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PREDEFINED EMULATORS AND TRAINING SETS

PREDEFINED EMULATORS

LaCE provides a set of predefined emulators that have been validated. These emulators are:

  • Neural network emulators:

    • Gadget emulators:
      • Cabayol23: Neural network emulating the optimal P1D of Gadget simulations fitting coefficients to a 5th degree polynomial. It goes to scales of 4Mpc^{-1} and z<=4.5.
      • Cabayol23+: Neural network emulating the optimal P1D of Gadget simulations fitting coefficients to a 5th degree polynomial. It goes to scales of 4Mpc^{-1} and z<=4.5. Updated version compared to Cabayol+23 paper.
      • Cabayol23_extended: Neural network emulating the optimal P1D of Gadget simulations fitting coefficients to a 7th degree polynomial. It goes to scales of 8Mpc^{-1} and z<=4.5.
      • Cabayol23+_extended: Neural network emulating the optimal P1D of Gadget simulations fitting coefficients to a 5th degree polynomial. It goes to scales of 4Mpc^{-1} and z<=4.5. Updated version compared to Cabayol+23 paper.
    • Nyx emulators:
      • Nyx_v0: Neural network emulating the optimal P1D of Nyx simulations fitting coefficients to a 6th degree polynomial. It goes to scales of 4Mpc^{-1} and z<=4.5.
      • Nyx_v0_extended: Neural network emulating the optimal P1D of Nyx simulations fitting coefficients to a 6th degree polynomial. It goes to scales of 8Mpc^{-1} and z<=4.5.
      • Nyx_alphap: Neural network emulating the optimal P1D of Nyx simulations fitting coefficients to a 6th degree polynomial. It goes to scales of 4Mpc^{-1} and z<=4.5.
      • Nyx_alphap_extended: Neural network emulating the optimal P1D of Nyx simulations fitting coefficients to a 6th degree polynomial. It goes to scales of 8Mpc^{-1} and z<=4.5.
      • Nyx_alphap_cov: Neural network under testing for the Nyx_alphap emulator.

  • Gaussian Process emulators:

    • Gadget emulators:
      • "Pedersen21": Gaussian process emulating the optimal P1D of Gadget simulations. Pedersen+21 paper.
      • "Pedersen23": Updated version of Pedersen21 emulator. Pedersen+23 paper.
      • "Pedersen21_ext": Extended version of Pedersen21 emulator.
      • "Pedersen21_ext8": Extended version of Pedersen21 emulator up to k=8 Mpc^-1.
      • "Pedersen23_ext": Extended version of Pedersen23 emulator.
      • "Pedersen23_ext8": Extended version of Pedersen23 emulator up to k=8 Mpc^-1.

PREDEFINED TRAINING SETS

Similarly, LaCE provides a set of predefined training sets that have been used to train the emulators. These training sets correspond to a simulations suite, a postprocessing and the addition (or not) of mean flux rescalings. The training sets are:

  • "Pedersen21": Training set used in Pedersen+21 paper. Gadget simulations without mean flux rescalings.
  • "Cabayol23": Training set used in Cabayol+23 paper. Gadget simulations with mean flux rescalings and measuring the P1D along the three principal axes of the simulation box.
  • "Nyx_Oct2023": Training set using Nyx version from October 2023.
  • "Nyx_Jul2024": Training set using Nyx version from July 2024.

CONNECTION BETWEEN PREDEFINED EMULATORS AND TRAINING SETS

The following table shows the default training set for each predefined emulator.

Emulator Training Set Simulation Type Description
Cabayol23 Cabayol23 Gadget NN Neural network emulator trained on Gadget simulations with mean flux rescaling
Cabayol23+ Cabayol23 Gadget NN Updated version of Cabayol23 emulator
Cabayol23_extended Cabayol23 Gadget NN Extended version of Cabayol23 emulator (k up to 8 Mpc^-1)
Cabayol23+_extended Cabayol23 Gadget NN Extended version of Cabayol23+ emulator (k up to 8 Mpc^-1)
Nyx_v0 Nyx_Oct2023 Nyx NN Neural network emulator trained on Nyx simulations
Nyx_v0_extended Nyx_Oct2023 Nyx NN Extended version of Nyx_v0 emulator (k up to 8 Mpc^-1)
Nyx_alphap Nyx_Oct2023 Nyx NN Neural network emulator trained on updated Nyx simulations
Nyx_alphap_extended Nyx_Oct2023 Nyx NN Extended version of Nyx_alphap emulator (k up to 8 Mpc^-1)
Nyx_alphap_cov Nyx_Jul2024 Nyx NN Testing version of Nyx_alphap emulator
Pedersen21 Pedersen21 Gadget GP GP emulator trained on Gadget simulations without mean flux rescaling
Pedersen23 Pedersen21 Gadget GP Updated version of Pedersen21 GP emulator
Pedersen21_ext Pedersen21 Gadget GP Extended version of Pedersen21 GP emulator
Pedersen21_ext8 Pedersen21 Gadget GP Extended version of Pedersen21 GP emulator (k up to 8 Mpc^-1)
Pedersen23_ext Pedersen21 Gadget GP Extended version of Pedersen23 GP emulator
Pedersen23_ext8 Pedersen21 Gadget GP Extended version of Pedersen23 GP emulator (k up to 8 Mpc^-1)