10 years of RXTE monitoring of anomalous X-ray pulsar 4U 0142+61: long-term variability

Dib, Rim; Kaspi, V. M.; Gavriil, F. P.

doi:10.1007/s10509-007-9313-2

Cited by 4 publications

(7 citation statements)

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“…We obtain a best spin period of P s = 8.6887(3) s (MJD 52318.0), P s = 8.6882(2) s (MJD 52663.0), P s = 8.688 56(1) s (MJD 53065.0) and P s = 8.688 575(2) s (MJD 53211.0), for the XMM–Newton observations from 2002 February to 2004 July 3 . All these periods are consistent within their 3σ errors with the more accurate RXTE timing analysis of this source (Dib, Kaspi & Gavriil 2007).…”

Section: Observations Data Analysis and Resultssupporting

confidence: 83%

“…The 0.3–10 keV peak‐to‐peak pulsed fraction shows a modest (∼2.5σ) increase between 2002 and 2004, possibly consistent with the evidence for a slow increase of the pulsed flux observed by RXTE (Dib et al 2007). However, this is not followed by a similar increase of the fundamental sine‐function pulsed fraction, which instead is within the errors relatively constant in time.…”

Section: Observations Data Analysis and Resultssupporting

confidence: 75%

“…We have done the same test between the two observations to asses the pulse profile variability in time for a given energy range: in all the energy ranges the probability of the 2004 March and July profiles having the same shape is ∼0.02, hence not very significant. However, note that RXTE monitoring over the last 10 yr did detect pulse profile variability with time, thanks to its higher sensitivity for pulse profile changes (Dib et al 2007).…”

Section: Observations Data Analysis and Resultsmentioning

confidence: 99%

“…The pulse profile of 4U 0142+614 is double peaked in the soft X‐rays, and largely variable as a function of energy; we also detect an increase with energy of the fundamental pulsed fraction component (see also hints for this increase in Israel et al 1999; Paul et al 2000; Göhler et al 2005). Furthermore, 10 yr of RXTE monitoring also revealed the pulse profile to be very variable in time, possibly related to the presence of glitches (Dib et al 2007; but see also Morii et al 2005).…”

Section: Discussionmentioning

confidence: 99%

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Very deep X-ray observations of the anomalous X-ray pulsar 4U 0142+614

Rea

Nichelli

Israel

et al. 2007

Monthly Notices of the Royal Astronomical Society

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We report on two new XMM–Newton observations of the anomalous X‐ray pulsar (AXP) 4U 0142+614 performed in 2004 March and July, collecting the most accurate spectrum for this source. Furthermore, we analyse two short archival observations performed in 2002 February and 2003 January in order to study the long‐term behaviour of this AXP. 4U 0142+614 appears to be relatively steady in flux between 2002 and 2004, and the phase‐averaged spectrum does not show any significant variability between the four epochs. We derive the deepest upper limits to date on the presence of lines in 4U 0142+614 spectrum as a function of energy: equivalent width in the 1–3 keV energy range <4 and <8 eV for narrow and broad lines, respectively. A remarkable energy dependence in both the pulse profile and the pulsed fraction is detected, and consequently pulse–phase spectroscopy shows spectral variability as a function of phase. By making use of XMM–Newton and INTEGRAL data, we successfully model the 1–250 keV spectrum of 4U 0142+614 with three models, namely the canonical absorbed blackbody plus two power laws, a resonant cyclotron scattering model plus one power‐law and two log‐parabolic functions.

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Section: Observations Data Analysis and Resultssupporting

confidence: 83%

Section: Observations Data Analysis and Resultssupporting

confidence: 75%

Section: Observations Data Analysis and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Very deep X-ray observations of the anomalous X-ray pulsar 4U 0142+614

Rea

Nichelli

Israel

et al. 2007

Monthly Notices of the Royal Astronomical Society

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“…In addition, Chandra's superior viewing windows relative to XMM have allowed rapid target-of-opportunity (ToO) observations in response to magnetar outbursts, as triggered, for example, by RXTE monitoring. The latter telescope, with its excellent scheduling agility, is ideal for regular snapshot monitoring of magnetars, especially the generally bright AXPs (36)(37)(38)(39)(40). However, RXTE provides only pulsed fluxes above 2 keV, and spectroscopy of very limited quality in these short observations, hence the need for occasional high-sensitivity focusing telescope observations.…”

Section: Magnetarsmentioning

confidence: 99%

Grand unification of neutron stars

Kaspi

2010

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

218

180

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The last decade has shown us that the observational properties of neutron stars are remarkably diverse. From magnetars to rotating radio transients, from radio pulsars to isolated neutron stars, from central compact objects to millisecond pulsars, observational manifestations of neutron stars are surprisingly varied, with most properties totally unpredicted. The challenge is to establish an overarching physical theory of neutron stars and their birth properties that can explain this great diversity. Here I survey the disparate neutron stars classes, describe their properties, and highlight results made possible by the Chandra X-Ray Observatory, in celebration of its 10th anniversary. Finally, I describe the current status of efforts at physical “grand unification” of this wealth of observational phenomena, and comment on possibilities for Chandra’s next decade in this field.

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Randomized algorithms for the low-rank approximation of matrices

Liberty

Woolfe

Martinsson

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

474

467

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We describe two recently proposed randomized algorithms for the construction of low-rank approximations to matrices, and demonstrate their application (inter alia) to the evaluation of the singular value decompositions of numerically low-rank matrices. Being probabilistic, the schemes described here have a finite probability of failure; in most cases, this probability is rather negligible (10 ؊17 is a typical value). In many situations, the new procedures are considerably more efficient and reliable than the classical (deterministic) ones; they also parallelize naturally. We present several numerical examples to illustrate the performance of the schemes.matrix ͉ SVD ͉ PCA L ow-rank approximation of linear operators is ubiquitous in applied mathematics, scientific computing, numerical analysis, and a number of other areas. In this note, we restrict our attention to two classical forms of such approximations, the singular value decomposition (SVD) and the interpolative decomposition (ID). The definition and properties of the SVD are widely known; we refer the reader to ref. 1 for a detailed description. The definition and properties of the ID are summarized in Subsection 1.1 below.Below, we discuss two randomized algorithms for the construction of the IDs of matrices. Algorithm I is designed to be used in situations where the adjoint A* of the m ϫ n matrix A to be decomposed can be applied to arbitrary vectors in a ''fast'' manner, and has CPU time requirements typically proportional to k⅐C A* ϩ k⅐m ϩ k 2 ⅐n, where k is the rank of the approximating matrix, and C A* is the cost of applying A* to a vector. Algorithm II is designed for arbitrary matrices, and its CPU time requirement is typically proportional to m⅐n⅐log(k) ϩ k 2 ⅐n. We also describe a scheme converting the ID of a matrix into its SVD for a cost proportional to k 2 ⅐(m ϩ n).Space constraints preclude us from reviewing the extensive literature on the subject; for a detailed survey, we refer the reader to ref. 2. Throughout this note, we denote the adjoint of a matrix A by A*, and the spectral (l 2 -operator) norm of A by ʈAʈ 2 ; as is well known, ʈAʈ 2 is the greatest singular value of A. Furthermore, we assume that our matrices have complex entries (as opposed to real); real versions of the algorithms under discussion are quite similar to the complex ones.This note has the following structure: Section 1 summarizes several known facts. Section 2 describes randomized algorithms for the low-rank approximation of matrices. Section 3 illustrates the performance of the algorithms via several numerical examples. Section 4 contains conclusions, generalizations, and possible directions for future research. Section 1: PreliminariesIn this section, we discuss two constructions from numerical analysis, to be used in the remainder of the note. Subsection 1.1: Interpolative Decompositions. In this subsection, we define interpolative decompositions (IDs) and summarize their properties.The following lemma states that, for any m ϫ n matrix A of rank k, there exist an...

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10 years of RXTE monitoring of anomalous X-ray pulsar 4U 0142+61: long-term variability

Cited by 4 publications

References 13 publications

Very deep X-ray observations of the anomalous X-ray pulsar 4U 0142+614

Very deep X-ray observations of the anomalous X-ray pulsar 4U 0142+614

Grand unification of neutron stars

Randomized algorithms for the low-rank approximation of matrices

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