Co-design of ground-based gravitational wave detector networks
Abstract
Discussions around the design philosophy and location of the next generation of ground-based gravitational wave detectors are still underway. In this context, we propose IfoScout, an innovative methodology for detector co-design based on state-of-the-art machine-learning (ML) techniques. As a simple demonstration, we present a two-stage simulation of a network of fictional L-shaped interferometers whose sensitivity is optimized within physical and geographical constraints, indirectly resulting in reducing the costs. To achieve this, we gather publicly available data for two token locations and establish the length and orientation with reinforcement learning (RL). Next, we optimize the internal detector parameters related to cavity stability to achieve the best possible sensitivity by means of differential programming (DP). We make the case that pipelines such as IfoScout could have a positive impact on the final design of new generation detectors (e.g. the Einstein Telescope, the Cosmic Explorer, etc.), given precise data and detailed, realistic simulations of the interferometers.
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BibTeX
@misc{lanchares2026codesign,
title = {{Co-design of ground-based gravitational wave detector networks}},
author = {Daniel Lanchares and Lysiane Mornas and Luigi Toffolatti and Pietro Vischia},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.27254},
eprint = {2607.27254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.27254},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Co-design of ground-based gravitational wave detector networks
AU - Daniel Lanchares
AU - Lysiane Mornas
AU - Luigi Toffolatti
AU - Pietro Vischia
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2607.27254
UR - https://arxiv.org/abs/2607.27254
AB - Discussions around the design philosophy and location of the next generation of ground-based gravitational wave detectors are still underway. In this context, we propose IfoScout, an innovative methodology for detector co-design based on state-of-the-art machine-learning (ML) techniques. As a simple demonstration, we present a two-stage simulation of a network of fictional L-shaped interferometers whose sensitivity is optimized within physical and geographical constraints, indirectly resulting in reducing the costs. To achieve this, we gather publicly available data for two token locations and establish the length and orientation with reinforcement learning (RL). Next, we optimize the internal detector parameters related to cavity stability to achieve the best possible sensitivity by means of differential programming (DP). We make the case that pipelines such as IfoScout could have a positive impact on the final design of new generation detectors (e.g. the Einstein Telescope, the Cosmic Explorer, etc.), given precise data and detailed, realistic simulations of the interferometers.
ER -