Foreground removal from WMAP 5yr temperature maps using an MLP neural network

One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5yr temperature data without using any auxiliary data. A simple \emph{multilayer perceptron} neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e.\ errors correlated with the Galactic foregrounds, are very small. With these results the neural network method is well prepared for dealing with the high - quality CMB data from the ESA Planck Surveyor satellite.Comment: 6 pages, 13 figure

Planck 2015 results. XXVII. The Second Planck Catalogue of Sunyaev-Zeldovich Sources

We present the all-sky Planck catalogue of Sunyaev-Zeldovich (SZ) sources detected from the 29 month full-mission data. The catalogue (PSZ2) is the largest SZ-selected sample of galaxy clusters yet produced and the deepest all-sky catalogue of galaxy clusters. It contains 1653 detections, of which 1203 are confirmed clusters with identified counterparts in external data-sets, and is the first SZ-selected cluster survey containing > $10^3$ confirmed clusters. We present a detailed analysis of the survey selection function in terms of its completeness and statistical reliability, placing a lower limit of 83% on the purity. Using simulations, we find that the Y5R500 estimates are robust to pressure-profile variation and beam systematics, but accurate conversion to Y500 requires. the use of prior information on the cluster extent. We describe the multi-wavelength search for counterparts in ancillary data, which makes use of radio, microwave, infra-red, optical and X-ray data-sets, and which places emphasis on the robustness of the counterpart match. We discuss the physical properties of the new sample and identify a population of low-redshift X-ray under- luminous clusters revealed by SZ selection. These objects appear in optical and SZ surveys with consistent properties for their mass, but are almost absent from ROSAT X-ray selected samples

Publications of the Jet Propulsion Laboratory 1989

This bibliography describes and indexes by primary author the externally distributed technical reporting, released during 1989, that resulted from scientific and engineering work performed, or managed, by JPL. Three classes of publications are included: JPL publications in which the information is complete for a specific accomplishment; articles from the quarterly Telecommunications and Data Acquisition (TDA) Progress Report; and articles published in the open literature

Linear mixing model applied to coarse resolution satellite data

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies

Foreground challenge to CMB polarization: present methodologies and new concepts

In this thesis, I focus on the issue of contamination to the polarization of the Cosmic Microwave Background (CMB) anisotropies from diffuse Galactic foregrounds, which is known to be one of the greatest challenges to the detection of the curl (B) modes of the signal, which might be sourced by cosmological gravitational waves. I take parallel approaches along these lines. I apply the most recent techniques capable of parametrizing, fitting, and removing the main known Galactic foregrounds in a multi-frequency CMB dataset to one of the forthcoming powerful CMB polarization experiments, the Large Scale Polarization Explorer (LSPE). I presented the result of the complete simulation done for the parametric component separation pipeline of this experiment. On the other hand, I explored the latest Machine Learning and Artificial Intelligence algorithms and their application in CMB data analysis, specifically component separation and foreground cleaning. I start the investigation of the relevance of Neural Networks (NNs) in the understanding of the physical properties of foregrounds, as it is necessary before the foreground removal layer, by implementing a novel algorithm, which I test on simulated data from future B-mode probes. The results of the implemented NN\u2019s prediction in discerning the correct foreground model address the high accuracy and suitability of this model as a preceding stage for the component separation procedure. Finally, I also investigate how different NNs, as a generative model, could be used for reconstructing CMB anisotropies where the removal is impossible, and data have to be abandoned in the analysis. Lots remain to be done along each of these three investigations, which have been published in scientific journals, and constitute the basis of my future research

Point Source Detection with Fully-Convolutional Networks: Performance in Realistic Simulations

Point sources (PS) are one of the main contaminants to the recovery of the cosmic microwave background (CMB) signal at small scales, and their detection is important for the next generation of CMB experiments. We develop a method (PoSeIDoN) based on fully convolutional networks to detect PS in realistic simulations, and we compare its performance against one of the most used PS detection method, the Mexican hat wavelet 2 (MHW2). We produce realistic simulations of PS taking into account contaminating signals as the CMB, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel'dovich effect, and the instrumental and PS shot noises. We first produce a set of training simulations at 217 GHz to train the network. Then we apply both PoSeIDoN and the MHW2 to recover the PS in the validating simulations at all 143, 217, and 353 GHz, comparing the results by estimating the reliability, completeness, and flux density accuracy and by computing the receiver operating characteristic curves. In the extra-galactic region with a 30{\deg} galactic cut, the network successfully recovers PS at 90% completeness corresponding to 253, 126, and 250 mJy for 143, 217, and 353 GHz respectively. The MHW2 with a 3$\sigma$ flux density detection limit recovers PS up to 181, 102, and 153 mJy at 90% completeness. In all cases PoSeIDoN produces a much lower number of spurious sources with respect to MHW2. The results on spurious sources for both techniques worsen when reducing the galactic cut to 10{\deg}. Our results suggest that using neural networks is a very promising approach for detecting PS, providing overall better results in dealing with spurious sources with respect to usual filtering approaches. Moreover, PoSeIDoN gives competitive results even at nearby frequencies where the network was not trained.Comment: 12 pages, 6 figures, accepted Astronomy & Astrophysic

Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

Context. Point source (PS) detection is an important issue for future cosmic microwave background (CMB) experiments since they are one of the main contaminants to the recovery of CMB signal on small scales. Improving its multi-frequency detection would allow us to take into account valuable information otherwise neglected when extracting PS using a channel-by-channel approach. Aims. We aim to develop an artificial intelligence method based on fully convolutional neural networks to detect PS in multi-frequency realistic simulations and compare its performance against one of the most popular multi-frequency PS detection methods, the matrix filters. The frequencies used in our analysis are 143, 217, and 353 GHz, and we imposed a Galactic cut of 30°. Methods. We produced multi-frequency realistic simulations of the sky by adding contaminating signals to the PS maps as the CMB, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel’dovich effect, and the instrumental and PS shot noises. These simulations were used to train two neural networks called flat and spectral MultiPoSeIDoNs. The first one considers PS with a flat spectrum, and the second one is more realistic and general because it takes into account the spectral behaviour of the PS. Then, we compared the performance on reliability, completeness, and flux density estimation accuracy for both MultiPoSeIDoNs and the matrix filters. Results. Using a flux detection limit of 60 mJy, MultiPoSeIDoN successfully recovered PS reaching the 90% completeness level at 58 mJy for the flat case, and at 79, 71, and 60 mJy for the spectral case at 143, 217, and 353 GHz, respectively. The matrix filters reach the 90% completeness level at 84, 79, and 123 mJy. To reduce the number of spurious sources, we used a safer 4σ flux density detection limit for the matrix filters, the same as was used in the Planck catalogues, obtaining the 90% of completeness level at 113, 92, and 398 mJy. In all cases, MultiPoSeIDoN obtains a much lower number of spurious sources with respect to the filtering method. The recovering of the flux density of the detections, attending to the results on photometry, is better for the neural networks, which have a relative error of 10% above 100 mJy for the three frequencies, while the filter obtains a 10% relative error above 150 mJy for 143 and 217 GHz, and above 200 mJy for 353 GHz. Conclusions. Based on the results, neural networks are the perfect candidates to substitute filtering methods to detect multi-frequency PS in future CMB experiments. Moreover, we show that a multi-frequency approach can detect sources with higher accuracy than single-frequency approaches also based on neural networks.We warmly thank the anonymous referee for the very useful comments on the original manuscript. J.M.C., J.G.N., L.B., M.M.C. and D.C. acknowledge financial support from the PGC 2018 project PGC2018-101948-B-I00 (MICINN, FEDER). DH acknowledges the Spanish MINECO and the Spanish Ministerio de Ciencia, Innovación y Universidades for partial financial support under project PGC2018-101814-B-I00. M.M.C. acknowledges PAPI-20-PF-23 (Universidad de Oviedo). J.D.C.J., M.L.S., S.L.S.G., J.D.S. and F.S.L. acknowledge financial support from the I+D 2017 project AYA2017-89121-P and support from the European Union’s Horizon 2020 research and innovation programme under the H2020-INFRAIA-2018-2020 grant agreement No 210489629. This research has made use of the python packages ipython (Pérez & Granger 2007), matplotlib (Hunter 2007), TensorFlow (Abadi et al. 2015), Numpy (Oliphant 2006) and Scipy (Jones et al. 2001), also the HEALPix (Górski et al. 2005) and healpy (Zonca et al. 2019) packages

GAIA: Composition, Formation and Evolution of the Galaxy

The GAIA astrometric mission has recently been approved as one of the next two `cornerstones' of ESA's science programme, with a launch date target of not later than mid-2012. GAIA will provide positional and radial velocity measurements with the accuracies needed to produce a stereoscopic and kinematic census of about one billion stars throughout our Galaxy (and into the Local Group), amounting to about 1 per cent of the Galactic stellar population. GAIA's main scientific goal is to clarify the origin and history of our Galaxy, from a quantitative census of the stellar populations. It will advance questions such as when the stars in our Galaxy formed, when and how it was assembled, and its distribution of dark matter. The survey aims for completeness to V=20 mag, with accuracies of about 10 microarcsec at 15 mag. Combined with astrophysical information for each star, provided by on-board multi-colour photometry and (limited) spectroscopy, these data will have the precision necessary to quantify the early formation, and subsequent dynamical, chemical and star formation evolution of our Galaxy. Additional products include detection and orbital classification of tens of thousands of extra-Solar planetary systems, and a comprehensive survey of some 10^5-10^6 minor bodies in our Solar System, through galaxies in the nearby Universe, to some 500,000 distant quasars. It will provide a number of stringent new tests of general relativity and cosmology. The complete satellite system was evaluated as part of a detailed technology study, including a detailed payload design, corresponding accuracy assesments, and results from a prototype data reduction development.Comment: Accepted by A&A: 25 pages, 8 figure