The CluMPR galaxy cluster-finding algorithm and DESI legacy survey galaxy cluster catalogue
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M J Yantovski-Barth↗
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Association between optically identified galaxy clusters and the underlying dark matter halos
Clusters of galaxies trace massive dark matter halos in the Universe, but they can include multiple halos projected along lines of sight. Here, we study the halos contributing to clusters using the Cardinal simulation, which mimics the Dark Energy Survey data. We use the red-sequence-based cluster finding algorithm redMaPPer as a case study. For each cluster, we identify the halos hosting its member galaxies, and we define the main halo as the one contributing the most to the cluster's richness ($λ$, the estimated number of member galaxies). At $z=0.3$, for clusters with $λ> 60$, the main halo typically contributes to $92\%$ of the richness, and this fraction drops to $67\%$ for $λ\approx 20$. Defining "clean" clusters as those with $\geq50\%$ of the richness contributed by the main halo, we find that $100\%$ of the $λ> 60$ clusters are clean, while $73\%$ of the $λ\approx 20$ clusters are clean. Three halos can usually account for more than $80\%$ of the richness of a cluster. The main halos associated with redMaPPer clusters have a completeness ranging from $98\%$ at virial mass $10^{14.6}~h^{-1}M_{\odot}$ to $64\%$ at $10^{14}~h^{-1}M_{\odot}$. In addition, we compare the inferred cluster centers with true halo centers, finding that $30\%$ of the clusters are miscentered with a mean offset $40\%$ of the cluster radii, in agreement with recent X-ray studies. These systematics worsen as redshift increases, but we expect that upcoming surveys extending to longer wavelengths will improve the cluster finding at high redshifts. Our results affirm the robustness of the redMaPPer algorithm and provide a framework for benchmarking other cluster-finding strategies.
79 ASTRONOMY AND ASTROPHYSICS↗
Finding Nuclear Clusters in the Short-Baseline Near Detector
Production of nuclear clusters such as deuterons, tritons, helions and alpha particles has only recently been simulated for neutrino-nucleus interactions, and has not yet been measured in neutrino experiments. The Short-Baseline Near Detector (SBND) is the first Liquid Argon Time Projection Chamber (LArTPC) with high enough resolution and a large enough neutrino flux to accurately measure the cross-section for the production of heavier-than-proton fragments in neutrino interactions. SBND is a 112-ton LArTPC, and lies 110 m downstream from the Booster Neutrino Beam target, where it has already collected the world's largest dataset of neutrino-argon interactions. In these processes, the intranuclear cascade and nuclear de-excitation stages can both produce nuclear clusters. With LArTPC technology's excellent reconstruction capabilities and SBND's unprecedented neutrino statistics, a measurement of these nuclear clusters could distinguish between nuclear models and improve neutrino energy reconstruction. This poster presents the status of simulation, reconstruction, and prospects for a measurement of nuclear clusters in SBND.
Beever, Anna [Sheffield U.] (ORCID:000900069339924↗
Measurement of the Splashback Feature Around SZ-Selected Galaxy Clusters With DES, SPT, and ACT
We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected red MaPPer clusters with similar mass and redshift distributions,rspis∼2σsmaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first in fall into the cluster.
T Shin↗
SZ–X-Ray Surface Brightness Fluctuations in the SPT-XMM Clusters
The hot plasma in galaxy clusters, the intracluster medium, is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the nonthermal pressure, which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent motions is thus of major astrophysical and cosmological interest. Characteristics of turbulent motions can be indirectly accessed through surface brightness fluctuations. This study expands on our pilot investigations of surface brightness fluctuations in the Sunyaev–Zel’dovich and in X-ray data by examining, for the first time, a large sample of 60 clusters using both SPT-SZ and XMM-Newton data and spans the redshift range 0.2 < z < 1.5, thus constraining the respective pressure and density fluctuations within 0.6 R $_{500}$. We deem density fluctuations to be of sufficient quality for 32 clusters, finding mild correlations between the peak of the amplitude spectra of density fluctuations and various dynamical parameters. We infer turbulent velocities from density fluctuations with an average Mach number $\mathcal{M}$ 3D = 0.52 ± 0.14, in agreement with numerical simulations. For clusters with inferred turbulent Mach numbers from fluctuations in both pressure, $\mathcal{M}$ P , and density, $\mathcal{M}$ ρ , we find broad agreement between $\mathcal{M}$ P and $\mathcal{M}$ ρ . Our results suggest either a bimodal or a skewed unimodal Mach number distribution, with the majority of clusters being turbulence-dominated (subsonic) while the remainder are shock-dominated (supersonic).
79 ASTRONOMY AND ASTROPHYSICS↗
Cluster-cluster clustering
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.
Barnes, J.↗
Cepheus OB2: Disk Evolution and Accretion at 3-10 Myr
We presented the results of MMT observations of young stars for our study of protoplanetary disks at ages 1-10 Myr in Tr 37 and NGC 7160. Using low-resolution optical spectra from the Hectospec multifiber spectrograph, we have tripled the number of known low-mass cluster members, identifying 130 new members in Tr 37 and 30 in NGC 7160. We used indicators of youth to identify and classify the low-mass cluster members. With the extended samples, we estimated the disk fraction in the clusters, finding that 40% of the low-mass stars in Tr 37 are actively accreting, whereas only 1 of the 55 low-mass stars in NGC 7160 shows indications of accretion. Optical photometry and theoretical isochrones are used to refine the ages of the clusters, confirming the estimates of 4 Myr for Tr 37 and 10 Myr for NGC 7160.
Sicilia-Aguilar, A.↗
The Compilation and Validation of the Spectroscopic Redshift Catalogs for the DESI-COSMOS and DESI-XMM-LSS Fields
Over several dedicated programs that include targets beyond the main cosmological samples, the Dark Energy Spectroscopic Instrument collected spectra for 304,970 unique objects in two fields centered on the COSMOS and XMM-LSS fields. In this work, we develop spectroscopic redshift robustness criteria for those spectra, validate these criteria using visual inspection, and provide two custom value-added catalogs with our redshift characterizations. With these criteria, we reliably classify 212,935 galaxies below z < 1.6, 9713 quasars, and 35,222 stars. The resulting catalogs achieve a redshift purity exceeding 99.4% across all galaxy samples. As a critical element in characterizing the selection function, we provide the description of 70 different algorithms that were used to select these targets from imaging data. To facilitate joint imaging/spectroscopic analyses, we provide row-matched photometry from the Dark Energy Camera, Hyper-Suprime Cam, and public COSMOS2020 photometric catalogs. Finally, we demonstrate example applications of these large catalogs to photometric redshift estimation, cluster finding, and completeness studies.
Ratajczak, J. [Univ. of Utah, Salt Lake City, UT (↗
How to cluster in parallel with neural networks
Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.
Kamgar-Parsi, Behzad↗
A US coordination Facility for the Spectrum-X-Gamma Observatory
We have completed our efforts in support of the Spectrum X Gamma mission under a NASA grant. These activities have included direct support to the mission, developing unifying tools applicable to SXG and other X-ray astronomy missions, and X-ray astronomy research to maintain our understanding of the importance and relevance of SXG to the field. SXG provides: 1) Simultaneous Multiwavelength Capability; 2) Large Field of View High Resolution Imaging Spectroscopy; 3) Sensitive Polarimetry with SXRP (Stellar X-Ray Polarimeter). These capabilities will ensure the fulfillment of the following objectives: understanding the accretion dynamics and the importance of reprocessing, upscattering, and disk viscosity around black holes; studying cluster mergers; spatially resolving cluster cooling flows to detect cooling gas; detecting cool gas in cluster outskirts in absorption; mapping gas in filaments around clusters; finding the 'missing' baryons in the Universe; determining the activity history of the black hole in the Galactic Center of our own central black hole; determining pulsar beam geometry; searching for the Lense-Thirring effect in black hole sources; constraining emission mechanisms and accretion geometry in AGN.
Forman, W.↗
Hyperspectral Image Classification using a Self-Organizing Map
The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.
Martinez, P.↗
A Stochastic Model of Space-Time Variability of Tropical Rainfall: I. Statistics of Spatial Averages
Global maps of rainfall are of great importance in connection with modeling of the earth s climate. Comparison between the maps of rainfall predicted by computer-generated climate models with observation provides a sensitive test for these models. To make such a comparison, one typically needs the total precipitation amount over a large area, which could be hundreds of kilometers in size over extended periods of time of order days or months. This presents a difficult problem since rain varies greatly from place to place as well as in time. Remote sensing methods using ground radar or satellites detect rain over a large area by essentially taking a series of snapshots at infrequent intervals and indirectly deriving the average rain intensity within a collection of pixels , usually several kilometers in size. They measure area average of rain at a particular instant. Rain gauges, on the other hand, record rain accumulation continuously in time but only over a very small area tens of centimeters across, say, the size of a dinner plate. They measure only a time average at a single location. In making use of either method one needs to fill in the gaps in the observation - either the gaps in the area covered or the gaps in time of observation. This involves using statistical models to obtain information about the rain that is missed from what is actually detected. This paper investigates such a statistical model and validates it with rain data collected over the tropical Western Pacific from ship borne radars during TOGA COARE (Tropical Oceans Global Atmosphere Coupled Ocean-Atmosphere Response Experiment). The model incorporates a number of commonly observed features of rain. While rain varies rapidly with location and time, the variability diminishes when averaged over larger areas or longer periods of time. Moreover, rain is patchy in nature - at any instant on the average only a certain fraction of the observed pixels contain rain. The fraction of area covered by rain decreases, as the size of a pixel becomes smaller. This means that within what looks like a patch of rainy area in a coarse resolution view with larger pixel size, one finds clusters of rainy and dry patches when viewed on a finer scale. The model makes definite predictions about how these and other related statistics depend on the pixel size. These predictions were found to agree well with data. In a subsequent second part of the work we plan to test the model with rain gauge data collected during the TRMM (Tropical Rainfall Measuring Mission) ground validation campaign.
Kundu, Prasun K.↗
The Advanced X-ray Spectroscopy and Imaging Observatory (AXSIO)
Following recommendations from the 2010 "New Worlds, New Horizons" (NWNH) report, the Advanced X-ray Spectroscopy and Imaging Observatory (AXSIO) concept streamlines the International X-ray Observatory (IXO) mission to concentrate on the science objectives that are enabled by high-resolution spectroscopic capabilities. AXSIO will trace orbits close to the event horizon of black holes, measure black hole spin for tens of supermassive black holes (SMBH), use spectroscopy to characterize outflows and the environment of AGN during their peak activity, observe 5MBH out to redshift z=6, map bulk motions and turbulence in galaxy clusters, find the missing baryons in the cosmic web using background quasars, and observe the process of cosmic feedback where black holes and supernovae inject energy on galactic and intergalactic scales. These measurements are enabled by a 0.9 sq m collecting area at 1.25 keV, a micro calorimeter array providing high-resolution spectroscopic imaging and a deployable high efficiency grating spectrometer. AXSIO delivers a 30-fold increase in effective area for high resolution spectroscopy. The key simplifications are guided by recommendations in the NWNH panel report include a reduction in focal length from 20m to 10m, eliminating the extendable optical bench, and a reduction in the instrument complement from six to two, avoiding a movable instrument platform. A focus on spectroscopic science allows the spatial resolution requirement to be relaxed to 10 arc sec (with a 5 arc sec goal). These simplifications decrease the total mission cost to under the $2B cost to NASA recommended by NWNH. AXSIO will be available to the entire astronomical community with observing allocations based on peer-review.
White, Nicholas E.↗
AXSIO and the NASA X-ray Mission Concept Study
The Advanced X-ray Spectroscopy and Imaging Observatory (AXSIO) focuses on the IXO science objectives ranked highly by the Decadal Survey: tracing orbits near 5MBH event horizons, measuring BH spin, characterizing outflows and the environment of AGN, observing 5MBH to z=6, mapping gas motion in clusters, finding the missing baryons, and observing cosmic feedback. AXSIO's streamlining of IXO includes reduction in the instrument complement to a calorimeter and a grating spectrometer, and relaxation of the angular resolution to 10". With 0.9 m2 effective area at 1.25 keV, AXSIO delivers a 30-fold performance increase over current missions for high-resolution spectroscopy and spectroscopic timing. NASA has also undertaken a study of notional missions to determine lower cost approaches to accomplishing IXO objectives over the next decade. Three concepts were studied; which as a group encompass the full range of IXO science. The capabilities and architecture of these missions are summarized.
Petre, Robert↗
Systematic characterization of unknown compounds via dimensionality reduction of time series
Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.
54 ENVIRONMENTAL SCIENCES↗
"A Richness Study of 14 Distant X-Ray Clusters from the 160 Square Degree Survey"
We have measured the surface density of galaxies toward 14 X-ray-selected cluster candidates at redshifts z(sub i) 0.46, and we show that they are associated with rich galaxy concentrations. These clusters, having X-ray luminosities of Lx(0.5-2 keV) approx. (0.5 - 2.6) x 10(exp 44) ergs/ sec are among the most distant and luminous in our 160 deg(exp 2) ROSAT Position Sensitive Proportional Counter cluster survey. We find that the clusters range between Abell richness classes 0 and 2 and have a most probable richness class of 1. We compare the richness distribution of our distant clusters to those for three samples of nearby clusters with similar X-ray luminosities. We find that the nearby and distant samples have similar richness distributions, which shows that clusters have apparently not evolved substantially in richness since redshift z=0.5. There is, however, a marginal tendency for the distant clusters to be slightly poorer than nearby clusters, although deeper multicolor data for a large sample would be required to confirm this trend. We compare the distribution of distant X-ray clusters in the L(sub X)-richness plane to the distribution of optically selected clusters from the Palomar Distant Cluster Survey. The optically selected clusters appear overly rich for their X-ray luminosities, when compared to X-ray-selected clusters. Apparently, X-ray and optical surveys do not necessarily sample identical mass concentrations at large redshifts. This may indicate the existence of a population of optically rich clusters with anomalously low X-ray emission, More likely, however, it reflects the tendency for optical surveys to select unvirialized mass concentrations, as might be expected when peering along large-scale filaments.
Jones, Christine↗
Dark Energy Survey Year 3 Results: Cosmological Constraints from Cluster Abundances, Weak Lensing, and Galaxy Clustering
Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the $\Lambda$CDM model. This work presents the first set of cosmological constraints derived with ~16,000 optically selected redMaPPer clusters across nearly 5,000 $\rm{deg}^2$ using DES Year 3 data sets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3x2pt), ensuring direct comparability with the DES-Y3 3x2pt analysis. We obtain constraints of $S_8 = 0.864 \pm 0.035$ and $\Omega_{\rm{m}} = 0.265^{+0.019}_{-0.031}$ from the cluster-based data vector. We find that cluster constraints and 3x2pt constraints are consistent under the $\Lambda$CDM model with a Posterior Predictive Distribution (PPD) value of $0.53$. The consistency between clusters and 3x2pt provides a stringent test of $\Lambda$CDM across different mass and spatial scales. Jointly analyzing clusters with 3x2pt further improves cosmological constraints, yielding $S_8 = 0.811^{+0.022}_{-0.020}$ and $\Omega_{\rm{m}} = 0.294^{+0.022}_{-0.033}$, a $24\%$ improvement in the $\Omega_{\rm{m}}-S_8$ figure-of-merit over 3x2pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of $0.6$, significantly reducing previous $S_8$ tension claims. Finally, combining DES 3x2pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of $\sum m_\nu < 0.26$ eV at 95% confidence under the $\Lambda$CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming Stage-IV surveys such as LSST, Euclid, and Roman.
79 ASTRONOMY AND ASTROPHYSICS↗
Dark energy survey year 3 results: Cosmological constraints from cluster abundances, weak lensing, and galaxy clustering
Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the ΛCDM model. This work presents the first set of cosmological constraints derived with ∼16,000 optically selected redMaPPer clusters across nearly 5000 deg 2 using DES year 3 datasets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3 × 2pt), ensuring direct comparability with the DES-Y3 3 × 2pt analysis. Here, we obtain constraints of 𝑆 8 = 0.864 ± 0.035 and Ω m = $0.265^{+0.019}_{−0.031}$ from the cluster-based data vector. We find that cluster constraints and 3 × 2pt constraints are consistent under the ΛCDM model with a posterior predictive distribution (PPD) value of 0.53. The consistency between clusters and 3 × 2pt provides a stringent test of ΛCDM across different mass and spatial scales. Jointly analyzing clusters with 3 × 2pt further improves cosmological constraints, yielding 𝑆 8 = $0.811^{+0.022}_{−0.020}$ and Ω m = $0.294^{+0.022}_{−0.033}$, a 24% improvement in the Ω m − 𝑆 8 figure of merit over 3 × 2pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of 0.6, significantly reducing previous 𝑆 8 tension claims. Finally, combining DES 3 × 2pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of ∑𝑚 𝜈 < 0.26 eV at 95% confidence under the ΛCDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming stage-IV surveys such as LSST, Euclid, and Roman.
Abbott, T. M. C. [NSF’s National Optical-Infrared ↗