Maria Funaro

Constraining dark energy models using the lookback time to galaxy clusters and the age of the universe

An impressive amount of different astrophysical data converges towards the picture of a spatially flat Universe undergoing today a phase of accelerated expansion. The nature of the dark energy dominating the energy content of the Universe is still unknown, and a lot of different scenarios are viable candidates to explain cosmic acceleration. Most of the methods employed to test these cosmological models are essentially based on distance measurements to a particular class of objects. A different method, based on the lookback time to galaxy clusters and the age of the Universe, is used here. In particular, we constrain the characterizing parameters of three classes of dark energy cosmological models to see whether they are in agreement with this kind of data, based on time measurements rather than distance observations.

Independent component analysis for artefact separation in astrophysical images

In this paper, we demonstrate that independent component analysis, a novel signal processing technique, is a powerful method for separating artefacts from astrophysical image data. When studying far-out galaxies from a series of consequent telescope images, there are several sources for artefacts that influence all the images, such as camera noise, atmospheric fluctuations and disturbances, cosmic rays, and stars in our own galaxy. In the analysis of astrophysical image data it is very important to implement techniques which are able to detect them with great accuracy, to avoid the possible physical events from being eliminated from the data along with the artefacts. For this problem, the linear ICA model holds very accurately because such artefacts are all theoretically independent of each other and of the physical events. Using image data on the M31 Galaxy, it is shown that several artefacts can be detected and recognized based on their temporal pixel luminosity profiles and independent component images. The obtained separation is good and the method is very fast. It is also shown that ICA outperforms principal component analysis in this task. For these reasons, ICA might provide a very useful pre-processing technique for the large amounts of available telescope image data.

Cosmological black holes as seeds of voids in the galaxy distribution

Deep surveys indicate a bubbly structure on cosmological large scales which should be the result of evolution of primordial density perturbations. Several models have been proposed to explain the origin and dynamics of such features but, till now, no exhaustive and fully consistent theory has been found. We discuss a model where cosmological black holes, deriving from primordial perturbations, are the seeds for large-scale-structure voids. We give details of the dynamics and accretion of the system voids-cosmological black holes from the epoch z � 10 3 till now, finding that a void of 40 h −1 Mpc diameter and under-density of −0.9 fits the observations without conflicting with the homogeneity and isotropy of the cosmic microwave background radiation.

Real time radiation dosimeters based on vertically aligned multiwall carbon nanotubes and graphene

Measurements of the absorbed dose and quality assurance programs play an important role in radiotherapy. Ionization chambers (CIs) are considered the most important dosimeters for their high accuracy, practicality and reliability, allowing absolute dose measurements. However, they have a relative large physical size, which limits their spatial resolution, and require a high bias voltage to achieve an acceptable collection of charges, excluding their use for in vivo dosimetry. In this paper, we propose new real time radiation detectors with electrodes based on graphene or vertically aligned multiwall carbon nanotubes (MWCNTs). We have investigated their charge collection efficiency and compared their performance with electrodes made of a conventional material. Moreover, in order to highlight the effect of nanocarbons, reference radiation detectors were also tested. The proposed dosimeters display an excellent linear response to dose and collect more charge than reference ones at a standard bias voltage, permitting the construction of miniaturized CIs. Moreover, an MWCNT based CI gives the best charge collection efficiency and it enables working also to lower bias voltages and zero volts, allowing in vivo applications. Graphene based CIs show better performance with respect to reference dosimeters at a standard bias voltage. However, at decreasing bias voltage the charge collection efficiency becomes worse if compared to a reference detector, likely due to graphenes semiconducting behavior.

Signal classification using Neural Networks

The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which allows to compress the data and extract robust features for the signal representation. For the classification stage, we have compared the performance of several neural models. An unsupervised method, based on the Principal Component Analysis (PCA) and the Mixture of Gaussian (MoG) clustering algorithm, gives a 70% percentage of correct classification. The Elman Recurrent Neural Nets (RNN) is able to reach 91% of correct classification on the test set. However this performance is strongly and critically dependent on the order of presentation of the events. Instead a MLP with a single hidden layer gives the 86% of correct classification on the test set, independently of the order of presentation of the patterns.

Finding Hidden Events in Astrophysical Data using PCA and Mixture of Gaussians Clustering

Abstract: The Principal Component Analysis (PCA) is applied to a set of astronomic data to obtain a separation between variations of luminosity and noisy fluctuations. A clustering with the Mixture of Gaussians method, performed in the principal subspace, allows us to classify the data according to the features of interest. Our results are compared with those obtained by the AGAPE (Andromeda Galaxy and Amplified Pixels Experiment) collaboration.