Interface Transparency of Nb/Pd Layered Systems

We have investigated, in the framework of proximity effect theory, the interface transparency T of superconducting/normal metal layered systems which consist of Nb and high paramagnetic Pd deposited by dc magnetron sputtering. The obtained T value is relatively high, as expected by theoretical arguments. This leads to a large value of the ratio $d_{s}^{cr}/ \xi_{s}$ although Pd does not exhibit any magnetic ordering.Comment: To be published on Eur. Phys. J.

Dissecting Photometric Redshift for Active Galactic Nucleus Using XMM- and Chandra-COSMOS Samples

In this paper, we release accurate photometric redshifts for 1692 counterparts to Chandra sources in the central square degree of the Cosmic Evolution Survey (COSMOS) field. The availability of a large training set of spectroscopic redshifts that extends to faint magnitudes enabled photometric redshifts comparable to the highest quality results presently available for normal galaxies. We demonstrate that morphologically extended, faint X-ray sources without optical variability are more accurately described by a library of normal galaxies (corrected for emission lines) than by active galactic nucleus (AGN) dominated templates, even if these sources have AGN-like X-ray luminosities. Preselecting the library on the bases of the source properties allowed us to reach an accuracy σ_(Δz/(1+z(spec))~0.015 with a fraction of outliers of 5.8% for the entire Chandra-COSMOS sample. In addition, we release revised photometric redshifts for the 1735 optical counterparts of the XMM-detected sources over the entire 2 deg^2 of COSMOS. For 248 sources, our updated photometric redshift differs from the previous release by Δz > 0.2. These changes are predominantly due to the inclusion of newly available deep H-band photometry (H_(AB) = 24 mag). We illustrate once again the importance of a spectroscopic training sample and how an assumption about the nature of a source together, with the number and the depth of the available bands, influences the accuracy of the photometric redshifts determined for AGN. These considerations should be kept in mind when defining the observational strategies of upcoming large surveys targeting AGNs, such as eROSITA at X-ray energies and the Australian Square Kilometre Array Pathfinder Evolutionary Map of the Universe in the radio band

ASTEP user's guide and software documentation

The Algorithm Simulation Test and Evaluation Program (ASTEP) is a modular computer program developed for the purpose of testing and evaluating methods of processing remotely sensed multispectral scanner earth resources data. ASTEP is written in FORTRAND V on the UNIVAC 1110 under the EXEC 8 operating system and may be operated in either a batch or interactive mode. The program currently contains over one hundred subroutines consisting of data classification and display algorithms, statistical analysis algorithms, utility support routines, and feature selection capability. The current program can accept data in LARSC1, LARSC2, ERTS, and Universal formats, and can output processed image or data tapes in Universal format

A Multiwavelength Study of a Sample of 70 μm Selected Galaxies in the COSMOS Field. II. The Role of Mergers in Galaxy Evolution

We analyze the morphological properties of a large sample of 1503 70 μm selected galaxies in the COSMOS field spanning the redshift range 0.01 10^(12) L_☉) being up to ~50%. We also find that the fraction of spirals drops dramatically with L_(IR). Minor mergers likely play a role in boosting the infrared luminosity for sources with low luminosities (L_(IR) 1 being difficult to classify and subject to the effects of bandpass shifting; therefore, these numbers can only be considered lower limits. At z 1, the fraction of major mergers is lower, but is at least 30%-40% for ULIRGs. In a comparison of our visual classifications with several automated classification techniques we find general agreement; however, the fraction of identified mergers is underestimated due to automated classification methods being sensitive to only certain timescales of a major merger. Although the general morphological trends agree with what has been observed for local (U)LIRGs, the fraction of major mergers is slightly lower than seen locally. This is in part due to the difficulty of identifying merger signatures at high redshift. The distribution of the U – V color of the galaxies in our sample peaks in the green valley (= 1.1) with a large spread at bluer and redder colors and with the major mergers peaking more strongly in the green valley than the rest of the morphological classes. We argue that, given the number of major gas-rich mergers observed and the relatively short timescale that they would be observable in the (U)LIRG phase, it is plausible for the observed red sequence of massive ellipticals (<10^(12) M_☉) to have been formed entirely by gas-rich major mergers

Finding counterparts for All-sky X-ray surveys with Nway: a Bayesian algorithm for cross-matching multiple catalogues

We release the AllWISE counterparts and Gaia matches to 106,573 and 17,665 X-ray sources detected in the ROSAT 2RXS and XMMSL2 surveys with |b|>15. These are the brightest X-ray sources in the sky, but their position uncertainties and the sparse multi-wavelength coverage until now rendered the identification of their counterparts a demanding task with uncertain results. New all-sky multi-wavelength surveys of sufficient depth, like AllWISE and Gaia, and a new Bayesian statistics based algorithm, NWAY, allow us, for the first time, to provide reliable counterpart associations. NWAY extends previous distance and sky density based association methods and, using one or more priors (e.g., colors, magnitudes), weights the probability that sources from two or more catalogues are simultaneously associated on the basis of their observable characteristics. Here, counterparts have been determined using a WISE color-magnitude prior. A reference sample of 4524 XMM/Chandra and Swift X-ray sources demonstrates a reliability of ~ 94.7% (2RXS) and 97.4% (XMMSL2). Combining our results with Chandra-COSMOS data, we propose a new separation between stars and AGN in the X-ray/WISE flux-magnitude plane, valid over six orders of magnitude. We also release the NWAY code and its user manual. NWAY was extensively tested with XMM-COSMOS data. Using two different sets of priors, we find an agreement of 96% and 99% with published Likelihood Ratio methods. Our results were achieved faster and without any follow-up visual inspection. With the advent of deep and wide area surveys in X-rays (e.g. SRG/eROSITA, Athena/WFI) and radio (ASKAP/EMU, LOFAR, APERTIF, etc.) NWAY will provide a powerful and reliable counterpart identification tool.Comment: MNRAS, Paper accepted for publication. Updated catalogs are available at www.mpe.mpg.de/XraySurveys/2RXS_XMMSL2 . NWAY available at https://github.com/JohannesBuchner/nwa

Improving the reliability of photometric redshift with machine learning

In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics

COSMOS2015 dataset machine learning photo-z

In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec<1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics