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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

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🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

Deep Learning-based Search for  Lensed Gravitational-Wave Signals from Binary Black Hole Events in GWTC-1 and -2

MINDS SEMINAR

period : 2022-07-11 ~ 2022-07-11

time : 13:00:00 ~ 14:00:00

개최 장소 : Math Bldg 404 & Online streaming (Zoom)

Topic : Deep Learning-based Search for  Lensed Gravitational-Wave Signals from Binary Black Hole Events in GWTC-1 and -2

개요

Date	2022-07-11 ~ 2022-07-11	Time	13:00:00 ~ 14:00:00
Speaker	Kyungmin Kim	Affiliation	Ewha Womans University
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Deep Learning-based Search for  Lensed Gravitational-Wave Signals from Binary Black Hole Events in GWTC-1 and -2
Contents	We present the result of a deep learning-based search for beating patterns, one of the gravitational lensing signatures, from the observed gravitational-wave signals. In this search, we examine the binary black hole events in the first and second gravitational-wave transient catalogs. This search is the first endeavor utilizing deep learning for searching lensing signatures from gravitational waves. Specifically, the search identifies beating patterns induced by lenses with masses between 10^3--10^5M⊙ from spectrograms of gravitational-wave signals. We train a deep learning model with spectrograms of simulated noisy gravitational-wave signals to classify the binary black hole events into two classes, lensed or unlensed signals. We introduce an ensemble learning with the deep learning model and employ a majority voting strategy for the predictions of all ensemble learners. The majority voting-based primary classification classifies one event, GW190707_093326, out of forty-six events, into the lensed class. However, upon estimating the p-value of the event, we observe that the uncertainty of p-value still includes the possibility of the event being unlensed. Therefore, we conclude the gravitational-wave signal of GW190707_093326 is likely an unlensed signal and, consequently, our search finds no significant evidence of beating patterns from the evaluated binary black hole events.