A New Approach for Measuring Power Spectra and Reconstructing Time Series in Active Galactic Nuclei

We provide a new approach to measure power spectra and reconstruct time series in active galactic nuclei (AGNs) based on the fact that the Fourier transform of AGN stochastic variations is a series of complex Gaussian random variables. The approach parameterizes a stochastic series in frequency domain and transforms it back to time domain to fit the observed data. The parameters and their uncertainties are derived in a Bayesian framework, which also allows us to compare the relative merits of different power spectral density models. The well-developed fast Fourier transform algorithm together with parallel computation enable an acceptable time complexity for the approach.Comment: 5 pages, 4 figures; accepted to MNRAS Letter; software available at https://github.com/LiyrAstroph/RECO

Identifying multi-scale communities in networks by asymptotic surprise

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asymptotic approximation of surprise. We discussed the critical behaviors of asymptotic surprise in phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we validated the critical behaviors of the asymptotic surprise further and the effectiveness of the improved Louvain algorithm, displayed its ability to solve the first-type resolution limit and stronger tolerance against the second-type resolution limit, and confirmed its effectiveness of revealing multi-scale community structures in multi-scale networks.Comment: 18 pages, 7 figure

Type-I X-ray bursts reveal a fast co-evolving behavior of the corona in an X-ray binary

The coronae in X-ray binaries (XRBs) still remain poorly understood, although they have been believed for a long time to play a key role in modeling the characteristic outbursts of XRBs. Type-I X-ray bursts, the thermonuclear flashes happening on the surface of a neutron star (NS), can be used as a probe to the innermost region of a NS XRB, where the corona is believed to be located very close to the NS. We report the discovery of a tiny life cycle of the corona that is promptly co-evolved with the type-I bursts superimposed on the outburst of the NS XRB IGR J17473$-$2721. This finding may serve as the first evidence of directly seeing the rapid disappearance and formation of a corona in an XRB with a cooling/heating timescale of less than a second, which can strongly constrain the accretion models in XRBs at work.Comment: The Astrophysical Journal, 2012, 752, L3

An accurate front capturing scheme for tumor growth models with a free boundary limit

We consider a class of tumor growth models under the combined effects of density-dependent pressure and cell multiplication, with a free boundary model as its singular limit when the pressure-density relationship becomes highly nonlinear. In particular, the constitutive law connecting pressure $p$ and density $\rho$ is $p(\rho)=\frac{m}{m-1} \rho^{m-1}$, and when $m \gg 1$, the cell density $\rho$ may evolve its support due to a pressure-driven geometric motion with sharp interface along the boundary of its support. The nonlinearity and degeneracy in the diffusion bring great challenges in numerical simulations, let alone the capturing of the singular free boundary limit. Prior to the present paper, there is lack of standard mechanism to numerically capture the front propagation speed as $m\gg 1$. In this paper, we develope a numerical scheme based on a novel prediction-correction reformulation that can accurately approximate the front propagation even when the nonlinearity is extremely strong. We show that the semi-discrete scheme naturally connects to the free boundary limit equation as $m \rightarrow \infty$, and with proper spacial discretization, the fully discrete scheme has improved stability, preserves positivity, and implements without nonlinear solvers. Finally, extensive numerical examples in both one and two dimensions are provided to verify the claimed properties and showcase good performance in various applications

Cosmological Evolution of Supermassive Black Holes: Mass Functions and Spins

We derive the mass function of supermassive black holes (SMBHs) over the redshift range 0<z<2, using the latest deep luminosity and mass functions of field galaxies. Applying this mass function, combined with the bolometric luminosity function of active galactic nuclei (AGNs), into the the continuity equation of SMBH number density, we explicitly obtain the mass-dependent cosmological evolution of the radiative efficiency for accretion. We suggest that the accretion history of SMBHs and their spins evolve in two distinct regimes: an early phase of prolonged accretion, plausibly driven by major mergers, during which the black hole spins up, then switching to a period of random, episodic accretion, governed by minor mergers and internal secular processes, during which the hole spins down. The transition epoch depends on mass, mirroring other evidence for "cosmic downsizing" in the AGN population.Comment: Proceedings of IAUS 290 "Feeding Compact Objects: Accretion on All Scales", C. M. Zhang, T. Belloni, M. Mendez & S. N. Zhang (eds.

Analysis and computation of some tumor growth models with nutrient: from cell density models to free boundary dynamics

In this paper, we study the tumor growth equation along with various models for the nutrient component, including the \emph{in vitro} model and the \emph{in vivo} model. At the cell density level, the spatial availability of the tumor density $n$ is governed by the Darcy law via the pressure $p(n)=n^{\gamma}$. For finite $\gamma$, we prove some a priori estimates of the tumor growth model, such as boundedness of the nutrient density, and non-negativity and growth estimate of the tumor density. As $\gamma \rightarrow \infty$, the cell density models formally converge to Hele-Shaw flow models, which determine the free boundary dynamics of the tumor tissue in the incompressible limit. We derive several analytical solutions to the Hele-Shaw flow models, which serve as benchmark solutions to the geometric motion of tumor front propagation. Finally, we apply a conservative and positivity preserving numerical scheme to the cell density models, with numerical results verifying the link between cell density models and the free boundary dynamical models

INTEGRAL and Swift observations of the Be X-ray binary 4U 1036-56 (RX J1037.5-5647) and its possible relation with gamma-ray transients

We present timing, spectral, and long-term temporal analysis of the high mass X-ray binary (HMXB) 4U 1036-56 using INTEGRAL and Swift observations. We show that it is a weak hard X-ray source spending a major fraction of the time in quiescence, and only occasionally characterized by X-ray outbursts. The outburst activity we report here lasts several days, with a dynamic range spanned by the luminosity in quiescence and in outburst as high as ~30. We report the detection of pulse period at 854.75+/-4.39 s during an outburst, which is consistent with previous measurements. Finally, we analyze the possibility of 4U 1036--56's association with the unidentified transient gamma-ray sources AGL J1037--5708 & GRO J1036--55, as prompted by its positional correlation.Comment: 21 pages, 6 figures; Accepted for publication in Ap

Identifying the Absorption Bump with Deep Learning

The pervasive interstellar dust grains provide significant insights to understand the formation and evolution of the stars, planetary systems, and the galaxies, and may harbor the building blocks of life. One of the most effective way to analyze the dust is via their interaction with the light from background sources. The observed extinction curves and spectral features carry the size and composition information of dust. The broad absorption bump at 2175 Angstrom is the most prominent feature in the extinction curves. Traditionally, statistical methods are applied to detect the existence of the absorption bump. These methods require heavy preprocessing and the co-existence of other reference features to alleviate the influence from the noises. In this paper, we apply Deep Learning techniques to detect the broad absorption bump. We demonstrate the key steps for training the selected models and their results. The success of Deep Learning based method inspires us to generalize a common methodology for broader science discovery problems. We present our on-going work to build the DeepDis system for such kind of applications

What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?

Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition. Compared with the natural image, the scale of remote sensing image is larger and the scene and the object it represents are more macroscopic. This study inquires whether remote sensing scene and natural scene recognitions differ and raises the following questions: What are the key factors in remote sensing scene recognition? Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding? We performed several experiments to explore the influence of the DCNN structure and the scale of remote sensing scene understanding from the perspective of scene complexity. Our experiment shows that understanding a complex scene depends on an in-depth network and multiple-scale perception. Using a visualization method, we qualitatively and quantitatively analyze the recognition mechanism in a complex remote sensing scene and demonstrate the importance of multi-objective joint semantic support

Alignments Of Black Holes With Their Warped Accretion Disks And Episodic Lifetimes Of Active Galactic Nuclei

Warped accretion disks have attracted intensive attention because of their critical role on shaping the spin of supermassive massive black holes (SMBHs) through the Bardeen-Petterson effect, a general relativistic effect that leads to final alignments or anti-alignments between black holes and warped accretion disks. We study such alignment processes by explicitly taking into account the finite sizes of accretion disks and the episodic lifetimes of AGNs that delineate the duration of gas fueling onto accretion disks. We employ an approximate global model to simulate the evolution of accretion disks, allowing to determine the gravitomagnetic torque that drives the alignments in a quite simple way. We then track down the evolutionary paths for mass and spin of black holes both in a single activity episode and over a series of episodes. Given with randomly and isotropically oriented gas fueling over episodes, we calculate the spin evolution with different episodic lifetimes and find that it is quite sensitive to the lifetimes. We therefore propose that spin distribution of SMBHs can place constraints on the episodic lifetimes of AGNs and vice versa. Applications of our results on the observed spin distributions of SMBHs and the observed episodic lifetimes of AGNs are discussed, although both the measurements at present are yet ambiguous to draw a firm conclusion. Our prescription can be easily incorporated into semi-analytic models for black hole growth and spin evolution.Comment: 11 pages, 8 figures, 1 table, to appear in the Astrophysical Journa