43,811 research outputs found

    Origin of the X-ray Quasi-Periodic Oscillations and Identification of a Transient Ultraluminous X-Ray Source in M82

    Full text link
    The starburst galaxy M82 contains two ultraluminous X-ray sources (ULXs), CXOM82 J095550.2+694047 (=X41.4+60) and CXOM82 J095551.1+694045 (=X42.3+59), which are unresolved by XMM-Newton. We revisited the two XMM-Newton observations of M82 and analyzed the surface brightness profiles using the known Chandra source positions. We show that the quasi-periodic oscillations (QPOs) detected with XMM-Newton originate from X41.4+60, the brightest X-ray source in M82. Correcting for the contributions of the unresolved sources, the QPO at a frequency of 55.8+/-1.3 mHz on 2001 May 06 had a fractional rms amplitude of 32%, and the QPO at 112.9+/-1.3 mHz on 2004 April 21 had an amplitude of 21%. The QPO frequency may possibly be correlated with the source flux, similar to the type C QPOs in XTE 1550-564 and GRS 1915+105, but at luminosities two orders of magnitude higher. X42.3+59, the second brightest source in M82, displayed a strikingly high flux of 1.4E-11 ergs/cm^2/s in the 2-10 keV band on 2001 May 6. A seven-year light curve of X42.3+59 shows extreme variability over a factor of 1000; the source is not detected in several Chandra observations. This transient behavior suggests accretion from an unstable disk. If the companion star is massive, as might be expected in the young stellar environment, then the compact object would likely be an IMBH.Comment: 9 pages, 6 figures, submitted to ApJ on May 08, 200

    The influence of laser hardening on wear in the valve and valve seat contact

    No full text
    In internal combustion engines it is important to manage the wear in the valve and valve seat contact in order to minimise emissions and maximise economy. Traditionally wear in this contact has been controlled by the use of a valve seat insert and the careful selection of materials for both the valve and the insert. More recently, due to the increasing demands for both performance and cost, alternative methods of controlling the wear, and the resulting valve recession, have been sought. Using the heating effect of a laser to induce localised phase transformations, to increase hardness and wear resistance, in materials has been used since the 1970s, however it is only in recent years that it has been able to compete with more established surface treatment techniques, particularly in terms of cost, as new laser hardware has been developed. In this work, a laser has been used to treat the valve seat area of a cast iron cylinder head. In order to optimise the laser parameters for use on the head, preliminary tests were carried out to investigate the fundamental wear characteristics of untreated cast iron and also cast iron with a range of laser treatments. Previous work has identified the predominant wear mechanism in the valve and valve seat contact as impact on valve closure. Two bespoke test machines, one for testing basic specimens and one for testing components, were used to identify the laser parameters most likely to yield acceptable results when applied to a cylinder head to be used in a fired dynamometer test. © 2009 Elsevier B.V. All rights reserved

    Inductive queries for a drug designing robot scientist

    Get PDF
    It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
    corecore