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  1. Abstract

    We present a measurement of the Hubble ConstantH0using the gravitational wave event GW190412, an asymmetric binary black hole merger detected by LIGO/Virgo, as a dark standard siren. This event does not have an electromagnetic counterpart, so we use the statistical standard siren method and marginalize over potential host galaxies from the Dark Energy Spectroscopic Instrument (DESI) survey. GW190412 is well-localized to 12 deg2(90% credible interval), so it is promising for a dark siren analysis. The dark siren value forH0=85.433.9+29.1km s−1 Mpc−1, with a posterior shape that is consistent with redshift overdensities. When combined with the bright standard siren measurement from GW170817 we recoverH0=77.965.03+23.0km s−1 Mpc−1, consistent with both early and late-time Universe measurements ofH0. This work represents the first standard siren analysis performed with DESI data, and includes the most complete spectroscopic sample used in a dark siren analysis to date.

     
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  2. Free, publicly-accessible full text available April 25, 2024
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  6. In this paper, we address the Online Unsupervised Domain Adapta- tion (OUDA) problem, where the target data are unlabelled and ar- riving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data. We pro- pose a multi-step framework for the OUDA problem, which insti- tutes a novel method to compute the mean-target subspace inspired by the geometrical interpretation on the Euclidean space. This mean- target subspace contains accumulative temporal information among the arrived target data. Moreover, the transformation matrix com- puted from the mean-target subspace is applied to the next target data as a preprocessing step, aligning the target data closer to the source domain. Experiments on four datasets demonstrated the con- tribution of each step in our proposed multi-step OUDA framework and its performance over previous approaches. 
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