Sky location of Galactic white dwarf binaries in space-based gravitational wave detection

Quickly localizing the identified white dwarf (WD) binaries is the basic requirement for the space-based gravitational wave (GW) detection. In fact, the amplitude of GW signals are modulated by the periodic motion of GW detectors on the solar orbit. The intensity of the observed signals is enhanced according to the observation time beyond a year to enhance a high signal to noise ratio (SNR). As data gap exists, the completeness of the data observed for a long time depends on filling gaps in the data. Actually, in a year period, the GW sources have a best observation orbit position of GW detectors, where the detector response intensity of GW is maximum. Thus, the best positions, where the direction of GW source is perpendicular to the detection arms, can be searched for the verified GW sources of the sky map to enhance SNR too. For the three arms response intensity of the GW signals changing more clearly with the location of the GW sources relative to the detector, the noises and the suppression of noise by time delay interferometer are ignored. In the four chosen sources, the two verification WD binaries: J0806 and V407 Vul are observed at the best orbit positions by TAIJI for the short time of 2 and 3 days respectively. The intensities of those GWs are above the values of the TAIJI sensitivity curve, significantly. Compared with a single detector, the network of two detectors does not significantly improve the accuracy of location of the verification binaries. The reason of that result is that one GW source can not be perpendicular to both detectors of TAIJI and LISA. These results imply that the searching of GW signals and parameter estimation of GW sources from the experimental data of the space-based mission do not ignore the orbit positions relevant to GW sources.Comment: 22 pages, 15 figure

Two Higgs Bi-doublet Model With Spontaneous P and CP Violation and Decoupling Limit to Two Higgs Doublet Model

The two Higgs bi-doublet left-right symmetric model (2HBDM) as a simple extension of the minimal left-right symmetric model with a single Higgs bi-doublet is motivated to realize both spontaneous P and CP violation while consistent with the low energy phenomenology without significant fine tuning. By carefully investigating the Higgs potential of the model, we find that sizable CP-violating phases are allowed after the spontaneous symmetry breaking. The mass spectra of the extra scalars in the 2HBDM are significantly different from the ones in the minimal left-right symmetric model. In particular, we demonstrate in the decoupling limit when the right-handed gauge symmetry breaking scale is much higher than the electroweak scale, the 2HBDM decouples into general two Higgs doublet model (2HDM) with spontaneous CP violation and has rich induced sources of CP violation. We show that in the decoupling limit, it contains extra light Higgs bosons with masses around electroweak scale, which can be directly searched at the ongoing LHC and future ILC experiments.Comment: 19 pages, discussions on fine-tuning problem added. Version to appear in Phys.Rev.

Movie101: A New Movie Understanding Benchmark

To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of actors. Existing works benchmark this challenge as a normal video captioning task via some simplifications, such as removing role names and evaluating narrations with ngram-based metrics, which makes it difficult for automatic systems to meet the needs of real application scenarios. To narrow this gap, we construct a large-scale Chinese movie benchmark, named Movie101. Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking. External knowledge, such as role information and movie genres, is also provided for better movie understanding. Besides, we propose a new metric called Movie Narration Score (MNScore) for movie narrating evaluation, which achieves the best correlation with human evaluation. Our benchmark also supports the Temporal Narration Grounding (TNG) task to investigate clip localization given text descriptions. For both two tasks, our proposed methods well leverage external knowledge and outperform carefully designed baselines. The dataset and codes are released at https://github.com/yuezih/Movie101.Comment: Accepted to ACL 202