X-ray spectral variability of seven LINER nuclei with XMM-Newton and Chandra data

1. X-ray spectral variability of seven LINER nuclei with XMM-Newton and Chandra data Author: Hernandez-Garcia, L; Gonzalez-Martin, O; Marquez, I; Masegosa. J Reporter: Jiayang Ni 2014.05.06

2. outline • Introduction • The sample and data • Data reduction • Methodology • Results • Discussion • conclusions

3. introduction • The unified model good representation of AGN, but a number of objects cannot be fitted • LINERs: strong low-ionization lines • An AGN is present when a point-like source is detected at hard X-ray energies • LINERs have lower luminosities, lower Eddington ratios and more massive black holes • Variability is one of the main properties that characterizes AGN

4. Quasars variability(Oetersib 1997) • Serfert galaxies variability (Risaliti et al. 2000,2010; Evans et al. 2005 etc) • LINERs variability (Maoz et al. 2005; Youns et al. 2011 etc)

5. The sample and the data • The sample: 82 type 1 and type 2 LINERs of G-M et al. (2009b) • Using some screen criteria, the final sample of LINERs contains seven objects • Hardness ratios, defined as HR=(H-S)/(H+S) • Five objects: observations at different epochs with the same instrument • Two objects: comparing XMM-Newton with Chandra data

6. General properties of the target galaxies for this study

7. The log of the observations

8. Data reduction • Chandra data reduction and analysis were carried out in a systematic, uniform way: CXC Chandra Interactive Analysis of Observations (CIAO) • Using many tasks, such as LC_CKEAN.SL, DMEXTRACT, MKASCIRMF, MKWARF, GRPPHA

9. XMM-Newton data were also reduced in a systematic, uniform way: Science Analysis Software (SAS) • Using different tasks, but their targets are almost same • Light curves in the 0.5-10 keV band for the source and background, for example NGC 5846

10. methodology • The spectral fitting process comprises two steps (1) individual analysis of each observation to determine the best fit for each spectrum (2) simultaneous fitting of the set of spectra of the same object at different epochs • The spectral fitting was done using XSPEC

11. Individual spectral analysis five different models were used: ME: a pure thermal model PL: a single power law model 2PL: a model containing two power laws with the same slope MEPL: a composite of a thermal plus a single power law model ME2PL: a composite of a thermal plus two power laws model

12. Simultaneous spectral analysis • we need to simultaneously fit the spectra for each object to the same model. • the simultaneous fit was made in three steps SMF0 SMF1 SMF2 the final best fit could be one of these three models • We need to be cautious when data from Chandra and XMM-Newton were used together

13. Flux variablity • using XSPEC compute X-ray luminosities • variable: luminosity variation larger than 3σ non-variable: variations below 1σ 3. Simultaneous XMM-Newton Optical Monitor (OM) data were used to compute the UV luminosities 4. X-ray to UV flux ratio defined as

14. Short time scale variability • use the light curves • consider the source to be variable if the count rate differed from the average above 3σ • the variability amplitude of the light curves, calculate the normalized excess variance

15. results • Individual objects for each objects, describe the following: • the observations used in the analysis (table 2) • variations of the hardness ratio (from Col. 8 in table 2)

16. 3. Individual and simultaneous best fit and the parameters varying in the model (table 3, 4, 5 and figure 1)

17. 4. X-ray flux variations: table 6 and figure 2

18. 5. The analysis of the annular region when data of Chandra and XMM-Newton were used together (table 7 and appendix B.1)

19. 6. Simultaneous fittings of these observations (table 8)

20. 7. Short term variability from the analysis of the light curves (table 9 and appendix C.1-C.7)

21. 8. UV luminosities when simultaneous data from the OM monitor was available (table 10, fig 2)

22. Summary of the variability

23. discussion • Main results: short term variability long term variability main driver for the variability • Short and long time scale variability • Accretion mechanism

24. conclusion • Variations greater than 20%in the HR always correspond to objects showing spectral variability • Individual fits of each observation provided composite models as the best fit • No short time scale variability was found, in agreement with predictions • Spectral X-ray variability was found in four out of six objects • Two relations are compatible with inefficient flows being the originof the accretion mechanism in these sources