 figures, Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, Nov. 202

Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators

A digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder modulator is obtained through machine learning techniques. The model is used to test optimization algorithms that automatically set the modulator control voltages under different operative conditions finding the optimum bias point

Gain profile characterization and modelling for an accurate EDFA abstraction and control

Relying on a two-measurement characterization phase, a gain profile model for dual-stage EDFAs is presented and validated in full spectral load condition. It precisely reproduces the EDFA dynamics varying the target gain and tilts parameters as shown experimentally on two commercial items from different vendors

Modeling Transceiver BER-OSNR Characteristic for QoT Estimation in Short-Reach Systems

A transceiver BER-OSNR model is validated and applied the Q-factor estimation for short-reach systems. Experiments using pluggable transceivers with commercial DSPs show that the modeling and estimation errors are less than 0.05 dB and 0.15 dB, respectively

Enhancing Lightpath QoT Computation with Machine Learning in Partially Disaggregated Optical Networks

Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented