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| 1 | A simple TOPMODEL-based runoff parameterization(SIMTOP)for use in global climate models显示文摘 | Niu G.Y Z.L.Yang R.E.Dickinson L.E.Gulden | | 0，，21： | 1 |
| 2 | Positive solutions to a system of second order nonlocal boundary value problems显示文摘 | Z.L.Yang | | 0，，62： | 1 |
| 3 | FAST Observations of an Extremely Active Episode of FRB 20201124A:Ⅰ.Burst Morphology显示文摘We report the properties of more than 600 bursts(including cluster-bursts)detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode on UTC 2021 September 25–28,in a series of four papers.The observations were carried out in the band of 1.0–1.5 GHz by using the center beam of the L-band 19-beam receiver.We monitored the source in sixteen1 hr sessions and one 3 hr session spanning 23 days.All the bursts were detected during the first four days.In this first paper of the series,we perform a detailed morphological study of 624 bursts using the two-dimensional frequencytime“waterfall”plots,with a burst(or cluster-burst)defined as an emission episode during which the adjacent emission peaks have a separation shorter than 400 ms.The duration of a burst is therefore always longer than 1 ms,with the longest up to more than 120 ms.The emission spectra of the sub-bursts are typically narrow within the observing band with a characteristic width of~277 MHz.The center frequency distribution has a dominant peak at about 1091.9 MHz and a secondary weak peak around 1327.9 MHz.Most bursts show a frequencydownwarddrifting pattern.Based on the drifting patterns,we classify the bursts into five main categories:downward drifting(263)bursts,upward drifting(3)bursts,complex(203),no drifting(35)bursts,and no evidence for drifting(121)bursts.Subtypes are introduced based on the emission frequency range in the band(low,middle,high and wide)as well as the number of components in one burst(1,2,or multiple).We measured a varying scintillation bandwidth from about 0.5 MHz at 1.0 GHz to 1.4 MHz at 1.5 GHz with a spectral index of 3.0. | D.J.Zhou J.L.Han B.Zhang K.J.Lee W.W.Zhu D.Li W.C.Jing W.-Y.Wang Y.K.Zhang J.C.Jiang J.R Niu R Luo H.Xu C.F.Zhang B.J.Wang J.W.Xu P.Wang Z.L.Yang Y.Feng | 2022 | Research in Astronomy and Astrophysics2022，22，12： | 0 |
| 4 | Cleaning Radio Frequency Interference in Pulsar-Folded Data Based on the Conditional Random Fields with an Adaptive Prior显示文摘Radio astronomy observations are frequently impacted by radio frequency interference(RFI).We propose a novel method,named 2σCRF,for cleaning RFI in the folded data of pulsar observations,utilizing a Bayesian-based model called conditional random fields(CRFs).This algorithm minimizes the“energy”of every pixel given an initial label.The standard deviations(i.e.,rms values)of the folded pulsar data are utilized as pixels for all subintegrations and channels.Non-RFI data without obvious interference is treated as“background noise,”while RFI-affected data have different classes due to their exceptional rms values.This initial labeling can be automated and is adaptive to the actual data.The CRF algorithm optimizes the label category for each pixel of the image with the prior initial labels.We demonstrate the efficacy of the proposed method on pulsar folded data obtained from Five-hundred-meter Aperture Spherical radio Telescope observations.It can effectively recognize and tag various categories of RFIs,including broadband or narrowband,constant or instantaneous,and even weak RFIs that are unrecognizable in some pixels but picked out based on their neighborhoods.The results are comparable to those obtained via manual labeling but without the need for human intervention,saving time and effort. | Xue Chen J.L.Han W.Q.Su Z.L.Yang D.J.Zhou | 2023 | Research in Astronomy and Astrophysics2023，23，10： | 0 |
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