Table Of ContentSpringer Theses
Recognizing Outstanding Ph.D. Research
Kana Moriwaki
Large-Scale
Structure of the
Universe
Cosmological Simulations and Machine
Learning
Springer Theses
Recognizing Outstanding Ph.D. Research
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IndexedbyzbMATH.
Kana Moriwaki
Large-Scale Structure
of the Universe
Cosmological Simulations and Machine
Learning
Doctoral Thesis accepted by
The University of Tokyo, Tokyo, Japan
Author Supervisor
Dr.KanaMoriwaki Prof.NaokiYoshida
DepartmentofPhysics DepartmentofPhysics
SchoolofScience SchoolofScience
TheUniversityofTokyo TheUniversityofTokyo
Tokyo,Japan Tokyo,Japan
ResearchCenterfortheEarlyUniverse ResearchCenterfortheEarlyUniverse
SchoolofScience SchoolofScience
TheUniversityofTokyo TheUniversityofTokyo
Tokyo,Japan Tokyo,Japan
KavliInstituteforthePhysics
andMathematicsoftheUniverse(WPI)
UTInstitutesforAdvancedStudy
TheUniversityofTokyo
Tokyo,Japan
InstituteforPhysicsofIntelligence
SchoolofScience
TheUniversityofTokyo
Tokyo,Japan
ISSN 2190-5053 ISSN 2190-5061 (electronic)
SpringerTheses
ISBN 978-981-19-5879-3 ISBN 978-981-19-5880-9 (eBook)
https://doi.org/10.1007/978-981-19-5880-9
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature
SingaporePteLtd.2022
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Supervisor’s Foreword
The last decade has witnessed an enormous increase of observational data in
astronomy. Both ground-based and space-borne telescopes have been used exten-
sively to find interesting celestial bodies and to look closely at new phenomena
occurring in space. The observational frontier is also expanding rapidly, reaching
almost the beginning of the universe. An array of next-generation telescopes is to
come online in 2020s, which are expected tocollect and deliver multi-exabytes of
data.Analyzingthesheervolumeofdataischallenging,andefficientextractionof
scientificinformationisakeytaskinmodernastronomyandcosmology.
Lineintensitymapping (LIM)isanemergingobservational method thatallows
ustoprobeavastareaoftheskyquickly.ALIMtelescopecollectsphotonsemitted
fromvirtuallyallthesourcesinitsfieldofviewonthesky.Photonswithdifferent
energies(wavelengths)carryinformationontheiroriginandontheepochwhenthey
areemitted,andthusLIMcanbeusedtoreconstructathree-dimensionaldistribution
oftheastronomicalobjectssuchasgalaxiesandblackholes.Tothisend,however,
there remains a critical problem called line confusion, in which photons happen
tohavethesamewavelengthdespiteoriginatingfromdifferentsourcesatdifferent
distances.LineconfusioncancompromiseconsiderablythescientificmeritofLIM
observations.
Inthisthesis,theauthorDr.Moriwakiproposesaseriesofnovelmethodsbased
ondeeplearning,inordertoextractdesignatedsignalsfromLIMobservations.An
efficientgenerativemodelisdevelopedfordenoisingandfordeconfusingmultiple
emission lines. Deep learning offers a powerful solution for the kind of task, only
if it is trained with realistic data of appropriate quality and quantity. The author
thusdevisesanefficientmethodtogenerateanextremelylargenumberofsimulated
observationaldata.Realisticconfigurationswiththeexperimentalnoiseforaspace
missionandforegroundcontaminationareconsidered.Aftertrainingwiththelarge
datasetandextensiveoptimization,themachinethatconsistsofmany-layerneural
networkssuccessfullyreconstructsthree-dimensionaldistributionsofgalaxies.
AnumberofLIMobservationsareplannedinthecomingdecade,forwhichthe
dataanalysismethodintroducedinthisthesiscanreadilybeapplied.Uponsuccessful
reconstructionofthelarge-scalestructureoftheuniverse,weareabletoknowthe
v
vi Supervisor’sForeword
geometry and the contents of the universe more accurately, and we can also study
theformationandevolutionofgalaxiesovercosmictime.
Tokyo,Japan NaokiYoshida
June2022
Partsofthisthesishavebeenpublishedinthefollowingjournalarticles:
(cid:129)
Kana Moriwaki, Naoki Yoshida, Marius B. Eide, and Benedetta Ciardi. Cross-
correlation between the 21-cm signal and [O iii] emitters during early cosmic
reionization.MNRAS,489(2):2471–2477,October2019.
(cid:129)
Kana Moriwaki, Nina Filippova, Masato Shirasaki, and Naoki Yoshida. Deep
learning for intensity mapping observations: component extraction. MNRAS,
496(1):L54–L58,July2020.
(cid:129)
KanaMoriwaki,MasatoShirasaki,andNaokiYoshida.DeepLearningforLine
IntensityMappingObservations:InformationExtractionfromNoisyMaps.ApJ,
906(1):L1,January2021.
(cid:129)
Kana Moriwaki and Naoki Yoshida. Deep-learning Reconstruction of Three-
dimensional Galaxy Distributions with Intensity Mapping Observations. ApJ,
923(1):L7,December2021.
vii
Acknowledgments
Firstofall,Iwouldliketoexpressmysinceregratitudetomysupervisor,Prof.Naoki
Yoshida,forhiscontinuoussupportandencouragement.Hisinsightfulfeedbackand
ideasbroughtmyresearchtoahigherlevel.Ilearnedhowtoconductresearch,find
a new topic, write a paper, and give a presentation from him. Even when I was
depressedaboutmyresearchnotgoingwell,discussionswithhimhelpedmeregain
mymotivation.Healsogavemeopportunitiestomeetmanyresearchers,whichnot
onlymadeasignificantcontributiontothisthesisbutwillbeinvaluableinmyfuture
researchlife.
I thank Marius Berge Eide and Benedetta Ciardi for providing simulation data
andfruitfuldiscussiononcosmicreionization.IamgratefultoMasatoShirasakifor
giving many helpful comments on my research. He also taught me how to train a
machinelearningmodel.IwouldalsoliketothankYasuhiroImotoforhiscomments
onmachinelearning.IwouldliketoexpressmyappreciationtoAnastasiaFialkovfor
acceptingmeasaguestremotememberofhergroupattheInstituteofAstronomy,
Cambridge,andgivingmanyinsightfulcomments.
Ithankthecommitteemembersofthisdissertation,MasahiroTakada,Hideyuki
Tagoshi,SatoshiYamamoto,KentaHotokezaka,andYoshiyukiKabashima,fortheir
insightfulcomments.Ialsothankthefollowingpeople:IkkohShimizuandShigeki
Inoue,whotaughtmehowtoanalyzethesimulationdata;TohruNagao,AkioInoue,
Takuya Hashimoto, Yoichi Tamura, Hiroshi Matsuo, Yuichi Matsuda, and Yuichi
Harikane,whoseinsightandknowledgehelpedmetobuildthelineemissionmodel;
membersoftheEoRworkinggroupoftheJapanSKAConsortium,whosecomments
deepenedmyresearch.
Iwouldliketoexpressmygratitudetothefollowingpeople:KazumiKashiyama,
forhismanycommentsonmyresearchandcasualbutactuallykindencouragement;
Tilman Hartwig, for the fruitful discussion on machine learning and helping me
to use the GPU; Yuta Tarumi, for lots of fun discussions about research and other
things. I also thank all the other members of the University of Tokyo Theoretical
AstrophysicalGroupandResearchCenterfortheEarlyUniverseformanyfruitful
discussionsandgoodtimesweshared.Ireallyenjoyedmyfiveyearsasagraduate
ix
x Acknowledgments
studentverymuchwithyou.IwouldliketogiveaspecialthankstoRuiLanZhang
forherhelpinproofreadingthisthesis.
Finally, I deeply thank my parents, Katsumi and Kazunori, and husband, Ryo,
for their patience and encouragement. I would not have been able to continue my
researchwithoutthem.
IacknowledgethesupportfromJSPSKAKENHIGrantNumberJP19J21379,the
Advanced Leading Graduate Course for Photon Science (ALPS) of the University
ofTokyo,andtheJSRFellowship,theUniversityofTokyo.Iappreciatethesupport
fromFirstLightSchoolinSaoPaulo.Partofthecalculationsinthisworkwascarried
outonCrayXC50atCenterforComputationalAstrophysics,NationalAstronomical
ObservatoryofJapan.
