The needle in the hundred square degree haystack: The hunt for binary neutron star mergers with LIGO and Palomar Transient Factory

14.5 MB·English

by  Leo P. Singer

The needle in the 100 deg2 haystack:
The hunt for binary neutron star mergers with LIGO and
Palomar Transient Factory
Thesisby
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LeoP.Singer
AllRightsReserved
iii
Totheloveofmylife,mywifeKristin,andourprecioussonIsaac.
Baconinhisinstructiontellsusthatthescientificstudentoughtnottobe
astheant,whogathersmerely,norasthespiderwhospinsfromher
ownbowels,butratherasthebeewhobothgathersandproduces. All
thisistrueoftheteachingaffordedbyanypartofphysicalscience.
Electricityisoftencalledwonderful,beautiful;butitissoonlyin
commonwiththeotherforcesofnature. Thebeautyofelectricityorof
anyotherforceisnotthatthepowerismysterious,andunexpected,
touchingeverysenseatunawaresinturn,butthatitisunderlaw,and
thatthetaughtintellectcanevennowgovernitlargely. Thehumanmind
isplacedabove,andnotbeneathit,anditisinsuchapointofviewthat
thementaleducationaffordedbyscienceisrenderedsuper-eminentin
dignity,inpracticalapplicationandutility;forbyenablingthemindto
applythenaturalpowerthroughlaw,itconveysthegiftsofGodtoman.
MichaelFaraday,Lecturenotesof1858,quotedinTheLifeandLettersof
Faraday(1870)byBenceJones,Vol. 2,p. 404
iv
Acknowledgments
Thisis LIGO DocumentNumber LIGO-P1400223-v10. Icarriedouttheworkpresentedinthis
thesiswithinthe LIGO ScientificCollaboration(LSC)andtheIntermediatePalomarTransient
Factory (iPTF) collaboration. The methods and results I present are under review and are
potentiallysubjecttochange. Theopinionsexpressedherearemyownandnotnecessarilythose
ofthe LSC or iPTF.
IgratefullyacknowledgefundingfromtheUnitedStatesNationalScienceFoundation(NSF)
fortheconstructionandoperationofthe LIGO Laboratory,whichprovidedsupportforthiswork.
LIGO wasconstructedbytheCaliforniaInstituteofTechnologyandMassachusettsInstituteof
Technologywithfundingfromthe NSF andoperatesundercooperativeagreementPHY-0107417.
Ithankthe NSF forsupportingmyresearchdirectlythroughaGraduateResearchFellowship.
This work is based on observations obtained with the Palomar 48-inch Oschin telescope and
the robotic Palomar 60-inch telescope at the Palomar Observatory as part of the Intermediate
Palomar Transient Factory project, a scientific collaboration among the California Institute of
Technology,LosAlamosNationalLaboratory,theUniversityofWisconsin,Milwaukee,theOskar
KleinCenter,theWeizmannInstituteofScience,theTANGOProgramoftheUniversitySystemof
Taiwan,andtheKavliInstituteforthePhysicsandMathematicsoftheUniverse. Theworkinthis
thesis is partly funded by Swift Guest Investigator Program Cycle 9 award 10522 (NASA grant
NNX14AC24G)andCycle10award10553(NASAgrantNNX14AI99G).
Thankyou,Mom,thankyouDad,foranupbringingfulloflove,learning,andloveoflearning.
Thankyou,mywifeKristin,thankyou,mysonIsaac,foryourloveandforyourpatiencewith
me.
v
Thankyou,SusanBates,foryourtutoringinproblemsolvingthathasresonatedwithmefrom
elementaryschoolthrougheverydayofmyscientificcareer.
Thankyou,JohnJacobson,AmandaVehslage,TambraWalker,andDr. PhilipTerry-Smith,for
themostinspiringcoursesinmyhighschooleducation,andformoldingmeintoaresponsible
andwell-roundedindividual.
Thankyou,Profs. LuisOrozcoandBetsyBeise,foryourmentoringandfriendshipaswellas
theUniversityofMarylandundergraduatephysicscoursesthatIenjoyedsomuch. Thankyoufor
initiatingmeintophysicsresearch,andforsendingmetograduateschoolsowellprepared.
Thankyou,Prof. AlanWeinstein,forbeinganoutstanding(and,whennecessary,forbearing)
thesis advisor, for engineering the many wonderful collaborations that I have been a part of at
Caltech,andforshowingmehowtothrivewithinaBigScienceexperiment.
Thankyou,Prof. ShriKulkarni,forrecruitingmeinto PTF,forengineeringatotallyoriginal
cross-disciplinaryresearchopportunityinphysicsandastronomy,andforplacingtrustinme. I
amcontinuallyinaweofhowthattrusthaspaidoff. Ithankmycolleaguesin PTF forwelcoming
meintotheirhighlycapableandexcitingteam.
Thank you, Prof. Christian Ott, for teaching me two formative courses. I was able to write
BAYESTAR,mygreatestcontributionsofartoAdvanced LIGO,onlybecausethelatterofthese
courses(Ay190: ComputationalAstrophysics)wasfreshinmyhead.
Thank you, Prof. David Reitze, for making me feel like the success of Advanced LIGO
dependsuponme. (Ithinkthatyouinspirethatsamefeelingineveryoneat LIGO Laboratory.)
Thank you, Rory Smith, my officemate, for ducking good-naturedly whenever I wanted to
chuck a chair out the window of 257 West Bridge. (Despite many strong oaths, no chairs were
actuallychuckedduringthewritingofthisthesis.)
Thankyou,NickFotopoulos,LarryPrice,BradCenko,andMansiKasliwal,foryourcollabora-
tionandfriendshipthroughoutmystudies,friendshipsthatIhopetokeepandtonurture.
vi
Abstract
The Advanced LIGO and Virgo experiments are poised to detect gravitational waves (GWs)
directlyforthefirsttimethisdecade. Theultimateprizewillbejointobservationofacompact
binarymergerinbothgravitationalandelectromagneticchannels. However, GW skylocations
that are uncertain by hundreds of square degrees will pose a challenge. I describe a real-time
detectionpipelineandarapidBayesianparameterestimationcodethatwillmakeitpossibleto
searchpromptlyforopticalcounterpartsinAdvanced LIGO. Havinganalyzedacomprehensive
population of simulated GW sources, we describe the sky localization accuracy that the GW
detector network will achieve as each detector comes online and progresses toward design
sensitivity. Next, in preparation for the optical search with the iPTF, we have developed a
unique capability to detect optical afterglows of gamma-ray bursts (GRBs) detected by the
Fermi Gamma-ray Burst Monitor (GBM). Its comparable error regions offer a close parallel
to the Advanced LIGO problem, but Fermi’s unique access to MeV–GeV photons and its near
all-sky coverage may allow us to look at optical afterglows in a relatively unexplored part of
the GRB parameter space. We present the discovery and broadband follow-up observations
(X-ray, UV, optical, millimeter, and radio) of eight GBM–iPTF afterglows. Two of the bursts
(GRB 130702A / iPTF13bxl and GRB 140606B / iPTF14bfu) are at low redshift (z = 0.145 and
z = 0.384, respectively), are sub-luminous with respect to “standard” cosmological bursts, and
havespectroscopicallyconfirmedbroad-linetypeIcsupernovae. Thesetwoburstsarepossibly
consistentwithmildlyrelativisticshocksbreakingoutfromtheprogenitorenvelopesratherthan
thestandardmechanismofinternalshockswithinanultra-relativisticjet. Onatechnicallevel,the
GBM–iPTF effortisaprototypeforlocatingandobservingopticalcounterpartsof GW eventsin
Advanced LIGO withtheZwickyTransientFacility.
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Contents
Acknowledgments iv
Abstract vi
1 TheroadtoAdvanced LIGO 1
1.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Aimsofthisthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Rangeandskyresolutionof GW detectornetworks 14
2.1 Basicmatchedfiltersearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Measuresofdetectorsensitivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3 Fisherinformationmatrix: singledetector . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Independenceofintrinsicandextrinsicerrors . . . . . . . . . . . . . . . . . . . . . . 22
2.5 Interpretationofphaseandtimeerrors . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 Positionresolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.6.1 Marginalizationovernuisanceparameters . . . . . . . . . . . . . . . . . . . . 29
2.6.2 Spatialinterpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.6.3 Outlineofcalculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.4 ExamplecalculationforHLVnetwork . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.5 Improvementinlocalizationduetocoherence . . . . . . . . . . . . . . . . . . 32
2.6.6 Revisionto LIGO observingscenariosdocument . . . . . . . . . . . . . . . . 37
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
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3 Earlywarning GW detection 41
3.1 Prospectsforearly-warningdetectionand EM follow-up. . . . . . . . . . . . . . . . 45
3.2 Novelreal-timealgorithmfor CBC detection. . . . . . . . . . . . . . . . . . . . . . . 49
3.2.1 Discrete-timerepresentationofamatchedfilter . . . . . . . . . . . . . . . . . 49
3.2.2 The LLOID method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2.2.1 Selectivelyreducingthesamplerateofthedataandtemplates . . . 53
3.2.2.2 Reducingthenumberoffilterswiththe SVD . . . . . . . . . . . . . 54
3.2.2.3 Early-warningoutput . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2.3 Comparisonofcomputationalcosts . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2.3.1 Conventional TD method . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2.3.2 Conventional FD method . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2.3.3 LLOID method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.3.4 Speedupof LLOID relativeto TD method . . . . . . . . . . . . . . 59
3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.1 Planningstage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.2 Filteringstage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4.2 Measured SNR loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4.3 Otherpotentialsourcesof SNR loss . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.4 Lowerboundsoncomputationalcostandlatencycomparedtoothermethods 70
3.4.5 ExtrapolationofcomputationalcosttoanAdvanced LIGO search . . . . . . 70
3.4.6 Measuredlatencyandoverhead . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 BAYESTAR: RapidBayesianskylocalizationof BNS mergers 76
4.1 Bayesianprobabilityandparameterestimation . . . . . . . . . . . . . . . . . . . . . . 78
4.2 The BAYESTAR likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.3 Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.4 Priorandproblemsetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.5 Marginalposterior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
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4.5.1 Integraloveranglesandtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.5.2 Integraloverdistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.5.2.1 AdaptiveGaussianquadraturemethod . . . . . . . . . . . . . . . . 87
4.5.2.2 FixedorderGaussianquadraturemethod . . . . . . . . . . . . . . . 90
4.6 Adaptivemeshrefinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.7 Runtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.8 Casestudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5 Thefirsttwoyearsof EM follow-upwithAdvanced LIGO andVirgo 96
5.1 Sourcesandsensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.1.1 Observingscenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.1.2 Simulatedwaveforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.1.3 Sensitivitytoassumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.1.4 Sourcelocations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.1.5 Dutycycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2 Detectionandpositionreconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2.1 Templatewaveforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.2 Detectionthreshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.3 Skylocalizationandparameterestimation . . . . . . . . . . . . . . . . . . . . 104
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.3.1 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.3.2 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.4.1 Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.4.2 Detectionscenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.4.3 Comparisonwithotherstudies . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6 Discovery and redshift of an optical afterglow in 71 square degrees: iPTF13bxl and
GRB 130702A 127
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.2 Discovery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
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6.3 Broadbandphotometricfollow-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.4 Opticalspectroscopyandhostgalaxyenvironment . . . . . . . . . . . . . . . . . . . 135
6.5 GRB 130702Aincontext . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7 Fermi, iPTF,andthe GRB–supernovaconnection 141
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
7.2 Searchmethodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.2.1 Automated TOO Marshal: alertsandtiling . . . . . . . . . . . . . . . . . . . 145
7.2.2 Triggeringthe P48 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
7.2.3 Automatedcandidateselection . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7.2.4 VisualscanninginTreasuresPortal . . . . . . . . . . . . . . . . . . . . . . . . 149
7.2.5 ArchivalvettingintheTransientMarshal . . . . . . . . . . . . . . . . . . . . . 151
7.2.6 Photometric,spectroscopic,andbroad-bandfollow-up . . . . . . . . . . . . . 152
7.2.7 Long-termmonitoringanddatareduction . . . . . . . . . . . . . . . . . . . . 154
7.3 The GBM–iPTF bursts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
7.3.1 GRB 130702A/iPTF13bxl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
7.3.2 GRB 131011A/iPTF13dsw . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
7.3.3 GRB 131231A/iPTF13ekl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7.3.4 GRB 140508A/iPTF14aue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
7.3.5 GRB 140606B/iPTF14bfu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
7.3.6 GRB 140620A/iPTF14cva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
7.3.7 GRB 140623A/iPTF14cyb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
7.3.8 GRB 140808A/iPTF14eag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
7.4 Thepopulationincontext . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
7.4.1 Selectioneffects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
7.4.2 GRBs asstandardcandles? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
7.4.3 Shockbreakout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
7.5 Lookingforward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
8 Conclusion 194
8.1 Nextsteps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195