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In spite of significant progress in the research of fast radio bursts (FRBs) in recent decade, their origin is still under extensive debate. Investigation on the population of FRBs can provide new insight into this interesting problem. In this paper, based on the first CHIME/FRB catalog, we construct a Bayesian framework to analyze the FRB population, with the selection effect of the CHIME telescope being properly taken into account. The energy function is modeled as the power-law with an exponential cutoff. Four redshift distribution models are considered, i.e., the star formation history (SFH) model, and three time-delayed models (Gaussian delay, log-normal delay, and power-law delay). The free parameters are simultaneously constrained using Bayesian inference method, and the Bayesian information criterion (BIC) is used in model comparison. According to BIC, the log-normal delay model fits the data best. The power-law delay model and Gaussian delay model can also give reasonable fits, although they are not as good as the log-normal delay model. However, the SFH model is strongly disfavored compared with the three time-delayed models. The energy function is tightly constrained and is almost independent of the redshift models, with the best-fitting power-law index $\alpha\approx 1.8$, and cut-off energy $\log(E_c/{\rm erg})\approx 42$. The FRB population shows on average $3\sim 5$ billion years time delay with respect to the SFH. Therefore, the hypothesis that the FRB population traces the SFH is conclusively ruled out., Comment: 12 pages, 2 figures, 2 tables. Accepted by Astrophysical Journal

We employ a sample of 135,873 RR Lyrae stars (RRLs) with precise photometric-metallicity and distance estimates from the newly calibrated $P$--$\phi_{31}$--$R_{21}$--[Fe/H] and $Gaia$ $G$-band $P$--$R_{21}$--[Fe/H] absolute magnitude-metallicity relations of Li et al., combined with available proper motions from $Gaia$ EDR3, and 6955 systemic radial velocities from $Gaia$ DR3 and other sources, in order to explore the chemistry and kinematics of the halo of the Milky Way (MW). This sample is ideally suited for characterization of the inner- and outer-halo populations of the stellar halo, free from the bias associated with spectroscopically selected probes, and for estimation of their relative contributions as a function of Galactocentric distance. The results of a Gaussian Mixture-Model analysis of these contributions are broadly consistent with other observational studies of the halo, and with expectations from recent MW simulation studies. We apply the HDBSCAN clustering method to the specific energies and cylindrical actions ($E$, J$_{r}$, J$_{\phi}$, J$_{z}$), identifying 97 Dynamically Tagged Groups (DTGs) of RRLs, and explore their associations with recognized substructures of the MW. The precise photometric-distance determinations ($\delta\, d/d < 5$\%), and the resulting high-quality determination of dynamical parameters, yield highly statistically significant (low) dispersions of [Fe/H] for the stellar members of the DTGs compared to random draws from the full sample, indicating that they share common star-formation and chemical histories, influenced by their birth environments., Comment: 16 pages, 8 figures, submitted to MNRAS

We present a sample of 135,873 RR Lyrae stars (RRLs) with precise photometric-metallicity and distance estimates from our newly calibrated $P-\phi_{31}-R_{21}-$[Fe/H]/$P-R_{21}-$[Fe/H] and $G$ band absolute magnitude-metallicity relations. The $P-\phi_{31}-R_{21}-$[Fe/H] and $P-R_{21}-$[Fe/H] relations for type RRab and type RRc stars are obtained from nearly 2700 Gaia-identified RRLs with precise $\phi_{31}$ and $R_{21}$ measurements from the light curves and metallicity estimates from spectroscopy. Using 236 nearby RRLs with accurate distances estimated from parallax measurements with Gaia EDR3, new $G$ band absolute magnitude-metallicity relations and near-infrared period-absolute magnitude-metallicity relations are constructed. External checks, using other high-resolution spectroscopic sample of field RRLs and RRL members of globular clusters (GCs), show that the typical uncertainties in our photometric-metallicity estimates are about 0.24 and 0.16 dex for type RRab/c stars, respectively, without significant systematic bias with respect to high-resolution spectroscopic metallicity measurements. The accuracies of these metallicity estimates are much improved, especially for RRab stars, when compared to those provided by the Gaia DR3 release. Validations of our distance estimates, again by using members of GCs, show that the typical distance errors are only 3-4%. The distance modulus $\mu_0=18.503\pm0.001(stat)\pm0.040(syst)$ mag for the LMC and $\mu_0=19.030\pm0.003(stat)\pm0.043(syst)$ mag for the SMC are estimated from our RRab sample, respectively, and are in excellent agreement with previous measurements. The mean metallicities of the LMC and SMC derived in this work are also consistent with the previous determinations. Using our sample, a steep metallicity gradient of $-0.024\pm0.001$ dex/kpc is found for the LMC, while a negligible metallicity gradient is obtained for the SMC., Comment: 16 pages, 18 figures and 6 tables, accepted by ApJ, the RRL catalog will be available at http://doi.org/10.5281/zenodo.6731860

Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents and provide supporting facts together with the exact answer. Existing works tend to utilize graph-based reasoning and question decomposition to obtain the reasoning chain, which inevitably introduces additional complexity and cumulative error to the system. To address the above issue, we propose a simple yet effective novel framework, From Easy to Hard (FE2H), to remove distracting information and obtain better contextual representations for the multi-hop QA task. Inspired by the iterative document selection process and the progressive learning custom of humans, FE2H divides both the document selector and reader into two stages following an easy-to-hard manner. Specifically, we first select the document most relevant to the question and then utilize the question together with this document to select other pertinent documents. As for the QA phase, our reader is first trained on a single-hop QA dataset and then transferred into the multi-hop QA task. We comprehensively evaluate our model on the popular multi-hop QA benchmark HotpotQA. Experimental results demonstrate that our method ourperforms all other methods in the leaderboard of HotpotQA (distractor setting).

The 500-meter Aperture Spherical Radio Telescope(FAST) has an active reflector. During the observation, the reflector will be deformed into a paraboloid of 300-meters. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. And the way to detect the nodes in the photos is the key problem in photogrammetry. This paper applies Convolutional Neural Network(CNN) with candidate regions to detect the nodes in the photos. The experiment results show a high recognition rate of 91.5%, which is much higher than the recognition rate of traditional edge detection., Comment: 13 pages, 12 figures, 2 tables, CNN applied on FAST's reflector measurement; matches the published version in RAA