configuration.yaml

---
# Configuration file for Fully Bayesian Forecast Example
# ======================================================
# This file mainly specifies information about the priors used and the
# frequency resolution of the imagined global signal experiment.
#
# It is placed here to make it either easier to consistently change these
# values or because they are used in multiple scripts.
#
#
# FREQUENCY RESOLUTION
# ====================
# The frequency resolution of the experiment in MHz.
frequency_resolution: 1.0
#
#
# PRIORS
# ======
# The priors used for each of global emus parameters.
# Each parameter has its own entry, specifying the prior type and the
# parameters of the prior. low and high are used in place of min and
# max to avoid clashing with python keywords.
priors:
  global_signal:
    f_star:
      type: log_uniform
      low: emu_min   # If given emu_min or emu_max, the value is taken from the
      high: emu_max  # minimum or maximum value of GlobalEmu was trained on.
    v_c:
      type: log_uniform
      low: emu_min
      high: 30.0
    f_x:
      type: log_uniform
      low: 0.001
      high: emu_max
    tau:
      type: truncated_gaussian
      mean: 0.054
      std: 0.007
      low: emu_min
      high: emu_max
    alpha:
      type: uniform
      low: emu_min
      high: emu_max
    nu_min:
      type: log_uniform
      low: emu_min
      high: emu_max
    R_mfp:
      type: uniform
      low: emu_min
      high: emu_max
  foregrounds:
    d0:
      type: uniform
      low: 1500.0  # K
      high: 2000.0 # K
    d1:
      type: uniform
      low: -1.0
      high: 1.0
    d2:
      type: uniform
      low: -0.05
      high: 0.05
    tau_e:
      type: uniform
      low: 0.005
      high: 0.200
    t_e:
      type: uniform
      low: 200.0   # K
      high: 2000.0 # K
#
#
# PREPROCESSING
# =============
# Settings to control the preprocessing of the data before being fed into the
# neural network.
whitening_transform: 'Cholesky'  # None, ZCA, PCA, Cholesky, ZCA-cor or PCA-cor
covariance_samples: 100_000  # Number of samples to use when calculating the
                             # covariance matrix for the whitening transform.
#
#
# VERIFICATION
# ============
# Number of data sets generated from each model to use when verifying the
# network against PolyChord. Evaluated in batches of fixed size due to
# HPC scheduling limitations.
verification_data_sets_per_model: 1000
verification_data_set_batch_size:    5
#
#
# PLOTTING
# ========
# Parameters that control details of the plots used to visualize the results.
br_evaluations_for_forecast: 1000000
detection_thresholds: ["2 sigma", "3 sigma", "5 sigma"]
parameters_to_plot: ["f_star", "f_x", "tau"]
parameters_to_log: ["f_star", "f_x"]