Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Intermediate-mass-ratio-inspirals in the Einstein Telescope. II.
  Parameter estimation errors   We explore the precision with which the Einstein Telescope (ET) will be able
to measure the parameters of intermediate-mass-ratio inspirals (IMRIs). We
calculate the parameter estimation errors using the Fisher Matrix formalism and
present results of a Monte Carlo simulation of these errors over choices for
the extrinsic parameters of the source. These results are obtained using two
different models for the gravitational waveform which were introduced in paper
I of this series. These two waveform models include the inspiral, merger and
ringdown phases in a consistent way. One of the models, based on the transition
scheme of Ori & Thorne [1], is valid for IMBHs of arbitrary spin, whereas the
second model, based on the Effective One Body (EOB) approach, has been
developed to cross-check our results in the non-spinning limit. In paper I of
this series, we demonstrated the excellent agreement in both phase and
amplitude between these two models for non-spinning black holes, and that their
predictions for signal-to-noise ratios (SNRs) are consistent to within ten
percent. We now use these models to estimate parameter estimation errors for
binary systems with masses 1.4+100, 10+100, 1.4+500 and 10+500 solar masses
(SMs), and various choices for the spin of the central intermediate-mass black
hole (IMBH). Assuming a detector network of three ETs, the analysis shows that
for a 10 SM compact object (CO) inspiralling into a 100 SM IMBH with spin
q=0.3, detected with an SNR of 30, we should be able to determine the CO and
IMBH masses, and the IMBH spin magnitude to fractional accuracies of 0.001,
0.0003, and 0.001, respectively. We also expect to determine the location of
the source in the sky and the luminosity distance to within 0.003 steradians,
and 10%, respectively. We also assess how the precision of parameter
determination depends on the network configuration.

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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: math