Bringing a Machine Learning Based Novelty Detection Software Tool from Research to Production

This paper presents the process of bringing a machine learning based novelty detection software tool from research to production. Moreover, it sums up the necessary changes that needed to be done for developing a scientific software library into a software product with an application in space operations. This process considers the needs and expectations of all stakeholders. The system for which this process is shown is the Automated Telemetry Health Monitoring System (ATHMoS) developed at the German Space Operations Center of the German Aerospace Center. In its early phase as a research software, it paved the way for the novelty detection research. After its value for the satellite engineer’s daily work became visible, it evolved to a robust and resilient software tool that can be used in a productive environment to support the engineers in their routine work. Furthermore, the integration of the system into our Visualization and Data Analysis framework is explained. This framework has a web-based front-end for the interactive exploration and analysis of satellite telemetry data

A Modern Approach to Visualise Structured and Unstructured Space Missions' Data

In this paper the Visualisation and Data Analysis (ViDA) project, currently being developed at the German Space Operations Center (GSOC), is presented. ViDA is a modern, interactive, web-based frontend tool designed to efficiently explore various types of data generated by space missions. It is more than just a telemetry display tool and, as such, includes features from business intelligence, data science and AI tools, while being focused on the multi-spacecraft operations use case. The paper describes how the big data challenges (volume, variety, variability, complexity, value) in the context of spacecraft operations have been addressed and how the adopted solutions have been integrated into ViDA. It also highlights the importance of contextual knowledge as crucial point for the design and implementation of ViDA. The techniques used for creating appropriate visual representations of the data and their relations are described. Such visualisations are specifically designed to deliver interpretable results to the users, thus helping them to quickly extract knowledge from them during their analytical process. Finally, the integration of ViDA into the ground system and its connections to the other tools in the telemetry/telecommand chain are discussed

A panchromatic view of infrared quasars: excess star formation and radio emission in the most heavily obscured systems

To understand the active galactic nuclei (AGNs) phenomenon and their impact on the evolution of galaxies, a complete AGN census is required; however, finding heavily obscured AGNs is observationally challenging. Here we use the deep and extensive multiwavelength data in the COSMOS field to select a complete sample of 578 infrared (IR) quasars (LAGN,IR > 1045 erg s−1) at z < 3, with minimal obscuration bias, using detailed UV-to-far-IR spectral energy distribution (SED) fitting. We complement our SED constraints with X-ray and radio observations to further investigate the properties of the sample. Overall, 322 of the IR quasars are detected by Chandra and have individual X-ray spectral constraints. From a combination of X-ray stacking and L2−10 kev – L6 μm analyses, we show that the majority of the X-ray faint and undetected quasars are heavily obscured (many are likely Compton thick), highlighting the effectiveness of the mid-IR band to find obscured AGNs. We find that 355 (≈61 per cent) IR quasars are obscured (NH > 1022 cm−2) and identify differences in the average properties between the obscured and unobscured quasars: (1) obscured quasars have star formation rates ≈3 times higher than unobscured systems for no significant difference in stellar mass and (2) obscured quasars have stronger radio emission than unobscured systems, with a radio-loudness parameter ≈ 0.2 dex higher. These results are inconsistent with a simple orientation model but in general agreement with either extreme host-galaxy obscuration towards the obscured quasars or a scenario where obscured quasars are an early phase in the evolution of quasars

NuSTAR Spectroscopy of Multi-Component X-ray Reflection from NGC 1068

We report on observations of NGC1068 with NuSTAR, which provide the best constraints to date on its $>10$~keV spectral shape. We find no strong variability over the past two decades, consistent with its Compton-thick AGN classification. The combined NuSTAR, Chandra, XMM-Newton, and Swift-BAT spectral dataset offers new insights into the complex reflected emission. The critical combination of the high signal-to-noise NuSTAR data and a spatial decomposition with Chandra allow us to break several model degeneracies and greatly aid physical interpretation. When modeled as a monolithic (i.e., a single N_H) reflector, none of the common Compton-reflection models are able to match the neutral fluorescence lines and broad spectral shape of the Compton reflection. A multi-component reflector with three distinct column densities (e.g., N_H~1.5e23, 5e24, and 1e25 cm^{-2}) provides a more reasonable fit to the spectral lines and Compton hump, with near-solar Fe abundances. In this model, the higher N_H components provide the bulk of the Compton hump flux while the lower N_H component produces much of the line emission, effectively decoupling two key features of Compton reflection. We note that ~30% of the neutral Fe Kalpha line flux arises from >2" (~140 pc), implying that a significant fraction of the <10 keV reflected component arises from regions well outside of a parsec-scale torus. These results likely have ramifications for the interpretation of poorer signal-to-noise observations and/or more distant objects [Abridged].Comment: Submitted to ApJ; 23 pages (ApJ format); 11 figures and 3 tables; Comments welcomed

The X-ray and mid-infrared luminosities in luminous type 1 quasars

Several recent studies have reported different intrinsic correlations between the active galactic nucleus (AGN) mid-IR luminosity (${L}_{\mathrm{MIR}}$) and the rest-frame 2–10 keV luminosity (L X) for luminous quasars. To understand the origin of the difference in the observed {L}_{{\rm{X}}}\mbox{--}{L}_{\mathrm{MIR}} relations, we study a sample of 3247 spectroscopically confirmed type 1 AGNs collected from Boötes, XMM-COSMOS, XMM-XXL-North, and the Sloan Digital Sky Survey quasars in the Swift/XRT footprint spanning over four orders of magnitude in luminosity. We carefully examine how different observational constraints impact the observed {L}_{{\rm{X}}}\mbox{--}{L}_{\mathrm{MIR}} relations, including the inclusion of X-ray-nondetected objects, possible X-ray absorption in type 1 AGNs, X-ray flux limits, and star formation contamination. We find that the primary factor driving the different {L}_{{\rm{X}}}\mbox{--}{L}_{\mathrm{MIR}} relations reported in the literature is the X-ray flux limits for different studies. When taking these effects into account, we find that the X-ray luminosity and mid-IR luminosity (measured at rest-frame $6\,\mu {\rm{m}}$, or ${L}_{6\mu {\rm{m}}}$) of our sample of type 1 AGNs follow a bilinear relation in the log–log plane: $\mathrm{log}{L}_{{\rm{X}}}=(0.84\pm 0.03)\times \mathrm{log}{L}_{6\mu {\rm{m}}}/{10}^{45}$ erg s−1 + (44.60 ± 0.01) for ${L}_{6\mu {\rm{m}}}\lt {10}^{44.79}$ erg s−1, and $\mathrm{log}{L}_{{\rm{X}}}=(0.40\pm 0.03)\times \mathrm{log}{L}_{6\mu {\rm{m}}}/{10}^{45}$ erg s−1 + (44.51 ± 0.01) for ${L}_{6\mu {\rm{m}}}\,\geqslant {10}^{44.79}$ erg s−1. This suggests that the luminous type 1 quasars have a shallower {L}_{{\rm{X}}}\mbox{--}{L}_{6\mu {\rm{m}}} correlation than the approximately linear relations found in local Seyfert galaxies. This result is consistent with previous studies reporting a luminosity-dependent {L}_{{\rm{X}}}\mbox{--}{L}_{\mathrm{MIR}} relation and implies that assuming a linear {L}_{{\rm{X}}}\mbox{--}{L}_{6\mu {\rm{m}}} relation to infer the neutral gas column density for X-ray absorption might overestimate the column densities in luminous quasars

Investigating the Covering Fraction Distribution of Swift/BAT AGNs with X-Ray and Infrared Observations

We present an analysis of a sample of 69 local obscured Swift/Burst Alert Telescope active galactic nuclei (AGNs) with X-ray spectra from NuSTAR and infrared (IR) spectral energy distributions from Herschel and WISE. We combine this X-ray and IR phenomenological modeling and find a significant correlation between reflected hard X-ray emission and IR AGN emission, with suggestive indications that this correlation may be stronger than the one between intrinsic hard X-ray and IR emissions. This relation between the IR and reflected X-ray emission suggests that both are the result of the processing of intrinsic emission from the corona and accretion disk by the same structure. We explore the resulting implications on the underlying distribution of covering fraction for all AGNs, by generating mock observables for the reflection parameter and IR luminosity ratio using empirical relations found for the covering fraction with each quantity. We find that the observed distributions of the reflection parameter and IR-to-X-ray ratio are reproduced with broad distributions centered around covering fractions of at least ~40%–50%, whereas narrower distributions match our observations only when centered around covering fractions of ~70%–80%. Our results are consistent with both independent estimates of the covering fractions of individual objects and the typical covering fraction obtained on the basis of obscured fractions for samples of AGNs. These results suggest that the level of reprocessing in AGNs, including X-ray reflection, is related in a relatively straightforward way to the geometry of the obscuring material

The XMM-SERVS Survey: XMM-Newton Point-source Catalogs for the W-CDF-S and ELAIS-S1 Fields

We present the X-ray point-source catalogs in two of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) fields, W-CDF-S (4.6 deg2) and ELAIS-S1 (3.2 deg2), aiming to fill the gap between deep pencil-beam X-ray surveys and shallow X-ray surveys over large areas. The W-CDF-S and ELAIS-S1 regions were targeted with 2.3 and 1.0 Ms of XMM-Newton observations, respectively; 1.8 and 0.9 Ms exposures remain after flare filtering. The survey in W-CDF-S has a flux limit of 1.0 × 10−14 erg cm−2 s−1 over 90% of its area in the 0.5–10 keV band; 4053 sources are detected in total. The survey in ELAIS-S1 has a flux limit of 1.3 × 10−14 erg cm−2 s−1 over 90% of its area in the 0.5–10 keV band; 2630 sources are detected in total. Reliable optical-to-IR multiwavelength counterpart candidates are identified for ≈89% of the sources in W-CDF-S and ≈87% of the sources in ELAIS-S1. A total of 3129 sources in W-CDF-S and 1957 sources in ELAIS-S1 are classified as active galactic nuclei (AGNs). We also provide photometric redshifts for X-ray sources; ≈84% of the 3319/2001 sources in W-CDF-S/ELAIS-S1 with optical-to-near-IR forced photometry available have either spectroscopic redshifts or high-quality photometric redshifts. The completion of the XMM-Newton observations in the W-CDF-S and ELAIS-S1 fields marks the end of the XMM-SERVS survey data gathering. The ≈12,000 pointlike X-ray sources detected in the whole ≈13 deg2 XMM-SERVS survey will benefit future large-sample AGN studies

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The fifteenth data release of the Sloan Digital Sky Surveys : first release of MaNGA derived quantities, data visualization tools and stellar library

Twenty years have passed since first light for the Sloan Digital SkySurvey (SDSS). Here, we release data taken by the fourth phase of SDSS(SDSS-IV) across its first three years of operation (July 2014-July2017). This is the third data release for SDSS-IV, and the fifteenth from SDSS (Data Release Fifteen; DR15). New data come from MaNGA - we release 4824 datacubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g. stellar and gas kinematics, emission line, andother maps) from the MaNGA Data Analysis Pipeline (DAP), and a new data visualisation and access tool we call "Marvin". The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials and examples of data use. While SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V(2020-2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.Publisher PDFPeer reviewe