An intermediate-mass black hole in the centre of the globular cluster 47 Tucanae [GA]

Intermediate mass black holes play a critical role in understanding the evolutionary connection between stellar mass and super-massive black holes. However, to date the existence of these species of black holes remains ambiguous and their formation process is therefore unknown. It has been long suspected that black holes with masses $10^{2}-10^{4}M_{\odot}$ should form and reside in dense stellar systems. Therefore, dedicated observational campaigns have targeted globular cluster for many decades searching for signatures of these elusive objects. All candidates found in these targeted searches appear radio dim and do not have the X-ray to radio flux ratio predicted by the fundamental plane for accreting black holes. Based on the lack of an electromagnetic counterpart upper limits of $2060 M_{\odot}$ and $470 M_{\odot}$ have been placed on the mass of a putative black hole in 47 Tucanae (NGC 104) from radio and X-ray observations respectively. Here we show there is evidence for a central black hole in 47 Tuc with a mass of M$_{\bullet}\sim2200 M_{\odot}$$_{-800}^{+1500}$ when the dynamical state of the globular cluster is probed with pulsars. The existence of an intermediate mass black hole in the centre of one of the densest clusters with no detectable electromagnetic counterpart suggests that the black hole is not accreting at a sufficient rate and therefore contrary to expectations is gas starved. This intermediate mass black hole might be a member of electromagnetically invisible population of black holes that are the elusive seeds leading to the formation of supermassive black holes in galaxies.

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B. Kiziltan, H. Baumgardt and A. Loeb
Thu, 9 Feb 17

Comments: Published in Nature

Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive "in silico" dataset [SSA]

Many real world systems exhibit cyclic behavior that is, for example, due to the nearly harmonic oscillations being perturbed by the strong fluctuations present in the regime of significant non-linearities. For the investigation of such sys- tems special techniques relaxing the assumption to periodicity are required. In this paper, we present the generalization of one of such techniques, namely the D2 phase dispersion statistic, to multidimensional datasets, especially suited for the analysis of the outputs from three-dimensional numerical simulations of the full magnetohydrodynamic equations. We present the motivation and need for the usage of such a method with simple test cases, and present an application to a solar-like semi-global numerical dynamo simulation covering nearly 150 magnetic cycles.

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N. Olspert, M. Kapyla and J. Pelt
Wed, 7 Dec 16

Comments: N/A

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference [CL]

Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally. In this paper, we report on a scalable, parallel version of Celeste, suitable for learning catalogs from modern large-scale astronomical datasets. Our algorithmic innovations include a fast numerical optimization routine for Bayesian posterior inference and a statistically efficient scheme for decomposing astronomical optimization problems into subproblems.
Our scalable implementation is written entirely in Julia, a new high-level dynamic programming language designed for scientific and numerical computing. We use Julia’s high-level constructs for shared and distributed memory parallelism, and demonstrate effective load balancing and efficient scaling on up to 8192 Xeon cores on the NERSC Cori supercomputer.

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J. Regier, K. Pamnany, R. Giordano, et. al.
Fri, 11 Nov 16

Comments: submitting to IPDPS’17

The Non-homogeneous Poisson Process for Fast Radio Burst Rates [HEAP]

This paper presents the non-homogeneous Poisson process (NHPP) for modeling the rate of fast radio bursts (FRBs) and other infrequently observed astronomical events. The NHPP, well-known in statistics, can model changes in the rate as a function of both astronomical features and the details of an observing campaign. This is particularly helpful for rare events like FRBs because the NHPP can combine information across surveys, making the most of all available information. The goal of the paper is two-fold. First, it is intended to be a tutorial on the use of the NHPP. Second, we build an NHPP model that incorporates beam patterns and a power law flux distribution for the rate of FRBs. Using information from 12 surveys including 15 detections, we find an all-sky FRB rate of 586.88 events per sky per day above a flux of 1 Jy (95\% CI: 271.86, 923.72) and a flux power-law index of 0.91 (95\% CI: 0.57, 1.25). Our rate is lower than other published rates, but consistent with the rate given in Champion et al. 2016.

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E. Lawrence, S. Wiel, C. Law, et. al.
Thu, 3 Nov 16

Comments: 19 pages, 2 figures

Neutron stars in the light of SKA: Data, statistics, and science [IMA]

The Square Kilometre Array (SKA), when it becomes functional, is expected to enrich neutron star (NS) catalogues by at least an order of magnitude over their current state. This includes the discovery of new NS objects leading to better sampling of under-represented NS categories, precision measurements of intrinsic properties such as spin period and magnetic field, as also data on related phenomena such as microstructure, nulling, glitching, etc. This will present a unique opportunity to seek answers to interesting and fundamental questions about the extreme physics underlying these exotic objects in the universe. In this paper, we first present a meta-analysis (from a methodological viewpoint) of statistical analyses performed using existing NS data, with a two-fold goal: First, this should bring out how statistical models and methods are shaped and dictated by the science problem being addressed. Second, it is hoped that these analyses will provide useful starting points for deeper analyses involving richer data from SKA whenever it becomes available. We also describe a few other areas of NS science which we believe will benefit from SKA which are of interest to the Indian NS community.

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M. Arjunwadkar, A. Kashikar and M. Bagchi
Thu, 27 Oct 16

Comments: To appear in Journal of Astrophysics and Astronomy (JOAA) special issue on “Science with the SKA: an Indian perspective”

Period estimation for sparsely-sampled quasi-periodic light curves applied to Miras [SSA]

We develop a non-linear semi-parametric Gaussian process model to estimate periods of Miras with sparsely-sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter over a dense grid.
A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting Period-Luminosity relations.

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S. He, W. Yuan, J. Huang, et. al.
Thu, 22 Sep 16

Comments: Accepted for publication in The Astronomical Journal. Software package and test data set available at this http URL

The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model [CEA]

Conventional Type Ia supernova (SN Ia) cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color-magnitude slopes. We construct a probabilistic generative model for the distribution of dusty extinguished absolute magnitudes and apparent colors as a convolution of the intrinsic SN Ia color-magnitude distribution and the host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude (M_B vs. B-V) slope beta_int differs from the host galaxy dust law R_B, this convolution results in a specific curve of mean extinguished absolute magnitude vs. apparent color. The derivative of this curve smoothly transitions from beta_int in the blue tail to R_B in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve at this transition near the average apparent color, resulting in an apparent slope beta_app between beta_int and R_B. We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a dataset of SALT2 optical light curve fits of a compilation of 277 nearby SN Ia at z < 0.10. The conventional linear fit obtains beta_app = 3. Our model finds a beta_int = 2.2 +/- 0.3 and a distinct dust law of R_B = 3.7 +/- 0.3, consistent with the average for Milky Way dust, while correcting a systematic distance bias of ~0.10 mag in the tails of the apparent color distribution.

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K. Mandel, D. Scolnic, H. Shariff, et. al.
Fri, 16 Sep 16

Comments: 22 pages, 16 figures, submitted to ApJ