85 research outputs found
The integrated Sachs-Wolfe effect in the AvERA cosmology
The recent AvERA cosmological simulation of R\'acz et al. (2017) has a
-like expansion history and removes the tension between
local and Planck (cosmic microwave background) Hubble constants. We contrast
the AvERA prediction of the integrated Sachs--Wolfe (ISW) effect with that of
. The linear ISW effect is proportional to the derivative
of the growth function, thus it is sensitive to small differences in the
expansion histories of the respective models. We create simulated ISW maps
tracing the path of light-rays through the Millennium XXL cosmological
simulation, and perform theoretical calculations of the ISW power spectrum.
AvERA predicts a significantly higher ISW effect than ,
times larger depending on the index of the spherical power
spectrum, which could be utilized to definitively differentiate the models. We
also show that AvERA predicts an opposite-sign ISW effect in the redshift range
, in clear contrast with . Finally,
we compare our ISW predictions with previous observations. While at present
these cannot distinguish between the two models due to large error bars, and
lack of internal consistency suggesting systematics, ISW probes from future
surveys will tightly constrain the models.Comment: 9 pages, 8 figures. Submitted to MNRA
Quantifying correlations between galaxy emission lines and stellar continua
We analyse the correlations between continuum properties and emission line
equivalent widths of star-forming and active galaxies from the Sloan Digital
Sky Survey. Since upcoming large sky surveys will make broad-band observations
only, including strong emission lines into theoretical modelling of spectra
will be essential to estimate physical properties of photometric galaxies. We
show that emission line equivalent widths can be fairly well reconstructed from
the stellar continuum using local multiple linear regression in the continuum
principal component analysis (PCA) space. Line reconstruction is good for
star-forming galaxies and reasonable for galaxies with active nuclei. We
propose a practical method to combine stellar population synthesis models with
empirical modelling of emission lines. The technique will help generate more
accurate model spectra and mock catalogues of galaxies to fit observations of
the new surveys. More accurate modelling of emission lines is also expected to
improve template-based photometric redshift estimation methods. We also show
that, by combining PCA coefficients from the pure continuum and the emission
lines, automatic distinction between hosts of weak active galactic nuclei
(AGNs) and quiescent star-forming galaxies can be made. The classification
method is based on a training set consisting of high-confidence starburst
galaxies and AGNs, and allows for the similar separation of active and
star-forming galaxies as the empirical curve found by Kauffmann et al. We
demonstrate the use of three important machine learning algorithms in the
paper: k-nearest neighbour finding, k-means clustering and support vector
machines.Comment: 14 pages, 14 figures. Accepted by MNRAS on 2015 December 22. The
paper's website with data and code is at
http://www.vo.elte.hu/papers/2015/emissionlines
Photometric redshifts for the SDSS Data Release 12
We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalog was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of 1, 976, 978 galaxies, and extends up to redshift z ≈ 0.8, with a useful coverage of up to z ≈ 0.6. We provide photometric redshifts and realistic error estimates for the 208, 474, 076 galaxies of the SDSS primary photometric catalog. We achieve an average bias of overline{Δ z_{norm}} = 5.84 × 10^{-5}, a standard deviation of σ(Δznorm) = 0.0205, and a 3σ outlier rate of Po = 4.11% when cross-validating on our training set. The published redshift error estimates and photometric error classes enable the selection of galaxies with high quality photometric redshifts. We also provide a supplementary error map that allows additional, sophisticated filtering of the data
A common explanation of the Hubble tension and anomalous cold spots in the CMB
The standard cosmological paradigm narrates a reassuring story of a universe
currently dominated by an enigmatic dark energy component. Disquietingly, its
universal explaining power has recently been challenged by, above all, the
tension in the values of the Hubble constant. Another, less
studied anomaly is the repeated observation of integrated Sachs-Wolfe imprints
stronger than expected in the CDM model from R>100
super-structures. Here we show that the inhomogeneous AvERA model of
emerging curvature is capable of telling a plausible albeit radically different
story that explains both observational anomalies without dark energy. We
demonstrate that while stacked imprints of R>100 supervoids in cosmic
microwave background temperature maps can discriminate between the AvERA and
CDM models, their characteristic differences may remain hidden using
alternative void definitions and stacking methodologies. Testing the extremes,
we then also show that the CMB Cold Spot can plausibly be explained in the
AvERA model as an ISW imprint. The coldest spot in the AvERA map is aligned
with multiple low- supervoids with R>100 and central underdensity
, resembling the observed large-scale galaxy density
field in the Cold Spot area. We hence conclude that the anomalous imprint of
supervoids may well be the canary in the coal mine, and existing observational
evidence for dark energy should be re-interpreted to further test alternative
models.Comment: 16 pages, 9 figures, accepted by MNRA
Photometric redshifts for the SDSS Data Release 12
We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalog was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of 1, 976, 978 galaxies, and extends up to redshift z ≈ 0.8, with a useful coverage of up to z ≈ 0.6. We provide photometric redshifts and realistic error estimates for the 208, 474, 076 galaxies of the SDSS primary photometric catalog. We achieve an average bias of overline{Δ z_{norm}} = 5.84 × 10^{-5}, a standard deviation of σ(Δznorm) = 0.0205, and a 3σ outlier rate of Po = 4.11% when cross-validating on our training set. The published redshift error estimates and photometric error classes enable the selection of galaxies with high quality photometric redshifts. We also provide a supplementary error map that allows additional, sophisticated filtering of the data
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