A Pipeline for the ROTSE-IIId Archival Data

0.41 MB·English

by  B. B. Güçsav

ExperimentalAstronomymanuscriptNo.
(willbeinsertedbytheeditor)
A Pipeline for the ROTSE–IIIdArchival Data
B.B.Güçsav · C.Yes¸ilyaprak · S.K.Yerli · N.Aksaker · U.Kızılog˘lu · D.Çoker ·
E.Dikiciog˘lu · M.E.Aydın
2
1
0
2
Received:date/Accepted:date
n
a
J
Abstract We haveconstructedanew,fast, robustandreli- 1 Introduction
2
able pipeline to detect variable stars from the ROTSE-IIId

] archivaldata.Turkishshare ofROTSE-IIIarchivecontains RoboticOpticalTransientSearchExperiment(ROTSE-Akerlofetal,
M approximatelyonemillionobjectsfromalargefieldofview 2003)isanetworkoftelescopeslocatedallaroundtheworld1.
◦
I (1.85 ) and itconsiderablycoversa largeportionof north- The primary goal of the ROTSE–IIIproject is to observe
h. ern sky (d >−25◦). The unfiltered ROTSE-III magnitude Gamma-Ray Bursts (GRB) in optical light. Each ROTSE–
p of the objects rangesfrom 7.7 to 16.9. The main stages of IIItelescope consists of 45 cm with a wide field of view
o- the newpipelineare asfollows:Sourceextraction,astrom- (1.85◦).ThetelescopeswerebuilttorespondrapidlytoGRBs
r etry of the objects, light curve generation and inhomoge- (<10 s) which are triggered by satellites such as Swift, In-
t
s neous ensemble photometry. A high performance comput- tegral and HETE. The ROTSE–IIIsystem runs unattended
a
ing (HPC) algorithm has also been implemented into the withfullyautomatedobservation,dataacquisitionandanal-
[
pipeline where we had a good performanceeven on a per- ysis(seeRykoffetal(2005)forthepipeline).
2
sonalcomputer.Runningthealgorithmsofthepipelineona TheROTSE–IIIcollaborationuses70%ofeachROTSE–
v
3 cluster decreasesanalysis time significantly from weeks to IIItelescope’sobservationtime.Therestofthetimeisallo-
2 hours.The pipelineis especially tested againstlong period catedfordiscretionbythelocalorganization.TheROTSE–
8 variablestarswithperiodsofafewhundreddays(e.gMira IIIdtelescope is located at TÜB˙ITAKNational Observatory
2
and SR) and variables having periods starting from a few (TUG)2, Bakırlıtepe, Antalya, Turkey. Scheduled observa-
.
8 daystoafewhundreddaysweredetected. tions were started in May 2004 and they were distributed
0
amongtheTurkishastronomersbyTUG.Inthiswork,allof
1
1 Keywords Software:Pipeline· Methods:dataanalysis· thepublicTurkishobservationshavebeenused.Someofar-
: Telescopes:ROTSE–IIId ticleswhichmadeofusingtheROTSE–IIIddataofTurkish
v
i share are as follows Baykaletal (2005), Biliretal (2006)
X
andKızılogˇluetal(2009).
r PACS (PACScodes)
a The aim of this work is to detect variability as well as
finding new variables using the ROTSE–IIIdarchival data.
Toachievethismaingoalamultistagealgorithmweredevel-
B.B.Güçsav·D.Çoker·M.E.Aydın
opedandthentheyarecraftedintoaseriesofroutines:the
Ankara Üniversitesi, Science Faculty, Astronomy & Space Sciences
Department,Ankara,Turkey pipeline.Insection2.1,ROTSE–IIIdobservationsandstruc-
C.Yes¸ilyaprak·E.Dikiciog˘lu tureofthedata,andinsection2.2thesummaryofROTSE–
AtatürkUniversity,FacultyofScience,DepartmentofPhysics,Erzu- IIIdpipelineare given.The data handling,our pipeline and
rum,Turkey its structure are given in section 3. The main stages of the
S.K.Yerli·U.Kızılog˘lu pipelineareasfollows:sourceextraction(§3.2),astrometry
OrtaDog˘uTeknikÜniversitesi,PhysicsDepartment,Ankara,Turkey offieldstars(§3.3),lightcurvegeneration(§3.4)andinho-
N.Aksaker
Çukurova Üniversitesi, Vocational School of Technical Sciences, 1 http://rotse.net/information/world/
Adana,Turkey 2 TUG:http://www.tug.tubitak.gov.tr/
2 GucsavB.etal.
Table1 MagnitudelimitsofROTSE–IIIdexposures. datareductionpipelineallowingthenearreal-timeprocess-
ingoftheCCDimagestakenbytheentireROTSE–IIInetwork.
ExposureTime SaturationMagnitude LimitingMagnitude
5s 7m.7 15m.6 Regardless of types of observation carried out on the tele-
20s 9m.0 16m.2 scope,allimagesarefedintotheROTSE–IIIdpipeline.Fur-
60s 10m.0 16m.9
thermore, CCD images of all observations (taken with ei-
ther alarmed or scheduled mode) were automatically pro-
cessed(biassubtracted,flatfieldedandfringecorrected)im-
mogeneousensemble photometry (§3.5). We conclude our mediately after the frame has been download to the disk
workwithresultsandsomesuggestionsforfuturework. (Rykoffetal,2005).SExtractorsoftware(BertinandArnouts,
1996) is then used to detect objects, to measure centroid
positionsand to determine instrumentalmagnitudes(using
2 ROTSEData 5 pixelsaperture).In additionto centroidpositions,round-
nessandsharpnessvaluesarealsousedtoeliminatenon-star
2.1 ROTSE–IIIdobservationsandstructureofthedata
like objects. Instrumentalmagnitudesof each objectin the
frame is calibrated by comparingall the field stars against
TheROTSE–IIIdhasan45cmprimarymirrorandisoutfit-
the“USNO–A2.0R–bandcatalog”(Monet,1998)(because
tedwitha2k×2kTEcooled,CCDcamerawith3.3"/pixels,
the R filter is the nearest to the QE maximum) to obtain
◦
making a 1.85 field of view. QE (Quantum Efficiency) of
ROTSE–IIImagnitudes.Bysimplyusingtriangulationmethod
the CCD peaks at 550 nm. The telescope and CCD were
from the USNO catalog each object’s coordinate and in-
describedinAkerlofetal(2003).
strumental magnitude are calibrated at the same time. The
The CCD observations have been carried out in three
pipeline finally outputsfiles of calibrated object catalogs
different exposure times: 5 (∼ 54%), 20 and 60 seconds.
whicharetabulateddataintheformofbinaryFITStables.
There are two important magnitude limits for these expo-
ThealgorithmofthepipelinewerecodedinIDLandsche-
suresgiveninTable1:saturation(meanofmaximumbright-
maticrepresentationisshowninFigure1(bluecoloredblocks).
nessofstarshavingnosaturatedpixelsintheirFullWidthat
Thus,theframe(andeachdetectedobjectintheframe)can
HalfMaximum-FWHM)andlimiting(meanofthefaintest nowbeusedinlightcurveanalysisusingobject’sa ,d ,mag-
star’smagnitudes).
nitudeanderrorinmagnitude.
TheunfilteredROTSE–IIImagnitudeoftheobjects,de-
pending on the exposure times, ranges from 7.7 to 16.9.
Another image quality check was the FWHM of the point
spreadfunctiononanimagewhichrangesbetween2to25
pixelsand onthe averageit istaken as5 pixels.The Turk-
ishshareofROTSE–IIIdarchivecovers2,210deg2 whichis
3.4%ofthewholesky.
3 TheNewPipeline
The journalof observationsused in the pipelineranges
betweenMay2004andJune2010anditconsistsof234,764
framesfrom645differentpointings.Thesizeofthearchived 3.1 DataHandling
data from the Turkish share (regardless of the pointings)
since2004isapproximately2TiB.
Inputdata forthe pipelinecan either bea calibratedobject
266 pointings were observed less than 100 times and
file or anycorrectedimagefile ofthe ROTSE–IIId.Thisis
they were not included in the pipeline. This limiting value
requirediftheframeisnotvalidi.e.theWCS(WorldCoor-
isanarbitrarychoice.However,tohavestatisticallyreliable
dinateSystem)headerswerewrongortherewererecorded
data sets we had to implement this minimum lower limit.
bad weather conditions, or technical problems. Otherwise
ThisnumberwerealsousedinotherlargevolumeROTSE–
theframeisvalidanditenterstothestage-Cofthepipeline.
IIIanalyses(Wozniak,2004).
Thus,usingthesefilteringmethods,atleast80%ofthearchive
washandledwiththepipeline.Thealgorithmofthepipeline
showninFigure1(yellowcoloredblocks).
2.2 ThesummaryofROTSE–IIIdpipeline
It seems that the first two stages of our pipeline dupli-
Thedataacquisitionsystemisconstructedontopofseveral catestheROTSE–IIIdpipeline(seeFigure1).However,since
daemons(e.g.weather,clamshell,camera).Thetelescopeis (a) SCAMP has been used in astrometry calibration (see
operatedundertwomodes:alarmedandscheduled.Thelat- §3.3) and (b) the parameterset that we use in our pipeline
termodeisusedintheentireTurkishshareandthus,inour wasnotproducedbytheROTSE–IIId,wehadtointroduce
pipeline. The ROTSE–IIItelescopes have a well designed ourownstagesinthepipeline.
SoftwareforROTSE–IIId. 3
tractor’s statistical output we have improved the reliabil-
ity of magnitudes calculated which wasn’t possible using
ROTSE–IIIdpipeline;itcontainedonlyafewcolumnsofin-
formation.
Asanexample;fortheROTSE–IIIdpointingof0006+4305
therewere12,037sourcesin“USNO–B1R”catalog(here-
afterUSNO–B;Monetetal,2005)form <18m.0.Whenthe
R
SExtractoris applied to this pointing, depending on atmo-
spheric conditions, number of sources varied between 279
and11,399.Thus,thestagemissesonly5%oftheUSNO-B
sources.
3.3 Stage-B:AstrometryofFieldStars
Theoutputofstage-AisusedasanINPUT. Similartothe
ROTSE–IIIdpipeline,thecalibratedobjectcatalogsarecal-
culatedasanOUTPUT.
TheSCAMP(Bertin,2006)softwarepackageisusedto
mapUSNO–Bstarswiththeinputtedfieldstarcoordinates
sothatatransformationmatrixcanbecalculated.Insteadof
USNO–B catalog, GSC, UCAC or 2MASS catalogs could
alsobeused.
SCAMPhasbeenimprovedforCCDframestakenfrom
largeFOV.ThecalculatedastrometricalerroroftheROTSE–
IIIdisapproximately1-2"foritsFOV(lessthanitspixelsize).
Therefore,SCAMPseemstobeasuitabletoolforastromet-
riccalibrationsinourpipeline.
Thepointingaccuracy(orerror)oftheROTSE–IIIdtelescope
′
isgivenasapproximately1(Akerlofetal,2003).
The mean of differences between “targeted frame cen-
ters” and “frame centers recorded in frame headers” gives
thepointingaccuracyofROTSE–IIId.Thesevaluesareap-
proximatelyDa =5.1′andDd =1.0′.Sinceunalignedframes
within a few arcmin (Bertin, 2006) can be handled with
SCAMP,thiserrordoesn’teffecttheresultantastrometry.
Fig.1 AcombinedflowchartviewofROTSE–IIId(bluecolored)and Duetothetelescopeoptics(Güçsav,2010),thebestas-
ournew(yellowcolored)pipelines.
trometricalignmentoftheframesweredonewithinthecen-
◦
tral 1.79 diameter of the whole ROTSE–IIIdframe. Thus,
starsfallingonlyintothisregionwereused(seeFig.2).
3.2 Stage-A:SourceExtraction
Since, SCAMP cannot handle frames having excessive
imagedeformations(e.g.badfocusing,weathereffects,tele-
ROTSE–IIIdcorrected images are used as INPUT data in
scope instability) they are automatically discarded and no
this stage. A catalog of stars with their instrumental mag-
outputisproduced.ThisfeatureofSCAMPisalsousedasa
nitudeandframecoordinatesisproducedasanOUTPUT.
“filtering”algorithmforfaultyframes.
SExtractorcode(BertinandArnouts,1996)isusedtoiden-
tify(andtoperformanaperturephotometry)thestarsinthe
frame.FourtypesofmagnitudesarecalculatedbySExtrac- 3.4 Stage-C:LightCurveGeneration
torwhere only one of them is used in the pipeline, namely
MAG_APER (magnitude from aperture). A classical value Theoutputofstage-BortheoutputofROTSE–IIIdpipeline
of5pixelsROTSE–IIIdaperture(e.g.Kızılogˇluetal,1995) isusedasanINPUT.
was used in the SExtractorsettings.The outputof SExtrac- Notethat,ourinputcontainsalistofequatorialcoordi-
torconsistsofinstrumentalmagnitude,framecoordinatesand nates and instrumentalmagnitudesof sources, namely it is
manystatisticalcalculationsforeachstar.ByusingtheSEx- called “calibrated object catalog” (see Fig. 1; grey colored
4 GucsavB.etal.
3.5 Stage-D:InhomogeneousEnsemblePhotometry
Theoutputofstage-CisusedasanINPUT.Cleanedlight
curvesarecreatedasanOUTPUT.
Inclassicalphotometry,asinglecomparisonstarisused
effectivelyformanyyearsbyastronomers(seeHendenandKaitchuck,
1990;Youngetal,1991).However,inrecentyearsdifferen-
tialphotometrywith“manycomparisonstars”hasincreased
both reliability and accuracy of the light curves. Ensem-
ble photometry is also a new kind of differential photom-
etrywhichworkswithinhomogeneousCCD datasets(e.g.
Honeycutt, 1992; Saesenetal, 2010). We have also imple-
mentedtheensemblephotometryinthepipelinetodecrease
statisticalerrorsininstrumentalmagnitudeofstars.Forex-
ample,lightcurveofa10m.0starwasfoundtobealmostcon-
stant.AccordingtoSExtractor,meanofRMSfluxerrorsof
thePSFfittingtothestarwascalculatedtobe0m.006.How-
ever,with the implementedensemble photometry,we have
reachedto ascatter valueof0m.002.Notethat,thislevelof
Fig.2 AsampleCCDimagefromROTSE–IIIdarchiveoverlayedwith noiseisalsorelatedtotheotherframestatistics(seescatter-
thechosenROTSE–IIIdFOV(i.e.1.79◦). errorgraphofthepointingthatthisstarislocated-Figure3);
as the magnitude of the source decreases, scatter increases
andthereforegoodnessofthePSFfittingdecreases.Ascan
beseeninthefigure,whilescatterofa10m.0magnitudestar
isaround2mmag,itincreasesto130–200mmagat16m.0.
boxatthemiddle).Eachinputtedstar’slightcurve(instru-
mentalmagnitudevs.JD)iscreatedasanOUTPUT. The main aim of the technique is to find non-varying
stars (i.e. reference stars) throughout the time span of the
In thisstage, the aim is to follow each star’s equatorial lightcurves.The objectsthat have non-starlike shapesare
coordinatesin each frame throughoutthe whole time span all ignored by using SExtractor’s roundnessand sharpness
andthencollectthecorrespondinginstrumentalmagnitudes analysis(see §2.2). Main criteriain choosingthe reference
atthatcoordinate.Asinthestage-B,coordinatesofUSNO– stars are (1) to choose the stars far enough from edges of
Bfieldstarsareusedtomatchcalibratedcatalogs.Inorder the frame, (2) to have the star isolated from the others, (3)
todothisafixedcircularaperture(namely“matchingaper- to have no flags set in the SExtractor’s output, (4) to have
ture”; hereafter f ) has to be chosen to scan through each roundness value close to zero, and (5) to have sharpness
m
catalog.Thef canbeneitheralowvalue(whichincreases valueclosetoone.Inordertonottofoulthetechnique,light
m
the chance to miss the target star) nor a high value (which curvesofmostlynon-variablestarshavetobeused.There-
increasesthechancetomulti-matchthetargetstars).There- fore,roughvariabilitydetectionhastobeappliedtothelight
fore, f was chosen according to ROTSE–IIIdpixel scale curves;namely‘scatter-and-error’relationofthelightcurve
m
(namely3.3"/pixel)andtoachieveastandardGaussianpho- hastobecalculated.Accordingtotheresultofthisrelation
ton distribution, 3 pixels range were taken. Thus, 10"was the star can nowbe accountedas a reference.Afterward,a
chosen as an optimum f value (f¯ ) which will be used meanreferenceleveliscalculatedfromallchosenreference
m m
throughoutthepipeline(Güçsav,2010). stars and it is used to calculate the relative(or differential)
magnitudeofotherstars.
As a side effect of the matching algorithm, especially Byapplyingthetechniquesecondtime(startingfromthe
in crowdedfields, there mightstill be multi-matchesin the scatter-and-errorrelation),reliabilityofreferencestarsisin-
field.Insuchcases,theneareststarinthecalibratedcatalog creased:starsofthefirstrunisinputtedasthestarlistinthe
totheUSNO–Bstarwaschosenasthematch.Thepipeline secondrun.Withthisrun,variationofreferencestarsisde-
can be fine-tuned manually for some of the overcrowded creasedandthereforetheaccuracyofthefinalcleanedlight
fields by decreasing f to 6"to be able to decrease multi- curvesis increased. As an example,a referencestar’s light
m
matches. In a future version, this fine-tuning can be inte- curves before and after ensemble photometry applied are
gratedintothepipelinebymarkingeachfieldwithacrowd- giveninFig.4.Asseeninthefigure,afterensemblephotom-
nessvaluewhichwouldmakeitpossibleto applydifferent etry(stage-D)applied,scatterofthelightcurve(i.e.sigma)
f valuestoeachfieldwheneveritisnecessary. decreased.
m
SoftwareforROTSE–IIId. 5
Fig.3 Meanmagnitude(inmagnitude)versus meanofRMSfluxerrors (inmmag)ofsources inanexamplepointingwhichcontains31,984
sourcesisgiven(see§3.5).Thebrightendofthegraphisextendedatupperleftasaninsetwiththesameunits.Similarly,dimendofthegraphis
markedwithgridlines.
Thistechniquehastheadvantageofremovingframeto In order to balance the CPU load between cores the same
framebackgroundvariationsduetoMoonlightandunstable amountofstar’s data(bothlightcurvesandstar’sinforma-
weatherconditions.Asadisadvantageofthistechnique,the tion)arestored.
absolutemagnitudeofstarscannotbecalculated.Asample TheprototypeofthepipelinewasstartedwithIDL.How-
of the final light curve of a variable from our pipeline is ever,to be able to have a robust and fast pipeline,they are
giveninFig.5. convertedandparallelizedintoC-language.Withtheproto-
type,ittookapproximatelyoneweektocreatelightcurves
of a single medium crowded pointing with a 4 cored PC.
3.6 ThePipelineStructure Thisdurationdecreasedtoabout2hourswiththepipeline.
The new pipeline works on a small cluster called Infini-
4 Conclusion
tuswhichmadeuseof8differentcomputerswith36cores
eachhaving8GHzCPUspeed.GNU/Linuxoperatingsys-
Wehaveconstructedanew,fast,robustandreliablepipeline
temandLustre1.6.7.4filesystemisusedonallcomputers.
to detect variable stars from the ROTSE–IIIdarchivaldata.
Computersareinterconnectedwithagigabyteethernet.The
The main stages of the pipeline were as follows: Object
pipelineismainlywritteninClanguage.Parallelalgorithms
identification,astrometryoftheobjects,lightcurvegenera-
havebeenusedineverystepofthepipelinewhichmadeuse
ofMPICH23library. tionandinhomogeneousensemblephotometry.Forthefirst
timeintheROTSE–IIIarchive,ahighperformancecomput-
In order to increase speed of the process a parallel file
ing(HPC)algorithmhasbeenimplementedintothepipeline.
reading method is used in the pipeline. The files of each
Dependingonthedataquality,eithercorrectedCCDimages
pointing (all the calibrated catalog stars in the entire time
(stage-A)orthe calibratedobjectcatalogsproducedbythe
span) is automatically separated into all processors so that
ROTSE–IIIdpipeline(stage-C)couldbeusedinthepipeline.
eachpointingstaysinthememoryuptoendofthestage-C.
Thelaststageofthepipeline(stage-D),namelyinhomoge-
3 http://www.mcs.anl.gov/research/projects/mpich2/neous ensemble photometry (implemented to the ROTSE–
6 GucsavB.etal.
Fig.4 Thelightcurvesofareference starbefore(toppanel)andaf-
ter(bottompanel)ensemblephotometryisapplied.Thesigmavalues
representthescatteringofthecurve.
IIIarchiveforthefirsttime),givesrelativelightvariationsof
themeasuredstarswithhighprecision.Within645pointings
(see section2.1) of observationsin2004–2010,lightcurve
ofapproximatelyonemillionstarsareproduced.
Workonidentifyingnewvariablesandsearchingforpe-
riodsofknownvariablesarestillinprogress.Thefollowing
statisticaltests todetectvariablestarshavebeenappliedto
Fig. 5 Light curves (differantial magnitudes vs. phase) of three se-
the light curves: Scatter-Error Analysis (commonly used),
lectedvariableswhichareautomaticlyproducedbyourpipeline.They
Abbe Index (Saesenetal, 2010) and Analysis of Variance allhavedifferentmeanmagnitudesandtheirmeanstatisticalerrorsare
(Wozniak,2004).Forperiodhuntingthefollowingtechniques alsoplottedoneachpoint(exceptthetopone;whichistoosmalltobe
plottedtogether).Fromtoptobottompanel,themeanmagnitudesare
havebeenused:PDM(PhaseDispersionMinimization;Stellingwerf,
8m.3,12m.4and15m.5;andtheirmeanstatisticalerrorsare1mmag,10
1978), Lomb–Scargle periodogram (Lomb, 1976; Scargle,
mmagand40mmag,respectively.
1982)andSIGnificanceSPECtrum(SigSpec;Reegen,2007).
A fewthousandsoflightcurveshavepassedfromallthree
statistical tests mentioned above. These light curves most tual periods of 78 Mira variables have been identified and
probablyareun-identifiedvariablestars. 18ofthemhaveaperiodforthefirsttime(Yes¸ilyapraketal,
Lightcurvesofapproximatelytenthousandsstarsfrom 2011)withearlyversionofthepipeline.Also,approximately
4pointingsareconvertedintophase-magnitudetable.These 300SRstarsareunderinvestigationand15ofthemwereex-
phasegraphswerethenvisuallyinspectedforrepeatingpat- aminedindetailsbyDikiciog˘lu(2011).Tabulatedvaluesof
terns. Amongthese reducedlist, 152stars show variability minimum and maximum values of both amplitude and pe-
andaccordingtoSIMBAD (Güçsav,2010),20ofthemare riodaregiveninTable2.
unknownvariableswhicharenotclassifiedyet. Asabyproductofthepipeline,deformationand/orde-
Asaresultofanearlyversionofthepipeline,Miraand generationofframescouldalsobedetected;e.g.background
SRstarsknowninSIMBADhavebeenexamined.Newac- variations due to Moon light and unstable weather condi-
SoftwareforROTSE–IIId. 7
Table2 Quantitativelimitsoflightcurvescreatedbythepipeline. Bilir S, Güver T, Aslan M (2006)Separation of dwarf and
giantstarswithROTSE-IIId.AstronomischeNachrichten
Amplitude Period(days) Reference
Min. 0m.1 0.17±0.02 Güçsav(2010) 327:693–697
Max. 5m.9 720.0±43.1 Yes¸ilyapraketal(2011) Dikiciog˘luE(2011)MultiplePeriodsforSemiregular(SR)
VariableStars.Master’sthesis,AtatürkUniversity,Erzu-
rum,Turkey
Güçsav BB (2010) Detection of Different Types of Celes-
tions. A surprisingresultfromdegradationof the ROTSE–
tial Objects from Robotic Telescope Archives. Master’s
IIIdmirrorwas also noticed:the decrease in the number of
thesis,AnkaraUniversity,Ankara,Turkey
detectedstars iscorrelatedwithonemagnitudedecreasein
HendenAA,KaitchuckRH(1990)Astronomicalphotome-
thelimitingmagnitude.
try : a text and handbookfor the advanced amateur and
Themainaimofthe pipelineusageistofindnewvari-
professionalastronomer
ables,periodsofknownvariables,andtoclassifythesevari-
HoneycuttRK(1992)CCDensemblephotometryonanin-
ablesusingtheaboveperiodhuntingtechniques.
homogeneousset of exposures.AstronomicalSociety of
The pipeline can easily be adapted to other ROTSE–
thePacific104:435–440
IIItelescopeswhichwillincreasetheskycoverageandnum-
KızılogˇluÜ, KızılogˇluN,BaykalA (1995)ROTSEObser-
berofdetectedunknownvariables,transientsetc.
vations of the Young Cluster IC 348. The Astronomical
Journal130:2766–2777
Acknowledgements ThisprojectutilizesdataobtainedbytheRobotic KızılogˇluÜ,OzbilgenS,KızılogˇluN,BaykalA(2009)Op-
OpticalTransientSearchExperiment(ROTSE).ROTSEisacollabora- ticalandX-rayoutburstsofBe/X-raybinarysystemSAX
tionofLawrenceLivermoreNationalLab,LosAlamosNationalLab
J2103.5+4545. Astronomy and Astrophysics 508:895–
andtheUniversityofMichigan(http://www.rotse.net).
900
All observations were made with the ROTSE–IIIdtelescope and
the archival data of ROTSE–IIIdobtained at the TÜB˙ITAK(Turkish Lomb NR (1976) Least-squares frequency analysis of un-
ScientificandResearchCouncil)NationalObservatory(TUG),sowe equally spaced data. Astrophysics and Space Science
thanktoROTSE–IIICollaborationandTUGfortheopticalandarchival 39:447–462
facilities(TUG-ROTSE–IIIdprojectsofTurkishobservers).
MonetDG(1998)The526,280,881ObjectsInTheUSNO-
WealsothankProf. Dr.Ü.Kızılog˘luforconsulting, suggestions
A2.0Catalog.BulletinoftheAmericanAstronomicalSo-
andhelps.
Thisstudywassupported byTÜB˙ITAKwiththeproject TBAG– ciety30:1427
108T475. Monet DG, Levine SE, Canzian B, Ables HD, Bird AR,
ThisresearchhasmadeuseoftheSIMBADdatabase,operatedat DahnCC, Guetter HH, HarrisHC, HendenAA, Leggett
CDS,Strasbourg,FranceandcdsclienttoollocatedatCDSandNASA SK,LevisonHF, LuginbuhlCB, MartiniJ, MonetAKB,
AstrophysicsDataSystemBibliographicServices.
Munn JA, Pier JR, Rhodes AR, Riepe B, Sell S, Stone
RC, Vrba FJ, Walker RL, Westerhout G, Brucato RJ,
ReidIN,SchoeningW,HartleyM,ReadMA,TrittonSB
(2005)TheUSNO-BCatalog.TheAstronomicalJournal
References
125:984–993
ReegenP(2007)SigSpec.I.Frequency-andphase-resolved
AkerlofCW,KehoeRL,McKayTA,RykoffES,SmithDA,
significance in Fourier space. Astronomy and Astro-
Casperson DE, McGowan KE, Vestrand WT, Wozniak
physics467:1353–1371
PR, Wren JA, Ashley MCB, Phillips MA, Marshall SL,
Rykoff ES, Aharonian F, Akerlof CW, Alatalo K, Ashley
EppsHW,SchierJA(2003)TheROTSE-IIIRoboticTele-
MCB,GüverT,HornsD,KehoeRL,KizilogˇluÜ,McKay
scopeSystem.ThePublicationsoftheAstronomicalSo-
TA,ÖzelM,PhillipsA,QuimbyRM,SchaeferBE,Smith
cietyofthePacific115:132–140
DA, Swan HF, Vestrand WT, Wheeler JC, Wren J, Yost
Baykal A, Kızılogˇlu Ü, Kızılogˇlu N, Balman S¸, Inam SÇ
SA (2005) A Search for Untriggered GRB Afterglows
(2005) X-ray outburst of 4U 0115+634and ROTSE ob-
with ROTSE-III. The Astrophysical Journal 631:1032–
servationsofitsopticalcounterpartV635Cas.Astronomy
1038
andAstrophysics439:1131–1134
Saesen S, Carrier F, Pigulski A, Aerts C, Handler G, Nar-
Bertin E (2006) Automatic Astrometric and Photometric
wid A, Fu JN, Zhang C, Jiang XJ, Vanautgaerden J,
Calibration with SCAMP. In: Astronomical Data Anal-
Kopacki G, Steslicki M, Acke B, Poretti E, Uytterho-
ysisSoftwareandSystemsXV, AstronomicalSociety of
even K, Gielen C, Ostensen R, De Meester W, Reed
thePacificConferenceSeries,vol351,pp112–115
MD, Kolaczkowski Z, Michalska G, Schmidt E, Yakut
BertinE,ArnoutsS(1996)SExtractor:Softwareforsource
K, Leitner A, Kalomeni B, Cherix M, Spano M, Prins
extraction. Astronomy and Astrophysics Supplement
S, van Helshoecht V, Zima W, Huygen R, Vandenbuss-
117:393–404
8 GucsavB.etal.
che B, Lenz P, Ladjal D, Puga Antolin E, Verhoelst T,
De Ridder J, Niarchos P, Liakos A, Lorenz D, Dehaes
S, ReyniersM, DavignonG, KimSL, KimDH, LeeYJ,
LeeCU,KwonJH,BroedersE,vanWinckelH,Vanholle-
beke E, Waelkens C, Raskin G, Blom Y, EggenJR, De-
groote P, Beck P, Puschnig J, Schmitzberger L, Gelven
GA, Steininger B, Blommaert J, Drummond R, Briquet
M,DebosscherJ(2010)Photometricmulti-sitecampaign
ontheopenclusterNGC884.I.Detectionofthevariable
stars.AstronomyandAstrophysics515:A16
ScargleJD(1982)Studiesinastronomicaltimeseriesanaly-
sis.II-Statisticalaspectsofspectralanalysisofunevenly
spaceddata.AstrophysicalJournal263:835–853
Stellingwerf RF (1978) Period determination using phase
dispersionminimization.AstrophysicalJournal224:953–
960
Wozniak WSJVWTGV P R (2004) Identifying Red Vari-
ablesintheNorthernSkyVariabilitySurvey.TheAstro-
nomicalJournal128:2965–2976
Yes¸ilyaprak C, Yerli S, Güçsav B, Aksaker N, Dikiciog˘lu
E, Helvacı M, Çoker D, Aydın M, Dinçel B, Uzun N
(2011) Long-Term Variations and Periods of Mira Stars
fromROTSE–IIId.AcceptedbyNewAstronomy
Young AT, Genet RM, Boyd LJ, Borucki WJ, Lockwood
GW,HenryGW,HallDS,SmithDP,BaliumasSL,Don-
ahueR, EpandDH (1991)Precise automaticdifferential
stellar photometry. Astronomical Society of the Pacific
103:221–242