arXiv Machine Learning By Callum Gray, Cassandra Hall, Stefano Santabarbara, Klaus Schmidt-Rohr, Andrew Ringham, Edward Gillen, Thomas J. Haworth, Christopher D. P. Duffy

An Agnostic Machine Learning Model of Photosynthetic Habitability

Read the original on arXiv Machine Learning →

arXiv:2606. 24458v1 Announce Type: cross Abstract: The search for exoplanet biosignatures is guided by whether planetary environments can sustain photosynthesis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
6d ago

Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study

arXiv:2608. 11261v1 Announce Type: cross Abstract: Organic photovoltaic (OPV) materials are promising candidates for distributed solar energy in tropical regions, yet existing virtual screening tools report static power conversion efficiency (PCE) values at standard testing conditions (STC) that fail to capture the temperature-driven performance degradation experienced under real deployment conditions.

By Steve Cabrel Teguia Kouam, Rockefeller Rockefeller, Raoult Dabou Teukam, Jean-Pierre Tchapet Njafa, Patrick Sorrel Mvoto Kongo, Jean-Pierre Nguenang, Serge Guy Nana Engo
arXiv Machine Learning
1d ago

Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference

arXiv:2608. 15282v1 Announce Type: new Abstract: Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process modelling within ecohydrology, which integrate EO, meteorological forcing and process models to characterise coupled water, energy and carbon dynamics in vegetation and soil across scales.

By Yi Yu, Jian Peng, Yucheng Lin, Trevor F. Keenan, Thomas F. A. Bishop
arXiv Machine Learning
Jun 24

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.

By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga