Cosmological Parameter Determination in Free-Form Strong Gravitational Lens Modeling

We develop a novel statistical strong lensing approach to probe the cosmological parameters by exploiting multiple redshift image systems behind galaxies or galaxy clusters. The method relies on free-form mass inversion of strong lenses and does not need any additional information other than gravitational lensing. Since in free-form lensing the solution space is a high-dimensional convex polytope, we consider Bayesian model comparison analysis to infer the cosmological parameters. The volume of the solution space is taken as a tracer of the probability of the underlying cosmological assumption. In contrast to parametric mass inversions, our method accounts for the mass-sheet degeneracy, which implies a degeneracy between the steepness of the profile and the cosmological parameters. Parametric models typically break this degeneracy, introducing hidden priors to the analysis that contaminate the inference of the parameters. We test our method with synthetic lenses, showing that it is able to infer the assumed cosmological parameters. Applied to the CLASH clusters, the method might be competitive with other probes.Comment: 11 pages, 5 figures. Accepted for publication in MNRA

Robust Stability Analysis of Nonlinear Hybrid Systems

We present a methodology for robust stability analysis of nonlinear hybrid systems, through the algorithmic construction of polynomial and piecewise polynomial Lyapunov-like functions using convex optimization and in particular the sum of squares decomposition of multivariate polynomials. Several improvements compared to previous approaches are discussed, such as treating in a unified way polynomial switching surfaces and robust stability analysis for nonlinear hybrid systems

A case study in model-driven synthetic biology

We report on a case study in synthetic biology, demonstrating the modeldriven design of a self-powering electrochemical biosensor. An essential result of the design process is a general template of a biosensor, which can be instantiated to be adapted to specific pollutants. This template represents a gene expression network extended by metabolic activity. We illustrate the model-based analysis of this template using qualitative, stochastic and continuous Petri nets and related analysis techniques, contributing to a reliable and robust design

Dynamical inference from a kinematic snapshot: The force law in the Solar System

If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: We infer the force law in the Solar System using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form a_r = -A [r/r_0]^{-\alpha}, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < \alpha < 2.052 (95 percent interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations

A Benchmarks Library for Extended Parametric Timed Automata

Parametric timed automata are a powerful formalism for reasoning on concurrent real-time systems with unknown or uncertain timing constants. In order to test the efficiency of new algorithms, a fair set of benchmarks is required. We present an extension of the IMITATOR benchmarks library, that accumulated over the years a number of case studies from academic and industrial contexts. We extend here the library with several dozens of new benchmarks; these benchmarks highlight several new features: liveness properties, extensions of (parametric) timed automata (including stopwatches or multi-rate clocks), and unsolvable toy benchmarks. These latter additions help to emphasize the limits of state-of-the-art parameter synthesis techniques, with the hope to develop new dedicated algorithms in the future.Comment: This is the author (and extended) version of the manuscript of the same name published in the proceedings of the 15th International Conference on Tests and Proofs (TAP 2021