Dwarf Galaxies in the Coma Cluster. I. Detection, Measurement and Classification Techniques

Deep B- and R-band CCD images of the central ~700 arcmin^2 of the Coma cluster core have been used to measure the dwarf-galaxy population in Coma. In this paper, we describe a newly developed code for automated detection, photometry and classification of faint objects of arbitrary shape and size on digital images. Intensity-weighted moments are used to compute the positions, radial structures, ellipticities, and integrated magnitudes of detected objects. We demonstrate that Kron-type 2r_1 aperture aperture magnitudes and surface brightnesses are well suited to faint-galaxy photometry of the type described here. Discrimination between starlike and extended (galaxy) objects is performed interactively through parameter-space culling in several possible parameters, including the radial moments, surface brightness, and integrated color versus magnitude. Our code is tested and characterized with artificial CCD images of star and galaxy fields; it is demonstrated to be accurate, robust and versatile. Using these analysis techniques, we detect a large population of dE galaxies in the Coma cluster core. These dEs stand out as a tight sequence in the R, (B-R) color-magnitude diagram.Comment: Accepted for publication PASP; 29 pages LaTeX (AASTeX using aaspp4.sty), with 9 EPS figures available from http://www.sci.wsu.edu/math/faculty/secker/ftp

The Early-type Dwarf-to-Giant Ratio and Substructure in the Coma Cluster

We have obtained new CCD photometry for a sample of $\simeq 800$ early-type galaxies (dwarf and giant ellipticals) in the central 700 arcmin$^2$ of the Coma cluster, complete in color and in magnitude to $R = 22.5$ mag ($M_R \simeq -12$ mag for $H_0 = 86$ km/sec/Mpc). The composite luminosity function for all galaxies in the cluster core (excluding NGC 4874 and NGC 4889) is modeled as the sum of a Gaussian distribution for the giant galaxies and a Schechter function for the dwarf elliptical galaxies. We determine that the early-type dwarf-to-giant ratio (EDGR) for Coma is identical to that measured for the less rich Virgo cluster; i.e., the EDGR does not increase as predicted by the EDGR-richness correlation. We postulate that the presence of substructure is an important factor in determining the cluster's EDGR; that is, the EDGR for Coma is consistent with the Coma cluster being built up from the merger of multiple less-rich galaxy clusters.Comment: 13 pages, LaTeX file, 3 EPS figures, uses aaspp4.sty; also available from: http://www.sci.wsu.edu/math/faculty/secker/secker.html; to be published in ApJ, 469 (Oct.1, 1996

Dwarf Galaxies in the Coma Cluster. II. Photometry and Analysis

We study the dwarf galaxy population in the central ~700 arcmin^2 of the Coma cluster, the majority of which are early-type dwarf elliptical (dE) galaxies. Analysis of the statistically-decontaminated dE galaxy sequence in the color-magnitude diagram reveals a highly significant trend of color with magnitude (\Delta (B-R)/\Delta R = -0.056\pm0.002 mag), in the sense that fainter dEs are bluer and thus presumably more metal-poor. The mean color of the faintest dEs in our sample is (B-R)~1.15 mag, consistent with a color measurement of the diffuse intracluster light in the Coma core. This intracluster light could then have originated from the tidal disruption of faint dEs in the cluster core. The total galaxy luminosity function (LF) is well modeled as the sum of a log-normal distribution for the giant galaxies, and a Schechter function for the dE galaxies with a faint-end slope \alpha = -1.41\pm0.05. This value of \alpha is consistent with those measured for the Virgo and Fornax clusters. The spatial distribution of the faint dE galaxies (19.0 < R \le 22.5 mag) has R_c = 22.15 arcmin (~0.46h^{-1} Mpc), significantly larger than the R_c = 13.71 arcmin (~0.29h^{-1} Mpc) found for the cluster giants and the brighter dEs (R \le 19.0 mag), consistent with tidal disruption of the faint dEs. Finally, we find that most dEs belong to the general Coma cluster potential rather than as satellites of individual giant galaxies: An analysis of the number counts around 10 cluster giants reveals that they each have on average 4\pm 1 dE companions within a projected radius of 13.9h^{-1} kpc. (Abridged)Comment: Accepted for publication in PASP; 36 pages LaTeX (AASTex using aaspp4.sty), with 14 EPS figures available from http://www.sci.wsu.edu/math/faculty/secker/ftp/ ; Single change in the Introduction (50 kpc corrected to read 50 pc

Automated Fact Checking in the News Room

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact-checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user-friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58\% of the time, and 59\% of the returned evidence was relevant

The Hubble Constant from Observations of the Brightest Red Giant Stars in a Virgo-Cluster Galaxy

The Virgo and Fornax clusters of galaxies play central roles in determining the Hubble constant H_0. A powerful and direct way of establishing distances for elliptical galaxies is to use the luminosities of the brightest red-giant stars (the TRGB luminosity, at M_I = -4.2). Here we report the direct observation of the TRGB stars in a dwarf elliptical galaxy in the Virgo cluster. We find its distance to be 15.7 +- 1.5 Megaparsecs, from which we estimate a Hubble constant of H_0 = 77 +- 8 km/s/Mpc. Under the assumption of a low-density Universe with the simplest cosmology, the age of the Universe is no more than 12-13 billion years.Comment: 12 pages, LaTeX, with 2 postscript figures; in press for Nature, July 199

Demonstration of a Heterogeneous Satellite Architecture During RIMPAC 2018

The Micro-Satellite Military Utility (MSMU) Project Arrangement (PA) is an agreement under the Responsive Space Capabilities (RSC) Memorandum of Understanding (MOU) involving the Departments and Ministries of Defence of Australia, Canada, Germany, Italy, Netherlands, New Zealand, Norway, United Kingdom and United States. MSMU’s charter is to inform a space enterprise that provides military users with reliable access to a broad spectrum of information in an opportunistic environment. The MSMU community participated on a non-interference basis in the biennial Rim of the Pacific (RIMPAC) exercise from 26 June to 2 August 2018. This provided an opportunity to explore the military utility of a heterogeneous space architecture of satellites including traditional government and commercial satellites, as well as micro-satellites and nanosatellites associated with the “new space” paradigm. The objective was to test the hypothesis that a heterogeneous space architecture, mostly composed of small satellites, can bring significant value to the operational theatre. This paper describes the results from the MSMU experiment, outlines the lessons learned in terms of the infrastructure required to support such an experiment, and offers insights into the military utility of the heterogeneous space architecture. It concludes that a cooperative heterogeneous space architecture does have advantages and value, and that micro-satellites and nanosatellites contribute significant capability

Demonstration of a Hybrid Space Architecture During RIMPAC 2020

The Micro-Satellite Military Utility (MSMU) Project Arrangement (PA) is an agreement under the Responsive Space Capabilities (RSC) Memorandum of Understanding (MOU) that involves the Departments and Ministries of Defence of Australia, Canada, Germany, Italy, the Netherlands, New Zealand, Norway, United Kingdom and United States. MSMU’s charter is to inform a space enterprise that provides military users with reliable access to a broad spectrum of information in an opportunistic environment. Research and Development teams from MSMU partner nations supported Exercise Rim of the Pacific (RIMPAC) 2020 which took place 17 to 31 August 2020 in the Hawaiian region. RIMPAC 2020 provided an opportunity to explore the military utility of a Hybrid Space Architecture (HSA) of satellites including traditional government and commercial satellites, as well as micro-satellites and nanosatellites, by leveraging contributions across the MSMU partner nations. The objective was to continue testing the hypothesis that an HSA, mostly composed of small satellites, can bring significant value to the operational theatre. The MSMU PA partner nations have leveraged several multi-national exercises, with the first being the Exercise RIMPAC 2018. Previous exercises enabled multinational technology advancements, interoperability testing, process refinement, and capability developments to make advancements towards MSMU’s goal to address the warfighter’s need for diverse ISR capabilities. The most recent accomplishment was a major integration effort across mission planning tools, space-based Intelligence, Surveillance and Reconnaissance (ISR) data providers, and exploitation tools. The MSMU team accessed ~256 space-based sensors (EO – Electro Optical, SAR – Synthetic Aperture Radar, AIS – Automatic Identification System) to collect maritime domain and ISR data over a harbor, airfields and open sea. Data was exploited via international channels in order to determine the success rate of capturing pertinent data to be later exploited and disseminated. This paper describes results from the experiment and offers insights into the HSA military utility

The Impact of Variability in SAR Satellite Imagery on Classification

Artificial intelligence (AI) can be a useful tool to gather intelligence from remote sensing data; it helps make sense of synthetic aperture radar (SAR) data via discovery and exploitation. The challenge of utilizing AI in SAR applications is obtaining (large enough) comprehensive sets of labeled training data because SAR data has significant variation across sensor-related characteristics, across processing parameters, and across the different collection plans. This work evaluates the impact of SAR satellite imagery variations on classification accuracy, and demonstrates this by classifying pixels of SAR imagery into land, water, and ship for varying conditions (area-of-interest, incidence angle, spatial resolution, etc.). Results showed that variations in the area-of-interest (AOI), incidence angle, and spatial resolution impacted the classification results obtained using an artificial neural network (ANN). This work also demonstrated that ANNs trained on SAR imagery can be used to infer training data labels of other SAR imagery obtained from different conditions, provided that the changes in condition produced less than a 5% classification error or increased class separation for some (or all) of the classes being discriminated