Revealing the X-ray variability of AGN with principal component analysis

Parker, M. L.; Fabian, A. C.; Matt, Giorgio; Koljonen, K. I. I.; Kara, Erin; Alston, W. N.; Walton, D. J.; Marinucci, Andrea; Brenneman, Laura; Risaliti, G.

doi:10.1093/mnras/stu2424

Cited by 47 publications

(42 citation statements)

References 86 publications

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“…Unsupervised spectral decomposition methods have been proven to be a powerful tool in separating a set of Xray spectra from X-ray binaries and active galactic nuclei into subcomponents (Vaughan & Fabian 2004;Malzac et al 2006;Koljonen et al 2013;Parker et al 2015;Koljonen 2015;Degenaar et al 2016). In general, the Xray spectra is decomposed to its constituent components by using matrix factorization techniques, e.g.…”

Section: Spectral Decompositionmentioning

confidence: 99%

Evidence of Spreading Layer Emission in a Thermonuclear Superburst

Koljonen¹,

Kajava

Kuulkers

2016

ApJ

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When a neutron star accretes matter from a companion star in a low-mass X-ray binary, the accreted gas settles onto the stellar surface through a boundary/spreading layer. On rare occasions the accumulated gas undergoes a powerful thermonuclear superburst powered by carbon burning deep below the neutron star atmosphere. In this paper, we apply the non-negative matrix factorization spectral decomposition technique to show that the spectral variations during a superburst from 4U 1636-536 can be explained by two distinct components: 1) the superburst emission characterized by a variable temperature black body radiation component, and 2) a quasi-Planckian component with a constant, ∼2.5 keV, temperature varying by a factor of ∼15 in flux. The spectrum of the quasi-Planckian component is identical in shape and characteristics to the frequency-resolved spectra observed in the accretion/persistent spectrum of neutron star low-mass X-ray binaries, and agrees well with the predictions of the spreading layer model by Inogamov & Sunyaev (1999). Our result is yet another observational evidence that superbursts -and possibly also normal X-ray bursts -induce changes in the disc-star boundary.

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Section: Spectral Decompositionmentioning

confidence: 99%

Evidence of Spreading Layer Emission in a Thermonuclear Superburst

Koljonen¹,

Kajava

Kuulkers

2016

ApJ

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“…However, these kind of eclipses are also observed in Seyfert 1s Sanfrutos et al 2013;Markowitz et al 2014;Agís-González et al 2014). When it is possible to estimate the cloud velocity, and thus the location of the absorbing material, the clouds appear to be located very close to the BLR or within the borderline between the BLR and the torus Walton et al 2014;Connolly et al 2014;Markowitz et al 2014;Parker et al 2015;Giustini et al 2016). The velocities of the clouds in Seyfert 1.8/1.9 (NGC 1365, NGC 2617, NGC 2992, and NGC 4395) and Seyfert 2 (Mark 1210, and NGC 4507) in our sample are greater than 10 3 km/s 10 (following the procedure in Risaliti et al 2010).…”

Section: Long-term Variationsmentioning

confidence: 96%

“…Nowadays we believe that the X-ray variations might be related to intrinsic changes of the nuclear source (e.g., Uttley et al 2005;Uttley 2007;Parker et al 2015), or to absorbing clouds that intersect the line of sight to the observer (e.g., Risaliti et al 2007). These changes can be studied by modelling the X-ray spectrum of AGN, whose continuum is dominated by a power-law component extending up to a cut-off at energies ≥100 keV (e.g., Zdziarski et al 1995;Guainazzi et al 2005;Fabian et al 2015).…”

Section: Introductionmentioning

confidence: 99%

X-ray variability of Seyfert 1.8/1.9 galaxies

Hernández-García

Masegosa

González‐Martín³

et al. 2017

A&A

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Context. Seyfert 1.8/1.9 are sources showing weak broad H α components in their optical spectra. According to unification schemes, they are seen with an edge on inclination, similarly to type 2 Seyfert galaxies, but with slightly lower inclination angles. Aims. We aim at testing whether Seyfert 1.8/1.9 have similar properties at UV and X-ray wavelengths. Methods. We use the 15 Seyfert 1.8/1.9 in the Véron Cetty and Véron catalogue with public data available from the Chandra and/or XMM-Newton archives at different dates, with timescales between observations ranging from days to years. All the spectra of the same source were simultaneously fitted with the same model and different parameters were left free to vary in order to select the variable parameter(s). Whenever possible, short-term variations from the analysis of the X-ray light curves and long-term UV variations from the optical monitor onboard XMM-Newton were studied. Our results are homogeneously compared with a previous work using the same methodology applied to a sample of Seyfert 2 (Hernández-García et al. 2015a).Results. X-ray variability is found in all 15 nuclei over the aforementioned ranges of timescales. The main variability pattern is related to intrinsic changes in the sources, which are observed in ten nuclei. Changes in the column density are also frequent, as they are observed in six nuclei, and variations at soft energies, possibly related to scattered nuclear emission, are detected in six sources. X-ray intra-day variations are detected in six out of the eight studied sources. Variations at UV frequencies are detected in seven out of nine sources. Conclusions. A comparison between the samples of Seyfert 1.8/1.9 and 2 shows that, even if the main variability pattern is due to intrinsic changes of the sources in the two families, these nuclei exhibit different variability properties in the UV and X-ray domains. In particular, variations in the broad X-ray band on short time-scales (days/weeks), and variations in the soft X-rays and UV on long time-scales (months/years) are detected in Seyfert 1.8/1.9 but not in Seyfert 2. Overall, we suggest that optically classified Seyfert 1.8/1.9 should be kept separated from Seyfert 2 galaxies in UV/X-ray studies of the obscured AGN population because their intrinsic properties might be different.

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“…The NMF method: Techniques such as PCA (Principal Components Analysis) have been used to decompose a multivariate dataset into a set of successive orthogonal components that explain the maximum amount of variance [5,6]. The goal is to project the data to a lower-dimensional space that preserves most of the variance.…”

Section: Spectral Decompositionmentioning

confidence: 99%

A New Statistical Approach to the Optical Spectral Variability in Blazars

et al. 2016

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Abstract:We present a spectral variability study of a sample of about 25 bright blazars, based on optical spectroscopy. Observations cover the period from the end of 2008 to mid 2015, with an approximately monthly cadence. Emission lines have been identified and measured in the spectra, which permits us to classify the sources into BL Lac-type or FSRQs, according to the commonly used EW limit. We have obtained synthetic photometry and produced colour-magnitude diagrams which show different trends associated with the object classes: generally, BL Lacs tend to become bluer when brighter and FSRQs become redder when brighter, although several objects exhibit both trends, depending on brightness. We have also applied a pattern recognition algorithm to obtain the minimum number of physical components which can explain the variability of the optical spectrum. We have used NMF (Non-Negative Matrix Factorization) instead of PCA (Principal Component Analysis) to avoid un-realistic negative components. For most targets we found that 2 or 3 meta-components are enough to explain the observed spectral variability.

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Revealing the X-ray variability of AGN with principal component analysis

Cited by 47 publications

References 86 publications

Evidence of Spreading Layer Emission in a Thermonuclear Superburst

Evidence of Spreading Layer Emission in a Thermonuclear Superburst

X-ray variability of Seyfert 1.8/1.9 galaxies

A New Statistical Approach to the Optical Spectral Variability in Blazars

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