Explore the Universe with Gravitational Waves
I am joining Nevada center of Astrophysics (NCfA) at University of Nevada, Las Vegas, as a postdoctoral Fellow.
We searched for precessing black hole binaries in the third observing run using the GstLAL detection pipeline. Though we didn't find new events, this shows good promise to make new discoveries in the future.
The paper "Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA" has been published in Physics Review D.
The paper "Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA’s fourth observing run" has been published in Physics Review D.
The paper "Rapid localization and inference on compact binary coalescences with the Advanced LIGO-Virgo-KAGRA gravitational-wave detector network" has been published in Physics Review D.
The paper "Extension of the Bayesian searches for anisotropic stochastic gravitational-wave background with nontensorial polarizations" has been published in Physics Review D.
The paper "Method for removing signal contamination during significance estimation of a GstLAL analysis" has been published in Physics Review D.
We extended the Bayesian searches for anisotropic stochastic gravitational-wave background to include non-GR polarizations.
The paper "Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences" has been published in Physics Review D.
We developed a rapid parameter estimation for GW signals from a compact binary, using the reduced-order-quadrature technique.
The paper "pygwb: A Python-based Library for Gravitational-wave Background Searches" has been published in The Astrophysical Journal.
We developed a tool to remove contamination due to GW signals leaking into the background model of GstLAL.
We improved the detection algorithm of the GstLAL search pipeline towards the LVK's fourth observing run, leading to the 20% increased sensitivity to GW signals.
We summarized the performance of the GstLAL search pipeline during a mock-data campaign as a preparation for the LVK's fourth observing run.
We developed a formalism to unify the information about the signal probability of a GW event across multiple search pipelines.
We developed a Python-based library for isotropic gravitational-wave background search, "pygwb".
The paper "Bayesian formalism for parameter inference of anisotropic stochastic gravitational wave background" has been published in Physics Review D.
We summarized the specification and new development of GstLAL's template bank toward the LVK's fourth observing run.
I gave an informal talk about black holes, gravitational waves, and LIGO in the podcast "バイリンガルニュース".
I am pleased to announce that recently I have got promoted as Assistant Research Professor in the LIGO group at Penn State.
We developed a Bayesian formalism for parameter inference of anisotropic stochastic gravitational wave background.
I was invited to APS April meeting to talk about "Observation of neutron stars during LIGO-Virgo-KAGRA's observing runs".
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For the first time, LIGO and Virgo scientific collaboration discovered gravitational signals from two mergers of neutron-star black-hole binaries!!
I am Leo Tsukada, an Postdoctoral Fellow in Nevada Center of Astrophysics at University of Nevada, Las Vegas (UNLV). Previously, I was an Assistant Research Professor in Institute for Gravitation and the Cosmos at the Pennsylvania State University. I received my Ph.D in Physics from the University of Tokyo (RESCEU). I am also a member of LIGO Scientific Collaboration and my research interest is mainly on the data analysis of the gravitational waves.
In particular, I have been interested in two kinds of gravitational wave signals; the low-latency detection of the gravitational waves from compact binary coalescences and the stochastic gravitational-wave background. Please come to Research page for more details.
I work closely with a LIGO group led by Prof. Carl-Johan Haster at UNLV, where we work on various projects related to gravitational-wave detection and inference.
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