pith. sign in

IVOA Recommendation: Simple Line Access Protocol Version 1.0

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

The Simple Line Access Protocol (SLAP) is an IVOA Data Access protocol which defines a protocol for retrieving spectral lines coming from various Spectral Line Data Collections through a uniform interface within the VO framework. These lines can be either observed or theoretical and will be typically used to identify emission or absorption features in astronomical spectra. It makes use of the Simple Spectral Line Data Model (SSLDM [1]) to characterize spectral lines through the use of uTypes [14]. Physical quantities of units are described by using the standard Units DM [15]. SLAP services can be registered in an IVOA Registry of Resources using the VOResource [12] Extension standard, having a unique ResourceIdentifier [13] in the Registry. The SLAP interface is meant to be reasonably simple to implement by service providers. A basic query will be done in a wavelength range for the different services. The service returns a list of spectral lines formatted as a VOTable. Thus, an implementation of the service may support additional search parameters (some which may be custom to that particular service) to more finely control the selection of spectral lines. The specification also describes how the search on extra parameters has to be done, making use of the support provided by the Simple Spectral Line Data Model (SSLDM [1])

fields

cs.AI 1

years

2026 1

verdicts

UNVERDICTED 1

representative citing papers

On the Geometry of Games and their Solvers

cs.AI · 2026-05-28 · unverdicted · novelty 7.0

Introduces a structure-aware solver synthesis method with a learned game representation that organizes solvability into a continuous geometry aligned with solver dynamics.

citing papers explorer

Showing 1 of 1 citing paper.

  • On the Geometry of Games and their Solvers cs.AI · 2026-05-28 · unverdicted · none · ref 7 · internal anchor

    Introduces a structure-aware solver synthesis method with a learned game representation that organizes solvability into a continuous geometry aligned with solver dynamics.