Continuing Adventures in the Discovery of Multiple Star Systems with AI/ML

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Early data-driven analyses of ozone chemistry sensitivity primarily relied on "ratio-based" indicators to partially linearize the non-linear aspects of urban ozone chemistry, which are influenced by pollution levels, light, and water vapor. With the development of more sophisticated algorithms, including machine learning techniques capable of fitting high-dimensional non-linear functions, we have shown that a highly effective parameterization of net ozone production rates (PO3) can be achieved. This approach eliminates the need for empirical linearization of ozone chemistry through various indicators and allows for the primary inputs to be accurately constrained using satellite observations.

Expected benefits

We need a better understanding of the worldwide spatiotemporal variability of ozone production rates. This is mainly due to the limited information we can gain from supersites or aircraft data by which we can generate observationally constrained PO3. However, we offer a significantly enhanced algorithm to parameterize PO3 using retrospective aircraft observations and a handful of variables that can be primarily informed by satellite observations that provide high spatial coverage. Our work shows the long-term maps of PO3 worldwide. This work has important implications for pollution exposure and regulations and can promote the use of satellite observations as an essential component for informing emission regulations beyond the current capabilities.

System outputs

All outputs related to the project are well documented in our website: https://www.ozonerates.space/

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported