Richard E. Griffiths

Bio: Richard E. Griffiths is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Galaxy & Active galactic nucleus. The author has an hindex of 73, co-authored 342 publications receiving 18259 citations. Previous affiliations of Richard E. Griffiths include University of Leicester & University of Michigan.

The X-Ray Observatory Suzaku

Abstract: High-sensitivity wide-band X-ray spectroscopy is the key feature of the Suzaku X-ray observatory, launched on 2005 July 10. This paper summarizes the spacecraft, in-orbit performance, operations, and data processing that are related to observations. The scientific instruments, the high-throughput X-ray telescopes, X-ray CCD cameras, non-imaging hard X-ray detector are also described.

COSMOS: Hubble Space Telescope Observations

Abstract: The Cosmic Evolution Survey (COSMOS) was initiated with an extensive allocation (590 orbits in Cycles 12-13) using the Hubble Space Telescope (HST) for high-resolution imaging. Here we review the characteristics of the HST imaging with the Advanced Camera for Surveys (ACS) and parallel observations with NICMOS and WFPC2. A square field (1.8 deg^2) has been imaged with single-orbit ACS I-band F814W exposures with 50% completeness for sources 0.5" in diameter at I_(AB) = 26.0 mag. The ACS is a key part of the COSMOS survey, providing very high sensitivity and high-resolution (0.09" FWHM and 0.05" pixels) imaging and detecting a million objects. These images yield resolved morphologies for several hundred thousand galaxies. The small HST PSF also provides greatly enhanced sensitivity for weak-lensing investigations of the dark matter distribution.

The Chandra COSMOS Survey. I. Overview and Point Source Catalog

Abstract: The Chandra COSMOS Survey (C-COSMOS) is a large, 1.8 Ms, Chandra program that has imaged the central 0.5 deg^2 of the COSMOS field (centered at 10 ^h , +02 ^o ) with an effective exposure of ~160 ks, and an outer 0.4 deg^2 area with an effective exposure of ~80 ks. The limiting source detection depths are 1.9 × 10^(–16) erg cm^(–2) s^(–1) in the soft (0.5-2 keV) band, 7.3 × 10^(–16) erg cm^(–2) s^(–1) in the hard (2-10 keV) band, and 5.7 × 10^(–16) erg cm^(–2) s^(–1) in the full (0.5-10 keV) band. Here we describe the strategy, design, and execution of the C-COSMOS survey, and present the catalog of 1761 point sources detected at a probability of being spurious of <2 × 10^(–5) (1655 in the full, 1340 in the soft, and 1017 in the hard bands). By using a grid of 36 heavily (~50%) overlapping pointing positions with the ACIS-I imager, a remarkably uniform (±12%) exposure across the inner 0.5 deg^2 field was obtained, leading to a sharply defined lower flux limit. The widely different point-spread functions obtained in each exposure at each point in the field required a novel source detection method, because of the overlapping tiling strategy, which is described in a companion paper. This method produced reliable sources down to a 7-12 counts, as verified by the resulting logN-logS curve, with subarcsecond positions, enabling optical and infrared identifications of virtually all sources, as reported in a second companion paper. The full catalog is described here in detail and is available online.

The performance and calibration of wfpc2 on the hubble space telescope

Abstract: The Wide Field Planetary Camera 2 (WFPC2) was installed in the Hubble Space Telescope (HST) in 1993 December. Since then, the instrument has been providing high-quality images. A significant among of calibration data has been collected to aid in the understanding of the on-orbit performance of the instrument. Generally, the behavior of the camera is similar to its performance during the system-level thermal vacuum test at JPL in 1993 May. Surprises were a significant charge-transfer-efficiency (CTE) problem and a significant growth rate in hot pixels at the original operating temperature of the CCDs (-76 deg C). The operating temperature of the WFPC2 CCDs was changed to -88 deg C on 1994 April 23, and significant improvements in CTE and hot pixels are seen at this temperature. In this paper we describe the on-orbit performance of the WFPC2. We discuss the optical and thermal history, the instrument throughput and stability, the Point Spread Function (PSF), the effects of undersampling on photometry, the properties of cosmic rays observed on-orbit, and the geometric distortion in the camera. We present the best techniques for the reduction of WFPC2 data, and describe the construction of calibration products including superbiases, superdarks, and flat fields.

COSMOS : Hubble Space Telescope Observations

Abstract: The Cosmic Evolution Survey (COSMOS) was initiated with an extensive allocation (590 orbits in Cycles 12-13) using the Hubble Space Telescope (HST) for high resolution imaging. Here we review the characteristics of the HST imaging with the Advanced Camera for Surveys (ACS) and parallel observations with NICMOS and WFPC2. A square field (1.8$\sq$\deg) has been imaged with single-orbit ACS I-F814W exposures with 50% completeness for sources 0.5\arcsec in diameter at I$_{AB} $ = 26.0 mag. The ACS imaging is a key part of the COSMOS survey, providing very high sensitivity and high resolution (0.09\arcsec FWHM, 0.05\arcsec pixels) imaging and detecting 1.2 million objects to a limiting magnitude of 26.5 (AB). These images yield resolved morphologies for several hundred thousand galaxies. The small HST PSF also provides greatly enhanced sensitivity for weak lensing investigations of the dark matter distribution.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

On the variation of the initial mass function

Abstract: A universal initial mass function (IMF) is not intuitive, but so far no convincing evidence for a variable IMF exists. The detection of systematic variations of the IMF with star-forming conditions would be the Rosetta Stone for star formation. In this contribution an average or Galactic-field IMF is defined, stressing that there is evidence for a change in the power-law index at only two masses: near 0.5 M⊙ and near 0.08 M⊙. Using this supposed universal IMF, the uncertainty inherent in any observational estimate of the IMF is investigated by studying the scatter introduced by Poisson noise and the dynamical evolution of star clusters. It is found that this apparent scatter reproduces quite well the observed scatter in power-law index determinations, thus defining the fundamental limit within which any true variation becomes undetectable. The absence of evidence for a variable IMF means that any true variation of the IMF in well-studied populations must be smaller than this scatter. Determinations of the power-law indices α are subject to systematic errors arising mostly from unresolved binaries. The systematic bias is quantified here, with the result that the single-star IMFs for young star clusters are systematically steeper by Δα≈0.5 between 0.1 and 1 M⊙ than the Galactic-field IMF, which is populated by, on average, about 5-Gyr-old stars. The MFs in globular clusters appear to be, on average, systematically flatter than the Galactic-field IMF (Piotto & Zoccali; Paresce & De Marchi), and the recent detection of ancient white-dwarf candidates in the Galactic halo and the absence of associated low-mass stars (Ibata et al.; Mendez & Minniti) suggest a radically different IMF for this ancient population. Star formation in higher metallicity environments thus appears to produce relatively more low-mass stars. While still tentative, this is an interesting trend, being consistent with a systematic variation of the IMF as expected from theoretical arguments.

Luminous infrared galaxies

Abstract: ▪ Abstract At luminosities above 1011 , infrared galaxies become the dominant population of extragalactic objects in the local Universe (z ≲ 0.3), being more numerous than optically selected starburst and Seyfert galaxies and quasi-stellar objects at comparable bolometric luminosity. The trigger for the intense infrared emission appears to be the strong interaction/merger of molecular gas-rich spirals, and the bulk of the infrared luminosity for all but the most luminous objects is due to dust heating from an intense starburst within giant molecular clouds. At the highest luminosities (Lir > 1012 ), nearly all objects appear to be advanced mergers powered by a mixture of circumnuclear starburst and active galactic nucleus energy sources, both of which are fueled by an enormous concentration of molecular gas that has been funneled into the merger nucleus. These ultraluminous infrared galaxies may represent an important stage in the formation of quasi-stellar objects and powerful radio galaxies. They may al...

Coevolution (Or Not) of Supermassive Black Holes and Host Galaxies

Abstract: Supermassive black holes (BHs) have been found in 85 galaxies by dynamical modeling of spatially resolved kinematics. The Hubble Space Telescope revolutionized BH research by advancing the subject from its proof-of-concept phase into quantitative studies of BH demographics. Most influential was the discovery of a tight correlation between BH mass and the velocity dispersion σ of the bulge component of the host galaxy. Together with similar correlations with bulge luminosity and mass, this led to the widespread belief that BHs and bulges coevolve by regulating each other's growth. Conclusions based on one set of correlations from in brightest cluster ellipticals to in the smallest galaxies dominated BH work for more than a decade. New results are now replacing this simple story with a richer and more plausible picture in which BHs correlate differently with different galaxy components. A reasonable aim is to use this progress to refine our understanding of BH-galaxy coevolution. BHs with masses of 105−106M...

Dwarf galaxies of the local group

Abstract: ▪ Abstract The Local Group dwarf galaxies offer a unique window to the detailed properties of the most common type of galaxy in the Universe. In this review, I update the census of Local Group dwarfs based on the most recent distance and radial velocity determinations. I then discuss the detailed properties of this sample, including (a) the integrated photometric parameters and optical structures of these galaxies, (b) the content, nature, and distribution of their interstellar medium (ISM), (c) their heavy-element abundances derived from both stars and nebulae, (d) the complex and varied star-formation histories of these dwarfs, (e) their internal kinematics, stressing the relevance of these galaxies to the “dark matter problem” and to alternative interpretations, and (f) evidence for past, ongoing, and future interactions of these dwarfs with other galaxies in the Local Group and beyond. To complement the discussion and to serve as a foundation for future work, I present an extensive set of basic observ...