Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Bayesian optimisation for likelihood-free cosmological inference

Many cosmological models have only a finite number of parameters of interest, but a very expensive data-generating process and an intractable likelihood function. We address the problem of performing likelihood-free Bayesian inference from such black-box simulation-based models, under the constraint of a very limited simulation budget (typically a few thousand). To do so, we adopt an approach based on the likelihood of an alternative parametric model. Conventional approaches to approximate Bayesian computation such as likelihood-free rejection sampling are impractical for the considered problem, due to the lack of knowledge about how the parameters affect the discrepancy between observed and simulated data. As a response, we make use of a strategy previously developed in the machine learning literature (Bayesian optimisation for likelihood-free inference, BOLFI), which combines Gaussian process regression of the discrepancy to build a surrogate surface with Bayesian optimisation to actively acquire training data. We extend the method by deriving an acquisition function tailored for the purpose of minimising the expected uncertainty in the approximate posterior density, in the parametric approach. The resulting algorithm is applied to the problems of summarising Gaussian signals and inferring cosmological parameters from the Joint Lightcurve Analysis supernovae data. We show that the number of required simulations is reduced by several orders of magnitude, and that the proposed acquisition function produces more accurate posterior approximations, as compared to common strategies.Comment: 16+9 pages, 12 figures. Matches PRD published version after minor modification

The Antares Collaboration : Contributions to the 34th International Cosmic Ray Conference (ICRC 2015, The Hague)

The ANTARES detector, completed in 2008, is the largest neutrino telescope in the Northern hemisphere. Located at a depth of 2.5 km in the Mediterranean Sea, 40 km off the Toulon shore, its main goal is the search for astrophysical high energy neutrinos. In this paper we collect the 21 contributions of the ANTARES collaboration to the 34th International Cosmic Ray Conference (ICRC 2015). The scientific output is very rich and the contributions included in these proceedings cover the main physics results, ranging from steady point sources, diffuse searches, multi-messenger analyses to exotic physics

Exclusion limits on the WIMP-nucleon cross-section from the Cryogenic Dark Matter Search

The Cryogenic Dark Matter Search (CDMS) employs low-temperature Ge and Si detectors to search for Weakly Interacting Massive Particles (WIMPs) via their elastic-scattering interactions with nuclei while discriminating against interactions of background particles. For recoil energies above 10 keV, events due to background photons are rejected with >99.9% efficiency, and surface events are rejected with >95% efficiency. The estimate of the background due to neutrons is based primarily on the observation of multiple-scatter events that should all be neutrons. Data selection is determined primarily by examining calibration data and vetoed events. Resulting efficiencies should be accurate to about 10%. Results of CDMS data from 1998 and 1999 with a relaxed fiducial-volume cut (resulting in 15.8 kg-days exposure on Ge) are consistent with an earlier analysis with a more restrictive fiducial-volume cut. Twenty-three WIMP candidate events are observed, but these events are consistent with a background from neutrons in all ways tested. Resulting limits on the spin-independent WIMP-nucleon elastic-scattering cross-section exclude unexplored parameter space for WIMPs with masses between 10-70 GeV c^{-2}. These limits border, but do not exclude, parameter space allowed by supersymmetry models and accelerator constraints. Results are compatible with some regions reported as allowed at 3-sigma by the annual-modulation measurement of the DAMA collaboration. However, under the assumptions of standard WIMP interactions and a standard halo, the results are incompatible with the DAMA most likely value at >99.9% CL, and are incompatible with the model-independent annual-modulation signal of DAMA at 99.99% CL in the asymptotic limit.Comment: 40 pages, 49 figures (4 in color), submitted to Phys. Rev. D; v.2:clarified conclusions, added content and references based on referee's and readers' comments; v.3: clarified introductory sections, added figure based on referee's comment

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN

On blind searches for noise dominated signals: a loosely coherent approach

We introduce a ‘loosely coherent’ method for detection of continuous gravitational waves that bridges the gap between semi-coherent and purely coherent methods. Explicit control over accepted families of signals is used to increase the sensitivity of a power-based statistic while avoiding the high computational costs of conventional matched filters. Several examples as well as a prototype implementation are discussed

Physics Behind Precision

This document provides a writeup of contributions to the FCC-ee mini-workshop on "Physics behind precision" held at CERN, on 2-3 February 2016.Comment: https://indico.cern.ch/event/469561