Survey presets#

Binny allows survey configurations to be stored as YAML survey presets. A preset groups together the parent redshift distribution, tomographic binning scheme, uncertainty model, and optional survey metadata such as footprint or galaxy density.

Using a YAML preset makes it easier to keep survey configurations consistent across forecasts, examples, and analyses.

LSST survey preset#

The example below shows the LSST survey preset included with Binny. It defines tomographic configurations for lens and source samples for both year 1 and year 10.

Show LSST YAML preset

LSST survey configuration#

name: lsst  # https://arxiv.org/abs/1809.01669

# Optional survey metadata (binning code can ignore unless requested)
survey_meta:
  footprint:
    # Can provide either survey_area [deg^2] or frac_sky (or both).
    nominal: {survey_area: 18000.0}
    years:
      # Year labels are strings on purpose (stable keys).
      '1':  {survey_area: 12000.0}
      '10': {survey_area: 14000.0}

# Redshift grid used for evaluating n(z) and all n_i(z) outputs
z_grid: {start: 0.0, stop: 3.5, n: 500}


tomography:
  # LENS (Y1)
  - role: lens  # optional label (e.g. lens/source); used for bookkeeping/keys
    year: '1'   # optional label; string is recommended for stable YAML keys

    # Optional population metadata (not needed to build bins)
    n_gal_arcmin2: 18.0  # surface density [gal/arcmin^2]; optional

    # Tomography kind (dispatcher). If omitted, default could be "photoz".
    kind: photoz

    nz:
      model: smail
      params: { z0: 0.26, alpha: 2.0, beta: 0.94}

    # Bin specification:
    # - Option A (scheme-based): provide scheme + n_bins (+ optional range)
    # - Option B (explicit edges): provide edges only (no scheme / n_bins)
    bins:
      scheme: equidistant
      n_bins: 5
      range: [0.2, 1.2]
      # edges: [0.2, 0.4, 0.6, 0.8, 1.0, 1.2]  # alternative to scheme-based

    # Photo-z uncertainty / error model parameters (name intentionally not "photoz")
    uncertainties:
      scatter_scale: 0.03  # sigma(z) ~ scatter_scale * (1+z) * mean_scale
      mean_offset: 0.0     # mu(z) ~ mean_scale * z - mean_offset
      # mean_scale: 1.0     # optional (scalar broadcasts), if supported

  # LENS (Y10)
  - role: lens
    year: '10'
    n_gal_arcmin2: 48.0  # surface density [gal/arcmin^2]; optional
    kind: photoz
    nz:
      model: smail
      params: { z0: 0.28, alpha: 2.0, beta: 0.9 }
    bins:
      scheme: equidistant
      n_bins: 10
      range: [0.2, 1.2]
    uncertainties:
      scatter_scale: 0.03
      mean_offset: 0.0

  # SOURCE (Y1)
  - role: source
    year: '1'
    n_gal_arcmin2: 10.0  # surface density [gal/arcmin^2]; optional
    kind: photoz
    nz:
      model: smail
      params: { z0: 0.13, alpha: 2.0, beta: 0.78 }
    bins:
      scheme: equal_number
      n_bins: 5
      # edges: [...]  # alternative to scheme-based
    uncertainties:
      scatter_scale: 0.05
      mean_offset: 0.0

  # SOURCE (Y10)
  - role: source
    year: '10'
    n_gal_arcmin2: 27.0  # surface density [gal/arcmin^2]; optional
    kind: photoz
    nz:
      model: smail
      params: { z0: 0.11, alpha: 2.0, beta: 0.68 }
    bins:
      scheme: equal_number
      n_bins: 5
    uncertainties:
      scatter_scale: 0.05
      mean_offset: 0.0

Visualizing the preset#

The figure below loads the LSST preset directly, constructs the corresponding tomographic bins, and plots the resulting redshift distributions for lens and source samples in years 1 and 10.

(Source code, png, hires.png, pdf)

Euclid survey preset#

Binny can also be used with a Euclid-inspired survey preset containing a photometric source sample and a spectroscopic lens sample.

In this simplified preset, the source sample follows the commonly used Euclid weak-lensing redshift distribution with 10 explicit tomographic bin edges, while the lens sample is represented as a spectroscopic sample over the Euclid clustering redshift range.

Visualizing the preset#

The figure below loads the Euclid preset and visualizes the resulting spectroscopic lens bins and photometric source bins.

(Source code, png, hires.png, pdf)

DES survey preset#

Binny also includes a simplified configuration inspired by the tomographic setup used in the Dark Energy Survey (DES).

Compared to LSST, DES covers a smaller area of the sky and has lower galaxy number densities, resulting in fewer tomographic bins and a shallower redshift reach.

Visualizing the preset#

The figure below loads the DES preset and visualizes the resulting lens and source tomographic bins.

(Source code, png, hires.png, pdf)

Roman survey preset#

Binny also includes a Roman-inspired survey preset based on the Wenzl22 HLS and wide survey configurations.

The preset contains photometric lens and source samples for the optimistic HLS, conservative HLS, and wide Roman scenarios.

Visualizing the preset#

The figure below loads the Roman preset and visualizes the resulting photometric lens and source tomographic bins. The top row compares the optimistic High Latitude Survey (HLS) configuration against the conservative HLS configuration, while the bottom row shows the wide survey configuration.

(Source code, png, hires.png, pdf)

DESI survey preset#

Binny also includes a DESI-inspired survey preset containing tabulated spectroscopic LRG and ELG lens redshift distributions.

The default preset uses the legacy Dani redshift windows: \(0.4 \leq z \leq 1.0\) for LRGs and \(0.6 \leq z \leq 1.5\) for ELGs.

Visualizing the preset#

The figure below loads the DESI preset and visualizes the tabulated LRG and ELG redshift distributions. The rows show the same redshift windows split into one, three, and five equal-width tomographic bins.

(Source code, png, hires.png, pdf)