6,906 research outputs found
Assembly Bias of Dwarf-sized Dark Matter Haloes
Previous studies indicate that assembly bias effects are stronger for lower
mass dark matter haloes. Here we make use of high resolution re-simulations of
rich clusters and their surroundings from the Phoenix Project and a large
volume cosmological simulation, the Millennium-II run, to quantify assembly
bias effects on dwarf-sized dark matter haloes. We find that, in the regions
around massive clusters, dwarf-sized haloes ([10^9,10^{11}]\ms) form earlier
( in redshift) and possess larger ()
than the field galaxies. We find that this environmental dependence is largely
caused by tidal interactions between the ejected haloes and their former hosts,
while other large scale effects are less important. Finally we assess the
effects of assembly bias on dwarf galaxy formation with a sophisticated
semi-analytical galaxy formation model. We find that the dwarf galaxies near
massive clusters tend to be redder () and have three times
as much stellar mass compared to the field galaxies with the same halo mass.
These features should be seen with observational data.Comment: 8 pages, 8 figures, accepted by MNRA
The First Photometric and Orbital Period Investigation of an Extremely Low Mass Ratio Contact Binary with a Sudden Period Change, TYC 4002-2628-1
Photometric observations for the totally eclipsing binary system TYC
4002-2628-1, were obtained between November 2020 and November 2021. To
determine the stellar atmospheric parameters, a spectral image was taken with
the 2.16 m telescope at National Astronomical Observatory of China (NAOC). TYC
4002-2628-1 is a low-amplitude (about 0.15 mag for band), short-period
(0.3670495 d), contact eclipsing binary with a total secondary eclipse.
Intrinsic light curve variations and the reversal of the O'Connell effect are
detected in the light curves, which may be due to spot activity. Based on the
photometric solutions derived from the multi-band time series light curves, TYC
4002-2628-1 is an extremely low mass ratio contact binary with a mass ratio of
0.0482 and a fill-out factor of . By analyzing the
variations, we find that its orbital period remains unchanged when BJD <
2458321 . Then the orbital period changed suddenly around BJD 2458743 and has
an increasing rate of
.
If confirmed, TYC 4002-2628-1 would be the contact binary with the highest
orbital period increasing rate so far. By investigating the ratio of orbital
angular momentum to the spin angular momentum ( / ) ,
the instability mass ratio () and the instability separation
(), TYC 4002-2628-1 can be regarded as a merger candidate.Comment: 9 page
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
Domain generalization (DG) intends to train a model on multiple source
domains to ensure that it can generalize well to an arbitrary unseen target
domain. The acquisition of domain-invariant representations is pivotal for DG
as they possess the ability to capture the inherent semantic information of the
data, mitigate the influence of domain shift, and enhance the generalization
capability of the model. Adopting multiple perspectives, such as the sample and
the feature, proves to be effective. The sample perspective facilitates data
augmentation through data manipulation techniques, whereas the feature
perspective enables the extraction of meaningful generalization features. In
this paper, we focus on improving the generalization ability of the model by
compelling it to acquire domain-invariant representations from both the sample
and feature perspectives by disentangling spurious correlations and enhancing
potential correlations. 1) From the sample perspective, we develop a frequency
restriction module, guiding the model to focus on the relevant correlations
between object features and labels, thereby disentangling spurious
correlations. 2) From the feature perspective, the simple Tail Interaction
module implicitly enhances potential correlations among all samples from all
source domains, facilitating the acquisition of domain-invariant
representations across multiple domains for the model. The experimental results
show that Convolutional Neural Networks (CNNs) or Multi-Layer Perceptrons
(MLPs) with a strong baseline embedded with these two modules can achieve
superior results, e.g., an average accuracy of 92.30% on Digits-DG
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