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Overview

🌌 The big picture​

The AO performance depends on the chosen set of Natural Guide Stars (NGSs). A set of guide stars used simultaneously forms an asterism, whose geometry and brightness directly impact the AO correction quality.
Depending on the instrument and AO architecture, an asterism may involve:

  • Mono-NGS - a single reference star (e.g. ERIS)
  • Multi-NGS - several stars combined (e.g. MAVIS with 3 NGS)

The Asterism Selection feature in TipTop aims to automatically determine the best possible asterism for a given instrument configuration, atmospheric forecast, and field of view.

TipTop lets you evaluate many candidate asterisms and provides the metrics (SR, FWHM, EE, penalty) - to rank them for your setup.

🌟 Why it matters​

Asterism selection is a key step in observation preparation, as it directly impacts image quality and scientific return. With TipTop, you can evaluate many candidate configurations quickly and objectively, using consistent performance metrics such as Strehl Ratio (SR), FWHM, Encircled Energy (EE), and jitter.

The TipTop Guide Star selection algorithm is currently being implemented into the standard ESO P2 tool, in particular in ObsPrep.

🤔 What TipTop adds​

Add a single block in your .ini file to drive the exploration:

[ASTERISM_SELECTION]​

This section tells TipTop:

  • how you provide the asterisms (the mode), and
  • the data needed (coordinates, fluxes, or a file with catalogs). Supported modes include:
  • Sets - you list explicit asterisms
  • Singles1 / Singles3 - you give a flat star list; TipTop builds all 1-star or 3-star combinations.
  • Generate - synthetic data (dev/tests).
  • File/ FileMono - load many fields from a NumPy recarray (.npy) and evaluate each field (triplets vs mono). See all the details and examples in Paramater files - [ASTERISM_SELECTION].

🔁 How it works (at a glance)​

  • Build candidates from your chosen mode.
  • Run AO simulations for each asterism.
  • Compute metrics per asterism (and per science target if applicable).
  • Rank candidates in your analysis (by a scalar penalty/jitter, lower is better).
  • Save outputs for fast reload and post-processing.
    For a step-by-step run, see Tutorial - Asterism Seclection.

📦 What you get (outputs)​

TipTop writes arrays to outputDir with the simulName prefix:

  • sr.npy – Strehl ratio per asterism (and per target if multi-target).
  • fw.npy – FWHM [mas] per asterism (and per target).
  • ee.npy – Encircled energy within your radius (or ensquared energy if requested).
  • covs.npy – Covariance ellipse parameters.
  • penalty.npy – scalar ranking metric (aka jitter); lower = better.

Arrays are saved in the global order of evaluation (fields concatenated). They are not pre-sorted.

You can reload all metrics without recomputation via reloadAsterismSelection(...) (see the Running selections page).

💡 Example use cases​

  • Compare candidate stars around a target (ERIS-like, Singles1).
  • Explore triplet combinations over a field (MAVIS-like, Singles3).
  • Batch-evaluate catalogue fields (File/FileMono) and pick the best per field.
  • Prototype what-if scenarios with synthetic inputs (Generate).

🔧 Requirements & tips​

Your INI must include a valid [ASTERISM_SELECTION] and the LO loop configuration (see sensor/RTC sections).

For File/FileMono, use the documented recarray format; a helper script is provided in the docs to create compatible files.

For analysis, the simulation object returned by asterismSelection is your friend: simul.cumAstSizes, simul.nfieldsSizes, simul.asterismsInputDataPolar/Cartesian, simul.penalty_Asterism, etc.

✔️ In Summary​

[ASTERISM_SELECTION] tells TipTop which stars to try and how to combine them. TipTop then simulates, scores, and saves metrics so you can rank and choose the best asterism for your AO configuration.