The Origins of Planets for ArieL (OPAL) Key Science Project: the end-to-end planet formation campaign for the ESA space mission Ariel

TL;DR

The OPAL project prepares for the ESA Ariel mission by creating a comprehensive library of synthetic exoplanet atmospheres, linking planetary compositions to their formation environments, and highlighting the diversity and complexity of planet formation.

Contribution

This work introduces a novel pipeline for generating realistic synthetic atmospheres and traces the genetic link between planets, disks, and stars, supporting Ariel's mission preparations.

Findings

Diverse outcomes emerge from initial conditions in planet formation.

High degeneracy in formation parameters leads to varied planetary compositions.

Interdisciplinary modeling is crucial for understanding exoplanet diversity.

Abstract

The growing body of atmospheric observations of exoplanets from space and ground-based facilities showcases how the great diversity of the planetary population is not limited to their physical properties but extends to their compositions. The ESA space mission Ariel will observe and characterise hundreds of exoplanetary atmospheres to explore and understand the roots of this compositional diversity. To lay the foundations for the Ariel mission, the OPAL Key Science Project is tasked with creating an unprecedented library of realistic synthetic atmospheres spanning tens of elements and hundreds of molecules on which the Ariel consortium will test and validate its codes and pipelines ahead of launch. In this work we describe the aims and the pipeline of codes of the OPAL project, as well as the process through which we trace the genetic link connecting planets to their native protoplanetary disks and host stars. We present the early results of this complex and unprecedented endeavour and discuss how they highlight the great diversity of outcomes that emerge from the large degeneracy in the parameter space of possible initial conditions to the planet formation process. This, in turn, illustrates the growing importance of interdisciplinary modelling studies supported by high-performance computing methods and infrastructures to properly investigate this class of high-dimensionality problems.