The Properties and Fate of the Galactic Center G2 Cloud

The object G2 was recently discovered descending into the gravitational potential of the supermassive black hole (BH) Sgr A*. We test the photoionized cloud scenario, determine the cloud properties, and estimate the emission during the pericenter passage. The incident radiation is computed starting from the individual stars at the locations of G2. The radiative transfer calculations are conducted with CLOUDY code and $2011$ broadband and line luminosities are fitted. The spherically symmetric, tidally distorted, and magnetically arrested cloud shapes are tested with both the interstellar medium dust and $10$~nm graphite dust. The best-fitting magnetically arrested model has the initial density $n_{\rm init}=1.8\times10^5{\rm cm}^{-3}$, initial radius $R_{\rm init}=2.2\times10^{15}{\rm cm}=17 {\rm mas}$, mass $m_{\rm cloud}=4M_{\rm Earth}$, and dust relative abundance $A=0.072$. It provides a good fit to $2011$ data, is consistent with the luminosities in $2004$ and $2008$, and reaches an agreement with the observed size. We revise down the predicted radio and X-ray bow shock luminosities to be below the quiescent level of Sgr A*, which readily leads to non-detection in agreement to observations. The magnetic energy dissipation in the cloud at the pericenter coupled with more powerful irradiation may lead to an infrared source with an apparent magnitude $m_{L'}\approx13.0$. No shock into the cloud and no X-rays are expected from cloud squeezing by the ambient gas pressure. Larger than previously estimated cloud mass $m_{\rm cloud}=(4-20)M_{\rm Earth}$ may produce a higher accretion rate and a brighter state of Sgr A* as the debris descend onto the BH.Comment: 12 pages, 5 figures, and 1 table, accepted to Ap

Detecting multiple periodicities in observational data with the multi-frequency periodogram. I. Analytic assessment of the statistical significance

We consider the "multi-frequency" periodogram, in which the putative signal is modelled as a sum of two or more sinusoidal harmonics with idependent frequencies. It is useful in the cases when the data may contain several periodic components, especially when their interaction with each other and with the data sampling patterns might produce misleading results. Although the multi-frequency statistic itself was already constructed, e.g. by G. Foster in his CLEANest algorithm, its probabilistic properties (the detection significance levels) are still poorly known and much of what is deemed known is unrigourous. These detection levels are nonetheless important for the data analysis. We argue that to prove the simultaneous existence of all $n$ components revealed in a multi-periodic variation, it is mandatory to apply at least $2^n-1$ significance tests, among which the most involves various multi-frequency statistics, and only $n$ tests are single-frequency ones. The main result of the paper is an analytic estimation of the statistical significance of the frequency tuples that the multi-frequency periodogram can reveal. Using the theory of extreme values of random fields (the generalized Rice method), we find a handy approximation to the relevant false alarm probability. For the double-frequency periodogram this approximation is given by an elementary formula $\frac{\pi}{16} W^2 e^{-z} z^2$, where $W$ stands for a normalized width of the settled frequency range, and $z$ is the observed periodogram maximum. We carried out intensive Monte Carlo simulations to show that the practical quality of this approximation is satisfactory. A similar analytic expression for the general multi-frequency periodogram is also given in the paper, though with a smaller amount of numerical verification.Comment: Accepted to MNRAS; 14 pages with 9 figures; the computation package is available at http://sourceforge.net/projects/frede

From Seed AI to Technological Singularity via Recursively Self-Improving Software

Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, review the relevant literature, analyze limits on computation restricting recursive self-improvement and introduce RSI Convergence Theory which aims to predict general behavior of RSI systems. Finally, we address security implications from self-improving intelligent software

Detecting multiple periodicities in observational data with the multifrequency periodogram - II. Frequency Decomposer, a parallelized time-series analysis algorithm

This is a parallelized algorithm performing a decomposition of a noisy time series into a number of sinusoidal components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After selection of the initial candidates, the algorithm performs a complete pass through all their possible combinations and computes the rigorous multifrequency statistical significance for each such frequency tuple. The largest combinations that still survived this thresholding procedure represent the outcome of the analysis. The parallel computing on a graphics processing unit (GPU) is implemented through CUDA and brings a significant performance increase. It is still possible to run FREDEC solely on CPU in the traditional single-threaded mode, when no suitable GPU device is available. To verify the practical applicability of our algorithm, we apply it to an artificial time series as well as to some real-life exoplanetary radial-velocity data. We demonstrate that FREDEC can successfully reveal several known exoplanets. Moreover, it detected a new $9.8$-day variation in the Lick data for the five-planet system of 55 Cnc. It might indicate the existence of a small sixth planet in the 3:2 commensurability with the planet 55 Cnc b, although this detection is model-dependent and still needs a detailed verification.Comment: Accepted by Astronomy & Computing; 13 pages. Accepted version v2 contains new section 7. The package can be downloaded at http://sourceforge.net/projects/fredec

Detecting non-sinusoidal periodicities in observational data: the von Mises periodogram for variable stars and exoplanetary transits

This paper introduces an extension of the linear least-squares (or Lomb-Scargle) periodogram for the case when the model of the signal to be detected is non-sinusoidal and depends on unknown parameters in a non-linear manner. The attention is paid to the problem of estimating the statistical significance of candidate periodicities found using such non-linear periodograms. This problem is related to the task of quantifying the distributions of maximum values of these periodograms. Based on recent results in the mathematical theory of extreme values of random field (the generalized Rice method), we give a general approach to find handy analytic approximation for these distributions. This approximation has the general form $e^{-z} P(\sqrt z)$, where $P$ is an algebraic polynomial and $z$ being the periodogram maximum. The general tools developed in this paper can be used in a wide variety of astronomical applications, for instance in the studies of variable stars and extrasolar planets. For this goal, we develop and consider in details the so-called von Mises periodogram: a specialized non-linear periodogram where the signal is modelled by the von Mises periodic function $\exp(\nu \cos \omega t)$. This simple function with an additional non-linear parameter $\nu$ can model lightcurves of many astronomical objects that show periodic photometric variability of different nature. We prove that our approach can be perfectly applied to this non-linear periodogram. We provide a package of auxiliary C++ programs, attached as the online-only material. They should faciliate the use of the von Mises periodogram in practice.Comment: 14 pages, 5 figures; published in MNRAS. Version 2 here is the accepted manuscript with a reduced fig. 5 file and without copy-edit changes of the final published version. Notice added with v2: the von Mises periodogram computation package, along with any future updates, can be downloaded at http://sourceforge.net/projects/vonmises

PlanetPack: a radial-velocity time-series analysis tool facilitating exoplanets detection, characterization, and dynamical simulations

We present PlanetPack, a new software tool that we developed to facilitate and standardize the advanced analysis of radial velocity (RV) data for the goal of exoplanets detection, characterization, and basic dynamical $N$-body simulations. PlanetPack is a command-line interpreter, that can run either in an interactive mode or in a batch mode of automatic script interpretation. Its major abilities include: (i) Advanced RV curve fitting with the proper maximum-likelihood treatment of unknown RV jitter; (ii) User-friendly multi-Keplerian as well as Newtonian $N$-body RV fits; (iii) Use of more efficient maximum-likelihood periodograms that involve the full multi-planet fitting (sometimes called as ``residual'' or ``recursive'' periodograms); (iv) Easily calculatable parametric 2D likelihood function level contours, reflecting the asymptotic confidence regions; (v) Fitting under some useful functional constraints is user-friendly; (vi) Basic tasks of short- and long-term planetary dynamical simulation using a fast Everhart-type integrator based on Gauss--Legendre spacings; (vii) Fitting the data with red noise (auto-correlated errors); (viii) Various analytical and numerical methods for the tasks of determining the statistical significance. It is planned that further functionality may be added to PlanetPack in the future. During the development of this software, a lot of effort was made to improve the calculational speed, especially for CPU-demanding tasks. PlanetPack was written in pure C++ (standard of 1998/2003), and is expected to be compilable and usable on a wide range of platforms.Comment: 15 pages; Revised version submitted to Astronomy & Computing; see http://sourceforge.net/projects/planetpack/ for downloa

Theory of G2 Cloud Multi-Wavelength Emission

An object called G2 was recently discovered moving towards the supermassive black hole in the Galactic Center. G2 emits infrared (IR) lines and continuum, which allows constraining its properties. The question is still unresolved whether G2 has a central windy star or it is a coreless cloud. Assuming the object is a cloud originating near the apocenter I perform line/continuum IR diagnostics, revisit estimates of non-thermal emission from pericenter passage, and speculate about future observational prospects. This work is partially reported in arXiv:1309.2282 and partially consists of new ideas discussed at the conference.Comment: 4 pages, conference proceedings, IAU Symposium 303 "The GC: Feeding and Feedback in a Normal Galactic Nucleus", 2013 Sep. 30 - Oct. 4, Santa Fe New Mexico (USA

PlanetPack software tool for exoplanets detection: coming new features

We briefly overview the new features of PlanetPack2, the forthcoming update of PlanetPack, which is a software tool for exoplanets detection and characterization from Doppler radial velocity data. Among other things, this major update brings parallelized computing, new advanced models of the Doppler noise, handling of the so-called Keplerian periodogram, and routines for transits fitting and transit timing variation analysis.Comment: 2 pages, to appear in the proceedings of IAUS310 "Complex Planetary Systems"; the PlanetPack2 package can be downloaded at http://sourceforge.net/projects/planetpac

The impact of red noise in radial velocity planet searches: Only three planets orbiting GJ581?

We perform a detailed analysis of the latest HARPS and Keck radial velocity data for the planet-hosting red dwarf GJ581, which attracted a lot of attention in recent time. We show that these data contain important correlated noise component ("red noise") with the correlation timescale of the order of 10 days. This red noise imposes a lot of misleading effects while we work in the traditional white-noise model. To eliminate these misleading effects, we propose a maximum-likelihood algorithm equipped by an extended model of the noise structure. We treat the red noise as a Gaussian random process with exponentially decaying correlation function. Using this method we prove that: (i) planets b and c do exist in this system, since they can be independently detected in the HARPS and Keck data, and regardless of the assumed noise models; (ii) planet e can also be confirmed independently by the both datasets, although to reveal it in the Keck data it is mandatory to take the red noise into account; (iii) the recently announced putative planets f and g are likely just illusions of the red noise; (iv) the reality of the planet candidate GJ581 d is questionable, because it cannot be detected from the Keck data, and its statistical significance in the HARPS data (as well as in the combined dataset) drops to a marginal level of $\sim 2\sigma$, when the red noise is taken into account. Therefore, the current data for GJ581 really support existence of no more than four (or maybe even only three) orbiting exoplanets. The planet candidate GJ581 d requests serious observational verification.Comment: 19 pages, 12 figures, 3 tables; accepted to MNRA

Detecting Qualia in Natural and Artificial Agents

The Hard Problem of consciousness has been dismissed as an illusion. By showing that computers are capable of experiencing, we show that they are at least rudimentarily conscious with potential to eventually reach superconsciousness. The main contribution of the paper is a test for confirming certain subjective experiences in a tested agent. We follow with analysis of benefits and problems with conscious machines and implications of such capability on future of computing, machine rights and artificial intelligence safety