Parametrizing growth in dark energy and modified gravity models

It is well-known that an extremely accurate parametrization of the growth function of matter density perturbations in $\Lambda$CDM cosmology, with errors below $0.25 \%$, is given by $f(a)=\Omega_{m}^{\gamma} \,(a)$ with $\gamma \simeq 0.55$. In this work, we show that a simple modification of this expression also provides a good description of growth in modified gravity theories. We consider the model-independent approach to modified gravity in terms of an effective Newton constant written as $\mu(a,k)=G_{eff}/G$ and show that $f(a)=\beta(a)\Omega_{m}^{\gamma} \,(a)$ provides fits to the numerical solutions with similar accuracy to that of $\Lambda$CDM. In the time-independent case with $\mu=\mu(k)$, simple analytic expressions for $\beta(\mu)$ and $\gamma(\mu)$ are presented. In the time-dependent (but scale-independent) case $\mu=\mu(a)$, we show that $\beta(a)$ has the same time dependence as $\mu(a)$. As an example, explicit formalae are provided in the DGP model. In the general case, for theories with $\mu(a,k)$, we obtain a perturbative expansion for $\beta(\mu)$ around the General Relativity case $\mu=1$ which, for $f(R)$ theories, reaches an accuracy below $1 \%$. Finally, as an example we apply the obtained fitting functions in order to forecast the precision with which future galaxy surveys will be able to measure the $\mu$ parameter.Comment: 12 pages, 12 figures. New section on applications to forecasts for galaxy surveys and new references included. Matches version published in PR

Representative galaxy age-metallicity relationships

The ongoing surveys of galaxies and those for the next generation of telescopes will demand the execution of high-CPU consuming machine codes for recovering detailed star formation histories (SFHs) and hence age-metallicity relationships (AMRs). We present here an expeditive method which provides quick-look AMRs on the basis of representative ages and metallicities obtained from colour-magnitude diagram (CMD) analyses. We have tested its perfomance by generating synthetic CMDs for a wide variety of galaxy SFHs. The representative AMRs turn out to be reliable down to a magnitude limit with a photometric completeness factor higher than $\sim$ 85 per cent, and trace the chemical evolution history for any stellar population (represented by a mean age and an intrinsic age spread) with a total mass within ~ 40 per cent of the more massive stellar population in the galaxy.Comment: 12 pages, 11 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Societ

Predicción de la frescura del aceite de oliva virgen extra durante el almacenamiento mediante espectroscopía de fluorescencia

Virgin olive oil quality relates to flavor and unique health benefits. Some of these properties are at the most desirable level when the oil is just extracted, since it is not a product that improves with age. On the contrary, the concentrations of many compounds change during its shelf-life. These changes reveal the aging of the oil but do not necessarily mean decay in sensory properties, so in some cases an aged oil from healthy olives may be better qualified than a fresh one from olives affected by fermentation. The aim of this work is to analyze different methodologies proposed for assessing the quality of virgin olive oil with implications in freshness and aging of the oil, and to highlight the possibilities of rapid spectrofluorimetric techniques for assessing oil freshness by checking the evolution of pigments during storage. The observed change in the selected spectral features and mathematical modelling over time was compared with the accepted model for predicting the amount of pyropheophytin a, which is based on isokinetic studies. The best regression was obtained for 655 nm (adjustedR2 = 0.91) wavelength, which matches the distinctive band of pigments. The two mathematical models described in this study highlight the usefulness of pigments in the prediction of the shelf-life of extra virgin olive oil.La calidad del aceite de oliva virgen está relacionada con su flavor y sus beneficios únicos para la salud. Algunas de estas propiedades se encuentran en el nivel más deseable cuando el aceite está recién extraído, ya que no es un producto que mejore con el tiempo. Por el contrario, las concentraciones de muchos compuestos cambian a lo largo de la vida útil. Estos cambios revelan el envejecimiento del aceite, pero no implican necesariamente la alteración de las propiedades sensoriales, por lo que en algunos casos un aceite envejecido procedente de aceitunas sanas puede presentar mejor calidad que uno fresco procedente de aceitunas afectadas por procesos de fermentación. El objetivo de este trabajo es estudiar diferentes metodologías propuestas para evaluar la calidad del aceite de oliva virgen con implicaciones en la frescura y el envejecimiento del aceite, destacando las posibilidades de las rápidas técnicas espectrofluorométricas para evaluar la frescura del aceite verificando la evolución de los pigmentos durante el almacenamiento. El cambio observado en las características espectrales seleccionadas y su modelado matemático a lo largo del tiempo se comparó con el modelo aceptado para predecir la cantidad de pirofeofitina a, que se basa en estudios isocinéticos. Los dos modelos matemáticos descritos en este estudio pusieron de manifiesto la utilidad de los pigmentos en la predicción de la vida útil del aceite de oliva virgen extra. La mejor regresión se obtuvo para 655 nm (R2 -ajustado = 0,91), longitud de onda que coincide con la banda distintiva de pigmentos.Secretaría de Estado de Investigación, Desarrollo e Innovación de España-AGL2015-69320-

The age-metallicity relationship in the Fornax spheroidal dwarf galaxy

We produce a comprehensive field star age-metallicity relationship (AMR) from the earliest epoch until ~ 1 Gyr ago for three fields in the Fornax dSph galaxy by using VI photometric data obtained with FORS1 at the VLT. We find that the innermost one does not contains dominant very old stars (age > 12 Gyr), whereas the relatively outer field does not account for representative star field populations younger than ~ 3 Gyr. When focusing on the most prominent stellar populations, we find that the derived AMRs are engraved by the evidence of a outside-in star formation process. The studied fields show bimodal metallicity distributions peaked at [Fe/H] = (-0.95 +- 0.15) dex and (-1.15 or -1.25 +- 0.05) dex, respectively, but only during the first half of the entire galaxy lifetime. Furthermore, the more metal-rich population appears to be more numerous in the outer fields, while in the innermost Fornax field the contribution of both metallicity populations seems to be similar. We also find that the metallicity spread ~ 6 Gyr ago is remarkable large, while the intrinsic metallicity dispersion at ~ 1-2 Gyr results smaller than that for the relatively older generations of stars. We interpret these outcomes as a result of a possible merger of two galaxies that would have triggered a star formation bursting process that peaked between ~ 6 and 9 Gyr ago, depending on the position of the field in the galaxy.Comment: 7 pages, 5 figures, MNRAS, in pres

Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy