Exoplanet Discovery with Machine Learning | NASA's Hamed Valizadegan at GHOST Day: AMLC скачать в хорошем качестве

Exoplanet Discovery with Machine Learning | NASA's Hamed Valizadegan at GHOST Day: AMLC

Abstract: The Kepler and TESS missions have yielded an astounding 100,000 potential transit signals, paving the way for an intricate process of distillation to identify viable exoplanet candidates. In response to this formidable challenge, we introduce ExoMiner, a groundbreaking deep neural network meticulously designed for the classification of transit signals in the search for exoplanets. ExoMiner played a pivotal role in validating and authenticating 301 previously undiscovered exoplanets. This keynote voyage commences with a sweeping overview of the captivating realm of exoplanetary exploration. As we delve into the heart of our presentation, we embark on an exploration of the distinctive attributes that set ExoMiner apart, unraveling the intricate web of factors that contribute to its remarkable accuracy. Our narrative extends beyond the confines of ExoMiner as we navigate through the integration of additional machine learning models seamlessly layered atop the ExoMiner architecture. This collaborative synthesis has already led to the identification of an additional 69 exoplanets, showcasing our unwavering commitment to pushing the boundaries of exoplanetary discovery. The journey of implementing machine learning in domains of such complexity is riddled with practical challenges. Guaranteeing consistent performance and earning the trust of domain experts in the reliability of results becomes an arduous task. Hamed Valizadegan will intricately delve into the practical intricacies, providing profound insights into the delicate balance between machine intelligence and the discerning eye of scientific scrutiny. About the speaker: Holder a PhD in computer science with a focus in Machine Learning, Dr. Valizadegan has more than 20 years of experience in Artificial Intelligence and Machine Learning. He is a data science manager and machine learning lead at USRA and a senior machine learning scientist at NASA through NASA Academic Mission Services (NAMS). Dr. Valizadegan has applied machine learning expertise in a diverse set of domains from engineering and biology to medicine and astronomy. At NASA, Dr. Valizadegan has been involved in several machine learning projects related to Hubble Space Telescope, Kepler and TESS missions, James Web Space Telescope (JWST) mission, Space Biology, and Orion Vehicle. Dr. Valizadegan has led a team of scientists who developed machine learning models to discover 370 new exoplanets to date.