adsabs/AstroCLIMB huggingface dataset:
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The prototype low-pass filter magnitude–frequency characteristics of the CSPFB and the OPFB.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The commutator used to achieve data decimation for M/D = 8/6 DPD-OPFB.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The synthesized wideband spectrum of J0332+5434 (a) by the CSPFB and (b) by the DPD-OPFB. Signal attenuation and spectral aliasing occur at the subband edges of the CSPFB.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The flowchart of the entire observation system.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The structure of the circular shift unit in a DPD-OPFB with a subband quantity M = 5 and a decimation factor D = 3.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The signal-processing flowchart of the design.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The dynamic spectrum of six subbands for pulsar J0332+5434.
Paper: A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array
Authors: Shaocong Guo, Qiao Meng, Hailong Zhang, Yazhou Zhang, Chenye Zhou, Gaojing Li, Jie Wu, Jianxun Shao
Figure caption: The Vivado implementation block design.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Comparison between the NNN predictions and the results obtained by using a small nuclear reaction network. The NNNs outperform the small net by several factors at small time steps. The top, upper-middle, lower-middle, and bottom panels present the errors in the electron fraction Ye, average mass number ﹩\bar{A}﹩, nuclear energy generation term enuc, and neutrino-loss term ϵν, respectively. Left panel: the errors for NNNs trained on data sets generated with the nuclear reaction network mesa_80. The green X markers are the average errors in the predictions of the NNNs compared to the target values from the BBQ runs with mesa_80 normalized by the average parameter value from BBQ. The blue line represents the relative errors that occur while running BBQ with the 22-isotope nuclear reaction network approx21_cr60_plus_co56. Right panel: same as the left panel for NNNs trained on data sets generated with the nuclear reaction network mesa_151.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: For training the NNNs, we consider a range of parameters that correspond to the conditions encountered in stellar models. We show the time evolution of the central temperature (top panel), central density (middle panel), and time steps taken by simulations (lower panel) of SN progenitors with initial masses of MZAMS = 20M⊙ and a nuclear reaction networks composed of 80 isotopes (orange; this paper) and 151 isotopes (purple; this paper), from post–oxygen core burning to collapse. The light blue curves present the same tracks for a MZAMS = 40M⊙ chemically homogeneous black hole progenitor taken from O. Gottlieb et al. 2024 (G24). We mark the depletion of oxygen, silicon, and the onset of core collapse with circles, diamonds, and squares, respectively. The shaded brown areas (top two panels) are the temperature and density regimes of the training sets, and the brown lines (bottom panel) indicate the time steps on which separate NNNs were trained.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Improvement in the results using NNNs compared to the small nuclear reaction network, obtained by dividing the small net error by the NNN error and converting the result to a percentage. NNN predictions are hundreds of percent more accurate than the small net for most of our explored time steps. The top, upper-middle, lower-middle, and bottom panels present the percentage of improvement in the values of the electron fraction Ye, average mass number ﹩\bar{A}﹩, nuclear energy generation term enuc, and neutrino-loss term ϵν, respectively. Orange (purple) pluses are for NNNs trained on mesa_80 (mesa_151) data sets.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: NNNs’ architecture. The emulators are composed of an input layer that contains the temperature T (on a logarithmic scale), density ρ (on a logarithmic scale) and initial composition {X}i, several hidden layers and an output layer that consists of the composition, normalized nuclear energy generation term enuc and normalized neutrino-loss term ϵν after a given time step. The input layer maps the initial conditions to a hidden layer of 1024 neurons, and from there they are mapped between the rest of the hidden layers with 2048 neurons each, until we obtain the composition and normalized energy terms predicted by the NNNs. For NNNs trained on data sets produced with the nuclear reaction network mesa_80 and mesa_151, the total number of layers is Nlayers = 12 and Nlayers = 9, respectively. Each component of the architecture, including the number of layers and the number of neurons per layer, was determined by a hyperparameter search.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Errors in the NNN predictions of the composition for different time steps. Z is the number of protons of each isotope, and N is its number of neutrons. Top row: errors in abundances for NNNs trained on data sets generated using the nuclear reaction network mesa_80 with a time step dt = 10−6 s (left panel), dt = 10−3 s (middle panel), and dt = 1 s (right panel). Bottom row: similar to the top row for NNNs trained on data sets generated using the nuclear reaction network mesa_151.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Comparison between the NNN predictions and the results obtained by using a small nuclear reaction network for different NNN depths. We show the relative error of the electron fraction (top panel), nuclear energy generation term (middle panel), and neutrino-loss term (bottom panel) for NNNs with a different number of layers trained on a time step of dt = 0.1 s. The green X markers are the errors of NNNs trained to predict the composition and energy generation of mesa_80 (left panel) and mesa_151 (right panel). The blue lines in both panels mark the average relative error between values found in the large networks and in the small net approx21_cr60_ plus_co56.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Comparison between the performance of our NNN in predicting a stellar composition and the isotopic abundances obtained using a small nuclear reaction network. We show the compositions of a stellar burning region evolved at a constant temperature of T = 5 × 109 K and a constant density of ρ = 2 × 108 K during dt = 0.1 s. Left panel: the composition resulting from a BBQ run performed with the large nuclear reaction network mesa_80 (the target composition). Middle panel: the composition predicted using our trained NNN model for dt = 0.1 s. Right panel: the composition resulting from a BBQ run performed with the small nuclear reaction network approx21_cr60_plus_co56.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Comparison between the computation time required to determine the composition and energy terms using NNNs and the accurate ODE solvers. Empty orange (purple) squares represent the time it takes BBQ to solve the nucleosynthesis ODEs for all test data sets (≃700 output files) with a nuclear reaction network that consists of 80 (151) isotopes. Filled orange (purple) circles indicate the time needed to predict these quantities using NNN models trained on 80 (151) isotopes. For comparison, we plot the computation time required for solving the ODEs using the small net (22 isotopes).
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Compositions in nuclear statistical equilibrium for nuclear reaction networks with different sizes. Left panel: equilibrium composition in the case of a 201-isotope network for a BBQ run where T = 7 × 109 K, ρ = 3 × 108 g cm−3 (for 80 and 833–3335-isotope networks, the final composition is similar). Middle panel: equilibrium composition for a BBQ run with the same initial parameters except the nuclear reaction network that contains 330 isotopes (for the 495 isotope network, the final composition is similar). Right panel: same as middle panel after fixing the bug in the nuclear reaction networks of MESA.
Paper: Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
Authors: Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer, Selma E. de Mink, Earl Patrick Bellinger, Chi-kwan Chan, Nutan Chen, Ebraheem Farag, Stephen Justham
Figure caption: Training (dark blue) and validation (pink) losses vs. the number of epochs for NNNs trained on mesa_data sets for dt = 0.1 s. The gray line marks the point where we applied early stopping since the composition predictions began to worsen.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: The distribution of all 45 GCs with their ΓM4 (listed in Table 1) within the FAST sky (delineated by green lines) in Galactic coordinates. Hexagons and diamonds are denoted as GCs with and without known pulsars, respectively. The symbols in different colors represent different values of ΓM4, while the five GCs without a measurement of ΓM4 are denoted as open white diamonds. The higher ΓM4 generally correlates with a higher possibility of finding pulsars. For the 14 clusters having pulsar discoveries, the size of the filled yellow star at each of these points corresponds to the number of known pulsars in that cluster.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Survey sensitivity as a function of period and DM, assuming an intrinsic pulse width of 8%. Each set of curves shows (from left to right; see example denoted for the curves of Parkes) the sensitivity calculated using DMs of 30, 200, and 500 pc cm−3. In each case, the DM = 30 pc cm−3 curve is the leftmost, and the DM = 500 pc cm−3 curve is the rightmost. To illustrate real detection performance, we plotted the mean flux density for three extreme cases—GLIMPSE-C01B, M14A, and M15L—calculated via Equations (1)–(5) using their measured DM and spin period from Table 3 and the integration times of 4 hr (S/N ∼ 3), 3 minutes (S/N ∼ 15), and 5 hr (S/N ∼ 50), respectively.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Companion mass vs. orbital period for GC pulsars. Pulsars in GCs from the ANTF pulsar catalog are shown as unfilled gray diamonds, known BWs, and redbacks are unfilled pentagons and triangles, respectively, and FAST discoveries are filled stars. The dashed gray lines show a constant semimajor axis ﹩a=x/\sin i﹩ (here, we assumed a pulsar mass of 1.35 M⊙ in the mass function). The line in the lower left corner denotes a = 1 light ms, and each successive line denotes an increase by a factor of 10. The dashed–dotted black line shows the numerical estimation of the non-conservative evolution of close binary systems with low-mass (1–2 M⊙) donor stars that were transferring material to the primary (T. M. Tauris & G. J. Savonije 1999).
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: The summary of the longest single observation durations for all 45 clusters within FAST’s sky. Red and purple horizontal lines denote the theoretical maximum observation times at zenith angles of 26﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5 and 40°, respectively. Clusters are ordered by their declinations (north to south). Within a zenith angle of 26﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5, FAST maintains a stable gain (∼16 K Jy−1) with a 300 m effective aperture, but beyond this angle, backlighting effects reduce the effective aperture to as low as 200 m, resulting in decreased gain.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Summary of GC FANS observations for 45 clusters within −13﹩\mathop{.}\limits^{\unicode{x000b0}}﹩1 < decl. < +47﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5 from 2018 May to 2024 September, plotted as a function of MJD and calendar date (YYYY-MM-DD). Clusters are sorted by their declinations (north to south). Blue markers denote individual observations, with the observation counts (N_obs) and total integration time (T) in hours shown on the right. Clusters with pulsar discoveries (e.g., M3) are regularly monitored, while those without pulsar detections are observed less frequently to optimize observing time.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Flux density vs. pulsar spin period P (left panel) and orbital period Pb (right panel) for pulsars in GCs discovered by different telescopes: Arecibo (lime), Effelsberg (deep pink), FAST (red), GBT (purple), MeerKAT (orange), and Parkes (cyan). The pulsars identified in the GC FANS demonstrate a notably deeper exploration of lower flux densities, with a wider range of P and Pb. The FAST observations highlight the minimum and maximum spin and orbital periods with solid markers. The dashed rectangles indicate the parameter space of spin and orbital periods vs. flux density of pulsars for each telescope. Most flux densities of pulsars at 1.4 GHz come from the ATNF catalog, except for FAST, where flux densities are estimated using Equation (1) and assumed 10% of the pulse width, based on the best S/N, P, DM, and the longest integration time. Additionally, the estimated minimum flux densities of 13 pulsars in ω Centauri (W. Chen et al. 2023) at 1.4 GHz and 13 pulsars in NGC 1851 (A. Ridolfi et al. 2022) at UHF are shown.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Timing residuals from the best-fit timing models presented in Tables 4 and 5 as a function of the observation date (MJD, left panel) and orbital phase (right panel) for M92A, NGC 6712A, and M71A to E. Affected by the companion atmosphere, the ToAs of NGC 6712A around orbital phase 0.25 have an extra delay, which is very likely caused by ionized gas emanating from the companion (Z. Yan et al. 2021). In the timing analysis, we exclude ToAs during eclipse phases: orbital phases 0.15−0.45 (M92A), 0.20−0.32 (NGC 6712A), and 0.15−0.34 (M71A). However, these excess delays remain visible in the residuals to illustrate the eclipsing signature.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Positions of M92A, NGC 6712A, and M71A-E (denoted as filled red stars), and detected X-ray sources (denoted as open blue circles), respectively. The details of those marked X-ray sources in M92, NGC 6712, and M71 can be found in T.-N. Lu et al. (2011), M. Geffert et al. (1994), and R. F. Elsner et al. (2008, and references therein), respectively. The center of these clusters (from W. E. Harris 2010) is shown as a green cross. The core radii (0﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩26, 0﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩76, and 0﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩63 for M92, NGC 6712, and M71, respectively) are represented by green dashed circles. A half-light radius (1﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩67) for M71 is denoted as an open green dotted circle. The background images are X-ray observations from the Chandra archive: M92 (ObsID 3778), NGC 6712 (ObsID 17849), and M71 (ObsID 5434). For reference, the beam size of FAST is 3′.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: Mass–inclination and mass–mass diagrams for M71D. The contours display the ﹩\cos i﹩–Mc and Mp–Mc planes, including 1σ, 2σ, and 3σ of a 2D pdf derived from the χ2 of TEMPO fit and the Bayesian technique described by E. M. Splaver et al. (2002), P. C. C. Freire et al. (2011). The red region shows the masses consistent with ﹩\dot{\omega }=0.0116\pm 0.0002\,{\rm{\deg }}\,{{\rm{yr}}}^{-1}﹩, assuming Einstein's theory of general relativity is valid. In the ﹩\cos i﹩–Mc plane, the gray areas are excluded by the condition that the pulsar mass must be positive; in the Mp–Mc plane, they are excluded by the mass function and the condition sini ≤ 1. The 1D pdfs for the ﹩\cos i﹩ (top left), Mp (top right), and Mc (right) are obtained from the marginalized 2D pdf. The orbital inclination remains unconstrained, as the median of the ﹩\cos i﹩ pdf is close to 0.5. The timing result for M71D shows no significant Shapiro delay or any other relativistic effects that could allow the determination of individual masses.
Paper: The FAST Globular Cluster Pulsar Survey (GC FANS)
Authors: Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
Figure caption: The central density as a function of the core radius for 44 clusters with known pulsars (excluding GLIMPSE-C01, which does not have available central density). The constant stellar interaction rate Γ and encounter rate per binary γ are shown as lines in the graph. Among these clusters, filled red, blue, and yellow circles indicate Nb > Ns, Ns > Nb, Ns ≃ Nb, where Nb and Ns are the numbers of binary pulsars and isolated pulsars, respectively.