Study of Type-B QPOs Observed in Black Hole X-Ray Binary Swift J1728.9-3613

We report the detection of type-B quasi-periodic oscillation(QPO)of the black hole X-ray binary Swift J1728.9-3613 observed by NICER during the 2019 outburst.A type-B QPO was observed for the first two days and it disappeared as flux increased,but again appeared at∼7.70 Hz when flux was dramatically decreased.The source was found in the soft intermediate state during these observations.We further studied the energy dependence of the QPO.We found that QPO was observed only for a higher energy range implying that the origin of QPO is possibly due to the corona emitting higher energy photons by the inverse Compton process.The variation of spectral parameters can be explained with the disk truncation model.The fractional rms was found to be monotonically increased with energy.The phase lag spectrum followed the“U-shaped”curve.The rms and phase lag spectrum are modeled and explained with the single-component Comptonization model vkompthdk.

Log-normal flux distribution of bright Fermi blazars

We present the results of the γ-ray flux distribution study on the brightest blazars which were observed by Fermi-LAT. We selected 50 of the brightest blazars based on the maximum number of detections reported in the Third LAT AGN Catalog. We performed standard unbinned maximum likelihood analysis on the LAT data during the period between August 2008 and December 2016, in order to obtain the average monthly flux. After quality cuts, blazars for which at least 90% of the total flux had survived were selected for further study, and this included 19 FSRQs and 19 BL Lacs. The Anderson-Darling and χ2 tests suggest that the integrated monthly flux follows a log-normal distribution for all sources, except for three FSRQs for which neither a normal nor a log-normal distribution was preferred. A double log-normal flux distribution tendency was observed in these sources, though this has to be confirmed with improved statistics. We also found that the standard deviation of the log-normal flux distribution increases with the mean spectral index of the blazar, and can be fitted with a line of slope 0.24±0.04. We repeat our study on three additional brightest unclassified blazars to identify their flux distribution properties. Based on the features of their log-normal flux distribution, we infer these unclassified blazars may be closely associated with FSRQs. We also highlight that considering the lognormal behavior of the flux distribution of blazars, averaging their long term flux on a linear scale can largely underestimate the nominal flux and this discrepancy can propagate down to the estimation of source parameters through spectral modeling.

Broadband spectral fitting of blazars using XSPEC

The broadband spectral energy distribution（SED） of blazars is generally interpreted as radiation arising from synchrotron and inverse Compton mechanisms. Traditionally,the underlying source parameters responsible for these emission processes,like particle energy density,magnetic field,etc.,are obtained through simple visual reproduction of the observed fluxes. However,this procedure is incapable of providing confidence ranges for the estimated parameters. In this work,we propose an efficient algorithm to perform a statistical fit of the observed broadband spectrum of blazars using different emission models. Moreover,we use the observable quantities as the fit parameters,rather than the direct source parameters which govern the resultant SED. This significantly improves the convergence time and eliminates the uncertainty regarding initial guess parameters. This approach also has an added advantage of identifying the degenerate parameters,which can be removed by including more observable information and/or additional constraints. A computer code developed based on this algorithm is implemented as a user-defined routine in the standard X-ray spectral fitting package,XSPEC. Further,we demonstrate the efficacy of the algorithm by fitting the well sampled SED of blazar 3 C 279 during its gamma ray flare in 2014.

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

A detailed case study of γ-hadron segregation for a ground based atmo- spheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation.

Gamma-ray bursts and their links with supernovae and cosmology

Gamma-ray bursts are the most luminous explosions in the Universe, whose origin and mechanism are the focus of intense interest. They appear connected to su- pernova remnants from massive stars or the merger of their remnants, and their bright- ness makes them temporarily detectable out to the largest distances yet explored in the universe. After pioneering breakthroughs from space and ground experiments, their study is entering a new phase with observations from the recently launched Fermi satellite, as well as the prospect of detections or limits from large neutrino and gravitational wave detectors. The interplay between such observations and theoretical models of gamma-ray bursts is reviewed, and cosmology. as well as their connections to supernovae

The neutron star zoo

中子星是一张很多样的人口，两在里面他们的观察并且他们的物理性质。他们喜欢在 X 光检查和 gamma 光线波长散发大多数他们的精力。但是他们的排放是否以旋转，生长，热，磁场或原子反应为动力，他们都是进化和内部作文其磁场仍然是一个谜的一样的动物的不同种类。这篇文章将广泛地考察中子星动物园的居民的性质，与他们的高精力的排放上的强调。

Gamma-ray bursts in the swift-Fermi era

Gamma 光线爆炸(GRB ) 在在宇宙的最强烈的出现之中。他们是强大的爆炸，对高 redshift，和思维到可见是黑洞出生的签名。他们是高度发光的事件并且提供远宇宙的优秀探针。GRB 研究极大地从斯威夫特，费密和一个活跃紧跟的社区与结果在过去的 10 年进展了。在这评论，我们调查在这些最近的观察和迅速的 GRB 排放和随后的晚霞的理论模型之间的相互影响。