The final redshift release of the 6dF Galaxy Survey (6dFGS) is a combined redshift and peculiar velocity survey over the southern sky (|b| > 10 degrees). Its 136,304 spectra have yielded 110,256 new extragalactic redshifts and a new catalogue of 125,071 galaxies making near-complete samples with limits in (K, H, J, r_F, b_J) (12.65, 12.95, 13.75, 15.60, 16.75). The median redshift of the survey is 0.053. The catalog includes basic data for the galaxies in the 6dFGS with redshifts, using the best 6dFGS redshifts (radial velocity quality flag Q =3 or 4) plus available redshifts from SDSS, 2dFGRS and ZCAT (124,647 entries in all). It supersedes the previous DR2 version (CDS Cat. VII/249). The home page of of the 6dFGS database is http://www-wfau.roe.ac.uk/6dFGS. Any use of these data should explicitly state that they come from the Final Release of 6dFGS and cite both the 6dGS DR3 paper (Jones et al. 2009, MNRAS, 399, 683) as well as the original 6dFGS survey paper (Jones et al. 2004, MNRAS, 355, 747). This table was created by the HEASARC in March 2011 based on CDS Catalog VII/259 file 6dfgs.dat. This is a service provided by NASA HEASARC .

The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.

Last Updated on 20 March 2025 - https://zenodo.org/record/6368347 - https://www.mattiavaccari.net/df/specz

Based on the Spitzer Data Fusion Project - https://doi.org/10.5281/zenodo.7850783 - https://mattiavaccari.net/df

Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available only from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available only from CAT1 and CAT2,so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.

See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.

SaaS Data Warehouse Observability Market Outlook

According to our latest research, the global SaaS Data Warehouse Observability market size reached USD 1.32 billion in 2024, with robust growth driven by the rapid adoption of cloud-native data warehousing and the escalating need for real-time data monitoring. The market is poised to expand at a compelling CAGR of 18.9% from 2025 to 2033, ultimately reaching a forecasted value of USD 6.02 billion by 2033. This dynamic growth is underpinned by organizations’ increasing focus on data-driven decision-making, compliance mandates, and the complexity of modern data architectures, all of which require advanced observability solutions for optimal performance and security.

The primary growth driver for the SaaS Data Warehouse Observability market is the exponential surge in cloud data warehouse deployments across industries. As enterprises migrate from traditional on-premises data warehouses to scalable, cloud-based platforms, the complexity of managing, monitoring, and optimizing these environments has intensified. This has led to a heightened demand for observability tools capable of providing end-to-end visibility into data pipelines, query performance, resource utilization, and data lineage. The proliferation of multi-cloud and hybrid data strategies further amplifies the need for unified observability solutions that can seamlessly integrate with diverse data sources and platforms, ensuring data reliability and business continuity.

Another significant factor fueling market expansion is the growing emphasis on data quality, governance, and regulatory compliance. Organizations are grappling with stringent data privacy laws such as GDPR, CCPA, and other regional mandates, which necessitate continuous monitoring of data flows, access controls, and anomaly detection. SaaS Data Warehouse Observability solutions are increasingly being leveraged to automate compliance reporting, detect unauthorized data access, and ensure data accuracy across complex data ecosystems. These capabilities not only mitigate risk but also foster trust among stakeholders and customers, making observability an integral component of enterprise data strategies.

Technological advancements and increasing investments in artificial intelligence and machine learning are also reshaping the SaaS Data Warehouse Observability market. Modern observability platforms are now incorporating AI-driven analytics, predictive monitoring, and automated remediation capabilities, enabling organizations to proactively identify and resolve issues before they impact business operations. This evolution is particularly critical for sectors such as BFSI, healthcare, and e-commerce, where data availability and integrity are paramount. As a result, vendors are focusing on innovation and differentiation, offering advanced features such as self-healing data pipelines, intelligent alerting, and deep integration with popular data warehouse platforms like Snowflake, Google BigQuery, and Amazon Redshift.

From a regional perspective, North America dominates the SaaS Data Warehouse Observability market due to the early adoption of cloud technologies, a mature digital infrastructure, and a strong presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digital transformation, expanding cloud adoption, and the proliferation of data-centric business models in countries such as China, India, and Japan. Europe and Latin America are also witnessing steady growth, driven by regulatory compliance requirements and increasing investments in data modernization initiatives. The Middle East & Africa region, while still nascent, is expected to exhibit promising growth as organizations accelerate their digital agendas.

Component Analysis

The Component segment of the SaaS Data Warehouse Observability market is bifurcated into software and services, each playing a pivotal role in shaping the overall market landscape. Software solutio

We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, The Galaxy Evolution Explorer All Sky Survey, and The Two Micron All Sky Survey using two new training-set methods. We utilize the broad-band photometry from the three surveys alongside Sloan Digital Sky Survey measures of photometric quality and galaxy morphology. Our first training-set method draws from the theory of ensemble learning while the second employs Gaussian process regression both of which allow for the estimation of redshift along with a measure of uncertainty in the estimation. The Gaussian process models the data very effectively with small training samples of approximately 1000 points or less. These two methods are compared to a well known Artificial Neural Network training-set method and to simple linear and quadratic regression. We also demonstrate the need to provide confidence bands on the error estimation made by both classes of models. Our results indicate that variations due to the optimization procedure used for almost all neural networks, combined with the variations due to the data sample, can produce models with variations in accuracy that span an order of magnitude. A key contribution of this paper is to quantify the variability in the quality of results as a function of model and training sample. We show how simply choosing the ``best" model given a data set and model class can produce misleading results. We also investigate supplemental information provided by the Sloan Digital Sky Survey photometric pipeline related to photometric quality and galaxy morphology tracers. We show that, using these additional quality and morphology indicators rather than only the Sloan Digital Sky Survey broad-band u,g,r,i,z imaging data commonly used, one can improve redshift accuracy by 10s of percent. Near Infrared LaTeX broad-band photometry provided from the Two Micron All Sky Survey and near-ultraviolet and far-ultraviolet broad-band data from The Galaxy Evolution Explorer All Sky Survey are also investigated where they overlap with the Sloan Digital Sky Survey. Our results show that robust photometric redshift errors as low as 0.02 RMS can regularly be obtained. We believe these can be expanded to other photometric surveys where sufficient redshift calibration objects exist.

Change Data Capture Market Outlook

According to our latest research, the global Change Data Capture (CDC) market size reached USD 1.95 billion in 2024, demonstrating robust momentum with a compound annual growth rate (CAGR) of 20.4% from 2025 to 2033. Driven by the surging need for real-time data integration and analytics across diverse industries, the market is forecasted to reach USD 7.94 billion by 2033. The exponential growth is primarily attributed to the increasing adoption of cloud-based solutions, the proliferation of big data and analytics platforms, and the growing demand for seamless data synchronization in complex enterprise ecosystems.

One of the key growth factors propelling the Change Data Capture (CDC) market is the escalating volume and complexity of enterprise data. Organizations today are generating and processing vast amounts of data from disparate sources, including transactional databases, IoT devices, web applications, and legacy systems. The need to capture, replicate, and synchronize data changes in real time has become paramount for ensuring data consistency, supporting business intelligence, and enabling agile decision-making. As enterprises increasingly migrate to hybrid and multi-cloud environments, CDC technologies offer a critical bridge, facilitating seamless data movement and integration without disrupting operational workflows. This trend is particularly pronounced in sectors such as BFSI, healthcare, and retail, where data accuracy and timeliness are crucial for regulatory compliance, personalized customer experiences, and operational efficiency.

Another significant driver is the rapid advancement and adoption of cloud-native architectures and microservices. As organizations modernize their IT infrastructure, there is a growing emphasis on agility, scalability, and resilience. CDC solutions, especially those delivered as cloud-based services, enable enterprises to decouple data ingestion from legacy systems and support real-time analytics, event-driven architectures, and data lakes. The integration of CDC with modern data platforms such as Apache Kafka, Snowflake, and Amazon Redshift further accelerates data-driven innovation. Additionally, the proliferation of artificial intelligence and machine learning applications is amplifying the need for up-to-date, high-quality data streams, positioning CDC as a foundational technology for next-generation analytics and automation initiatives.

The Change Data Capture market is also benefiting from the rising focus on digital transformation across various industries. Organizations are investing heavily in data modernization projects to enhance customer engagement, streamline operations, and gain competitive advantages. CDC plays a pivotal role in enabling seamless data migration, legacy system modernization, and the integration of disparate data sources into unified analytics environments. The adoption of CDC is further supported by the increasing availability of user-friendly, low-code/no-code solutions that democratize data integration for business users and IT professionals alike. However, the market faces challenges such as data security concerns, integration complexities, and the need for specialized expertise, which are being addressed through continuous innovation and strategic partnerships among leading vendors.

From a regional perspective, North America continues to dominate the Change Data Capture market, accounting for the largest revenue share in 2024, driven by the presence of major technology providers, high adoption rates of advanced analytics, and a mature digital infrastructure. Europe and Asia Pacific are also witnessing significant growth, fueled by increasing investments in cloud computing, the expansion of e-commerce, and the digitalization of traditional industries. The Asia Pacific region, in particular, is expected to exhibit the highest CAGR during the forecast period, supported by the rapid digital transformation of emerging economies such as China, India, and Southeast Asia. Meanwhile, Latin America and the Middle East & Africa are gradually embracing CDC technologies, albeit at a slower pace, as organizations in these regions prioritize data-driven strategies and regulatory compliance.

Component Analysis

The Change Data Capture market by component is segmented into software, hardware, and services, each playing a distinct role in the overall CDC ecosystem. The software segment dominates the market, accounting for the largest share in 2024, as organ

The authors have compiled a sample of X-ray-selected galaxy groups and clusters from the XMM-Newton Serendipitous Source Catalog (2XMMi-DR3) with optical confirmation and redshift measurement from the Sloan Digital Sky Survey (SDSS). In their paper, they present an analysis of the X-ray properties of this new sample with particular emphasis on the X-ray luminosity-temperature (L_X - T) relation. The X-ray cluster candidates were selected from the 2XMMi-DR3 catalog in the footprint of the SDSS-DR7. The authors developed a finding algorithm to search for overdensities of galaxies at the positions of the X-ray cluster candidates in the photometric redshift space and to measure the redshifts of the clusters from the SDSS data. For optically confirmed clusters with good quality X-ray data, they derived the X-ray flux, luminosity, and temperature from proper spectral fits, while the X-ray flux for clusters with low-quality X-ray data was obtained from the 2XMMi-DR3 catalogue. The detection algorithm provides the photometric redshift of 530 galaxy clusters. Of these, 310 clusters have a spectroscopic redshift for at least one member galaxy. About 75 percent of the optically confirmed cluster sample are newly discovered X-ray clusters. Moreover, 301 systems are known as optically selected clusters in the literature while the remainder are new discoveries in X-ray and optical bands. The optically confirmed cluster sample spans a wide redshift range 0.03 to 0.70 (median z = 0.32). In this paper, they present the catalog of X-ray-selected galaxy groups and clusters from the 2XMMi/SDSS galaxy cluster survey. The catalog has two subsamples: (i) a cluster sample comprising 345 objects with their X-ray spectroscopic temperature and flux from the spectral fitting; (these objects are identified by having values for the table_sample parameter of 1 in this HEASARC implementation of the catalog) and (ii) a cluster sample consisting of 185 systems with their X-ray flux from the 2XMMi-DR3 catalog, because their X-ray data are insufficient for spectral fitting (these objects are identified by having values for the table_sample parameter of 2 herein). For each cluster, the catalog also provides the X-ray bolometric luminosity and the cluster mass at R₅₀₀ based on scaling relations and the position of the likely brightest cluster galaxy (BCG). The updated L_X - T relation of the current sample with X-ray spectroscopic parameters is presented in the paper. The authors found the slope of the L_X - T relation to be consistent with published ones. They see no evidence for evolution in the slope and intrinsic scatter of the L_X - T relation with redshift when excluding the low-luminosity groups. This catalog of X-ray selected galaxy clusters and groups supersedes and subsumes the first release of the 2XMMi/SDSS Galaxy Cluster Survey, comprising 175 clusters of galaxies, which was presented in Takey et al. (2011, A&A, 534, A120). This table was created by the HEASARC in October 2013 based on CDS catalog J/A+A/558/A75 files table1.dat and table2.dat. This is a service provided by NASA HEASARC .

This table contains data from the first data release (DR1) from the UV-bright Quasar Survey (UVQS) for new z ~ 1 active galactic nuclei (AGN) across the sky. Using simple GALEX UV and WISE near-IR color selection criteria, the authors generated a list of 1,450 primary candidates with FUV < 18.5 mag, that is contained in the HEASARC table (entries with source_sample = 'P'). They obtained discovery spectra, primarily on 3m-class telescopes, for 1,040 of these candidates and confirmed 86% as AGN, with redshifts generally at z > 0.5. Including a small set of observed secondary candidates, the authors report the discovery of 217 AGN with GALEX FUV magnitudes < 18 mag that previously had no reported spectroscopic redshifts. These are excellent potential targets for UV spectroscopy before the end of the Hubble Space Telescope mission. The main data products of UVQS are publicly available through the Mikulski Archive for Space Telescopes (MAST). The authors have performed an all-sky survey for z ~ 1, FUV-bright quasars selected from GALEX and WISE photometry. In several of the observing runs, conditions were unexpectedly favorable and we exhausted the primary candidates at certain right ascension ranges. To fill the remaining observing time, they generated a secondary candidate list. This secondary set of 2,010 candidates is also contained in this HEASARC table (entries with source_sample = 'S'). The authors proceeded to obtain discovery-quality long-slit spectra (i.e., low-dispersion, large-wavelength coverage, modest signal-to-noise ratio (S/N) of their UV-bright Quasar Survey (UVQS) candidates in one calendar year. The principal facilities were: (i) the dual Kast spectrometer on the 3m Shane telescope at the Lick Observatory; (ii) the Boller & Chivens (BCS) spectrometer on the Irenee du Pont 100-inch telescope at the Las Campanas Observatory; and (iii) the Calar Alto Faint Object Spectrograph on the CAHA 2.2-meter telescope at the Calar Alto Observatory (CAHA). They acquired an additional ~20 spectra on larger aperture telescopes (Keck/ESI, MMT/MBC, Magellan/MagE) during twilight or under poor observing conditions. Typical exposure times were limited to < ~200s, with adjustments for fainter sources or sub-optimal observing conditions. Table 3 in the reference paper provides a list of the details of the observations of these candidates. From the total candidates list of 3,460 objects, the authors measured high-quality redshifts (redshift quality flag values of 3 or greater) for 1,121 sources. They assumed that every source with a recessional velocity v_r = z * c < 500 km s^-1 was "Galactic", which they associate with the Galaxy and members of the Local Group. This included sources where the eigenspectra fits were poor yet a low v_r was indisputable (e.g., stars). Many of these were assigned z = 0 exactly. The remainder of the UVQS sources were assumed to be extragalactic AGN, and the derived redshift information for these sources (which was given in Table 4 of the reference paper) has been incorporated into this HEASARC representation of UVQS. Finally, there were 93 sources with good-quality spectra for which we cannot the authors could not recover a secure redshift. The majority of these have been previously cataloged as blazars (or BL Lac objects). Table 6 in the reference paper lists the sample of these unknown or insecure redshift objects. This table was created by the HEASARC in April 2017 based upon the CDS Catalog J/AJ/152/25 files table1.dat, table2.dat, and table4.dat. This is a service provided by NASA HEASARC .

The all-sky ACO (Abell, Corwin and Olowin 1989, ApJS, 70, 1) Catalog of 4073 rich clusters of galaxies and 1175 southern poor or distant S-clusters has been searched for published redshifts. Data for 1059 of them were found and classified into various quality classes, e.g. to reduce the problem of foreground contamination of redshifts. Taking the ACO selection criteria for redshifts, a total of 992 entries remain, 21 percent more than ACO. Redshifts for rich clusters are now virtually complete out to a redshift z of 0.05 in the north and of 0.04 in the south. In the north, the magnitude-redshift (m_10 - z) relation agrees with that of Kalinkov et al. (1985, Astr. Nachr., 306, 283). For the southern rich clusters, minor adjustments to the m_10 - z relation of ACO are suggested, while for the S-clusters the redshifts are about 30 percent lower than estimated. This table was created by the HEASARC in May 2010 based on CDS Catalog VII/165A file catalog.dat. This is a service provided by NASA HEASARC .