From hidden symmetry to extra dimensions: a five dimensional formulation of the Degenerate BESS model

We consider the continuum limit of a moose model corresponding to a generalization to N sites of the Degenerate BESS model. The five dimensional formulation emerging in this limit is a realization of a RS1 type model with SU(2)_L x SU(2)_R in the bulk, broken by boundary conditions and a vacuum expectation value on the infrared brane. A low energy effective Lagrangian is derived by means of the holographic technique and corresponding bounds on the model parameters are obtained.Comment: Latex file, 40 pages and 5 figure

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers

We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup, power values of four first-order counter-propagating pumps are optimized to achieve the desired 2D power profile. The pump power optimization framework includes a convolutional neural network (CNN) followed by differential evolution (DE) technique, applied online to the amplifier setup to automatically achieve the target 2D power profiles. The results on achievable 2D profiles show that the framework is able to guarantee very low maximum absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles. Moreover, the framework is tested in a multi-objective design scenario where the goal is to achieve the 2D profiles with flat gain levels at the end of the span, jointly with minimum spectral excursion over the entire fiber length. In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values. The simulation results also prove that with enough pump power available, better gain deviation (less than 0.6 dB) for higher target gain levels is achievable

Galeno Introduzione alla logica

La pi\uf9 chiara esposizione della dottrina sillogistica aristotelica "curvata" in senso probabilistico per l'applicazione nel campo della diagnostica medica a opera del 'principe' dei medici dell'et\ue0 romana tardo-imperiale, Galen

Optimization of Raman amplifiers: a comparison between black-, grey- and white-box modeling

Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offline optimization of optical amplifiers relies on models ranging from white-box models deeply rooted in physics to black-box data-driven physics-agnostic models. Here, we compare the capabilities of white-, grey- and black-box models to achieve a target frequency-distance amplification in a bidirectional Raman amplifier. We show that any of the studied methods can achieve down to 1 dB of frequency-distance flatness over the C-band in a 100-km span. Then, we discuss the models' applicability, advantages, and drawbacks based on the target application scenario, in particular in terms of optimization speed and access to training data

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly-accurate gain profile designs and noise figure predictions, with a maximum error on average of ~0.3dB. This framework provides the comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of the next-generation optical communication systems, expected to employ Raman amplification.Comment: 4 pages, 5 figure

Machine learning applied to inverse systems design

In this work, we will give an overview of some of the most recent and successful applications of machine learning based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems

Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

In this paper, we show that by combining experimental data from different optical fibers, we can build a fiber-agnostic neural-network to model the Raman amplifier. The fiber-agnostic NN model can predict the gain profile of a new fiber type (unseen by the model during training) with a maximum absolute error as low as 0.22 dB. We show that this generalization is only possible when the unseen fiber parameters are similar to the fibers used to build the model. Therefore, a training dataset with a wide range of optical fibers parameters is needed to enhance the chance of accurately predicting the gain of a new fiber. This implies that time-consuming experimental measurements of various fiber types can be avoided. For that, here we extend and improve our general model by numerically generating the dataset. By doing so, it is possible to generate uniformly distributed data that covers a wide range of optical fiber types. The results show that the averaged maximum prediction error is reduced when compared to the limited experimental data-based general models. As the second and final contribution of this work, we propose the use of transfer learning (TL) to re-train the numerical data-based general model using just a few experimental measurements. Compared with the fiber-specific models, this TL-upgraded general model reaches very similar accuracy, with just 3.6% of the experimental data . These results demonstrate that the already fast and accurate NN-based RA models can be upgraded to have strong fiber generalization capabilities

Experimental characterization of Raman amplifier optimization through inverse system design

Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this paper, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5~dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase.Comment: 11 pages, 12 figure

Modified spontaneous symmetry breaking pattern by brane-bulk interaction terms

We show how translational invariance can be broken by the vacuum that drives the spontaneous symmetry breaking of extra-dimensional extensions of the Standard Model, when delta-like interactions between brane and bulk scalar fields are present. We explicitly build some examples of vacuum configurations, which induce the spontaneous symmetry breaking, and have non trivial profile in the extra coordinate.Comment: 13 pages, two figure