the challenges analysing gaia time seriescschafer/scma6/eyer.pdf · epoch photometric precision...
TRANSCRIPT
ThechallengesanalysingGaiaTimeSeries1
NamiMowlavi1,DafyddW.Evans2,BerryHoll1,LorenzoRimoldini1,AlessandroLanzafame3,LeanneGuy1,ShayZucker4,BrandonTingley5,IsabelleLecoeur-Taïbi1,MaroussiaRoelens1,JanCuypers6,JorisDeRidder7,SaraRegibo7,ManuelLópez8,JonasDebosscher7,MariaSüveges1,LuisSarro9,GisellaClemenXni10,SilvioLeccia11,VincenzoRipepi11,FabioBarblan1,AndréMoiXnho12,KrzysztofNienartowicz1,DiegoOrdoñez-Blanco1,JonathanCharnas1,GrégoryJévardatdeFombelle1
1DepartmentofAstronomy,UniversityofGeneva,Versoix,Switzerland2InsXtuteofAstronomy,UniversityofCambridge,Cambridge,UnitedKingdom3DiparXmentodiFisicaeAstronomia,UniversitadiCatania,Catania,Italy4DepartmentofGeosciences,TelAvivUniversity,TelAviv,Israel5InsXtutforFysikogAstronomi,AarhusUniversitet,Aarhus,Denmark6RoyalObservatoryofBelgium,Brussels,Belgium7InsXtuutvoorSterrenkunde,KULeuven,Leuven,Belgium8CentrodeAstrobiologia,DepartamentodeAstrofisica,VillanuevadelaCanada,Spain9DepartamentodeInteligenciaArXficial,UNED,Madrid,SpainI10INAFOsservatorioAstronomicodiBologna,Bologna,Italy11INAF-OsservatorioAstronomicodiCapodimonte,Napoli,Italy12FaculdadedeCienciasdeUniversidadedeLisboa,Lisboa,Portugal
Sta3s3calChallengesinModernAstronomyCarnegieMellonUniversity,Pi=sburgh,USAWednesdayJune8,2016
Laurent Eyer
2021
Introduc3on:PtolemyvsHipparcosvsGaiacatalogues
>>10kilometersofbooks!
1997
Courtesy of Berry Holl
AlmagestPtolemy~150ADHereed.of1528
Hipparcoscatalogueinmyoffice
LBV
Stars
AGN
Stars
Asteroids
RotationEclipse
Microlensing CataclysmicEruptive PulsationSecular
Novae
(DAV) H-WDs
Variability Tree
Extrinsic Intrinsic
NSupernovae
SN
Symbiotic
ZAND
Dwarf novae
UG
Eclipse
Asteroid occultation
Eclipsing binary
Planetary transits
EA
EB
EW
Rotation
ZZ CetiPG 1159
Solar-like
(PG1716+426, Betsy)long period sdB
V1093 Her
(W Vir)Type II Ceph.
δ Cepheids
RR Lyrae
CWCEP
RRCredit : L. Eyer & N. Mowlavi (03/2009)
δ Scuti
γ Doradus
Slowlypulsating B stars
α Cygni
β Cephei
λ Eri
SX Phoenicis
SXPHE
Hot OB Supergiants
ACYG
BCEP
SPB
SPBe
GDOR
DST
PMSδ Scuti
roAp
Miras
Irregulars
Semi-regulars
M
SRL
RV
SARVSmall ampl. red var.
(DO,V GW Vir)He/C/O-WDs
PV TelHe star
Be stars
RCB
GCASFU
UV Ceti
Binary red giants
α2 Canes VenaticorumMS (B8-A7) withstrong B fields
SX ArietisMS (B0-A7) withstrong B fields
Red dwarfs(K-M stars)
ACV
BY Dra
ELL
FKCOMSingle red giants
WR
SXA
β Per, α Vir
RS CVn
PMS
S Dor
Eclipse
(DBV) He-WDs
V777 Her
(EC14026)short period sdB
V361 Hya
RV Tau
Photom. PeriodFG SgeSakurai,
V605 Aql
R Hya (Miras)δ Cep (Cepheid)
DY Per
GoalofVariabilityProcessingandAnalysisGaiameasurementsforsource“i”:
AstrometrySpectro/PhotometricXmesseries
Spectra ⎬
?? UKNOWN ??
ChallengesofGaiaintermsof3meseriesanalysis
•Howtodealwithlargedataset?(e.g.reshuffledatatogetXmeseriespersource)
•HowtogatherqualitaXvelydifferentquanXXes?(astrometry,photometry,spectra)
•Howtohandledifferentnumberofmeasurements(from40to250),sparseanddifferentsamplings?
•HowtodealwithheteroscedasXcdata?
•HowtodowhentherearepooresXmatesofuncertainXes?
•Howtosearchfor“small”signalsin”noisy"data?
•HowtoperformmodelselecXon?
•Howtocomputethesignificanceofpeaksinaperiodogram
•Howtodealwiththealiasingproblem
•Howtoclassifyvariableobjects?
•Howdowecrossmath?
•Howtorankobjects?
•etc…
Fiveyearnominalscanninglaw(NSL)
FullscanninglawmovieonYouTube:h=ps://www.youtube.com/watch?v=lRhe2grA9wE
Courtesy of Berry Holl
180-180
Fiveyearnominalscanninglaw(NSL)
FullscanninglawmovieonYouTube:h=ps://www.youtube.com/watch?v=lRhe2grA9wE
-180 180
Epochphotometricprecision
Greenpoints:currentactualscauerinindividualconstantsourceXmeseries
Purplestars:requirementsonGaiaperformancewebsiteBlackline:pre-launchesXmateredline:esXmateswithstray-light
Courtesy of Dafydd W.Evans
Pragma3capproach
•Developa“method”
- Testonsimulateddata(ovenverysimple)
- Testonrealdata
•ApplyittorealGaiadata
•Starton“easy“signal,e.g.largesignaltonoiseraXo
•Startwithlawnumbersofobjects
•Interactwiththedata(subsamples)atalllevelsoftheanalysis
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
1billionsourcesobservedbyGaia
Timeseriesof70(40-250)measurementsover5years790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
Selec3onofsourcesobservedbyGaiafromEclip3cPoleScanningLaw(790,000sources)
(Equatorial coordinates, deg)
~28days
180-180
90
-90
Warning:Samplingregular,differentfromNominalScanninglaw
SouthEclip3cPoleregion(partofLargeMagellanicCloud):Gaiaandothersurveys
(Equatorial coordinates, deg)
TogetthedataflavourComparisonwithOGLE
GAIA OGLE
ImageoftheWeek(March05,2015):RRLyraestars
Credits:ESA/Gaia/DPAC/CU5/CU7/INAF-OABo,GisellaClemen@ni,DafyddEvans,LaurentEyer,KrzysztofNienartowicz,LorenzoRimoldiniandthe
GenevaCU7/DPCGandCU7/INAF-OACNteams.
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
GeneralVariabilityDetec3on
5 95
80 20
CONSTANT
VARIABLE
VARIABLE
CONSTAN
T
Contamination 8 13
546
376
Classifierresult:Theconfusionmatrix
Threshold of
DetecXonwasdonewithaclassifier(RandomForest)auributeswerecomputedatrainingsetwasdefined(basedonOGLE)
TwofundamentalquanXXestoesXmate:—Completeness—Contamina3on
ClassicalhypothesistesXngcan’tbeappliedbecauseof“poor”esXmatesofuncertainXes
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
Characterisa3on
Goal: To define attributes
• statistical parameters
• Modelling
—Period search
—Fourier Series and polynomial fit
Time series per object:
Time(i), G-, BP-, RP- mag(i) [ or radial velocity(i) ]
i=1,…, number of measurements
0.0 0.2 0.4 0.6 0.8 1.0
20.0
19.6
19.2
18.8
4659728285679659008, P: 0.5152 d, Dist: 0.55, 0.75, 0.09
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.020.0
19.6
19.2
18.8
4659764878786343424, P: 0.4616 d, Dist: 0.67, 0.78, 0.1
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
19.6
19.2
18.8
18.4 4659864144078321920, P: 0.5294 d, Dist: 0.55, 0.82, 0.11
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
19.4
19.0
18.6
4659759621733746304, P: 0.1548 d, Dist: 0.39, 0.66, 0.11
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
20.0
19.6
19.2
18.8
4659879846459603840, P: 0.5591 d, Dist: 0.62, 0.81, 0.11
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
19.2
19.0
18.8
4660473479656479488, P: 0.1208 d, Dist: 0.65, 0.91, 0.12
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
19.8
19.4
19.0
18.6 5284191241736438912, P: 0.5201 d, Dist: 0.73, 0.91, 0.12
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
0.0 0.2 0.4 0.6 0.8 1.0
19.4
19.0
18.6
5284412140479472640, P: 0.6449 d, Dist: 0.53, 0.87, 0.13
Phase
Mag
nitu
de
1670
1675
1680
1685
1690
1695
1700
Phases
mag
nitu
des
times
(BJD
in T
CB−
2455
197.
5)
Phase Phase
Characterisa3on:fewexamplesofmodelling
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
Classifica3on
MulXstagetree:Bayesiannetworks
MulXstagetree:Gaussianmixture RandomForest
Supervisedclassifica3on(severalmethods):
TreeforGaussianMixture:Furnishtrainingset
builtfromCrossmatcheddata
Classifica3onConfusionmatrixofRandomForestusingcross-matcheddata(OGLE,Hipparcos,AAVSO,Milliquas)
10050 5014 29 29 2925 12 6230 19 7 30 153 1 2 9413 4 12 69 2 14 3 84 7 1 1 17 88 2 379 8 5 9
UGDCEPDSCTELLLPV
RRLYROTHER
ECLQSO
CONSTANT
CONSTAN
T
QSO
ECL
OTH
ER
RRLYR
LPV
ELL
DSC
T
DCEP
UG
Contamination 34 20 19 34 5 43 100 − − −227827106108135100103
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
Classifica3onofRRLyraeandCepheidstarsGisellaClemenXni,SilvioLeccia,VincenzoRipepi,NamiMowlavi,IsabelleLecoeur
Credits: ESA/Gaia/DPAC/CU5/DPCI/CU7/INAF-OABo/INAF-OACn Gisella Clementini, Vincenzo Ripepi, Silvio Leccia, Laurent Eyer, Lorenzo Rimoldini, Isabelle Lecoeur-Taibi, Nami Mowlavi, Dafydd Evans, Geneva CU7/DPCG and the whole CU7 team. The photometric data reduction was done with the PhotPipe pipeline at DPCI; processing data were received from the IDT pipeline at DPCE.
ClassicalovertoneCepheid3candidateanomalousCepheidsType2Cepheid
SpecificObjectStudies:EclipsingbinariesEclipsingbinariesgotoadedicatetreatment(UniversitéLibredeBruxelles)forafullmodelling
Here,simplemodelling(fitwith“twoGaussians”)aremadeThesoluXonsenablearanking
Highestrank
Phase
G m
ag
Lowestrank
SpecificObjectStudies:EclipsingbinariesG
mag
Phase
GaiaVariabilityProcessingandAnalysis
General Variability Detection (GVD)
Characterization
Classification
Specific Object Studies (SOS)
Special Variability Detection (SVD)
Ast
rom
etri
c ch
ar (
CU
3+C
U4)
Spec
tros
copi
c ch
ar (
CU
6)A
stro
phys
ical
par
am (
CU
8)
Glo
bal
Vari
ab
ilit
y S
tud
ies
(GV
S)
Calibrated photometry (CU5)Radial velocities (CU6)
Vari
able
s ca
talo
gue
(CU
7)
Unexpected Features Analyses Supplementary Observations
790,000REALGaiadatafromEclip3cPoles
Timeseriesupto170measurementsover28days
Persourceprocessing
Globalprocessing
GlobalVariabilitystudies
Courtesy of L. Sarro (modified by L.Eyer)
Log (frequency [1/day])
Log(
Ampl
itude
[G &
I m
ag])
Comparisonofdistribu3onfunc3onsofRRLyraestars
Thedatareleases
Release1:
‣ αandδ,meanG-magnitudefor1billionstars
‣ 100Kpropermo3onstars(Hipparcos+Gaia)
nominalmissionend
extendedmissionend?
Release2:
‣ 5-parameterastrometricsolu3onsforsinglestar(parallax)
‣ IntegratedBP/RP+Astrophysicalparameters
‣ MeanVrad(fornonvariable)
spacecraft operationsstart
2014 20222021202020192018201720162015
Release3:
‣ MeanVrad
‣ ObjectclassificaXonsandAstrophysicalParameters
‣ MeanRVS,BP,RPspectra
‣ Orbitalsolu3onofbinaries
Release4:
‣ Variablestarsclassifica3on‣ Non-singlestarcatalogue
‣ Solarsystemobjects
Finalrelease:
‣ everything!
‣ +Groupsofvariables:RRLyraestars&Cepheids(~3,300stars)
Courtesy of B.Holl
‣ +TGAS:Tycho-Gaiaastrometricsolu3on(2millionstars)
‣ +Allsky:RRLyraestars&Cepheids&ShortTimescalevariables+?(50,000-100,000stars?)
Datareleasecaveats/limita3ons• Allsourceshavebeentreatedassinglestars
• Somefilteringhasbeenapplied,e.g.;-OmitsourceswithtoofewobservaXons,withoutastrometryand/orphotometry,withveryelongatedposiXonalerrorellipses,…-Upperlimitonerrorsinparallax,posiXon,andphotometry
• Nohigh-proper-moXonstars(μ>3.5arcsec/year)
• Variousunmodeledeffectslevinthedata(chromaXcity,CTI,micro-meteoroidhits,micro-clanks,…)
• Basic-angle-variaXoncorrecXonderivedfromon-boardmetrology
• Cross-matchinglimitedbycrudea{tude,IGSL,andspurioussources
• Cyclicprocessingloopsnotyetclosed
• Alloftheseissueswillbeaddressedinupcomingreleases!
Warning:theaboveweaknesseswillleadtospaXally-correlatedsystemaXcsinDR1:donotblindlyaverageastrometricquanXXes
Courtesy of Jos de Bruijne