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Register a new userWhich galaxy mass estimator can we trust?
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Added by
Luis Peralta de Arriba
Publication date
2015
fields
Physics
and research's language is
English
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We address the problem that dynamical masses of high-redshift massive galaxies, derived using virial scaling, often come out lower than stellar masses inferred from population fitting to multi-band photometry. We compare dynamical and stellar masses for various samples spanning ranges of mass, compactness and redshift, including the SDSS. The discrepancy between dynamical and stellar masses occurs both at low and high redshifts, and systematically increases with galaxy compactness. Because it is unlikely that stellar masses show systematic errors with galaxy compactness, the correlation of mass discrepancy with compactness points to errors in the dynamical mass estimates which assume homology with massive, nearby ellipticals. We quantify the deviations from homology and propose specific non-virial scaling of dynamical mass with effective radius and velocity dispersion.
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Possible inaccuracies in the determination of periods from short-term time series caused by disregard of the real course of light curves and instrumental trends are documented on the example of the period analysis of simulated TESS-like light curve by notorious Lomb-Scargle method.
Recently, speech enhancement (SE) based on deep speech prior has attracted much attention, such as the variational auto-encoder with non-negative matrix factorization (VAE-NMF) architecture. Compared to conventional approaches that represent clean speech by shallow models such as Gaussians with a low-rank covariance, the new approach employs deep generative models to represent the clean speech, which often provides a better prior. Despite the clear advantage in theory, we argue that deep priors must be used with much caution, since the likelihood produced by a deep generative model does not always coincide with the speech quality. We designed a comprehensive study on this issue and demonstrated that based on deep speech priors, a reasonable SE performance can be achieved, but the results might be suboptimal. A careful analysis showed that this problem is deeply rooted in the disharmony between the flexibility of deep generative models and the nature of the maximum-likelihood (ML) training.
How far can we use multi-wavelength cross-identifications to deconvolve far-infrared images? In this short research note I explore a test case of CLEAN deconvolutions of simulated confused 850 micron SCUBA-2 data, and explore the possible scientific applications of combining this data with ostensibly deeper TolTEC Large Scale Structure (LSS) survey 1.1mm-2mm data. I show that the SCUBA-2 can be reconstructed to the 1.1mm LMT resolution and achieve an 850 micron deconvolved sensitivity of 0.7 mJy RMS, an improvement of at least ~1:5x over naive point source filtered images. The TolTEC/SCUBA-2 combination can constrain cold (<10K) observed-frame colour temperatures, where TolTEC alone cannot.
In large optical survey at high redshifts ($z>2$), the CIV broad emission line is the most practical alternative to estimate the mass ($M_{text{BH}}$) of active super-massive black holes (SMBHs). However, mass determinations obtained with this line are known to be highly uncertain. In this work we use the Sloan Digital Sky Survey Data Release 7 and 12 quasar catalogues to statistically test three alternative methods put forward in the literature to improve CIV-based $M_{text{BH}}$ estimations. These methods are constructed from correlations between the ratio of the CIV line-width to the low ionization line-widths (H$alpha$, H$beta$ and MgII) and several other properties of rest-frame UV emission lines. Our analysis suggests that these correction methods are of limited applicability, mostly because all of them depend on correlations that are driven by the linewidth of the CIV profile itself and not by an interconnection between the linewidth of the CIV line with the linewidth of the low ionization lines. Our results show that optical CIV-based mass estimates at high redshift cannot be a proper replacement for estimates based on IR spectroscopy of low ionization lines like H$alpha$, H$beta$ and MgII
We construct a simple and robust approach for deriving constraints on magnetic fields in galaxy clusters from rotation measure (RM) maps. Relaxing the commonly used assumptions of a correlation between the magnetic field strength and the plasma density and of a power-law (in wave number) magnetic field power spectrum, and using an efficient numerical analysis method, we test the consistency of a wide range of magnetic field models with RM maps of 11 extended sources in 5 clusters, for which the data were made available to us. We show that the data reveal no indication for a radial dependence of the average magnetic field strength, and in particular no indication for a correlation between the gas density and the field strength. The RM maps of a considerable fraction of the sources either require or are consistent with the presence of a spatially uniform magnetic field of a relatively small strength, 0.02-0.3 muG, which contributes significantly to the RM. The RM maps of all but one source do not require a power-law magnetic field power spectrum, and most are consistent with a power spectrum dominated by a single wave length. The uncertainties in the magnetic field strengths (and spatial correlation lengths) derived from RM maps exceed an order of magnitude (and often more). These uncertainties imply, in particular, that there is no indication in current RM data for a systematic difference between the magnetic field strengths in radio-halo clusters and in radio-quiet clusters. With the improvement expected in the near future of the quality and quantity of RM data, our analysis method will enable one to derive more accurate constraints on magnetic fields in galaxy clusters.
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