September 24, 2024:

Speaker Dr. Floyd Nichols

Title: 

A Gentle Introduction to Machine Learning for Astrobiology, Part 2: Supervised Learning

Abstract:

Modern instrumentation and laboratory techniques have resulted in an exponential rise in the volume and complexity of data collected for astrobiological research. As such, a shift from standard statistical approaches to more advanced statistical techniques is necessary to adequately understand and examine these more complex datasets. Currently, machine learning methods are underutilized in astrobiology; however, such techniques excel at revealing structure and hidden patterns in large and/or complex data. Machine learning can be daunting to implement in astrobiology work due to the plethora of algorithms available, a requirement of some programming knowledge, and data science and statistical literacy. This talk will provide the basics and fundamentals of artificial intelligence (AI) and machine learning (ML) to equip astrobiologists with a foundation for implementing available AI/ML techniques in their own research. More specifically, Part 2 of this series will focus on the fundamentals and applications of supervised learning techniques. Additionally, I will provide a publicly available python notebook tutorial designed to teach and introduce machine learning approaches including unsupervised and supervised methods in the context of astrobiology. 

Google Colab Tutorial Link:

https://colab.research.google.com/drive/1hYCYH9rMlqMxbndAgMV03ufBKR8nAAPV?usp=sharing

Video recording: https://www.youtube.com/watch?v=erQezPVG5ig

September 10, 2024:

Speaker Dr. Floyd Nichols

Title: 

A Gentle Introduction to Machine Learning for Astrobiology, Part 1: Unsupervised Learning

Abstract:

Modern instrumentation and laboratory techniques have resulted in an exponential rise in the volume and complexity of data collected for astrobiological research. As such, a shift from standard statistical approaches to more advanced statistical techniques is necessary to adequately understand and examine these more complex datasets. Currently, machine learning methods are underutilized in astrobiology; however, such techniques excel at revealing structure and hidden patterns in large and/or complex data. Machine learning can be daunting to implement in astrobiology work due to the plethora of algorithms available, a requirement of some programming knowledge, and data science and statistical literacy. This talk will provide the basics and fundamentals of artificial intelligence (AI) and machine learning (ML) to equip astrobiologists with a foundation for implementing available AI/ML techniques in their own research. More specifically, Part 1 of this series will focus on the fundamentals and applications of unsupervised learning techniques. Additionally, I will provide a publicly available python notebook tutorial designed to teach and introduce machine learning approaches including unsupervised and supervised methods in the context of astrobiology. 

Google Colab Tutorial Link:

https://colab.research.google.com/drive/1hYCYH9rMlqMxbndAgMV03ufBKR8nAAPV?usp=sharing

Video recording: https://www.youtube.com/watch?v=6TE1uDQAh0k

August 7, 2024:

Speaker Dr. Bethany Theiling

Title:

Building an Onboard AI to Act as an Advance Science Team

Abstract:

Scientists and science-fiction alike dream of exploring distant worlds that could harbor life outside of Earth. While humans cannot currently travel to the most promising candidates for life in our solar system – ocean worlds such as Europa and Enceladus – our spacecraft can. Our group has been developing a collaborative, onboard AI capability that draws not only from the expertise of human scientists, but one that can learn ‘on-the-fly’. We use multiple machine learning (ML) algorithms developed for mass spectrometry as a priori models – effectively as subject matter expertise in our AI framework. The framework analyzes data onboard, and multiple ML models act as a ‘science team’ with different perspectives. Here I will discuss how we use ML algorithms as an onboard science team and present a simulated mission scenario to Enceladus that demonstrates our onboard AI capability during a possible life detection event. Having this capability onboard would empower missions to collect more observations and Earth-based science teams to focus on interpretation and discovery. 

Video recording: https://www.youtube.com/watch?v=aR3MK3tt-1Q

July 16, 2024:

Speakers Dr. Anirudh Prabhu and Dr. Michael Wong

Title:

Searching for life in the solar system using machine learning + pyrolysis–GCMS

Abstract: 

The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We recap recent efforts to search for life on nearby worlds and explore the rationale to develop so-called “agnostic biosignatures” for detecting life as we don’t know it. Then, we detail a novel biosignature method we’ve developed using pyrolysis–gas chromatography coupled to mass spectrometry, followed by machine learning. We analyzed a suite of 134 chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine-learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine-learning methods do not rely on precise compound identification; rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method’s utility for detecting alien biology.

Video recording: https://www.youtube.com/watch?v=5odOQ8g7xJM

April 25, 2024:

Speaker Dr. Ehsan “Sam” Gharib-Nezad

Title:

TelescopeML: Convolutional Neural Networks for Predicting Brown Dwarf Atmospheric Parameters

Abstract:

We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes such as the James Webb Space Telescope (JWST). High-resolution, high-SNR spectra from exoplanet and brown dwarf atmospheres have been collected over the past few decades, requiring the development of accurate and reliable pipelines and tools for their analysis. For example, accurately and swiftly determining the parameters of brown dwarf atmospheres from observational spectra is crucial for understanding their atmospheric composition and guiding future follow-up observations. Spectral analysis using traditional methods is typically time-consuming, involving several statistical steps and assumptions. In this talk, we will discuss the implementation of the Convolutional Neural Networks (CNNs) method as a high-performance deep-learning algorithm, which was trained with synthetic brown dwarf spectra generated across a broad range of atmospheric conditions. The model has been tested on T-dwarf benchmarks and predicted effective temperature, surface gravity, carbon-to-oxygen ratio (C/O), and metallicity [M/H]. All the ML operators, visualization, and statistical functions are consolidated into multiple modules and published as a Python PyPI package, named TelescopeML. They are made publicly available along with a well-documented online tutorial covering installation, deployment, and core concepts.

Tutorial link: https://ehsangharibnezhad.github.io/TelescopeML/index.html

GitHub link: https://github.com/EhsanGharibNezhad/TelescopeML

Zenodo link: https://zenodo.org/records/11043721

Video recording: https://www.youtube.com/watch?v=FcAO7neags4