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