arXiv subjects

Mark Moussa

Publications and source records attributed to Mark Moussa.

5 recordsLinked to original sources

arXiv2026

Life, Machine Learning, and the Search for Habitability: Predicting Biosignature Fluxes for the Habitable Worlds Observatory

Future direct-imaging flagship missions, such as NASA's Habitable Worlds Observatory (HWO), face critical decisions in prioritizing observations due to extremely stringent time and resource constraints. In this paper, we introduce two advanced machine-learning architectures tailored for predicting biosignature species fluxes from exoplanetary reflected-light spectra: a Bayesian Convolutional Neural Network (BCNN) and our novel model architecture, the Spectral Query Adaptive Transformer (SQuAT). The BCNN robustly quantifies both epistemic and aleatoric uncertainties, offering reliable predictions under diverse observational conditions, whereas SQuAT employs query-driven attention mechanisms to enhance interpretability by explicitly associating spectral features with specific biosignature species. We demonstrate that both models achieve comparably high predictive accuracy on an augmented dataset spanning a wide range of exoplanetary conditions, while highlighting their distinct advantages in uncertainty quantification and spectral interpretability. These capabilities position our methods as promising tools for accelerating target triage, optimizing observation schedules, and maximizing scientific return for upcoming flagship missions such as HWO.

cs.LG↗

arXiv2026

Habitable Worlds Observatory (HWO): Living Worlds Community Working Group: The Search for Life on Potentially Habitable Exoplanets

The discovery of a biosphere on another planet would transform how we view ourselves, and our planet Earth, in relation to the rest of the cosmos. We now know Earth is one planet among eight circling our sun; our sun is part of a swirling galaxy of over one hundred billion other suns; and our galaxy is one of untold billions in the universe. While we do not yet know how many, if any, other biospheres exist on the countless worlds orbiting countless other suns, we stand at the precipice of a new era of discovery, enabled by powerful new facilities able to peer across the light years into the atmospheres of planets similar to our own. This article is an adaptation of a science case document (SCDD) developed for the NASA Astrophysics Flagship mission the Habitable Worlds Observatory (HWO) Science, Technology, and Architecture Review Team (START) Living Worlds Community Working Group.

astro-ph.IM↗

arXiv2026

NASA Decadal Astrobiology Research and Exploration Strategy (NASA-DARES 2025) White Paper -- Habitable Worlds Observatory Living Worlds Science Cases: Research Gaps and Needs

Executive Summary: The Habitable Worlds Observatory (HWO) is the first astrophysics flagship mission with a key cross-divisional astrobiology science goal of searching for signs of life on rocky planets beyond our solar system. The Living Worlds Working Group under the Science, Technology, and Architecture Review Team (START) was charged with investigating how HWO could characterize potentially habitable exoplanets orbiting stars in the solar neighborhood, search for signs of life, and interpret potential biosignatures within a false positive and false negative framework. In particular, we focused on (1) identifying biosignatures that have spectral features in the UV-Vis-NIR wavelength range and defining their measurement requirements, (2) determining additional information needed from the planet and planet system to interpret biosignatures and assess the likelihood of false positives, and (3) assembling current knowledge of likely HWO target stars and identify which properties of host stars and systems are most critical to know in advance of HWO. The Living Worlds atmospheric biosignatures science case is considered one of the key drivers in the design of the observatory. An additional 10 astrobiology science cases were developed that collectively revealed key research gaps and needs required to fully explore the observatory parameter space and perform science return analyses. Investment in these research gaps will require coordination across the Science Mission Directorate and fall under the purview of the new Division-spanning astrobiology strategy.

astro-ph.IM↗

arXiv2025

PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery

Rapid and accurate wildfire detection is crucial for emergency response and environmental management. In airborne and spaceborne missions, real-time algorithms must distinguish between no fire, active fire, and post-fire conditions, and estimate fire intensity. Multispectral and hyperspectral thermal imagers provide rich spectral information, but high data dimensionality and limited onboard resources make real-time processing challenging. As wildfires increase in frequency and severity, the need for low-latency and computationally efficient onboard detection methods is critical. We present a systematic evaluation of multiple deep learning architectures, including custom Convolutional Neural Networks (CNNs) and Transformer-based models, for multi-class fire classification. We also introduce PyroFocus, a two-stage pipeline that performs fire classification followed by fire radiative power (FRP) regression or segmentation to reduce inference time and computational cost for onboard deployment. Using data from NASA's MODIS/ASTER Airborne Simulator (MASTER), which is similar to a next-generation fire detection sensor, we compare accuracy, inference latency, and resource efficiency. Experimental results show that the proposed two-stage pipeline achieves strong trade-offs between speed and accuracy, demonstrating significant potential for real-time edge deployment in future wildfire monitoring missions.

cs.CV↗

arXiv2024

Generative AI for Overall Mission Effectiveness at the Habitable Worlds Observatory

Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START) AI/ML Working Group. Collectively, our group mission statement is "Where is the Human-in-the-loop as Gen AI systems become more powerful and autonomous?" with an emphasis on the ethical applications of Gen AI, guided by using these systems to remove drudgery from human work while simultaneously increasing opportunities for humans to experience more collective creativity and innovation. The HWO mission stands to benefit dramatically from generative models for different data types including text, time series/spectra, and image data. These cover a wide range of applications in science and engineering for HWO, including: mission development acceleration, data analysis and interpretation, enhancing imaging capabilities, anomaly detection, predictive modeling and simulation, data augmentation for machine learning, instrument calibration and optimization, public engagement and education, and assisting in mission planning. As an example, through sensitivity analysis of simulated exoplanet population science data sets of various generative model complexity, we can reverse engineer the measurement uncertainty requirements for HWO instruments to produce data that can constrain population models and thus inform HWO design requirements. This approach to HWO design is one example of a strategy that can ensure that HWO remains AI-ready. Through presenting herein a combination of visionary ideas balanced with grounded validated use case examples, we aim to support the development of a long-term strategy to keep HWO AI-ready as it moves forward.

astro-ph.IM↗