14,020 research outputs found
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 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 ~nm graphite dust. The best-fitting magnetically arrested model
has the initial density , initial
radius , mass , and dust relative abundance . It provides a
good fit to data, is consistent with the luminosities in and
, 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 . 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 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
components revealed in a multi-periodic variation, it is mandatory to apply
at least significance tests, among which the most involves various
multi-frequency statistics, and only 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 , where stands for
a normalized width of the settled frequency range, and 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 -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 , where is an algebraic polynomial and 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 . This simple function with an additional non-linear parameter 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 -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 -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
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 , 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
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
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
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