Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDeep 3 GHz Number Counts from a P(D) Fluctuation Analysis
117
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Radio source counts constrain galaxy populations and evolution, as well as the global star formation history. However, there is considerable disagreement among the published 1.4-GHz source counts below 100 microJy. Here we present a statistical method for estimating the microJy and even sub-microJy source count using new deep wide-band 3-GHz data in the Lockman Hole from the Karl G. Jansky Very Large Array (VLA). We analyzed the confusion amplitude distribution P(D), which provides a fresh approach in the form of a more robust model, with a comprehensive error analysis. We tested this method on a large-scale simulation, incorporating clustering and finite source sizes. We discuss in detail our statistical methods for fitting using Monte Carlo Markov chains, handling correlations, and systematic errors from the use of wide-band radio interferometric data. We demonstrated that the source count can be constrained down to 50 nJy, a factor of 20 below the rms confusion. We found the differential source count near 10 microJy to have a slope of -1.7, decreasing to about -1.4 at fainter flux densities. At 3GHz the rms confusion in an 8arcsec FWHM beam is ~ 1.2 microJy/beam, and a radio background temperature ~ 14mK. Our counts are broadly consistent with published evolutionary models. With these results we were also able to constrain the peak of the Euclidean normalized differential source count of any possible new radio populations that would contribute to the cosmic radio background down to 50 nJy.
rate research
Read More
Dusty, star forming galaxies contribute to a bright, currently unresolved cosmic far-infrared background. Deep Herschel-SPIRE images designed to detect and characterize the galaxies that comprise this background are highly confused, such that the bulk lies below the classical confusion limit. We analyze three fields from the HerMES programme in all three SPIRE bands (250, 350, and 500 microns); parameterized galaxy number count models are derived to a depth of ~2 mJy/beam, approximately 4 times the depth of previous analyses at these wavelengths, using a P(D) (probability of deflection) approach for comparison to theoretical number count models. Our fits account for 64, 60, and 43 per cent of the far-infrared background in the three bands. The number counts are consistent with those based on individually detected SPIRE sources, but generally inconsistent with most galaxy number counts models, which generically overpredict the number of bright galaxies and are not as steep as the P(D)-derived number counts. Clear evidence is found for a break in the slope of the differential number counts at low flux densities. Systematic effects in the P(D) analysis are explored. We find that the effects of clustering have a small impact on the data, and the largest identified systematic error arises from uncertainties in the SPIRE beam.
We describe the application of a statistical method to estimate submillimeter galaxy number counts from confusion limited observations by the Balloon-borne Large Aperture Submillimeter Telescope (BLAST). Our method is based on a maximum likelihood fit to the pixel histogram, sometimes called P(D), an approach which has been used before to probe faint counts, the difference being that here we advocate its use even for sources with relatively high signal-to-noise ratios. This method has an advantage over standard techniques of source extraction in providing an unbiased estimate of the counts from the bright end down to flux densities well below the confusion limit. We specifically analyse BLAST observations of a roughly 10 sq. deg. map centered on the Great Observatories Origins Deep Survey South (GOODS-S) field. We provide estimates of number counts at the three BLAST wavelengths, 250, 350, and 500 microns; instead of counting sources in flux bins we estimate the counts at several flux density nodes connected with power-laws. We observe a generally very steep slope for the counts of about -3.7 at 250 microns and -4.5 at 350 and 500 microns, over the range ~0.02-0.5 Jy, breaking to a shallower slope below about 0.015 Jy at all three wavelengths. We also describe how to estimate the uncertainties and correlations in this method so that the results can be used for model-fitting. This method should be well-suited for analysis of data from the Herschel satellite.
Our velocity relative to the cosmic microwave background (CMB) generates a dipole from the CMB monopole, which was accurately measured by COBE. The relative velocity also modulates and aberrates the CMB fluctuations, generating a small signature of statistical isotropy violation in the covariance matrix. This signature was first measured by Planck 2013. Galaxy surveys are similarly affected by a Doppler boost. The dipole generated from the number count monopole has been extensively discussed, and measured (at very low accuracy) in the NVSS and TGSS radio continuum surveys. For the first time, we present an analysis of the Doppler imprint on the number count fluctuations, using the bipolar spherical harmonic formalism to quantify these effects. Next-generation wide-area surveys with a high redshift range are needed to detect the small Doppler signature in number count fluctuations. We show that radio continuum surveys with the SKA should enable a detection at $gtrsim 3 sigma$ in Phase 2, with marginal detection possible in Phase 1.
Deep Swift UV/Optical Telescope (UVOT) imaging of the Chandra Deep Field South is used to measure galaxy number counts in three near ultraviolet (NUV) filters (uvw2: 1928 A, uvm2: 2246 A, uvw1: 2600 A) and the u band (3645 A). UVOT observations cover the break in the slope of the NUV number counts with greater precision than the number counts by the Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) and the Galaxy Evolution Explorer (GALEX), spanning a range from 21 < m_AB < 25. Number counts models confirm earlier investigations in favoring models with an evolving galaxy luminosity function.
Sunyaev-Zeldovich (SZ) surveys are promising probes of cosmology - in particular for Dark Energy (DE) -, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects and maximum likelihood estimators may present biases for such sample sizes. In this work we use the Monte Carlo approach to determine the presence of bias on cosmological parameter estimators from cluster abundance as a function of the area and depth of the survey, and the number of cosmological parameters fitted. Assuming perfect knowledge of mass and redshift some estimators have non-negligible biases. For example, the bias of $sigma_8$ corresponds to about $40%$ of its statistical error bar when fitted together with $Omega_c$ and $w_0$. Including a SZ mass-observable relation decreases the relevance of the bias, for the typical sizes of current surveys. The biases become negligible when combining the SZ data with other cosmological probes. However, we show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the South Pole Telescope (SPT) sensitivity. The results of this work validate the use of the current maximum likelihood methods for present SZ surveys, but highlight the need for further studies for upcoming experiments. [abridged]
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
