Keywords

The occurrence of the first significant digits from real world sources is usually not equally distributed, but is consistent with a logarithmic distribution instead, known as Benford's law. In this work, we perform a comprehensive investigation on the first digit distributions of the duration, fluence, and energy flux of gamma-ray bursts (GRBs) for the first time. For a complete GRB sample detected by the Fermi satellite, we find that the first digits of the duration and fluence adhere to Benford's law. However, the energy flux shows a significant departure from this law, which may be due to the fact that a considerable part of the energy flux measurements is restricted by lack of spectral information. Based on the conventional duration classification scheme, we also check if the durations and fluences of long and short GRBs (with duration T₉₀ > 2 s and T₉₀ ≤ 2 s, respectively) obey Benford's law. We find that the fluences of both long and short GRBs still agree with the Benford distribution, but their durations do not follow Benford's law. Our results hint that the long–short GRB classification scheme does not directly represent the intrinsic physical classification scheme.

The correlation between close-in super Earths and distant cold Jupiters in planetary systems has important implications for their formation and evolution. Contrary to some earlier findings, a recent study conducted by Bonomo et al. suggests that the occurrence of cold Jupiter companions is not excessive in super-Earth systems. Here we show that this discrepancy can be seen as a Simpson's paradox and is resolved once the metallicity dependence of the super-Earth–cold Jupiter relation is taken into account. A common feature is noticed that almost all the cold Jupiter detections with inner super-Earth companions are found around metal-rich stars. Focusing on the Sun-like hosts with super-solar metallicities, we show that the frequency of cold Jupiters conditioned on the presence of inner super Earths is ${39}_{-11}^{+12} \%$, whereas the frequency of cold Jupiters in the same metallicity range is no more than 20%. Therefore, the occurrences of close-in super Earths and distant cold Jupiters appear correlated around metal-rich hosts. The relation between the two types of planets remains unclear for stars with metal-poor hosts due to the limited sample size and the much lower occurrence rate of cold Jupiters, but a correlation between the two cannot be ruled out.

Cloud cover plays a pivotal role in assessing observational conditions for astronomical site-testing. Except for the fraction of observing time, its fragmentation also wields a significant influence on the quality of nighttime sky clarity. In this article, we introduce the function ${\rm{\Gamma }}\in \left[0,1\right]$, designed to comprehensively capture both the fraction of available observing time and its continuity. Leveraging in situ measurement data gathered at the Muztagh-Ata site between 2017 and 2021, we showcase the effectiveness of our approach. The statistical result illustrates that the Muztagh-Ata site affords approximately 122 nights that were absolutely clear and 205 very good nights annually, corresponding to Γ ≥ 0.9 and Γ ≥ 0.36 respectively.

We tested a new model of CMOS detector manufactured by the Gpixel Inc, for potential space astronomical application. In laboratory, we obtain some bias images under the typical application environment. In these bias images, clear random row noise pattern is observed. The row noise also contains some characteristic spatial frequencies. We quantitatively estimated the impact of this feature to photometric measurements, by making simulated images. We compared different bias noise types under strict parameter control. The result shows the row noise will significantly deteriorate the photometric accuracy. It effectively increases the readout noise by a factor of 2–10. However, if it is properly removed, the image quality and photometric accuracy will be significantly improved.

Pulsar detection has become an active research topic in radio astronomy recently. One of the essential procedures for pulsar detection is pulsar candidate sifting (PCS), a procedure for identifying potential pulsar signals in a survey. However, pulsar candidates are always class-imbalanced, as most candidates are non-pulsars such as RFI and only a tiny part of them are from real pulsars. Class imbalance can greatly affect the performance of machine learning (ML) models, resulting in a heavy cost as some real pulsars are misjudged. To deal with the problem, techniques of choosing relevant features to discriminate pulsars from non-pulsars are focused on, which is known as feature selection. Feature selection is a process of selecting a subset of the most relevant features from a feature pool. The distinguishing features between pulsars and non-pulsars can significantly improve the performance of the classifier even if the data are highly imbalanced. In this work, an algorithm for feature selection called the K-fold Relief-Greedy (KFRG) algorithm is designed. KFRG is a two-stage algorithm. In the first stage, it filters out some irrelevant features according to their K-fold Relief scores, while in the second stage, it removes the redundant features and selects the most relevant features by a forward greedy search strategy. Experiments on the data set of the High Time Resolution Universe survey verified that ML models based on KFRG are capable of PCS, correctly separating pulsars from non-pulsars even if the candidates are highly class-imbalanced.

The prompt emission mechanism of gamma-ray bursts (GRBs) is still unclear, and the time-resolved spectral analysis of GRBs is a powerful tool for studying their underlying physical processes. We performed a detailed time-resolved spectral analysis of 78 bright long GRB samples detected by Fermi/Gamma-ray Burst Monitor. A total of 1490 spectra were obtained and their properties were studied using a typical Band-shape model. First, the parameter distributions of the time-resolved spectrum are given as follows: the low-energy spectral index α ∼ − 0.72, high-energy spectral index β ∼ − 2.42, the peak energy E_p ∼ 221.69 keV, and the energy flux F ∼ 7.49 × 10⁻⁶ erg cm⁻² s⁻¹. More than 80% of the bursts exhibit the hardest low-energy spectral index ${\alpha }_{\max }$ exceeding the synchrotron limit (−2/3). Second, the evolution patterns of α and E_p were statistically analyzed. The results show that for multi-pulse GRBs the intensity-tracking pattern is more common than the hard-to-soft pattern in the evolution of both E_p and α. The hard-to-soft pattern is generally shown in single-pulse GRBs or in the initial pulse of multi-pulse GRBs. Finally, we found a significant positive correlation between F and E_p, with half of the samples exhibiting a positive correlation between F and α. We discussed the spectral evolution of different radiation models. The diversity of spectral evolution patterns indicates that there may be more than one radiation mechanism occurring in the GRB radiation process, including photospheric radiation and synchrotron radiation. However, it may also involve only one radiation mechanism, but more complicated physical details need to be considered.

We present a study of low surface brightness galaxies (LSBGs) selected by fitting the images for all the galaxies in α.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models (disk+bulge): single exponential, single sérsic, exponential+deVaucular (exp+deV), and exponential+sérsic (exp+ser). Under the criteria of the B band disk central surface brightness ${\mu }_{0,\mathrm{disk}}(B)\geqslant 22.5\ \mathrm{mag}\ {\mathrm{arcsec}}^{-2}$ and the axis ratio b/a > 0.3, we selected four none-edge-on LSBG samples from each of the models which contain 1105, 1038, 207, and 75 galaxies, respectively. There are 756 galaxies in common between LSBGs selected by exponential and sérsic models, corresponding to 68.42% of LSBGs selected by the exponential model and 72.83% of LSBGs selected by the sérsic model, the rest of the discrepancy is due to the difference in obtaining μ₀ between the exponential and sérsic models. Based on the fitting, in the range of 0.5 ≤ n ≤ 1.5, the relation of μ₀ from two models can be written as ${\mu }_{0,{\rm{s}}\acute{{\rm{e}}}\mathrm{rsic}}-{\mu }_{0,\exp }=-1.34(n-1)$. The LSBGs selected by disk+bulge models (LSBG_2comps) are more massive than LSBGs selected by single-component models (LSBG_1comp), and also show a larger disk component. Though the bulges in the majority of our LSBG_2comps are not prominent, more than 60% of our LSBG_2comps will not be selected if we adopt a single-component model only. We also identified 31 giant low surface brightness galaxies (gLSBGs) from LSBG_2comps. They are located at the same region in the color–magnitude diagram as other gLSBGs. After we compared different criteria of gLSBGs selection, we find that for gas-rich LSBGs, M_⋆ > 10¹⁰M_⊙ is the best to distinguish between gLSBGs and normal LSBGs with bulge.

For high-precision pulsar timing analysis and low-frequency gravitational wave detection, it is essential to accurately determine pulsar pulse times of arrival (ToAs) and associated uncertainties. To measure the ToAs and their uncertainties, various cross-correlation-based techniques can be employed. We develop methodologies to investigate the impact of the template-matching method, profile shape, signal-to-noise ratio of both template and observation on ToA uncertainties. These methodologies are then applied to data from the International Pulsar Timing Array. We demonstrate that the Fourier domain Markov chain Monte Carlo method is generally superior to other methods, while the Gaussian interpolation shift method outperforms other methods in certain cases, such as profiles with large duty cycles or smooth profiles without sharp features. However, it is important to note that our study focuses solely on ToA uncertainty, and the optimal method for determining both ToA and ToA uncertainty may differ.

Spectral classification plays a crucial role in the analysis of astronomical data. Currently, stellar spectral classification primarily relies on one-dimensional (1D) spectra and necessitates a sufficient signal-to-noise ratio (S/N). However, in cases where the S/N is low, obtaining valuable information becomes impractical. In this paper, we propose a novel model called DRC-Net (Double-branch celestial spectral classification network based on residual mechanisms) for stellar classification, which operates solely on two-dimensional (2D) spectra. The model consists of two branches that use 1D convolutions to reduce the dimensionality of the 2D spectral composed of both blue and red arms. In the following, the features extracted from both branches are fused, and the fused result undergoes further feature extraction before being fed into the classifier for final output generation. The data set is from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, comprising 15,680 spectra of F, G, and K types. The preprocessing process includes normalization and the early stopping mechanism. The experimental results demonstrate that the proposed DRC-Net achieved remarkable classification precision of 93.0%, 83.5%, and 86.9% for F, G, and K types, respectively, surpassing the performance of 1D spectral classification methods. Furthermore, different S/N intervals are tested to judge the classification ability of DRC-Net. The results reveal that DRC-Net, as a 2D spectral classification model, can deliver superior classification outcomes for the spectra with low S/Ns. These experimental findings not only validate the efficiency of DRC-Net but also confirm the enhanced noise resistance ability exhibited by 2D spectra.

A negative correlation was found to exist between the low-energy spectral index and the redshift of gamma-ray bursts (GRBs) by Amati et al. It was later confirmed by Geng & Huang and Gruber et al., but the correlation was also found to be quite dispersive when the sample size was significantly expanded. In this study, we have established two even larger samples of GRBs to further examine the correlation. One of our samples consists of 316 GRBs detected by the Swift satellite, and the other one consists of 80 GRBs detected by the Fermi satellite. It is found that there is no correlation between the two parameters for the Swift sample, but there does exist a weak negative correlation for the Fermi sample. The correlation becomes even more significant when the spectral index at the peak flux is considered. It is argued that the absence of the correlation in the Swift sample may be due to the fact that Swift has a very narrow energy response so that it could not measure the low-energy spectral index accurately enough. Further studies based on even larger GRB samples are solicited.