Non-universality of the mass function: dependence on the growth rate and power spectrum shape

The abundance of dark matter haloes is one of the key probes of the growth of structure and expansion history of the Universe. Theoretical predictions for this quantity usually assume that, when expressed in a certain form, it depends only on the mass variance of the linear density field. However, cosmological simulations have revealed that this assumption breaks, leading to 10-20% systematic effects. In this paper we employ a specially-designed suite of simulations to further investigate this problem. Specifically, we carry out cosmological N-body simulations where we systematically vary growth history at a fixed linear density field, or vary the power spectrum shape at a fixed growth history. We show that the halo mass function generically depends on these quantities, thus showing a clear signal of non-universality. Most of this effect can be traced back to the way in which the same linear fluctuation grows differently into the nonlinear regime depending on details of its assembly history. With these results, we propose a parameterization with explicit dependence on the linear growth rate and power spectrum shape. Using an independent suite of simulations, we show that this fitting function accurately captures the mass function of haloes over cosmologies spanning a vast parameter space, including massive neutrinos and dynamical dark energy. Finally, we employ this tool to improve the accuracy of so-called cosmology-rescaling methods and show they can deliver 2% accurate predictions for the halo mass function over the whole range of currently viable cosmologies

Consistent and simultaneous modelling of galaxy clustering and galaxy-galaxy lensing with Subhalo Abundance Matching

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Subhalo Abundance Matching, dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift-space galaxy correlation function and galaxy-galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r = [0.6-30] Mpc/h. Modelling the baryonic processes in galaxy-galaxy lensing with a baryonification scheme allows SHAMe's range of validity to be extended to r = [0.1-30] Mpc/h. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy-galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys.Comment: 14 pages, 7 figures. Submitted to MNRA

Recognition of Intentional Violations of Active Constraints in Cooperative Manipulation Tasks

Active Constraints (ACs) are high-level control algorithms deployed to assist a human operator in man-machine cooperative tasks [1], and define regions within which it is safe for the robot to move and cut [2]. To enhance the performance in cooperative surgical tasks, adaptive constraints have been exploited to optimally adjust the provided level of assistance according to some knowledge of the task, hardware or user. In [3] Hidden Markov Models were used for the run-time detection of the user intention to leave a guidance constraint to circumvent an obstacle. In this work, we present a novel, Neural Network (NN)-based method for the runtime classification of intentional and unintentional violations of ACs, that is trained on either statistical or frequency features from the enforced constraint forces. We investigate which set of parameters yield faster and more reliable classification results, both for guidance and regional constraints

Nafion-TiO2 composite DMFC membranes: Physico-chemical properties of the filier versus electrochemical performance

TiO2 nanometric powders were prepared via a sol-gel procedure and calcined at various temperatures to obtain different surface and bulk properties. The calcined powders were used as fillers in composite Nafion membranes for application in high temperature direct methanol fuel cells (DMFCs). The powder physico-chemical properties were investigated by X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and pH measurements. The observed characteristics were correlated to the DMFC electrochemical behaviour. Analysis of the high temperature conductivity and DMFC performance reveals a significant influence of the surface characteristics of the ceramic oxide, such as oxygen functional groups and surface area, on the membrane electrochemical behaviour. A maximum DMFC power density of 350 mW cm-2 was achieved under oxygen feed at 145°C in a pressurized DMFC (2.5 bar, anode and cathode) equipped with TiO2 nano-particles based composite membranes. © 2004 Elsevier Ltd. All rights reserved