Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions

The goal of population spectral synthesis (PSS) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique has been established as a fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current PSS codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently, ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy. In this article, we present FADO (Fitting Analysis using Differential evolution Optimization): a conceptually novel, publicly available PSS tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis. The innovative character of FADO is further augmented by its mathematical foundation: FADO is the first PSS code employing genetic differential evolution optimization. This, in conjunction with other unique elements in its mathematical concept (e.g., optimization of the spectral library using artificial intelligence, convergence test, quasi-parallelization) results in key improvements with respect to computational efficiency and uniqueness of the best-fitting SFHs.Comment: 25 pages, 12 figures, A&A accepte

Measuring the delay time distribution of binary neutron stars. II. Using the redshift distribution from third-generation gravitational wave detectors network

We investigate the ability of current and third-generation gravitational wave (GW) detectors to determine the delay time distribution (DTD) of binary neutron stars (BNS) through a direct measurement of the BNS merger rate as a function of redshift. We assume that the DTD follows a power law distribution with a slope $\Gamma$ and a minimum merger time $t_{\rm min}$, and also allow the overall BNS formation efficiency per unit stellar mass to vary. By convolving the DTD and mass efficiency with the cosmic star formation history, and then with the GW detector capabilities, we explore two relevant regimes. First, for the current generation of GW detectors, which are only sensitive to the local universe, but can lead to precise redshift determinations via the identification of electromagnetic counterparts and host galaxies, we show that the DTD parameters are strongly degenerate with the unknown mass efficiency and therefore cannot be determined uniquely. Second, for third-generation detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE), which will detect BNS mergers at cosmological distances, but with a redshift uncertainty inherent to GW-only detections ($\delta(z)/z\approx 0.1z$), we show that the DTD and mass efficiency can be well-constrained to better than 10\% with a year of observations. This long-term approach to determining the DTD through a direct mapping of the BNS merger redshift distribution will be supplemented by more near term studies of the DTD through the properties of BNS merger host galaxies at $z\approx 0$ (Safarzadeh & Berger 2019).Comment: 10 pages, Accepted to ApJ Letter

Joint deprojection of Sunyaev-Zeldovich and X-ray images of galaxy clusters

We present two non-parametric deprojection methods aimed at recovering the three-dimensional density and temperature profiles of galaxy clusters from spatially resolved thermal Sunyaev-Zeldovich (tSZ) and X-ray surface brightness maps, thus avoiding the use of X-ray spectroscopic data. In both methods, clusters are assumed to be spherically symmetric and modeled with an onion-skin structure. The first method follows a direct geometrical approach. The second method is based on the maximization of a single joint (tSZ and X-ray) likelihood function, which allows one to fit simultaneously the two signals by following a Monte Carlo Markov Chain approach. These techniques are tested against a set of cosmological simulations of clusters, with and without instrumental noise. We project each cluster along the three orthogonal directions defined by the principal axes of the momentum of inertia tensor. This enables us to check any bias in the deprojection associated to the cluster elongation along the line of sight. After averaging over all the three projection directions, we find an overall good reconstruction, with a small (<~10 per cent) overestimate of the gas density profile. This turns into a comparable overestimate of the gas mass within the virial radius, which we ascribe to the presence of residual gas clumping. Apart from this small bias the reconstruction has an intrinsic scatter of about 5 per cent, which is dominated by gas clumpiness. Cluster elongation along the line of sight biases the deprojected temperature profile upwards at r<~0.2r_vir and downwards at larger radii. A comparable bias is also found in the deprojected temperature profile. Overall, this turns into a systematic underestimate of the gas mass, up to 10 percent. (Abridged)Comment: 17 pages, 15 figures, accepted by MNRA

Non-parametric deprojection of NIKA SZ observations: Pressure distribution in the Planck-discovered cluster PSZ1 G045.85+57.71

The determination of the thermodynamic properties of clusters of galaxies at intermediate and high redshift can bring new insights into the formation of large-scale structures. It is essential for a robust calibration of the mass-observable scaling relations and their scatter, which are key ingredients for precise cosmology using cluster statistics. Here we illustrate an application of high resolution $(< 20$ arcsec) thermal Sunyaev-Zel'dovich (tSZ) observations by probing the intracluster medium (ICM) of the \planck-discovered galaxy cluster \psz\ at redshift $z = 0.61$, using tSZ data obtained with the NIKA camera, which is a dual-band (150 and 260~GHz) instrument operated at the IRAM 30-meter telescope. We deproject jointly NIKA and \planck\ data to extract the electronic pressure distribution from the cluster core ($R \sim 0.02\, R_{500}$) to its outskirts ($R \sim 3\, R_{500}$) non-parametrically for the first time at intermediate redshift. The constraints on the resulting pressure profile allow us to reduce the relative uncertainty on the integrated Compton parameter by a factor of two compared to the \planck\ value. Combining the tSZ data and the deprojected electronic density profile from \xmm\ allows us to undertake a hydrostatic mass analysis, for which we study the impact of a spherical model assumption on the total mass estimate. We also investigate the radial temperature and entropy distributions. These data indicate that \psz\ is a massive ($M_{500} \sim 5.5 \times 10^{14}$ M$_{\odot}$) cool-core cluster. This work is part of a pilot study aiming at optimizing the treatment of the NIKA2 tSZ large program dedicated to the follow-up of SZ-discovered clusters at intermediate and high redshifts. (abridged)Comment: 16 pages, 10 figure

A study of the sensitivity of shape measurements to the input parameters of weak lensing image simulations

Improvements in the accuracy of shape measurements are essential to exploit the statistical power of planned imaging surveys that aim to constrain cosmological parameters using weak lensing by large-scale structure. Although a range of tests can be performed using the measurements, the performance of the algorithm can only be quantified using simulated images. This yields, however, only meaningful results if the simulated images resemble the real observations sufficiently well. In this paper we explore the sensitivity of the multiplicative bias to the input parameters of Euclid-like image simulations.We find that algorithms will need to account for the local density of sources. In particular the impact of galaxies below the detection limit warrants further study, because magnification changes their number density, resulting in correlations between the lensing signal and multiplicative bias. Although achieving sub-percent accuracy will require further study, we estimate that sufficient archival Hubble Space Telescope data are available to create realistic populations of galaxies.Comment: 18 pages, accepted for publications in MNRA