35 research outputs found
S-PLUS DR1 galaxy clusters and groups catalogue using PzWav
We present a catalogue of 4499 groups and clusters of galaxies from the first
data release of the multi-filter (5 broad, 7 narrow) Southern Photometric Local
Universe Survey (S-PLUS). These groups and clusters are distributed over 273
deg in the Stripe 82 region. They are found using the PzWav algorithm,
which identifies peaks in galaxy density maps that have been smoothed by a
cluster scale difference-of-Gaussians kernel to isolate clusters and groups.
Using a simulation-based mock catalogue, we estimate the purity and
completeness of cluster detections: at S/N>3.3 we define a catalogue that is
80% pure and complete in the redshift range 0.1<z<0.4, for clusters with
M. We also assessed the accuracy of the catalogue
in terms of central positions and redshifts, finding scatter of
kpc and , respectively. Moreover, less than 1% of
the sample suffers from fragmentation or overmerging. The S-PLUS cluster
catalogue recovers ~80% of all known X-ray and Sunyaev-Zel'dovich selected
clusters in this field. This fraction is very close to the estimated
completeness, thus validating the mock data analysis and paving an efficient
way to find new groups and clusters of galaxies using data from the ongoing
S-PLUS project. When complete, S-PLUS will have surveyed 9300 deg of the
sky, representing the widest uninterrupted areas with narrow-through-broad
multi-band photometry for cluster follow-up studies.Comment: 17 pages, 15 figures, paper accepted for publication by MNRA
The miniJPAS survey: clusters and galaxy groups detection with AMICO
Samples of galaxy clusters allow us to better understand the physics at play
in galaxy formation and to constrain cosmological models once their mass,
position (for clustering studies) and redshift are known. In this context,
large optical data sets play a crucial role. We investigate the capabilities of
the Javalambre-Physics of the Accelerating Universe Astrophysical Survey
(J-PAS) in detecting and characterizing galaxy groups and clusters. We analyze
the data of the miniJPAS survey, obtained with the JPAS-Pathfinder camera and
covering deg centered on the AEGIS field to the same depths and with
the same 54 narrow band plus 2 broader band near-UV and near-IR filters
anticipated for the full J-PAS survey. We use the Adaptive Matched Identifier
of Clustered Objects (AMICO) to detect and characterize groups and clusters of
galaxies down to in the redshift range . We detect 80, 30
and 11 systems with signal-to-noise ratio larger than 2.5, 3.0 and 3.5,
respectively, down to . We derive mass-proxy scaling
relations based on Chandra and XMM-Newton X-ray data for the signal amplitude
returned by AMICO, the intrinsic richness and a new proxy that incorporates the
galaxies' stellar masses. The latter proxy is made possible thanks to the J-PAS
filters and shows a smaller scatter with respect to the richness. We fully
characterize the sample and use AMICO to derive a probabilistic membership
association of galaxies to the detected groups that we test against
spectroscopy. We further show how the narrow band filters of J-PAS provide a
gain of up to 100% in signal-to-noise ratio in detection and an uncertainty on
the redshift of clusters of only placing J-PAS in
between broadband photometric and spectroscopic surveys. The performances of
AMICO and J-PAS with respect to mass sensitivity, mass-proxies qualityComment: 15 pages, 12 figures, 3 tables, submitted to A&
Hepatitis reactivation in patients with rheumatic diseases after immunosuppressive therapy—a report of long-term follow-up of serial cases and literature review
BEST PRACTICE FOR MEASURING WIND SPEEDS AND TURBULENCE OFFSHORE THROUGH IN-SITU AND REMOTE SENSING TECHNOLOGIES
The motivation for making offshore wind measurements for the wind energy industry is summarized including identifying the key parameters of interest, and providing a limited summary of available best practice recommendations for the offshore wind energy industry. A précis of in situ measurement technologies, installation guidance and uncertainty analyses, and best practice recommendations is provided. Next wind measurement devices are reviewed that are based on optical remote sensing (ground-based, airborne, and satellite-based techniques and applications) are relevant to the demands of the wind energy industry. Emphasis is placed on a review of lidar measurement techniques and use of lidar. For both ground-based and satellite-borne instrumentation, a précis of the technologies, uncertainty analyses, and best practice recommendations are given. Finally, the report concludes by providing a number of recommendations for future work.Acknowledgment: “This material is based upon work supported by the Department of Energy under Award Number #DE-EE0005379.”
Disclaimer: “This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
FAST.Farm development and validation of structural load prediction against large eddy simulations
Errors in radial velocity variance from Doppler wind lidar
A high-fidelity lidar turbulence measurement technique relies on accurate
estimates of radial velocity variance that are subject to both systematic
and random errors determined by the autocorrelation function of radial velocity,
the sampling rate, and the sampling duration. Using both statistically simulated
and observed data, this paper quantifies the effect of the volumetric averaging in
lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which
the stationarity assumption is valid for atmospheric flows, the systematic
error is negligible but the random error exceeds about 10 %