Nonextensive scalar field theories and dark energy models

Bibcode
2004PhyA..340..459B
DOI
10.1016/j.physa.2004.04.041
Beck, C.
Bibliographical reference

Physica A, Volume 340, Issue 1-3, p. 459-466.

Advertised on:
9
2004
Number of authors
1
IAC number of authors
0
Citations
17
Refereed citations
14
Description
Current astronomical measurements indicate that approximately 73% of the universe is made up of dark energy. Stochastically quantized self-interacting scalar fields can serve as suitable models to generate dark energy. We study a particular model where the scalar field theory underlying dark energy exhibits strongest possible chaotic behaviour. It is shown that the fluctuating chaotic field momenta obey a generalized (nonextensive) statistical mechanics, with an entropic index q given by q=3, respectively, q=-1 if the escort formalism is used.
Type
Refereed
Dark energy

It may interest you

  • Refereed
    An ultra-short-period super-Earth with an extremely high density and an outer companion
    We present the discovery and characterization of a new multi-planetary system around the Sun-like star K2-360 (EPIC 201595106). K2-360 was first identified in K2 photometry as the host of an ultra-short-period (USP) planet candidate with a period of 0.88 d. We obtained follow-up transit photometry, confirming the star as the host of the signal
    Livingston, John H. et al.

    Advertised on:

    11
    2024
    Bibcode
    2024NatSR..1427219L
    Citations
    0
  • Refereed
    Neural calibration of imaging Stokes polarimeters
    Current polarimetric calibration techniques derive the instrument modulation matrix by numerically fitting an instrumental model to measurements of a set of calibration Stokes vectors. These techniques are typically limited to an error on the retrieved normalized Stokes Q, U and V parameters, in the $1\times10^{-2}$ to $1\times10^{-3}$ range. This
    Iglesias, F. A. et al.

    Advertised on:

    8
    2024
    Bibcode
    2024BAAA...65..281I
    Citations
    0
  • Refereed
    Model-based identification of solar coronal mass ejections using deep neural networks
    Coronal mass ejections (CMEs) are a major driver of space weather and thus can have important negative technological and social impacts. To assess their geoeffectiveness once they are ejected, it is crucial their prompt identification in coronagraph images. In the last decade, deep neural networks (DNN) have experienced enormous improvements in
    Iglesias, F. A. et al.

    Advertised on:

    8
    2024
    Bibcode
    2024BAAA...65...17I
    Citations
    0