185 research outputs found

    Foreground component separation with generalised ILC

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    The 'Internal Linear Combination' (ILC) component separation method has been extensively used to extract a single component, the CMB, from the WMAP multifrequency data. We generalise the ILC approach for separating other millimetre astrophysical emissions. We construct in particular a multidimensional ILC filter, which can be used, for instance, to estimate the diffuse emission of a complex component originating from multiple correlated emissions, such as the total emission of the Galactic interstellar medium. The performance of such generalised ILC methods, implemented on a needlet frame, is tested on simulations of Planck mission observations, for which we successfully reconstruct a low noise estimate of emission from astrophysical foregrounds with vanishing CMB and SZ contamination.Comment: 11 pages, 6 figures (2 figures added), 1 reference added, introduction expanded, V2: version accepted by MNRA

    Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects

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    International audienceIn various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term "modality" for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or "challenges" , are common to multiple domains. This paper deals with two key questions: "why we need data fusion" and "how we perform it". The first question is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second question, "diversity" is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the datasets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects and opportunities that it holds

    Extracting HI cosmological signal with Generalized Needlet Internal Linear Combination

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    HI intensity mapping is a new observational technique to map fluctuations in the large-scale structure of matter using the 21 cm emission line of atomic hydrogen (HI). Sensitive radio surveys have the potential to detect Baryon Acoustic Oscillations (BAO) at low redshifts (z < 1) in order to constrain the properties of dark energy. Observations of the HI signal will be contaminated by instrumental noise and, more significantly, by astrophysical foregrounds, such as Galactic synchrotron emission, which is at least four orders of magnitude brighter than the HI signal. Foreground cleaning is recognised as one of the key challenges for future radio astronomy surveys. We study the ability of the Generalized Needlet Internal Linear Combination (GNILC) method to subtract radio foregrounds and to recover the cosmological HI signal for a general HI intensity mapping experiment. The GNILC method is a new technique that uses both frequency and spatial information to separate the components of the observed data. Our results show that the method is robust to the complexity of the foregrounds. For simulated radio observations including HI emission, Galactic synchrotron, Galactic free-free, radio sources and 0.05 mK thermal noise, we find that we can reconstruct the HI power spectrum for multipoles 30 < l < 150 with 6% accuracy on 50% of the sky for a redshift z ~ 0.25.Comment: 20 pages, 13 figures. Updated to match version accepted by MNRA

    Of `Cocktail Parties' and Exoplanets

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    The characterisation of ever smaller and fainter extrasolar planets requires an intricate understanding of one's data and the analysis techniques used. Correcting the raw data at the 10^-4 level of accuracy in flux is one of the central challenges. This can be difficult for instruments that do not feature a calibration plan for such high precision measurements. Here, it is not always obvious how to de-correlate the data using auxiliary information of the instrument and it becomes paramount to know how well one can disentangle instrument systematics from one's data, given nothing but the data itself. We propose a non-parametric machine learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a `blind' signal de-mixing is commonly known as the `Cocktail Party problem' in signal-processing. Given multiple simultaneous observations of the same exoplanetary eclipse, as in the case of spectrophotometry, we show that we can often disentangle systematic noise from the original light curve signal without the use of any complementary information of the instrument. In this paper, we explore these signal extraction techniques using simulated data and two data sets observed with the Hubble-NICMOS instrument. Another important application is the de-correlation of the exoplanetary signal from time-correlated stellar variability. Using data obtained by the Kepler mission we show that the desired signal can be de-convolved from the stellar noise using a single time series spanning several eclipse events. Such non-parametric techniques can provide important confirmations of the existent parametric corrections reported in the literature, and their associated results. Additionally they can substantially improve the precision exoplanetary light curve analysis in the future.Comment: ApJ accepte

    Non-Gaussianity in CMB analysis: bispectrum estimation and foreground subtraction

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    The focus of this work is the development of statistical and numerical methods forthe study of non-Gaussian and/or anisotropic features in cosmological surveys of themicrowave sky. We focus on two very different types of non-Gaussian (NG) signals. The former is primordial non-Gaussianity (PNG), generated in the very Early Universeduring the inflationary expansion stage. In this case the aim of our study will be that ofexploiting the NG component in order to extract useful cosmological information. The latter is non-Gaussianity generated by astrophysical foreground contamination. In thiscase, the goal is instead that of using non-Gaussianity as a tool to help in removingthese spurious, non-cosmological components (of course foregrounds themselves contain relevant astrophysical information, but the focus in this thesis is on Cosmology, thereforeforegrounds are regarded here only as a contaminant). Considerable efforts have been put so far in the search for deviations from Gaussianity in the CMB anisotropies, that are expected to provide invaluable information aboutthe Inflationary epoch. Inflation is in fact expected to produce an isotropic and nearly-Gaussian fluctuation field. However, a large amount of models also predicts very small,but highly model dependent NG signatures. This is the main reason behind the largeinterest in primordial NG studies. Of course, the pursuit for primordial non-Gaussianity must rely on beyond power spectrum statistics. It turns out that the most important higher order correlator produced by interactions during Inflation is the three pointfunction, or, more precisely, its Fourier space counterpart, called the bispectrum. Toovercome the issue of computing the full bispectrum of the observed field, that would require a prohibitive amount of computational time, the search for PNG features is carriedout by fitting theoretically motivated bispectrum templates to the data. Among those, one can find bispectrum templates with a scale-dependent (SD) bispectrum amplitude. Such templates have actually received little attention so far in the literature, especiallyas long as NG statistical estimation and data analysis are concerned. This is why a significant part of this thesis will be devoted to the development and application of efficientstatistical pipelines for CMB scale-dependent bispectra estimation. We present here theresults of the estimation of several primordial running bispectra obtained from WMAP9 year and Planck data-set. iiiThe second part of this thesis deals instead, as mentioned iin the beginning, withthe component separation problem, i.e. the identification of the different sources thatcontributes to the microwave sky brightness. Foreground emission produces several,potentially large, non-Gaussian signatures that can in principle be used to identify andremove the spurious components from the microwave sky maps. Our focus will be onthe development of a foreground cleaning technique relying on the hypothesis that, ifthe data are represented in a proper basis, the foreground signal is sparse. Sparsenessimplies that the majority of the signal is concentrated in few basis elements, that can be used to fit the corresponding component with a thresholding algorithm. We verifythat the spherical needlet frame has the right properties to disentangle the coherentforeground emission from the isotropic stochastic CMB signal. We will make clear inthe following how sparseness in needlet space is actually in several ways linked to thecoherence, anisotropy and non-Gaussianity of the foreground components.. The mainadvantages of our needlet thresholding technique are that it does not requires multi-frequency information as well as that it can be used in combination with other methods. Therefore it can represent a valuable tool in experiments with limited frequency coverage,as current ground-based CMB surveys

    Separation of Synchronous Sources

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    This thesis studies the Separation of Synchronous Sources (SSS) problem, which deals with the separation of signals resulting from a linear mixing of sources whose phases are synchronous. While this study is made in a form independent of the application, a motivation from a neuroscience perspective is presented. Traditional methods for Blind Source Separation, such as Independent Component Analysis (ICA), cannot address this problem because synchronous sources are highly dependent. We provide sufficient conditions for SSS to be an identifiable problem, and quantify the effect of prewhitening on the difficulty of SSS. We also present two algorithms to solve SSS. Extensive studies on simulated data illustrate that these algorithms yield substantially better results when compared with ICA methods. We conclude that these algorithms can successfully perform SSS in varying configurations (number of sources, number of sensors, level of additive noise, phase lag between sources, among others). Theoretical properties of one of these algorithms are also presented. Future work is discussed extensively, showing that this area of study is far from resolved and still presents interesting challenges

    Annual Report 2007

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    Applying unmixing to gene expression data for tumor phylogeny inference

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    <p>Abstract</p> <p>Background</p> <p>While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.</p> <p>Results</p> <p>The present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.</p> <p>Conclusions</p> <p>Unmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.</p
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