Clues to the nature of dark matter from first galaxies

We use thirty-eight high-resolution simulations of galaxy formation between redshift 10 and 5 to study the impact of a 3 keV warm dark matter (WDM) candidate on the high-redshift Universe. We focus our attention on the stellar mass function and the global star formation rate and consider the consequences for reionization, namely the neutral hydrogen fraction evolution and the electron scattering optical depth. We find that three different effects contribute to differentiate warm and cold dark matter (CDM) predictions: WDM suppresses the number of haloes with mass less than few $10^9$ M$_{\odot}$; at a fixed halo mass, WDM produces fewer stars than CDM; and finally at halo masses below $10^9$ M$_{\odot}$, WDM has a larger fraction of dark haloes than CDM post-reionization. These three effects combine to produce a lower stellar mass function in WDM for galaxies with stellar masses at and below $\sim 10^7$ M$_{\odot}$. For $z > 7$, the global star formation density is lower by a factor of two in the WDM scenario, and for a fixed escape fraction, the fraction of neutral hydrogen is higher by 0.3 at $z \sim 6$. This latter quantity can be partially reconciled with CDM and observations only by increasing the escape fraction from 23 per cent to 34 per cent. Overall, our study shows that galaxy formation simulations at high redshift are a key tool to differentiate between dark matter candidates given a model for baryonic physics.Comment: 11 pages, 8 figures, submitted to MNRA

Phase shifts in nonresonant coherent excitation

Far-off-resonant pulsed laser fields produce negligible excitation between two atomic states but may induce considerable phase shifts. The acquired phases are usually calculated by using the adiabatic-elimination approximation. We analyze the accuracy of this approximation and derive the conditions for its applicability to the calculation of the phases. We account for various sources of imperfections, ranging from higher terms in the adiabatic-elimination expansion and irreversible population loss to couplings to additional states. We find that, as far as the phase shifts are concerned, the adiabatic elimination is accurate only for a very large detuning. We show that the adiabatic approximation is a far more accurate method for evaluating the phase shifts, with a vast domain of validity; the accuracy is further enhanced by superadiabatic corrections, which reduce the error well below $10^{-4}$. Moreover, owing to the effect of adiabatic population return, the adiabatic and superadiabatic approximations allow one to calculate the phase shifts even for a moderately large detuning, and even when the peak Rabi frequency is larger than the detuning; in these regimes the adiabatic elimination is completely inapplicable. We also derive several exact expressions for the phases using exactly soluble two-state and three-state analytical models.Comment: 10 pages, 7 figure

Model Matching Theory: A Framework for Examining the Alignment between Game Mechanics and Mental Models

The primary aim of this article is to provide a comprehensive review and elaboration of model matching and its theo- retical propositions. Model matching explains and predicts individuals’ outcomes related to gameplay by focusing on the interrelationships among games’ systems of mechanics, relevant situations external to the game, and players’ mental mod- els. Formalizing model matching theory in this way provides researchers a unified explanation for game-based learning, game performance, and related gameplay outcomes while also providing a theory-based direction for advancing the study of games more broadly. The propositions explicated in this article are intended to serve as the primary tenets of model matching theory. Considerations for how these propositions may be tested in future games studies research are discussed

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems