The Latent Heat of Single Flavor Color Superconductivity in a Magnetic Field

We calculate the energy release associated with first-order phase transition between different types of single flavor color superconductivity in a magnetic field.Comment: Updated version accepted by PRD, with minor change

The electromagnetic and gravitational-wave radiations of X-ray transient CDF-S XT2

Binary neutron star (NS) mergers may result in remnants of supra-massive or even stable NS, which have been supported indirectly by observed X-ray plateau of some gamma-ray bursts (GRBs) afterglow. Recently, Xue et al. (2019) discovered a X-ray transient CDF-S XT2 that is powered by a magnetar from merger of double NS via X-ray plateau and following stepper phase. However, the decay slope after the plateau emission is a little bit larger than the theoretical value of spin-down in electromagnetic (EM) dominated by losing its rotation energy. In this paper, we assume that the feature of X-ray emission is caused by a supra-massive magnetar central engine for surviving thousands of seconds to collapse black hole. Within this scenario, we present the comparisons of the X-ray plateau luminosity, break time, and the parameters of magnetar between CDF-S XT2 and other short GRBs with internal plateau samples. By adopting the collapse time to constrain the equation of state (EOS), we find that three EOSs (GM1, DD2, and DDME2) are consistent with the observational data. On the other hand, if the most released rotation energy of magnetar is dominated by GW radiation, we also constrain the upper limit of ellipticity of NS for given EOS, and it is range in $[0.32-1.3]\times 10^{-3}$. Its GW signal can not be detected by aLIGO or even for more sensitive Einstein Telescope in the future.Comment: 13 pages, 5 figures,1 table. Accepted for publication by Research in Astronomy and Astrophysic

Impact of LHCb 13 TeV WW and ZZ pseudo-data on the Parton Distribution Functions

We study the potential of the LHCb 13 TeV single $W^{\pm}$ and $Z$ boson pseudo-data for constraining the parton distribution functions (PDFs) of the proton. As an example, we demonstrate the sensitivity of the LHCb 13 TeV data, collected with integrated luminosities of 5 fb$^{-1}$ and 300 fb$^{-1}$, to reducing the PDF uncertainty bands of the CT14HERA2 PDFs, using the error PDF updating package {\sc ePump}. The sensitivities of various experimental observables are compared. Generally, sizable reductions in PDF uncertainties can be observed in the 300 fb$^{-1}$ data sample, particularly in the small-$x$ region. The double-differential cross section measurement of $Z$ boson $p_T$ and rapidity can greatly reduce the uncertainty bands of $u$ and $d$ quarks in almost the whole $x$ range, as compared to various single observable measurements.Comment: 25 pages, 13 figure

cigFacies: a massive-scale benchmark dataset of seismic facies and its application

Seismic facies classification is crucial for seismic stratigraphic interpretation and hydrocarbon reservoir characterization but remains a tedious and time-consuming task that requires significant manual effort. The data-driven deep learning approaches are highly promising to automate the seismic facies classification with high efficiency and accuracy, as they have already achieved significant success in similar image classification tasks within the field of computer vision (CV). However, unlike the CV domain, the field of seismic exploration lacks a comprehensive benchmark dataset for seismic facies, severely limiting the development, application, and evaluation of deep learning approaches in seismic facies classification. To address this gap, we propose a comprehensive workflow to construct a massive-scale benchmark dataset of seismic facies and evaluate its effectiveness in training a deep learning model. Specifically, we first develop a knowledge graph of seismic facies based on the geological concepts and seismic reflection configurations. Guided by the graph, we then implement three strategies of field seismic data curation, knowledge-guided synthesization, and GAN-based generation to construct a benchmark dataset of 8000 diverse samples for five common seismic facies. Finally, we use the benchmark dataset to train a network and then apply it on two 3-D seismic data for automatic seismic facies classification. The predictions are highly consistent with expert interpretation results, demonstrating the diversity and representativeness of our benchmark dataset is sufficient to train a network that can generalize well in seismic facies classification across field data. We have made this dataset, the trained model and associated codes publicly available for further research and validation of intelligent seismic facies classification