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Register a new userRecovering Redshift Distributions with Cross-Correlations: Pushing The Boundaries
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Samuel Schmidt
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Determining accurate redshift distributions for very large samples of objects has become increasingly important in cosmology. We investigate the impact of extending cross-correlation based redshift distribution recovery methods to include small scale clustering information. The major concern in such work is the ability to disentangle the amplitude of the underlying redshift distribution from the influence of evolving galaxy bias. Using multiple simulations covering a variety of galaxy bias evolution scenarios, we demonstrate reliable redshift recoveries using linear clustering assumptions well into the non-linear regime for redshift distributions of narrow redshift width. Including information from intermediate physical scales balances the increased information available from clustering and the residual bias incurred from relaxing of linear constraints. We discuss how breaking a broad sample into tomographic bins can improve estimates of the redshift distribution, and present a simple bias removal technique using clustering information from the spectroscopic sample alone.
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The search for detection of gamma-rays from distant AGNs by Imaging Atmospheric Cherenkov Telescopes (IACTs) is challenging at high redshifts, not only because of lower flux due to the distance of the source, but also due to the consequent absorption of gamma-rays by the extragalactic background light (EBL). Before the MAGIC discoveries reported in this work, the farthest source ever detected in the VHE domain was the blazar PKS 1424+240, at z>0.6. MAGIC, a system of two 17 m of diameter IACTs located in the Canary island of La Palma, has been able to go beyond that limit and push the boundaries for VHE detection to redshifts z~ 1. The two sources detected and analyzed, the blazar QSO B0218+357 and the FSRQ PKS 1441+25 are located at redshift z=0.944 and z=0.939 respectively. QSO B0218+357 is also the first gravitational lensed blazar ever detected in VHE. The activity, triggered by Fermi-LAT in high energy gamma-rays, was followed up by other instruments, such as the KVA telescope in the optical band and the Swift-XRT in X-rays. In the present work we show results on MAGIC analysis on QSO B0218+357 and PKS 1441+25 together with multiwavelength lightcurves. The collected dataset allowed us to test for the first time the present generation of EBL models at such distances.
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An understanding of astrophysical feedback is important for constraining models of galaxy formation and for extracting cosmological information from current and future weak lensing surveys. The thermal Sunyaev-Zeldovich effect, quantified via the Compton-$y$ parameter, is a powerful tool for studying feedback, because it directly probes the pressure of the hot, ionized gas residing in dark matter halos. Cross-correlations between galaxies and maps of Compton-$y$ obtained from cosmic microwave background surveys are sensitive to the redshift evolution of the gas pressure, and its dependence on halo mass. In this work, we use galaxies identified in year one data from the Dark Energy Survey and Compton-$y$ maps constructed from Planck observations. We find highly significant (roughly $12sigma$) detections of galaxy-$y$ cross-correlation in multiple redshift bins. By jointly fitting these measurements as well as measurements of galaxy clustering, we constrain the halo bias-weighted, gas pressure of the Universe as a function of redshift between $0.15 lesssim z lesssim 0.75$. We compare these measurements to predictions from hydrodynamical simulations, allowing us to constrain the amount of thermal energy in the halo gas relative to that resulting from gravitational collapse.
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We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGB$alpha$ planes within a reference view frustum and renders novel views by projecting this content into the target viewpoints. We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to $4times$ the lateral viewpoint movement allowed by prior work. Our method ameliorates two specific issues that limit the range of views renderable by prior methods: 1) We expand the range of novel views that can be rendered without depth discretization artifacts by using a 3D convolutional network architecture along with a randomized-resolution training procedure to allow our model to predict MPIs with increased disparity sampling frequency. 2) We reduce the repeated texture artifacts seen in disocclusions by enforcing a constraint that the appearance of hidden content at any depth must be drawn from visible content at or behind that depth. Please see our results video at: https://www.youtube.com/watch?v=aJqAaMNL2m4.
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