A simple and natural interpretations of the DAMPE cosmic-ray electron/positron spectrum within two sigma deviations

The DArk Matter Particle Explorer (DAMPE) experiment has recently announced the first results for the measurement of total electron plus positron fluxes between 25 GeV and 4.6 TeV. A spectral break at about 0.9 TeV and a tentative peak excess around 1.4 TeV have been found. However, it is very difficult to reproduce both the peak signal and the smooth background including spectral break simultaneously. We point out that the numbers of events in the two energy ranges (bins) close to the 1.4 TeV excess have $1\sigma$ deficits. With the basic physics principles such as simplicity and naturalness, we consider the $-2\sigma$, $+2\sigma$, and $-1\sigma$ deviations due to statistical fluctuations for the 1229.3~GeV bin, 1411.4~GeV bin, and 1620.5~GeV bin. Interestingly, we show that all the DAMPE data can be explained consistently via both the continuous distributed pulsar and dark matter interpretations, which have $\chi^{2} \simeq 17.2$ and $\chi^{2} \simeq 13.9$ (for all the 38 points in DAMPE electron/positron spectrum with 3 of them revised), respectively. These results are different from the previous analyses by neglecting the 1.4 TeV excess. At the same time, we do a similar global fitting on the newly released CALET lepton data, which could also be interpreted by such configurations. Moreover, we present a $U(1)_D$ dark matter model with Breit-Wigner mechanism, which can provide the proper dark matter annihilation cross section and escape the CMB constraint. Furthermore, we suggest a few ways to test our proposal.Comment: 18 pages, 6 figures, 5 tables. Figures and Bibs update

Unveiling the Intricate Symphony of Nonlinear Pulsation Mode Interactions in High-Amplitude δ\delta Scuti Stars

People can diagnose the interiors of stars by sensing their pulsations. Pulsation modes, which are determined by the internal state and structure of a star, are typically considered stable over short timescales. These independent pulsation modes have been used in asteroseismology to reconstruct the interior structure of stars. Here, we report the discovery of peculiar pulsation mode interaction details in the high-amplitude $\delta$ Scuti star KIC 6382916 (J19480292+4146558), challenging the reliability of independent pulsation modes as indicators of the star's internal structure. Through analysis of archival data, we found distinct variations in amplitudes and frequencies of three independent pulsation modes and their harmonics/combinations over approximately 20 days. These variations can reach amplitudes of about 140% and frequency variations of about 12%. Correlation analysis of amplitude and frequency variations revealed additional pulsation mode interaction details and patterns. Notably, our findings regarding the phenomena related to harmonics of independent pulsation modes challenge the traditional understanding in this area. These discoveries serve as cornerstones for future research and advance nonlinear asteroseismology.Comment: 16 pages, 10 figures, 1 tabl

Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients

BackgroundNephron sparing nephrectomy may not reduce the prognosis of nephroblastoma in the absence of involvement of the renal capsule, sinus vessels, and lymph nodes, However, there is no accurate preoperative noninvasive evaluation method at present.Materials and methods105 nephroblastoma patients underwent contrast-enhanced CT scan between 2013 and 2020 in our hospital were retrospectively collected, including 59 cases with localized stage and 46 cases with non-localized stage, and then were divided into training cohort (n= 73) and validation cohort (n= 32) according to the order of CT scanning time. After lesion segmentation and data preprocessing, radiomic features were extracted from each volume of interest. The multi-step procedure including Pearson correlation analysis and sequential forward floating selection was performed to produce radiomic signature. Prediction model was constructed using the radiomic signature and Logistic Regression classifier for predicting the localized nephroblastoma in the training cohort. Finally, the model performance was validated in the validation cohort.ResultsA total of 1652 radiomic features have been extracted, from which TOP 10 features were selected as the radiomic signature. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity of the prediction model were 0.796, 0.795, 0.732 and 0.875 for the training cohort respectively, and 0.710, 0.719, 0.611 and 0.857 for the validation cohort respectively. The result comparison with prediction models composed of different machine learning classifiers and different parameters also manifest the effectiveness of our radiomic model.ConclusionA logistic regression model based on radiomic features extracted from preoperative CT images had good ability to noninvasively predict nephroblastoma without renal capsule, sinus vessel, and lymph node involvement