A hierarchical Bayesian model to infer PL(Z) relations using Gaia parallaxes

Aims. We aim at creating a Bayesian model to infer the coefficients of PL or PLZ relations that propagates uncertainties in the observables in a rigorous and well founded way. Methods. We propose a directed acyclic graph to encode the conditional probabilities of the inference model that will allow us to infer probability distributions for the PL and PL(Z) relations. We evaluate the model with several semi-synthetic data sets and apply it to a sample of 200 fundamental mode and first overtone mode RR Lyrae stars for which Gaia DR1 parallaxes and literature Ks-band mean magnitudes are available. We define and test several hyperprior probabilities to verify their adequacy and check the sensitivity of the solution with respect to the prior choice. Results. The main conclusion of this work is the absolute necessity of incorporating the existing correlations between the observed variables (periods, metallicities and parallaxes) in the form of model priors in order to avoid systematically biased results, especially in the case of non-negligible uncertainties in the parallaxes. The tests with the semi-synthetic data based on the data set used in Gaia Collaboration et al. (2017) reveal the significant impact that the existing correlations between parallax, metallicity and periods have on the inferred parameters. The relation coefficients obtained here have been superseded by those presented in Muraveva et al. (2018a), that incorporates the findings of this work and the more recent Gaia DR2 measurements.Comment: 14 pages, 12 figures. Submitted to A&

Bosonic Seesaw in the Unparticle Physics

Recently, conceptually new physics beyond the Standard Model has been proposed by Georgi, where a new physics sector becomes conformal and provides "unparticle" which couples to the Standard Model sector through higher dimensional operators in low energy effective theory. Among several possibilities, we focus on operators involving the (scalar) unparticle, Higgs and the gauge bosons. Once the Higgs develops the vacuum expectation value (VEV), the conformal symmetry is broken and as a result, the mixing between the unparticle and the Higgs boson emerges. In this paper, we consider a natural realization of bosonic seesaw in the context of unparticle physics. In this framework, the negative mass squared or the electroweak symmetry breaking vacuum is achieved as a result of mass matrix diagonalization. In the diagonalization process, it is important to have zero value in the (1,1)-element of the mass matrix. In fact, the conformal invariance in the hidden sector can actually assure the zero of that element. So, the bosonic seesaw mechanism for the electroweak symmetry breaking can naturally be understood in the framework of unparticle physics.Comment: 5 pages, no figure; added one more referenc

New mathematical model for extended arrival management capabilities

The Extended arrival management (E-AMAN) concept is based on starting the arrival traffic sequencing earlier than is the case by the arrival management (AMAN). The E-AMAN extends the horizon at which to start sequencing from the airport terminal area further upstream, to enable more smooth traffic management through speeding up, or slowing down arriving flights. Current application of E-AMAN at Heathrow, with the horizon at 350NM reduces delay, operational costs, CO2 emission and smooths delivery of arrival traffic to the runways. Here we propose an E-AMAN model that extends to 500NM. More specifically, the model incorporates three horizons: Tactical Horizon (100NM), Command Horizon (500NM), and Data Horizon (600NM). When a flight enters Data Horizon, the flight intentions are sent to the E-AMAN. When a flight enters Command Horizon, the optimizer is run to find optimal slot for that flight at the runway. Compared to previous optimisation processes, the E-AMAN takes into account the cost of delay and fuel for the airline instead of delay alone, and uses information on the distribution time of arrival to manage uncertainty (e.g. due to wind). Based on the optimal slot assigned, the E-AMAN issues a command to the flight, that can be to maintain initial speed, to speed up, or to slow down. It also assigns minutes of holding if delay cannot be absorbed during cruise. We will present some evidence of the efficiency of the optimisation process, in particular compared to a baseline scenario where the E-AMAN takes only delay into account and no uncertainty. We will show how this efficiency changes in different conditions, in particular relative to wind uncertainty

Estimating economic severity of Air Traffic Flow Management regulations

The development of trajectory-based operations and the rolling network operations plan in European air traffic management network implies a move towards more collaborative, strategic flight planning. This opens up the possibility for inclusion of additional information in the collaborative decision-making process. With that in mind, we define the indicator for the economic risk of network elements (e.g., sectors or airports) as the expected costs that the elements impose on airspace users due to Air Traffic Flow Management (ATFM) regulations. The definition of the indicator is based on the analysis of historical ATFM regulations data, that provides an indication of the risk of accruing delay. This risk of delay is translated into a monetary risk for the airspace users, creating the new metric of the economic risk of a given airspace element. We then use some machine learning techniques to find the parameters leading to this economic risk. The metric is accompanied by an indication of the accuracy of the delay–cost prediction model. Lastly, the economic risk is transformed into a qualitative economic severity classification. The economic risks and consequently economic severity can be estimated for different temporal horizons and time periods providing an indicator which can be used by Air Navigation Service Providers to identify areas which might need the implementation of strategic measures (e.g., resectorisation or capacity provision change), and by Airspace Users to consider operation of routes which use specific airspace regions

Domino D2.1 - Data management and sources

This deliverable presents the approach of Domino for the data management, and details the data sources considered

Local Unitary Quantum Cellular Automata

In this paper we present a quantization of Cellular Automata. Our formalism is based on a lattice of qudits, and an update rule consisting of local unitary operators that commute with their own lattice translations. One purpose of this model is to act as a theoretical model of quantum computation, similar to the quantum circuit model. It is also shown to be an appropriate abstraction for space-homogeneous quantum phenomena, such as quantum lattice gases, spin chains and others. Some results that show the benefits of basing the model on local unitary operators are shown: universality, strong connections to the circuit model, simple implementation on quantum hardware, and a wealth of applications.Comment: To appear in Physical Review

Mercury: an open source platform for the evaluation of air transport mobility

The Mercury simulator is a platform developed over several years during exploratory research projects. It features a detailed description of the air transportation system at the European level, including passengers and aircraft, plus various important actors such as the Network Manager, airports, etc. This article presents the possibilities offered by the simulator’s last and now open-source version. We describe the core Mercury functionalities and highlight its modularity and the possibility of using it with other tools. We present its new interface, which supports user-friendly interaction, exploring its data input/output and setting its various parameters. We emphasise its possible uses as a solution performance assessment tool, usable early in the innovation pipeline to better estimate the impact of new changes to the air transportation system, particularly with respect to other system components. We hope opening the simulator may encourage other models to become available, allowing faster prototyping of SESAR Solutions early in the innovation pipeline and an in fine standardisation and higher performance of simulation-based performance assessment tools

Domino D2.2 - Database structure

This is a technical deliverable describing the database used in Domino. The structure of the database along with information on the data sources used are included. This database has been used to store the input and outputs of the executions of the investigative case studies reported in D5.2 – Investigative case studies results. The deliverable includes a diagram of the relational database and a description of the different tables used with information on the different fields that define these tables. Information on the precomputation of data to create the required input for the model is also included. Current shortcomings of the database are identified and potential solutions highlighted