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doctoral thesis
Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:820088
Zečević, Petar
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Institutional repository: FER Repository

Cite this document

Zečević, P. (2023). Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:820088

Zečević, Petar. "Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:820088

Zečević, Petar. "Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:820088

Zečević, P. (2023). 'Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:820088

Zečević P. Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2025 January 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:820088

P. Zečević, "Fast distributed cross-matching of big astronomical data and parameter estimation of moving point source model", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:820088

Metadata
TitleFast distributed cross-matching of big astronomical data and parameter estimation of moving point source model
Title (english)Brzo raspodijeljeno uparivanje velikih astronomskih podataka i procjena parametara modela gibajućega točkastog izvora
AuthorPetar Zečević
MentorSven Lončarić (mentor)
MentorMario Jurić https://orcid.org/0000-0003-1996-9252 (mentor)
Committee memberMarko Subašić (predsjednik povjerenstva)
Committee memberZvonko Kostanjčar (član povjerenstva)
Committee memberVernesa Smolčić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronic Systems and Information Processing)
Zagreb
Defense date and country2023-11-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
The thesis develops two original methods: “Distributed Zones Algorithm” and “Online Multifit”. “Distributed Zones Algorithm” is a zone-based method for fast distributed crossmatching of big astronomical data. It is developed based on the Zones Algorithm and comprises a distributed data organization scheme and an efficient method of positionally joining data using a moving window. The algorithm is implemented within a system called AXS (Astronomical Extensions for Spark), based on Apache Spark and extended with astronomy-specific functionalities. The system’s cross-matching performance has been extensively tested in single-machine and “cloud” environments. “OnlineMultifit” is a method for parameter estimation of a moving point-source model based on sequential Bayesian updating. It was developed by extending the Multifit method. OnlineMultifit utilizes Bayesian statistics to obtain the full posterior probability distribution of moving point source model parameters but it does so by processing one image at a time using “sequential updating” technique, updating the posteriors after each image. The method approximates posteriors as mixtures of Gaussians and the main challenge here is to reduce the errors introduced by these approximations. The final result is that the method uses less computational resources while obtaining full posterior estimates. Furthermore, the estimates can be stored in a compressed form (the means and covariances of Gaussian distributions).
Abstract (english)
Disertacijom se razvijaju dvije originalne metode: “algoritam baziran na distribuiranim zonama” i “slijedni Multifit”. “Algoritam baziran na distribuiranim zonama” je metoda za brzo distribuirano uparivanje velikih astronomskih podataka, razvijena na temelju “algoritma baziranom na zonama”. Metoda se sastoji od sheme za distribuiranu organizaciju podataka i efikasne metode za poziciono uparivanje podataka pomoću pomičnog prozora. Algoritam je implementiran unutar sustava nazvanog AXS (eng. Astronomical Extensions for Spark), baziranog na Apache Spark platformi koja je proširena funkcionalnostima specifičnim za astronomiju. Performanse sustava u uparivanju detaljno su testirane na jednoj mašini te u okolini “u oblaku”. “Slijedni Multifit” je metoda za estimaciju parametara modela gibajućeg točkastog izvora temeljena na slijednom Bayesovom osvježavanju. Razvijena je nadogradnjom Multifit metode. Slijedni Multifit koristi Bayesovu statistiku za dobivanje pune posteriorne distribucije vjerojatnosti parametara modela gibajućeg točkastog izvora, no to čini obradom jedne po jedne slike, metodom “slijednog osvježavanja”, osvježavajući posterior nakon svake slike. Metoda aproksimira posteriore kao skup Gaussovih distribucija te joj je glavni izazov u smanjenju grešaka koje takva aproksimacija unosi. Krajnji je rezultat taj da metoda koristi manje računalnih resursa, a procjenjuje potpune posteriorne distribucije vjerojatnosti. Štoviše, procjene se mogu spremiti u kompresiranoj formi (u obliku središnjih vrijednosti i kovarijanci Gaussovih distribucija vjerojatnosti).
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:820088
Promotion2023
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extent101 str. : graf. prikazi u boji ; 30 cm
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-05-10 13:52:00