tilepy: Smart Scheduling for Multi-Messenger Astronomy from Earth to Orbit

TL;DR

tilepy is a Python tool designed to optimize follow-up observations of poorly localized astrophysical transients, integrating ground and space-based observatories, and incorporating AI for scheduling to enhance multi-messenger astronomy research.

Contribution

The paper introduces tilepy, a novel Python library that improves scheduling and coordination of multi-messenger transient follow-ups, including space-based observatories and AI optimization.

Findings

Successfully integrates with multiple observatories.

Handles diverse field of view shapes.

Incorporates AI for optimized scheduling.

Abstract

The rise of direct detection of gravitational waves (GWs) started a new era in multi-messenger astrophysics. Like GWs, many other astrophysical transient sources suffer from poor localization, which can span tens to thousands of square degrees in the sky. Moreover, as the detection horizon for these transients widens and the detection rate increases, current electromagnetic follow-up facilities require tools to optimize the follow-up of poorly localized events and save valuable telescope time for their time-domain astrophysics programs. We present \texttt{tilepy}, a Python library, and a tool to optimize the follow-up of poorly localized transient events. \texttt{tilepy} is used for GWs as well as other poorly localized events such as gamma-ray bursts detected by Fermi-GBM and neutrino candidates from IceCube. \texttt{tilepy} has also been optimized to integrate smoothly with multiple ground-based observatories operating individually or simultaneously with diverse observational configurations. In this contribution, we introduce the latest developments from \texttt{tilepy}, mainly the ability to operate with space-based observatories while taking into consideration factors such as Earth, Sun, and Moon occultation and South Atlantic Anomaly passage. We present innovations to the platform, handling a variety of field of view shapes, the possibility of optimizing observation scheduling with artificial intelligence tools and examples of its use on transient astrophysical events.