7,976 research outputs found
White dwarf binary modulation can help stochastic gravitational wave background search
For the stochastic gravitational wave backgrounds (SGWBs) search centred at
the milli-Hz band, the galactic foreground produced by white dwarf binaries
(WDBs) within the Milky Way contaminates the extra-galactic signal severely.
Because of the anisotropic distribution pattern of the WDBs and the motion of
the spaceborne gravitational wave interferometer constellation, the time-domain
data stream will show an annual modulation. This property is fundamentally
different from those of the SGWBs. In this Letter, we propose a new filtering
method for the data vector based on the annual modulation phenomenon. We apply
the resulted inverse variance filter to the LISA data challenge. The result
shows that for the weaker SGWB signal, such as energy density , the filtering method can enhance the posterior
distribution peak prominently. For the stronger signal, such as , the method can improve the Bayesian evidence from
`substantial' to `strong' against null hypotheses. This method is
model-independent and self-contained. It does not ask for other types of
information besides the gravitational wave data.Comment: 6 pages, 5 figure
C-Silicon-based metasurfaces for aperture-robust spectrometer/imaging with angle integration
Compared with conventional grating-based spectrometers, reconstructive
spectrometers based on spectrally engineered filtering have the advantage of
miniaturization because of the less demand for dispersive optics and free
propagation space. However, available reconstructive spectrometers fail to
balance the performance on operational bandwidth, spectral diversity and
angular stability. In this work, we proposed a compact silicon metasurfaces
based spectrometer/camera. After angle integration, the spectral response of
the system is robust to angle/aperture within a wide working bandwidth from
400nm to 800nm. It is experimentally demonstrated that the proposed method
could maintain the spectral consistency from F/1.8 to F/4 (The corresponding
angle of incident light ranges from 7{\deg} to 16{\deg}) and the incident
hyperspectral signal could be accurately reconstructed with a fidelity
exceeding 99%. Additionally, a spectral imaging system with 400x400 pixels is
also established in this work. The accurate reconstructed hyperspectral image
indicates that the proposed aperture-robust spectrometer has the potential to
be extended as a high-resolution broadband hyperspectral camera
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Offline goal-conditioned RL (GCRL) offers a way to train general-purpose
agents from fully offline datasets. In addition to being conservative within
the dataset, the generalization ability to achieve unseen goals is another
fundamental challenge for offline GCRL. However, to the best of our knowledge,
this problem has not been well studied yet. In this paper, we study
out-of-distribution (OOD) generalization of offline GCRL both theoretically and
empirically to identify factors that are important. In a number of experiments,
we observe that weighted imitation learning enjoys better generalization than
pessimism-based offline RL method. Based on this insight, we derive a theory
for OOD generalization, which characterizes several important design choices.
We then propose a new offline GCRL method, Generalizable Offline
goAl-condiTioned RL (GOAT), by combining the findings from our theoretical and
empirical studies. On a new benchmark containing 9 independent identically
distributed (IID) tasks and 17 OOD tasks, GOAT outperforms current
state-of-the-art methods by a large margin.Comment: Accepted by Proceedings of the 40th International Conference on
Machine Learning, 202
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