Limits on Arcminute Scale Cosmic Microwave Background Anisotropy with the BIMA Array

We have used the Berkeley-Illinois-Maryland-Association (BIMA) millimeter array outfitted with sensitive cm-wave receivers to search for Cosmic Microwave Background (CMB) anisotropies on arcminute scales. The interferometer was placed in a compact configuration which produces high brightness sensitivity, while providing discrimination against point sources. Operating at a frequency of 28.5 GHz, the FWHM primary beam of the instrument is 6.6 arcminutes. We have made sensitive images of seven fields, five of which where chosen specifically to have low IR dust contrast and be free of bright radio sources. Additional observations with the Owens Valley Radio Observatory (OVRO) millimeter array were used to assist in the location and removal of radio point sources. Applying a Bayesian analysis to the raw visibility data, we place limits on CMB anisotropy flat-band power Q_flat = 5.6 (+3.0 -5.6) uK and Q_flat < 14.1 uK at 68% and 95% confidence. The sensitivity of this experiment to flat band power peaks at a multipole of l = 5470, which corresponds to an angular scale of approximately 2 arcminutes. The most likely value of Q_flat is similar to the level of the expected secondary anisotropies.Comment: 15 pages, 5 figures, LaTex, aas2pp4.sty, ApJ submitte

Predicted Planck Extragalactic Point Source Catalogue

An estimation of the number and amplitude (in flux) of the extragalactic point sources that will be observed by the Planck Mission is presented in this paper. The study is based on the Mexican Hat wavelet formalism introduced by Cayon et al. 2000. Simulations at Planck observing frequencies are analysed, taking into account all the possible cosmological, Galactic and Extragalactic emissions together with noise. With the technique used in this work the Planck Mission will produce a catalogue of extragalactic point sources above fluxes: 1.03 Jy (857 GHz), 0.53 Jy (545 GHz), 0.28 Jy (353 GHz), 0.24 Jy (217 GHz), 0.32 Jy (143 GHz), 0.41 Jy (100 GHz HFI), 0.34 Jy (100 GHz LFI), 0.57 Jy (70 GHz), 0.54 Jy (44 GHz) and 0.54 Jy (30 GHz), which are only slightly model dependent (see text). Amplitudes of these sources are estimated with errors below 15%. Moreover, we also provide a complete catalogue (for the point sources simulation analysed) with errors in the estimation of the amplitude below 10%. In addition we discuss the possibility of identifying different point source populations in the Planck catalogue by estimating their spectral indices.Comment: 13 pages, 2 figures, submitted to MNRA

Filtering techniques for the detection of Sunyaev-Zel'dovich clusters in multifrequency CMB maps

The problem of detecting Sunyaev-Zel'dovich (SZ) clusters in multifrequency CMB observations is investigated using a number of filtering techniques. A multifilter approach is introduced, which optimizes the detection of SZ clusters on microwave maps. An alternative method is also investigated, in which maps at different frequencies are combined in an optimal manner so that existing filtering techniques can be applied to the single combined map. The SZ profiles are approximated by the circularly-symmetric template $\tau (x) = [1 +(x/r_c)^2]^{-\lambda}$, with $\lambda \simeq \tfrac{1}{2}$ and $x\equiv |\vec{x}|$, where the core radius $r_c$ and the overall amplitude of the effect are not fixed a priori, but are determined from the data. The background emission is modelled by a homogeneous and isotropic random field, characterized by a cross-power spectrum $P_{\nu_1 \nu_2}(q)$ with $q\equiv |\vec{q}|$. The filtering methods are illustrated by application to simulated Planck observations of a $12.8^\circ \times 12.8^\circ$ patch of sky in 10 frequency channels. Our simulations suggest that the Planck instrument should detect $\approx 10000$ SZ clusters in 2/3 of the sky. Moreover, we find the catalogue to be complete for fluxes $S > 170$ mJy at 300 GHz.Comment: 12 pages, 7 figures; Corrected figures. Submitted to MNRA

Cosmological applications of a wavelet analysis on the sphere

The cosmic microwave background (CMB) is a relic radiation of the Big Bang and as such it contains a wealth of cosmological information. Statistical analyses of the CMB, in conjunction with other cosmological observables, represent some of the most powerful techniques available to cosmologists for placing strong constraints on the cosmological parameters that describe the origin, content and evolution of the Universe. The last decade has witnessed the introduction of wavelet analyses in cosmology and, in particular, their application to the CMB. We review here spherical wavelet analyses of the CMB that test the standard cosmological concordance model. The assumption that the temperature anisotropies of the CMB are a realisation of a statistically isotropic Gaussian random field on the sphere is questioned. Deviations from both statistical isotropy and Gaussianity are detected in the reviewed works, suggesting more exotic cosmological models may be required to explain our Universe. We also review spherical wavelet analyses that independently provide evidence for dark energy, an exotic component of our Universe of which we know very little currently. The effectiveness of accounting correctly for the geometry of the sphere in the wavelet analysis of full-sky CMB data is demonstrated by the highly significant detections of physical processes and effects that are made in these reviewed works.Comment: 17 pages, 8 figures; JFAA invited review, in pres

A low CMB variance in the WMAP data

We have estimated the CMB variance from the three-year WMAP data, finding a value which is significantly lower than the one expected from Gaussian simulations using the WMAP best-fit cosmological model, at a significance level of 98.7 per cent. This result is even more prominent if we consider only the north ecliptic hemisphere (99.8 per cent). Different analyses have been performed in order to identify a possible origin for this anomaly. In particular we have studied the behaviour of single radiometers and single year data as well as the effect of residual foregrounds and 1/f noise, finding that none of these possibilities can explain the low value of the variance. We have also tested the effect of varying the cosmological parameters, finding that the estimated CMB variance tends to favour higher values of $n_s$ than the one of the WMAP best-fit model. In addition, we have also tested the consistency between the estimated CMB variance and the actual measured CMB power spectrum of the WMAP data, finding a strong discrepancy. A possible interpretation of this result could be a deviation from Gaussianity and/or isotropy of the CMB.Comment: 13 pages, 5 figures. Some new tests added. Section 5 largely modified. Accepted for publication in MNRA

Characterizing Planetary Orbits and the Trajectories of Light

Exact analytic expressions for planetary orbits and light trajectories in the Schwarzschild geometry are presented. A new parameter space is used to characterize all possible planetary orbits. Different regions in this parameter space can be associated with different characteristics of the orbits. The boundaries for these regions are clearly defined. Observational data can be directly associated with points in the regions. A possible extension of these considerations with an additional parameter for the case of Kerr geometry is briefly discussed.Comment: 49 pages total with 11 tables and 10 figure

Bayesian modelling of clusters of galaxies from multi-frequency pointed Sunyaev--Zel'dovich observations

We present a Bayesian approach to modelling galaxy clusters using multi-frequency pointed observations from telescopes that exploit the Sunyaev--Zel'dovich effect. We use the recently developed MultiNest technique (Feroz, Hobson & Bridges, 2008) to explore the high-dimensional parameter spaces and also to calculate the Bayesian evidence. This permits robust parameter estimation as well as model comparison. Tests on simulated Arcminute Microkelvin Imager observations of a cluster, in the presence of primary CMB signal, radio point sources (detected as well as an unresolved background) and receiver noise, show that our algorithm is able to analyse jointly the data from six frequency channels, sample the posterior space of the model and calculate the Bayesian evidence very efficiently on a single processor. We also illustrate the robustness of our detection process by applying it to a field with radio sources and primordial CMB but no cluster, and show that indeed no cluster is identified. The extension of our methodology to the detection and modelling of multiple clusters in multi-frequency SZ survey data will be described in a future work.Comment: 12 pages, 7 figures, submitted to MNRA

Interactional positioning and narrative self-construction in the first session of psychodynamic-interpersonal psychotherapy

The purpose of this study is to identify possible session one indicators of end of treatment psychotherapy outcome using the framework of three types of interactional positioning; client’s self-positioning, client’s positioning between narrated self and different partners, and the positioning between client and therapist. Three successful cases of 8-session psychodynamic-interpersonal (PI) therapy were selected on the basis of client Beck Depression Inventory scores. One unsuccessful case was also selected against which identified patterns could be tested. The successful clients were more descriptive about their problems and demonstrated active rapport-building, while the therapist used positionings expressed by the client in order to explore the positionings developed between them during therapy. The unsuccessful case was characterized by lack of positive self-comment, minimization of agentic self-capacity, and empathy-disrupting narrative confusions. We conclude that the theory of interactional positioning has been useful in identifying patterns worth exploring as early indicators of success in PI therapy

Neural networks and separation of Cosmic Microwave Background and astrophysical signals in sky maps

The Independent Component Analysis (ICA) algorithm is implemented as a neural network for separating signals of different origin in astrophysical sky maps. Due to its self-organizing capability, it works without prior assumptions on the signals, neither on their frequency scaling, nor on the signal maps themselves; instead, it learns directly from the input data how to separate the physical components, making use of their statistical independence. To test the capabilities of this approach, we apply the ICA algorithm on sky patches, taken from simulations and observations, at the microwave frequencies, that are going to be deeply explored in a few years on the whole sky, by the Microwave Anisotropy Probe (MAP) and by the {\sc Planck} Surveyor Satellite. The maps are at the frequencies of the Low Frequency Instrument (LFI) aboard the {\sc Planck} satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic diffuse emissions from thermal dust and synchrotron. We show that the ICA algorithm is able to recover each signal, with precision going from 10% for the Galactic components to percent for CMB; radio sources are almost completely recovered down to a flux limit corresponding to $0.7\sigma_{CMB}$, where $\sigma_{CMB}$ is the rms level of CMB fluctuations. The signal recovering possesses equal quality on all the scales larger then the pixel size. In addition, we show that the frequency scalings of the input signals can be partially inferred from the ICA outputs, at the percent precision for the dominant components, radio sources and CMB.Comment: 15 pages; 6 jpg and 1 ps figures. Final version to be published in MNRA