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Register a new userMADX -- A simple technique for source detection and measurement using multi-band imaging from the Herschel-ATLAS survey
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We describe the method used to detect sources for the Herschel-ATLAS survey. The method is to filter the individual bands using a matched filter, based on the point-spread function (PSF) and confusion noise, and then form the inverse variance weighted sum of the individual bands, including weights determined by a chosen spectral energy distribution. Peaks in this combined image are used to estimate the source positions. The fluxes for each source are estimated from the filtered single-band images, interpolated to the exact sub-pixel position. We test the method by creating simulated maps in three bands with PSFs, pixel sizes and Gaussian instrumental noise that match the 250, 350 and 500 micron bands of Herschel-ATLAS. We use our method to detect sources and compare the measured positions and fluxes to the input sources. The multi-band approach allows reliable source detection a factor 1.2 to 3 lower in flux compared to single-band source detection, depending on the source colours. The false detection rate is reduced by a factor between 4 and 10, and the variance of the source position errors is reduced by about a factor 1.5. We also consider the effect of confusion noise and find that the appropriate matched filter gives a further improvement in completeness and noise over the standard PSF filter approach. Overall the two modifications give a factor of 1.5 to 3 improvement in the depth of the recovered catalogues compared to a single-band PSF filter approach.
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This paper describes the Bayesian Technique for Multi-image Analysis (BaTMAn), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real Integral-Field Spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of two. Our analysis reveals that the algorithm prioritizes conservation of all the statistically-significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a `black box to improve the signal-to-noise ratio, but as a new approach to characterize spatially-resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn .
We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SV) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands g,r,i, and z. We use three deterministic light-curve models to fit burst-like transients and one stochastic light curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGN). We assess the quality of fit of the models band-wise source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications. We use our clustering method to characterize 4361 extragalactic image difference detected sources in the first 2.5 years of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGN, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 active galactic nuclei and 812 supernovae. We use these samples to identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.
The Physics of the Accelerating Universe (PAU) Survey is an international project for the study of cosmological parameters associated with Dark Energy. PAUs 18-CCD camera (PAUCam), installed at the prime focus of the William Herschel Telescope at the Roque de los Muchachos Observatory (La Palma, Canary Islands), scans part of the northern sky, to collect low resolution spectral information of millions of galaxies with its unique set of 40 narrow-band filters in the optical range from 450 nm to 850 nm, and a set of 6 standard broad band filters. The PAU data management (PAUdm) team is in charge of treating the data, including data transfer from the observatory to the PAU Survey data center, hosted at Port dInformacio Cientifica (PIC). PAUdm is also in charge of the storage, data reduction and, finally, of making the results available to the scientific community. We describe the technical solutions adopted to cover different aspects of the PAU Survey data management, from the computing infrastructure to support the operations, to the software tools and web services for the data process orchestration and exploration. In particular we will focus on the PAU database, developed for the coordination of the different PAUdm tasks, and to preserve and guarantee the consistency of data and metadata.
We have reduced the data taken with the Spectral and Photometric Imaging Receiver (SPIRE) photometer on board the Herschel Space Observatory in the Science Demonstration Phase (SDP) of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). We describe the data reduction, which poses specific challenges, both because of the sheer size of the data, and because only two scans are made for each region. We implement effective solutions to process the bolometric timelines into maps, and show that correlations among detectors are negligible, and that the photometer is stable on time scales up to 250 s. This is longer than the time the telescope takes to cross the observed sky region, and it allows us to use naive binning methods for an optimal reconstruction of the sky emission. The maps have equal contribution of confusion and white instrumental noise, and the latter is estimated to 5.3, 6.4, and 6.7 mJy/beam (1-{sigma}), at 250, 350, and 500 mu{m}, respectively. This pipeline is used to reduce other H-ATLAS observations, as they became available, and we discuss how it can be used with the optimal map maker implemented in the Herschel Interactive Processing Environment (HIPE), to improve computational efficiency and stability. The SDP dataset is available from http://www.h-atlas.org/.
The Lunar Cherenkov technique is a promising method for UHE neutrino and cosmic ray detection which aims to detect nanosecond radio pulses produced during particle interactions in the Lunar regolith. For low frequency experiments, such as NuMoon, the frequency dependent dispersive effect of the ionosphere is an important experimental concern as it reduces the pulse amplitude and subsequent chances of detection. We are continuing to investigate a new method to calibrate the dispersive effect of the ionosphere on lunar Cherenkov pulses via Faraday rotation measurements of the Moons polarised emission combined with geomagnetic field models. We also extend this work to include radio imaging of the Lunar surface, which provides information on the physical and chemical properties of the lunar surface that may affect experimental strategies for the lunar Cherenkov technique.
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