XMM-Newton observations of the Lockman Hole-V. Time variability of the brightest AGN

21 Pages·2007·1.02 MB·English

A&A473,105–120(2007) Astronomy
DOI:10.1051/0004-6361:20077909 &
(cid:1)c ESO2007 Astrophysics
XMM-Newtonobservations of the Lockman Hole
(cid:1),(cid:1)(cid:1)
V. Time variability of the brightest AGN
S.Mateos1,X.Barcons2,F.J.Carrera2,M.J.Page3,M.T.Ceballos2,G.Hasinger4,andA.C.Fabian5
1 X-rayAstronomyGroup,DepartmentofPhysicsandAstronomy,LeicesterUniversity,LeicesterLE17RH,UK
e-mail:[email protected]
2 InstitutodeFísicadeCantabria(CSIC-UC),39005Santander,Spain
3 MSSL,UniversityCollegeLondon,HolmburySt.Mary,Dorking,SurreyRH56NT,UK
4 Max-Planck-InstitutfürExtraterrestrischePhysik,Giessenbachstrasse,Garching85748,Germany
5 InstituteofAstronomy,UniversityofCambridge,MadingleyRoad,CambridgeCB3OHA,UK
Received18May2007/Accepted10July2007
ABSTRACT
ThispaperpresentstheresultsofastudyofX-rayspectralandfluxvariabilityontimescalesfrommonthstoyears,ofthe123brightest
objects (including 46 type-1 AGN and 28 type-2 AGN) detected withXMM-Newton inthe Lockman Holefield. Wedetected flux
variabilitywithasignificance ≥3σin∼50%of theobjects, including 68± 11%and48 ±15%among our samples of type-1and
type-2 AGN.However wefound that thefractionof sourceswithbest qualitylight curvesthat exhibit fluxvariabilityonthetime
scalessampledbyourdatais≥80%,i.ethegreatmajorityoftheAGNpopulationmayactuallyvaryinfluxonlongtimescales.The
meanrelativeintrinsicamplitudeoffluxvariabilitywasfoundtobe∼0.15althoughwithalargedispersioninmeasuredvalues,from
∼0.1to∼0.65.ThefluxvariabilitypropertiesofoursamplesofAGN(fractionofvariableobjectsandamplitudeofvariability)donot
significantlydependontheredshiftorX-rayluminosityoftheobjectsandseemtobesimilarforthetwoAGNtypes.Usingabroad
bandX-raycolourwefoundthatthefractionofsourcesshowingspectralvariabilitywithasignificance≥3σis∼40%i.e.lesscommon
thanfluxvariability.Spectralvariabilitywasfoundtobemorecommonintype-2AGNthanintype-1AGNwithasignificanceof
morethan99%.Thisresultisconsistentwiththefactthatpartofthesoftemissionintype-2AGNcomesfromscatteredradiation,and
thiscomponentisexpectedtobemuchlessvariablethanthehardcomponent.Theobservedfluxandspectralvariabilitypropertiesof
ourobjectsandespeciallythelackofcorrelationbetweenfluxandspectralvariabilityinmostofthemcannotbeexplainedasbeing
producedbyvariabilityofonespectralcomponentalone,forexamplechangesinΓassociatedwithchangesinthemassaccretionrate,
orvariabilityintheamountofX-rayabsorption.AtleasttwospectralcomponentsmustvaryinordertoexplaintheX-rayvariability
ofourobjects.
Keywords.X-rays:general–galaxies:active
1. Introduction these results are consistent with recent results based on power
spectrum analysis of light curves of AGN (Markowitz et al.
Active Galactic Nuclei (AGN) are strongly variable sources in 2003a;McHardyetal.2006).Therearealsoindicationsthatthe
all wave bands (Edelson et al. 1996; Nandra et al. 1997) and strength of the anti-correlation might decrease towards longer
ondifferenttimescales.Thelargestamplitudeandfastestvaria- timescales(Markowitzetal.2004).
bility is usually observed in X-rays. This is taken as evidence
Allthesefindingsareconsistentwithascenariowheremore
for X-ray emission in AGN originatingin a small region close
luminoussources,hostingmoremassiveblackholes,havelarger
to the central object, which is thought to be a supermassive
(106−109 M(cid:4))blackhole. X-rayemittingregions,andthereforethevariabilityneedsmore
timetopropagatethroughtheX-rayemittingregion.
Ithas beenfoundthat AGN are generallymore variableon
longtimescalesthanonshorttimescales(seeBarr&Mushotzky X-ray spectral variability studies of nearby objects have
1986;Nandraetal.1997;Markowitz&Edelson2001).Inaddi- shownthatmostAGNtendtobecomesofterwhentheybrighten
tion, it is well known that variability amplitude on short (Barr (seee.g.Markowitzetal.2003b;Nandraetal.1997)similarto
& Mushotzky 1986; Nandra et al. 1997; Turner et al. 1999; the differentflux-spectralstates seen in black hole X-ray bina-
Lawrence & Papadakis1993) and long (Markowitz & Edelson ries (BHXRB) (McClintocketal. 2003). Inaddition,nearlyall
2001) time scales, when measured over a fixed temporal fre- AGNexhibitstrongervariabilityatsoftX-raysonalltimescales
quency, is anti-correlated with the X-ray luminosity, i.e. lower (Markowitzetal.2004).
variability amplitudes are seen in more luminous sources. All Two phenomenologicalmodel parameterisationshave been
proposed to explain the variability patterns observed in AGN
(cid:2) Table 4 is only available in electronic form at the CDS via (see e.g. Taylor et al. 2003): in the two-component spectral
anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via model(McHardyetal.1998;Shihetal.2002)asoftercontinuum
http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/473/105 emission power law component of constant slope but variable
(cid:2)(cid:2) Appendicesareonlyavailableinelectronicformat flux(probablyassociatedwiththeemissionfromthehotcorona)
http://www.aanda.org is superimposed on a harder spectral componentwith constant
Article published by EDP Sciences and available at http://www.aanda.org or http://dx.doi.org/10.1051/0004-6361:20077909
106 S.Mateosetal.:X-rayspectraLH.V.
flux(likelyassociatedwiththeComptonreflectionhump).This 2. XMM-NewtondeepsurveyintheLockmanHole
modeldoes notrequire the form of anyof the spectralcompo-
The XMM-Newton observatory has carried out its deepest ob-
nentstovary,whatvariesistherelativecontributionofthesoft
servationin the directionofthe LockmanHole field,centredat
and hard spectral components with time. When the source be-
RA: 10:52:43 and Dec: +57:28:48(J2000). The XMM-Newton
comesbrighterthesoftcomponentdominatesthespectrumand
deep survey in the Lockman Hole is composed of 16 observa-
thesourcebecomessofter.Inthetwo-componentspectralmodel
tionscarriedoutfrom2000to2002,whichallowustostudythe
it is expected that as the sources become brighter their X-ray
X-ray variabilitypropertiesof oursourceson longtime scales,
spectral slope will saturate to the slope of the soft component.
from months to years. The Lockman Hole was also observed
However, some bright objects show evidence for variations in
during revolutions 071 (∼61 ks) and 344 (∼80 ks duration).
theirunderlyingcontinuumspectrum(e.g.NGC4151seePerola
However at the time of this analysis there was no Observation
etal.1986;Yaqoob&Warwick1991;Yaqoobetal.1993).For
DataFile(ODF)availablefortheobservationinrevolution071,
these objectsthetwo componentspectralmodelcannotexplain
and hence, we could not use that data. We could not use the
theirvariabilityproperties.
data from revolution 344 because most of the observation was
An alternative parameterisation is the spectral pivoting
affectedbyhighandflaringbackground.Wereportthesummary
model: in this model flux-correlated changes in the continuum
oftheXMM-NewtonobservationsintheLockmanHoleusedin
shape are due to changes in a single variable component, that
ourstudyinTable1.Thefirstcolumnshowstherevolutionnum-
becomessofterasthesourcebrightens(seee.g.Zdziarskietal.
ber and observation identifier. The second column shows the
2003).
nameofthephaseofobservation(PVforobservationsduringthe
Although X-ray spectral variability in a significant number
PayloadVerificationPhase,andAO1andAO2forobservations
of AGN can be explained as due to changes in the primary
duringthefirstandsecondAnnouncementsofOpportunity).The
continuumshape,changesinthecolumndensityofthecoldgas
thirdandfourthcolumnslistthepointingcoordinatesoftheob-
responsible for the absorptionin X-rayshave been reportedon
servation.Columnfiveliststheobservationdates,whilethelast
time-scales of months-years in both type-1 and type-2 Seyfert
twocolumnsshowthefiltersthatwereusedduringeachobser-
galaxies(Risalitietal.2002;Maliziaetal.1997;Puccettietal.
vation for the EPIC-pn X-ray detector,together with the expo-
2004). Theabsorbingmaterialinthese objectsmustbeclumpy
sure times after removal of periods of high background. The
andduetothetimescalesinvolved,itmustbeclosetothecentral
16 XMM-Newton observations gave a total exposure time (af-
blackhole,muchnearerthanthestandardtorusofAGNunifica-
ter removalof periods of high background)of ∼650 ks for the
tionmodels(Antonuccietal.1993).
EPIC-pndata.
In this paper we present the results of a study of the flux
andspectralvariabilitypropertiesofthe123brightestobjectsde-
tectedwithXMM-NewtonintheLockmanHolefield,forwhich 2.1.Sampleofsources
their time-averaged X-ray spectral properties are known, and
AdetaileddescriptionofhowthelistofXMM-Newtondetected
were presented in a previouspaper (Mateos et al. 2005b). The
sources in the total observation of the Lockman Hole was ob-
aim ofthis studyis to providefurtherinsightinto the originof
tained can be found in Mateos et al. (2005b). In brief, the
the X-ray emission in AGN, by analysing the variability pro-
XMM-Newton Science Analysis Software (SAS, Gabriel et al.
pertiesoftheobjectsonlongtimescales,frommonthstoyears.
2004) was used to analyse the X-ray data. The event files of
Itisimportanttonotethatthevariabilityanalysispresentedhere
each observation were filtered to remove periods of time af-
doesnotallowustostudy,forexample,fluctuationsintheaccre-
fected by high background. Images, exposure maps and back-
tiondisc,asthedynamicaltimescales(light-crossingtime)are
groundmapswereobtainedforeachindividualobservationand
much shorterthanthe onessampledin ouranalysis. Intra-orbit
variability studies (i.e. on time scales <2 days) cannot be per- for the EPIC-pn detector on the five standard XMM-Newton
energy bands (0.5−2, 2−4.5, 4.5−7.5, 7.5−12 keV). The data
formedontheLockmanHolesources,astheyaretoofaint.With
were summed to obtain the total observation of the field
our analysis we can detect, for example, variability related to
for each energy band. The SAS source detection algorithm
changesintheglobalaccretionrate,suchasvariationsintheac-
eboxdetect-emldetectwasrunonthefiveXMM-Newtonen-
cretionratepropagatinginwards(Lyubarskii1997).Inaddition,
ergybandssimultaneously1 to producean X-raysourcelist for
we caninvestigatewhetherthedetectedlong-termX-rayvaria-
thetotalobservation.
bilityinourobjectscanbeexplainedasbeingduetovariations
From the final list of sources the 123 brightest objects
intheobscurationofthenuclearengine.
(withmorethan500source-backgroundsubtracted0.2−12keV
This paper is organised as follows: Sect. 2 describes the
counts) were selected for analysis. Because the main goal was
X-ray data thatwas used for the analysisand explainshow we
tostudytheX-raypropertiesofAGN,objectsidentifiedasclus-
builtoursampleofobjects;inSect.3wedescribetheapproach
ters ofgalaxiesor starswere excludedfromthe sample.Atthe
thatwefollowedtocarryoutourvariabilityanalysis;theresults
time oftheanalysis,74(∼60%)oftheselectedobjectshadop-
of the study of flux and spectral variability in our objects are
tical spectroscopic identifications available. Of these, 46 were
shown in Sects. 4 and 5; Sect. 6 explores the true fraction of
opticallyclassifiedastype-1AGNand28astype-2AGN.
sources where either flux or spectral variability are present; a
discussionoftheresultsandpossibleinterpretationsfortheori-
ginofthelong-termvariabilityarepresentedinSect.7;Sect.8 3. X-rayvariabilityanalysis
presentsthevariabilitypropertiesofthetype-2AGNinoursam-
The three XMM-Newton EPIC cameras (M1, M2 and pn) have
plewithoutanysignofX-rayabsorptionintheirco-addedX-ray
differentgeometries,thereforeforagivenobservation,itiscom-
spectra; finally the results of our analysis are summarised in
montofindthatasignificantnumberofserendipitouslydetected
Sect.9.
ThroughoutthispaperwehaveadoptedtheWMAPconcor- 1 This increases the sensitivity of detection of sources on each in-
dance cosmology model parameters (Spergel et al. 2003) with dividual energyband, andallowstoobtainsourceparametersoneach
H0 =70kms−1Mpc−1,ΩM =0.3andΩΛ =0.7. individualenergyband.
S.Mateosetal.:X-rayspectraLH.V. 107
Table1.SummaryofXMM-NewtonEPIC-pnobservationsintheLockmanHole.
Rev/ObsId Obsphase RA Dec Obs.date Filter GTI
(1) (2) (3) (4) (5) (6) (7)
070/0123700101 PV 10 5243.0 [phone] Th 34
073/0123700401 PV 10 5243.0 [phone] Th 14
074/0123700901 PV 10 5241.8 [phone] Th 5
081/0123701001 PV 10 5241.8 [phone] Th 27
345/0022740201 AO1 10 5243.0 [phone] M 40
349/0022740301 AO1 10 5243.0 [phone] M 35
522/0147510101 AO2 10 5103.4 [phone] M 79
523/0147510801 AO2 10 5127.7 [phone] M 55
524/0147510901 AO2 10 5243.0 [phone] M 55
525/0147511001 AO2 10 5208.1 [phone] M 78
526/0147511101 AO2 10 5317.9 [phone] M 45
527/0147511201 AO2 10 5358.3 [phone] M 30
528/0147511301 AO2 10 5429.5 [phone] M 28
544/0147511601 AO2 10 5243.0 [phone] M 104
547/0147511701 AO2 10 5240.6 [phone] M 98
548/0147511801 AO2 10 5245.3 [phone] M 86
Columnsareasfollows:(1)XMM-Newtonrevolutionandobservationidentifier;(2)Observationphase:VerificationPhase(PV),firstandsecond
announcementsofopportunity(AO1andAO2);(3)and(4)pointingcoordinatesinrightascensionanddeclination(J2000);(5)Observationdate;
(6)and(7)EPIC-pnblockingfilteraoftheobservationandgoodtimeinterval(inks)aftersubtractingperiodsoftheobservationaffectedbyhigh
backgroundflares.
aBlockingfilters:Th:Thinat40nmA1;M:Mediumat80nmA1.
objectsfallnearorinsideCCD gapsinatleastoneofthecam- the radius that contains 80% of the telescope PSF on each de-
eras. This means that, for the same object, we will have light tectorpoint.We usedthesemaskstoremoveautomaticallynon
curves from different data sets (i.e. data from different revolu- desireddata.
tions) for each camera and therefore the sampling is different. Each point on the light curves corresponds to the average
Inaddition,becauseoftheirdifferentinstrumentalresponseswe countrateofthesourcesinthatrevolution.Theexposuretimes
cannot combine the count rates from the three EPIC cameras. of the EPIC-pnobservationsvaried significantlybetweenrevo-
The fact that light curvesfrom each EPIC camera are sampled lutionsandthereforealsotheuncertaintyinthemeasuredcount
differentlyinmostsources,makescomparingtheresultsofour rate values. In addition, the time intervalbetween pointsis not
variability analysis between detectorsvery difficult. Due to the constant,withsomedatapointsinthelightcurvesseparatedby
geometry of the pn detector, the number of sources falling in days,andothersbyyears.Thereforewearenotstudyingvaria-
thepnCCDgapswillbelargerthaninMOSdetectors;however bilitypropertiesonawelldefinedtemporalscale.Inadditionwe
theformerprovidesthedeepestobservationofthefieldforeach donothavethesamenumberofpointsinthelightcurvesforall
exposure2, which allows us to detect lower variability ampli- sources.Allthisneedstobetakenintoaccountwhencomparing
tudes,andtomeasurebetterthevariabilityamplitude.Therefore, thevariabilitypropertiesofdifferentsources.
wehaveusedonlypndataintheanalysispresentedinthispaper.
The pn source lists obtained for each revolution were visually
screenedtoremovespuriousdetectionsofhotpixelsstillpresent 4. Fluxvariability
intheX-rayimages.
Inordertosearchforfluxvariability,wecreatedalightcurvefor
Sourcedetectionwascarriedoutonthe EPIC-pndatafrom
each source using the count rates in the observed 0.2−12 keV
each individual revolution to obtain all the relevant parame-
energy band in each revolution. We took this energy band be-
ters for the selected sources from all observations where they
weredetected.Wehaveusedmeasured0.2−12keVcountrates3 cause it was used to study the time averaged spectral emission
properties of the sources and therefore we can refer our varia-
fromeach individualobservationwhere sourceswere detected,
bility studies to the time averaged spectral properties. In addi-
tobuildlightcurves.
tion,using0.2−12keVcountrateswehavemorethan10back-
Source parameters become very uncertain for objects de-
groundsubtractedcountsoneachpointofthelightcurves,and
tected close to CCD gaps. To avoid this problem, we did not
thereforewecanassumeGaussianstatisticsduringtheanalysis.
usedatafromobservationswheretheobjectsweredetectednear
UsingEPIC-pn0.2−12keVcountratesweobtainedlightcurves
CCD gaps,bad columns,or nearthe edgeof theField of View
withatleasttwodatapointsfor120outofthe123sourcesinour
(FOV). To remove these cases we created detector masks, one
sample,including45type-1AGNand27type-2AGN.
foreachobservation,increasingthesizeoftheCCDgapsupto
NotallEPIC-pnobservationswerecarriedoutwiththesame
blockingfilter(seeTable1).Wecannotdirectlycomparetheob-
2 Notethatingeneralpnobservationsreceive∼twicethenumberof served countrates, because blockingfilters affect in a different
photons than M1 and M2, and hence pn observations are in general
deeper,byafactorof∼2,thanM1andM2observations.Althoughthe way the low energy photons and hence measured 0.2−12 keV
count rates will be different even for a non varying source.
totalexposuretimeoftheEPIC-pndataislowerthantheexposuretimes
ofM1andM2data,thepntotalobservationstillcollectsmorecounts. Moreover,changesinbackgroundmodelling/calibrationduring
3 The SAS source detection task emldetect provides count rates thelifetimeofthemissioncanintroducesystematicdifferences
correctedforvignettingandPointSpreadFunction(PSF)lossesoneach between measured count rates. The observations we are using
energyband. were carried out in a time interval spanning two years, and
108 S.Mateosetal.:X-rayspectraLH.V.
therefore we need to study whether any instrumental drift is Table 2. Summary of detection of flux and spectral variability in the
present in our data, and whether it is affecting the measured LockmanHolesources.
variabilitypropertiesofoursources.Inordertocorrectforboth
different filters and instrumental drifts, we have calculated the Group Ntot nflux fraccflux(%) nsp fraccsp(%)
mean (averaged over all sources) deviation of the count rates (1) (2) (3) (4) (5) (6)
All 120 62 51±7 24 20±6
measuredoneachobservationfromthemeancountratesofthe
type-1AGN 45 31 68±11 6 14±8
lightcurves.Wethencorrectedthecountratesfromthesemean
type-2AGN 27 13 48±15 9 34±14
deviations.ThisisexplainedindetailinAppendixA.Allcount Unidentified 48 18 37±11 9 19±9
ratesusedforthestudypresentedinthispaperarecorrectedfor
thisotherwisesmalleffect(<∼10%). Columnsareasfollows:(1)groupofsources;(2)totalnumber ofob-
To search for deviations from the null hypothesis (that the jects in the group; (3) number of sources with detected flux variabi-
0.2−12keVmeancountratesoftheobjectshaveremainedcon- lity (confidence ≥3σ); (4) fraction (corrected for spurious detections;
stantduringallobservations)weusedχ2 seeMateosetal.2005a)ofsourcesingroupwithdetectedfluxvaria-
bility; (5) number of sources withdetected spectral variability (confi-
(cid:1)N (x −(cid:5)x(cid:6))2 dence≥3σ);(6)fraction(correctedforspuriousdetections)ofsources
χ2 = i (1) in group with detected spectral variability. Errors correspond to the
σ2 1σconfidenceinterval.
i=1 i
where x arethe0.2−12keVcountratesofeachsourceoneach
i
binandσ thecorresponding1σstatisticalerrors,Nisthenum- meanfluxlevel.Someexamplesoflightcurvesusedinouranal-
i
berofpointsoneachlightcurveand(cid:5)x(cid:6)istheunweightedmean ysisareshowninFig.12.
count rate for that source. We accepted a source as variable if
the significanceofχ2 beinghigherthanthe obtainedvaluejust
4.1.Amplitudeoffluxvariability
bychanceislowerthan2.7×10−3i.e.a3σdetectionconfidence.
Theresultsofdetectionoffluxvariabilityaresummarisedin The method we have used to calculate the amplitude of
Cols. 3 and4 of Table 2. We correctedthe fractionsof sources flux variability (noise-subtracted) in our light curves is fully
with detected flux variability for spurious detections for the describedinAlmainietal.(2000).Thismethodisthemostap-
givenconfidencelevel(3σ)usingthebayesianmethoddescribed propriate in the regime of Gaussian statistics, an assumption
inMateosetal.(2005a)andinStevensetal.(2005):theproba- that, as we said before, is satisfied by our data. In addition,
bilitydistributionofthe“true”fractionofobjectswithadetected this method is appropriate for light curves with points having
property is calculated as a function of the selected confidence significantlydifferentmeasurementerrors.
level, the sample size and the measured number of detections. The method assumes that the measured dispersion in the
The latter has two different contributions, the spurious detec- lightcurveshastwodifferentcontributions:
tions allowed by the selected confidence level and the “true”
detections. σ2total =σ2noise+σ2Q. (2)
Overall we detect flux variability in ∼50% of the sources
with a significance of more than 3σ. Flux variability was de- The first contribution, σnoise, is the statistical error in the data
tectedin31outof45(68±11%)type-1AGNand13outof27 pointsandthesecond,σQ,thetruefluctuationinthesourceflux.
(48±15%)type-2AGN. Weareinterestedinthesecondquantity,σQ,asitmeasuresthe
intrinsic variability in flux of the objects. In order to calculate
The fractionof AGN with detected flux variability was not
found to vary with redshift, i.e., the differentsampling of rest- σQ,amaximumlikelihoodmethodisused.
frameenergiesforsourcesatdifferentredshiftsdoesnotseemto Foreachobject,aprobabilitydistributionforσQisobtained.
affectthedetectionoffluxvariability.Wecomparedtheobserved Themaximumofthisfunctiongivesthemostprobablevalueof
fractions of varying sources for different samples of objects σQforthegivensetofdatapoints,whiletheuncertaintyinσQis
obtainedbyintegratingthefunctionfromthemaximumvalueto
usingthemethoddescribedinMateosetal.(2005a).We found
thedifferenceinthefractionsoffluxvariableobjectsamongour bothsidesuntilthedesiredprobabilityisencompassed.Wehave
calculated1σerrorsforthemeasuredvariabilityamplitudes.In
samplesoftype-1andtype-2AGNtobesignificantatonlythe
∼75%confidencelevel.Thereforethere isno evidencethatthe the cases where the lower error bound of the integrals reached
fraction of sources showing flux variability in the 0.2−12 keV zerowecalculated1σupperlimitsforσQ.
band on long time scales is significantly different for type-1 To allow comparison of variability amplitudes for sources
withdifferentmeancountrateswehavecalculatedthevalueof
and type-2 AGN. We see that the fraction of variable sources
thenormalisedexcessvariance,σ ,foreachsource,
amongunidentifiedobjectsis lowerthanthe fractionsobtained intr
fortype-1andtype-2AGN,andforthewholesampleofobjects. σ
σ = Q (3)
This couldbe due to the factthat mostunidentifiedobjectsare intr (cid:5)CR(cid:6)
amongthefaintestsourcesinthesample,andthereforetheywill
tendtohavelightcurveswithlowerquality,wherevariationsare where (cid:5)CR(cid:6) is the unweightedmean 0.2−12 keV count rate of
more difficult to detect. In addition,some of these unidentified thatsource.Thisparametergivesthefractionofthetotalfluxthat
sourcesmightnotbevariable.Forexample,unidentifiedheavily isvariable,andthereforecanbeusedtocompareamplitudesof
obscuredAGN (i.e. muchmoreobscuredthan the type-2AGN fluxvariabilityforsourceswithdifferentfluxes.Figure1shows
identified in our sample) may show only reprocessed emission typical probability distributions of σ of sources without de-
intr
whichwillalsohavelowervariabilityamplitude. tected(top)anddetected(bottom)variability.
Wefoundoursourcestoexhibitawholerangeoffluxvaria- Thedistributionofσ valuesobtainedfromthelightcurves
intr
bility patterns. Some objects became fainter with time, while ofoursourcesisshowninFig.2forthewholesampleofobjects
others became brighter. However for a significant fraction of (solidline)andforobjectswherefluxvariabilitywasdetectedin
sources,we foundirregularfluxvariationswithrespectto their termsoftheχ2testwithaconfidence≥3σ(filledhistogram).
S.Mateosetal.:X-rayspectraLH.V. 109
Table3.Measuredvaluesof0.2−12keVfluxvariabilityamplitudesinLockmanHolesources.
Sample (cid:5)σ (cid:6) (cid:5)σ (cid:6)w (cid:5)σ (cid:6) (cid:5)σ (cid:6)w σ
intr all intr all intr var intr var intr
(1) (2) (3) (4) (5) (6)
All 0.22±0.01 0.15±0.01 0.34±0.01 0.28±0.01 ≤0.36
type-1AGN 0.27±0.02 0.18±0.02 0.34±0.02 0.27±0.02 0.27+0.47
type-2AGN 0.21±0.03 0.15±0.02 0.34±0.04 0.28±0.02 ≤0.−305.08
Unidentified 0.19±0.02 0.11±0.02 0.35±0.03 0.30±0.02 ≤0.33
Columnsareasfollows:(1)groupofsources;(2)arithmeticmeansandcorresponding1σerrorsofthemeasuredvariabilityamplitudesfordifferent
typesofsources;(3)weightedmeansandcorresponding1σerrorsofthemeasuredvariabilityamplitudesfordifferenttypesofsources;(4)and
(5) arithmetic and weighted means of the measured variability amplitudes considering only objects with detected variability from the χ2 test;
(6)modeand1σconfidenceintervalsfromtheaverageP(σ )distributions.ForthecaseswheretheintegraloftheP(σ )distributiontotheleft
intr intr
reachedzero68%confidenceupperlimitsaregiven.
Fig.2.Distributionofmeasuredamplitudeofvariabilityforallobjects
(solidline)andforthesourceswherefluxvariabilitywasdetectedwith
aconfidencelevel≥3σ(filledhistogram).
weseethat,forvariabilityamplitudeslowerthan20%,thevalues
of the amplitude formally obtained are very rarely significant
at3σ.
Therefore our distribution of observed flux variability am-
plitudes for variableobjects shouldnot be interpretedas a true
distribution of variability amplitudesin our sample of sources.
Indeedwemightbemissingsourceswithlowvariabilityampli-
tudesorfaintsourceswithstrongvariabilityduetothelargesta-
tisticalerrorsinthedata.WewillreturntothispointinSect.4.3.
Fig.1.Typicalprobabilitydensitydistributionsoftheexcessvariance,
σintr,forsourceswithoutdetectedvariability(top)anddetectedvariabi- 4.2.Meanvariabilityamplitude
lity(bottom).
Mean values of σ for the whole sample of sources and for
intr
type-1 and type-2 AGN and unidentified subsamples are listed
Weseethatourvariablesourceshaveabroadrangeofvalues in Table 3. We show values obtained using both the arithmetic
ofthemeasuredexcessvariancefrom∼10%to∼65%,withthe andweightedmeanforcomparison.Weseethataveragevalues
maximumofthedistribution(forobjectswithdetectedvariabi- from the weighted mean are significantly lower that those ob-
lity4) being at a value of ∼30%. Values of the excess variance tained with the arithmetic mean in all cases. This is because,
above∼50−60%arenotcommoninoursources. aswewanttostudythevariabilitypropertiesofoursample,we
We expect the efficiency of detection of variability to be a includedinthecalculationsallmeasuredvaluesofσintrindepen-
dentlyonwhetherwedetectedornotsignificantvariabilityinthe
strongfunctionofthequalityofthelightcurves,butalsoofthe
lightcurves.Becauseofthat,wehaveanumberofsourceswith
amplitude of variability. Thereforethe fraction of sources with
detected variability from the χ2 test should decrease for lower measuredσintr valuesof zerobutwith errorsof the same order
variabilityamplitudes.ThiseffectisevidentfromFig.2,where astheonesforσintr >0.Thesevaluesshifttheweightedmeans
tolowervalues.
4 Note that the values outside the filled area correspond to sources We see in Table 3 that the mean values for the amplitude
withundetectedvariability(<3σ)andthereforetheycannotbeconsid- of flux variability for type-1 and type-2 AGN do not differ
eredsignificantdetections. significantly.
110 S.Mateosetal.:X-rayspectraLH.V.
Fig.4. Distribution of intrinsic amplitude of flux variability, P(σ ),
intr
obtainedfromoursimulations(solidline).Notethatinoursimulations
(seeAppendixB)wereferredtothemeasuredamplitudeoffluxvaria-
bilityasσ orS insteadofσ .Thedistributionofvaluesobtained
obs o intr
foroursampleofsources(excludingobjectswithlessthan5pointsin
theirlightcurves,seeSect.4.3)isshownforcomparison(dashedline).
theco-addedspectraofourobjects(fromMateosetal.2005b).
The next three columns list the results of the detection of flux
andspectralvariability.
4.3.Probabilitydistributionoftheexcessvariance
Thereareanumberoffactorsthatcouldbeaffectingthevalues
and distribution of measured excess variances that we utilised
Fig.3. Mean probability density functions of the excess variance,
P(σ ),forallobjects(top)andfortype-1andtype-2AGN(bottom). tomodelthefluxvariability.Themostclearexampleisthelow
intr quality in our lightcurves. We have made simulations in order
Thedistributionswereobtainedfromthemeanoftheindividualproba-
bilitydistributionsofσ . toobtainthetrueprobabilitydistributionofexcessvariancefor
intr our sources, after accountingfor all the selection effects in our
sampleofobjects.Thedetailsonhowthesimulationswerecar-
We havecalculatedthe meanprobabilitydistributionofthe riedoutaregiveninAppendixB.Thesesimulationsassumethat
excess variance, P(σ ), for our sample of sources as the un- thefluxvariabilitypropertiesarenotdependentonthesource’s
intr
weighted average of the probability distributions of σ ob- flux (or count rate), an assumption that is consistent with our
intr
tainedfromeachlightcurve.Aswementionedbefore,because analysis.
wewanttodescribetheX-rayfluxvariabilitypropertiesforthe Our simulations have shown that the observed amplitude
sampleasawhole,wehaveincludedinthecalculationtheprob- of variability depends strongly on the number of points in the
ability distributions of σ for both variable and non variable light curves, in the sense that systematically lower amplitudes
intr
sources.ThesemeanprobabilitydistributionsareshowninFig.3 of variability are measured in objects with smaller number of
forallsources(top)andforthetwoAGNsamples(bottom).The pointsinthelightcurves.Thereforeinthissectionweuseonly
mostprobablevalues(modes)andthecorresponding1σuncer- the 103sourceswith at least 5 pointsin their lightcurves.The
tainties obtainedfromthese distributionsare listed in Col. 6 of “corrected”probabilitydistribution,P(σ ),obtainedusingour
intr
Table 3. The mode for the whole sample of sources is σ ∼ simulationsisshowninFig.4(solidline).Towitnesstheeffects
intr
0.15 (68% upper limit = 0.36), consistent with the value ob- that go into this, we also have included the observed probabil-
tained from the arithmetic mean of the values for each source, itydistributionofσ values(dashed-line)foroursources,ex-
intr
asexpected. cludingobjectswithlessthan5pointsintheirlightcurves(see
We seethattheaverageprobabilitydistributionsofσ for Fig.2).
intr
type-1andtype-2AGNdonotdiffersignificantly,althoughthe Weseethatbothdistributionspeakatsimilarvaluesofσ ∼
intr
mostprobablevalueofσ ismarginallylowerfortype-2AGN 0.2andthereforeeffectsastheoneslistedbefore,arenotaffect-
intr
thanfortype-1AGN. ing significantly the mode of the distribution of amplitudes of
The flux variability properties for all our sources flux variability. However the corrected distribution of intrinsic
(significance of detection and amplitude of variability) are excess variances shows a long tail towards high values of σ
intr
listed in Table 4. The first and second columns list the (≥0.6),whichwefailedtodetectintherealdata.Duringoursi-
XMM-NewtonandROSATidentificationnumberofthesources. mulationsworkwefoundthatforsourceswithlightcurveswith
Columns 3 and 4 show the coordinates of the sources while the lowest number of bins, we always measured values of the
Cols. 5 and 6 list the optical class and spectroscopic redshift. variabilityamplitudesignificantlylowerthantheinputvalue,no
Column 7 lists the best fit model from the spectral analysis of matter how large this value was (see Fig. B.3). Hence, we can
S.Mateosetal.:X-rayspectraLH.V. 111
Fig.5.Dependenceoftheexcessvariance,σ ,ontheabsorptioncorrected2−10keVluminosity(obtainedfromthebestfitmodelofeachobject,
intr
left)andredshift(right)foroursampleofAGN.Errorscorrespondtothe1σconfidenceinterval.
explainthescarcityofsourcesinwhichwehavedetectedlarge and
(≥0.6)variabilityamplitudesasbeingduetoasmallnumberof
pointsinthelightcurve. σ =(−0.062±0.028)× z+(0.378±0.053). (5)
intr
4.4.Dependenceoffluxvariabilitywithluminosity We see that we do not find any anticorrelationbetween excess
andredshift variance and luminosity. However it is important to note that
theknownanticorrelationwasfoundwhenusingthesamerest-
Ithasbeensuggestedthatfluxvariabilityamplitude,whenmea- frame frequencyinterval for all sources, while the light curves
sured on a fixed temporal frequency, correlates inversely with of our sources are not uniformly distributed in time and cover
X-rayluminosity(thishasbeenconfirmedonshorttimescales, muchlongertimescalesthantheonesusedinthoseworks.
∼1day)fornearbySeyfert1galaxies(Barretal.1986;Lawrence We have used the same observed energy band to study the
etal.1993;Nandraetal.1997),inthesensethatmoreluminous variability properties of our sources, but because our sources
sources show lower variability amplitudes than less luminous span a broad range in redshifts we are sampling different rest-
sources. However there is significant scatter in this correlation frameenergies(harderenergiesforhigherredshiftsources).This
onbothshortandlongtimescales,whichhasbeenattributedfor could be a problem when comparing observed variability pro-
example,toadependenceoftheamplitudeofvariabilityonthe pertiesbetweensources,ifthereexistsadependenceofvariabi-
spectralpropertiesofthesources(Fioreetal.1998;Greenetal. litypropertieswithenergy(strongervariabilityinthesoftband
1993).Furthermore,therearealsoindicationsthatthestrengthof compared to the hard band, has been observed in a number of
thecorrelationdecreasestowardslongertimescales(Markowitz Seyfert1 galaxies).By plottingthe excessvarianceversusred-
etal.2004)andmightbeweakerforsourcesathigherredshifts shift we should be able to see whether this effect is present
(Almainietal.2000;Mannersetal.2002). in our data. This dependenceof σ with redshift is shown in
intr
We have analysed whether there exists any dependence of Fig.5(right).TheresultsfromthegeneralisedKendall’stautest
the detected flux variability in our sources with their X-ray lu- (probability of detection of 97.7%) and from the linear regres-
minosityandredshift.Figure5(left)showsthemeasuredvaria- sion analysis (Eq. (5)) suggest that there is a weak correlation
bility amplitudes for our AGN as a function of the 2−10 keV betweenσ andtheredshift.
intr
luminosity. We have used the absorption-corrected 2−10 keV To enhance any underlying correlation between the ampli-
luminositiesobtainedfromthebestfitoftheco-addedspectrum tude of variability and the X-ray luminosity or redshift, we
of each object. The values we used are reported in Table 8 of haveobtainedaverageprobabilitydistributionsforoursampleof
Mateosetal.(2005b).Inorderto determinethesignificanceof type-1AGN (wecannotrepeattheexperimentfortype-2AGN
anycorrelationbetweenthe two quantitieswe usedthe version asthesamplesizeistoosmall)inbinsofredshiftandluminosity.
ofthegeneralisedKendall’stautestprovidedintheAstronomy Inordertohaveenoughobjectsperbinandenoughdatapoints
SurvivalAnalysispackage(ASURV;Lavalleyetal.1992)which we haveusedonlytwo binsinredshiftandluminosity(allbins
implements the methods described in Isobe et al. 1986 for the having the same number of objects). The results are shown in
caseofcensoreddata.TheresultsoftheKendall’sτtestinclud- Fig.6whilethevaluesofσ obtainedfromthesedistributions
ingtheprobabilitythatacorrelationispresentareτ=0.05(4%) (modes)areshowninTablei5nt.rThevariabilityamplitudeappears
for σintr-L2−10keV and τ = 2.25(97.7%) for σintr-redshift. We to be independentof the 2−10 keV luminosity, confirming the
have derived linear regression parameters using the “estimate aboveresults.Thesameresultholdsforthedependenceofvaria-
and maximise” (EM) and the Buckley-James regression meth- bilityamplitudewithredshiftasweseethatthereissomeindi-
ods also included in the ASURV package, however as both cation that the amplitude of flux variability is lower for higher
methodsagreedwithintheerrors,weonlygivetheresultsfrom redshiftsources,althoughtheeffectisnotverysignificant.
theEMtest.We foundtherelationsbetweenσintr-L2−10keV and In addition, we do not find that the detection of flux varia-
σ -redshifttobe
intr bilitychangeswithredshift.Insummary,neitherthefractionof
sourceswithvariabilityoritsamplitudechangewithredshiftin
σintr =(−0.012±0.044)× log(L2−10keV)+(1.054±1.937) (4) oursample.
112 S.Mateosetal.:X-rayspectraLH.V.
Fig.6.Left:averageprobabilitydistributionsofσ forsourceswithredshiftsbelow1.5andabove1.5.Right:averageprobabilitydistributionsof
intr
σ forsourcesat2−10keVluminositiesaboveandbelow1044 ergs−1.TheprobabilitydistributionswereobtainedusingallAGN,variableand
intr
nonvariableintermsoftheχ2test.
Table 5. Amplitudes of variability obtained from the mean probabil- 2−10 keV rest-frame band will correspond to a change in the
ity distributions of σ for type-1 AGN at different luminosities and spectralslopeof∆Γ∼0.2.Therelationbetweenspectralshape
intr
redshifts. and intensity is less clear for a corona not dominatedby pairs,
butqualitativelysignificantspectralvariationscouldbeobtained
Group σintr forsmallchangesintheobservedcountrateinthiscase.
(1) (2) InordertoprovidemoreinsightintothenatureoftheX-ray
type-1AGNz<1.5 0.32+0.17 variabilityinourobjects,wehavestudiedwhichfractionofthe
−0.23
type-1AGNz>1.5 0.18+0.17 sourcesinoursampleshowspectralvariabilityandinthesecases
−0.17
type-1AGNL(2−12keV)>1044 ≤0.38 whetherthereexistssomecorrelationbetweentheobservedflux
type-1AGNL(2−12keV)<1044 0.29−+00..2200 andspectralvariability.
Columns are as follows: (1) type-1 AGN group; (2) variability am-
plitude, σ , obtained from the mode of the mean probability distri- 5.1.VariabilityofthebroadbandX-raycolours
intr
butions, P(σ ), of each group of sources. Errors correspond to the
intr Becauseoursourcesaretypicallyfaint,wecannotusetheirspec-
1σ confidence interval. In the cases where the lower error bound of
theintegralsreachedzerowecalculated90%upperlimitsforσ . tra from each revolution, as the uncertainties in the measured
intr spectralparameterswillbeinmostcasestoolarge.Instead,we
haveusedabroadbandX-raycolourorhardnessratio(HR),that
allowsustosearchforchangesinthebroadbandspectralshape
5. Spectralvariability
of the sources. We calculated for each source a “colour” curve
Comptonization models are most popular in explaining the whereeachpointistheX-rayhardnessratiofromeachobserva-
X-ray emission in AGN (Haardt et al. 1997). In these models tionwhichisobtainedas
the X-ray emission is produced by Compton up-scattering of HRr =(CRr −CRr)/(CRr +CRr) (6)
optical/UV photons in a hot electron corona above the accre- h s h s
tion disc. One of the predictions of these models is that, if the whereCRrs andCRrh arecorrectedcountrates(seeAppendixA)
nuclear emission increases, the Comptoncoolingin the corona in the 0.5−2 keV (soft) and 2−12keV (hard)energybandsfor
shouldincreasetoo,andthereforethecoronawillbecomecolder, revolutionrrespectively.Weusedthesetwoenergybanddefini-
andtheX-rayspectrumbecomessofter. tionstoseparatebetterthemostimportantspectralcomponents
This flux-spectral behaviour is similar to the one observed foundintheco-addedspectraofourobjects.Changesinthein-
in different states of BHXRB (McClintock et al. 2003): in the trinsicabsorbingcolumndensityorinthepropertiesofthesoft
low/hardstate5,mostcommonlyobservedintheseobjects,their excessemissioncomponentwillaffectmostlythesoftcountrate,
X-rayfluxesarelowandthe2−10keVX-rayspectraaredomi- whilechangesintheshapeofthebroadbandcontinuumwillaf-
natedbynonthermalemission,bestreproducedbyahard(Γ∼ fectbothsoftandhardbandcountrates.
1.5−2.1) power law, along with a weak or undetected thermal In order to search for objects with spectral variability, we
component(multi-temperatureaccretiondiskcomponent).Inthe usedtheχ2 test(seeSect.4)andathresholdinthesignificance
high/softstate their fluxesare highand their 2−10keV spectra of detectionof3σ. Results aresummarisedin Cols. 5and 6 of
are soft, dominated by the thermalcomponentwith a tempera- Table2.Thefractionsofsourceswithdetectedspectralvariabi-
ture∼1keV. lity havebeencorrectedforthe expectedspuriousdetectionsat
The accretion disk-static hot corona model of Haardt et al. theselectedconfidencelevelasshowninMateosetal. (2005a)
(1997) predicts that, if the corona is dominated by electron- and Stevenset al. (2005).We havedetectedspectralvariability
positronpairs,anintensityvariabilityofafactorof10overthe aboveaconfidencelevelof3σin24outofthe120objectswith
light curves with at least 2 points. Spectral variability was de-
5 Thesoftstateisusuallyseenathigher 2−10keVluminositythan tectedin6outof45type-1AGNand9outof27type-2AGN.
the hard state motivating the names high/soft and low/hard states for Amongsourcesstillnotidentifiedwedetectedspectralvariabi-
BHXRBs. lityin9outof48(butnoteagainthepossibleidentificationbias).
S.Mateosetal.:X-rayspectraLH.V. 113
Fig.7. Observed spectral variability of three objects in our sample for which their co-added spectra were best fittedwith asimple power law.
Variationofthecontinuumshape(Γ)withtime(errorscorrespondtothe90%confidenceinterval).Horizontallinesindicatethebestfitcontinuum
shape(dashedlines)andthecorresponding90%confidenceintervals(solidlines)measuredintheco-addedspectra.
Theseresultsindicatethatspectralvariabilityismuchlesscom- withenoughqualityforthisanalysis.Webuiltforeachobjecta
monthanfluxvariability.Thisresultalsoholdsforbothsamples co-addedspectrumusingthedatafromeachobservationalphase.
oftype-1andtype-2AGN. Thisisnotan unexpectedresult,as Thisapproachallowedustoconductstandardspectralfittingand
weknowthatwithalimitednumberofcounts,spectralvariabi- study spectralvariabilitydirectly ontime scales of monthsand
lityismoredifficulttodetectthanfluxvariability.Aswewillsee years.Wewereabletocarryoutthisstudyin109outof123ob-
inSect.7(seeFig.11),foraspectralpivotingmodel,achange jectsforwhichwehadmorethanonespectrumavailable.Inor-
inspectralslopeof∆Γ∼0.2correspondstoachangeintheob- dertosearchforspectralvariability,wefittedthespectraofeach
served X-ray colour of ∆HR ∼ 0.1 while ∆HR ∼ 0.2 requires observationphasewiththebestfitmodel(seeCol.7inTable4)
∆Γ∼ 0.4−0.6.ThetypicaldispersioninHR valuesobservedin and best fit parameters obtained from the overall spectrum of
oursourceswithdetectedspectralvariabilityis∼0.1−0.2while eachobject(seeTable8inMateosetal.2005b).
most of the measurement errors in HR in a single observation
We left free the normalisation of the continuum emission
are of similar size. Therefore changes in the continuum shape
sinceweareonlyinterestedhereinsearchingforspectralvaria-
∆Γ ≤ 0.3 will be detectable only in a small number of objects
bility, and we have seen that flux variability is common. For
inoursample.Wewillcomebacktothispointinmoredetailin
sources with detected soft excess emission, we kept fixed the
Sects.6and7.
ratio of the soft excess to the power law normalisations that
Finally,wehavenotfoundevolutionwithredshiftofthefrac- we found in the best fit of the co-added spectra. We then re-
tionofobjectswithdetectedspectralvariability,indicatingthat peated the fits using the same spectralmodels,but allowingall
theeffectofsamplingharderrest-frameenergybandsathigher fitting parameters to vary. We compared the χ2 of the two fits
redshiftsdoesnotreducethedetectionofspectralvariability. andsearchedforallcaseswhere∆χ2 =9foroneparameterand
We havecomparedthefractionsoftype-1andtype-2AGN ∆χ2=11.8fortwoparameters,i.e.wherewefoundasignificant
withdetectedspectralvariabilityusingthemethoddescribedin (≥3σ) improvementin the quality of the fits varying the spec-
Mateos et al. (2005a). We found that the significance of these tralshape.Fromthisanalysiswedetectedspectralvariabilityin
fractions being different is 99%. This constitutes marginalevi- 8(7%)sources(XMM-Newtonidentificationnumbers400,90,
dencethatspectralvariabilitymightbemorecommonintype-2 148,171,191,332,342and469,seeTable4),including6type-1
AGN,althoughweshouldrecallthatwearedealingwithasmall and 1 type-2AGN. The fractionof objectswith detected spec-
number of sources. If true, this might be due to the contribu- tral variability increased to ∼17% if we used instead a confi-
tiontotheX-rayemissionfromscatteredradiationbeinghigher dencelevelof2σ.Weillustratetheobservedspectralvariability
in type-2 AGN. This scattered componentwill not be variable, propertiesofthesourceswithdetectedspectralvariabilityfrom
as it will be related to the very distant reflector. The detected this analysis in Fig. 7 for the 3 type-1 AGN best fitted with a
X-rayemissionin type-2AGNis dominatedbythe hardX-ray powerlaw (absorbedbythe Galaxy).Figure8 showssome ex-
component(i.e.,inthenuclearemissiontransmittedthroughthe amplesofcontourdiagramsofΓ−kT andΓ−N for variable
H
absorbing material) while most of the non-variable soft X-ray AGN whereabsorptionorsoftexcesswere detected.We found
emissionwillnotbesignificantlyabsorbed.Therefore,changes thatinallobjectswithdetectedspectralvariability(includingall
intheintensityofthehardX-raycomponentalonewouldresult AGN),thiswasassociatedmostlywithchangesintheshapeof
inlargerchangesintheobservedX-raycolour. thebroadbandcontinuum(∆Γ ∼0.2−0.3forallobjectsexcept
source 90, where the observed maximum change in Γ ∼ 1 al-
thoughwithverylargeuncertainties).Wedidnotseeimportant
5.2.VariabilityoftheX-rayemissioncomponents
changesinotherspectralcomponentssuchasthetemperatureof
the soft excess component(when modelledwith a black body)
Inordertogivemoreinsightintothenatureofthedetectedspec-
or the amount of intrinsic absorption. This is the case also for
tralvariabilityinourobjects,wegroupedtheXMM-Newtonob-
servationsinfourdifferentperiodsoftimecorrespondingtothe the one type-2 AGN in our sample with detected variability in
itsspectralfittingparameters.
XMM-Newton phases PV, AO1 and AO2a and AO2b (see sec-
ond column in Table 1). As we have said before, most of the Note that amongthe objects with detected spectral variabi-
objects in our sample are too faint to search for spectral varia- lity from this spectral fitting, we only detected spectral varia-
bilitycomparingtheemissionpropertiesmeasuredontheX-ray bility usingthebroadbandX-raycolourin onesource,90(see
spectrumfromeachrevolution.Asignificantfractionoftheex- Table4).Theseresultssupporttheargumentthatthelowfraction
posuretimewasachievedduringtheAO2phase,soweseparated ofobjectswithdetectedspectralvariabilitycouldbeduetothe
theAO2dataintotwocontiguousgroupsandstillobtaineddata factthattypicalchangesinthecontinuumshapeareoftheorder
114 S.Mateosetal.:X-rayspectraLH.V.
Fig.8.Contourdiagramsshowingtheobservedspectralvariabilityintype-1andtype-2AGN.Contourscorrespondto1σ,2σand3σconfidence.
of∆Γ∼0.2−0.3,whichcorrespondsto∆HR∼0.1,undetectable
inmostofthesourcesinoursample.
Ifthisisthecase,thentheresultsshowninthissectionsup-
porttheideathatthemaindriver(butnottheonlyone,seelater)
forspectralvariabilityonmonth-yearsscalesinAGNmightbe
changes in the mass accretion rate, resulting in changes in the
underlyingpowerlawΓ.Wewilldiscussthispointinmorede-
tailinSect.7.
6. WhatisthetruefractionofsourceswithX-ray
variability?
InFig.9(top)weshowthefractionofobjectsinoursamplewith
detected flux variability as a functionof the mean 0.2−12keV
countrate.Weseethatitisastrongfunctionofthemeancount
rate,andhencethenumberweobtainedforthewholesampleof
objects(∼50%)hastobetakenasalowerlimit.Indeedtheplot
suggeststhatthe fractionof sourceswith fluxvariabilitymight
be80%(themaximumfraction)orhigher.
Theresultthatthemajorityofoursourcesarevariableinflux
is in agreement with previous variability analyses, that found
long time scale variability in AGN to be more common than
short time scale variability (see e.g. Ciliegi et al. 1997; Grandi
etal.1992).Howevernotallsourcesinoursampleareexpected
to be variable as some of the objects still not identified might
eithernotbeAGN,orbeheavilyobscuredAGN.
Figure9(bottom)showsthefractionofobjectsinoursam-
ple with detected spectral variability as a functionof the mean
errorinthehardnessratio.Weseethatthedetectionofspectral
variability varies with the quality of our data, although in this
case a clear dependenceis onlyevidentfor the first bin, which
suggeststhatthefractionofsourcesinoursamplewithspectral Fig.9.Top:fractionofsourceswithdetectedfluxvariabilityasafunc-
variabilitycouldbe∼40%orhigher. tionofthemean0.2−12keVcountrateoftheobjects.Bottom:fraction
Even if the fraction of sources with spectral variability is ofsourceswithdetectedspectralvariabilityasafunctionoftheerrorin
as high as ∼40%, this fraction is still significantly lower than theirmeanX-raycolour.
the fraction of sources with flux variability. If spectral variabi-
lityislesscommonthanfluxvariabilityinoursample,thenfor
objects in our sample. Indeed38 sourcesin oursample exhibit
manysourcesfluxvariabilitywillnotbeaccompaniedbyspec-
fluxvariabilitybutnotspectralvariability,while9showspectral
tralvariability.
butnotfluxvariability(seeTable4).
Inaddition,noevidentcorrelationbetweenfluxandspectral
variabilitywasfoundforthe15objectsforwhichbothwerede-
7. Onthelong-termX-rayvariability
tected.TheHR-CRplotsfortheseobjectsareshowninFig.10.
Theresultsofourvariabilityanalysissuggestthatfluxandspec- We see in Fig. 10 that significant changes in flux without ev-
tralvariabilityarenotcorrelatedforasignificantfractionofthe ident changes in the X-ray colour are common in our objects.