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Whansun Kim

Publications and source records attributed to Whansun Kim.

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A superconducting tensor detector for mid-frequency gravitational waves: its multi-channel nature and main astrophysical targets

Mid-frequency band gravitational-wave detectors will be complementary for the existing Earth-based detectors (sensitive above 10 Hz or so) and the future space-based detectors such as LISA, which will be sensitive below around 10 mHz. A ground-based superconducting omnidirectional gravitational radiation observatory (SOGRO) has recently been proposed along with several design variations for the frequency band of 0.1 to 10 Hz. For three conceptual designs of SOGRO (e.g., pSOGRO, SOGRO and aSOGRO), we examine their multi-channel natures, sensitivities and science cases. One of the key characteristics of the SOGRO concept is its six detection channels. The response functions of each channel are calculated for all possible gravitational wave polarizations including scalar and vector modes. Combining these response functions, we also confirm the omnidirectional nature of SOGRO. Hence, even a single SOGRO detector will be able to determine the position of a source and polarizations of gravitational waves, if detected. Taking into account SOGRO's sensitivity and technical requirements, two main targets are most plausible: gravitational waves from compact binaries and stochastic backgrounds. Based on assumptions we consider in this work, detection rates for intermediate-mass binary black holes (in the mass range of hundreds up to $10^{4}$ $M_\odot$) are expected to be $0.0014-2.5 \,\, {\rm yr}^{-1}$. In order to detect stochastic gravitational wave background, multiple detectors are required. Two aSOGRO detector networks may be able to put limits on the stochastic background beyond the indirect limit from cosmological observations.

gr-qc

Generating Event Triggers Based on Hilbert-Huang Transform and Its Application to Gravitational-Wave Data

We present a new event trigger generator based on the Hilbert-Huang transform, named EtaGen ($η$Gen). It decomposes a time-series data into several adaptive modes without imposing a priori bases on the data. The adaptive modes are used to find transients (excesses) in the background noises. A clustering algorithm is used to gather excesses corresponding to a single event and to reconstruct its waveform. The performance of EtaGen is evaluated by how many injections in the LIGO simulated data are found. EtaGen is viable as an event trigger generator when compared directly with the performance of Omicron, which is currently the best event trigger generator used in the LIGO Scientific Collaboration and Virgo Collaboration.

astro-ph.IM