A Python-based tool for constructing observables from the DSN's closed-loop archival tracking data files

TL;DR

This paper introduces a Python tool that converts complex NASA DSN radio science data files into accessible observables, enabling modern analysis of historical data for improved scientific insights.

Contribution

The paper presents a novel Python-based preprocessing tool that transforms archived DSN radio science data into usable observables, facilitating reanalysis with current methods.

Findings

Enables processing of historical DSN data with modern software

Produces Doppler and range observables from ATDFs

Facilitates reanalysis of legacy radio science data

Abstract

Radio science data collected from NASA's Deep Space Networks (DSNs) are made available in various formats through NASA's Planetary Data System (PDS). The majority of these data are packed in complex formats, making them inaccessible to users without specialized knowledge. In this paper, we present a Python-based tool that can preprocess the closed-loop archival tracking data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along with ancillary information. ATDFs are primitive closed-loop radio science products with limited available documentation. Early in the 2000s, DSN deprecated ATDF and replaced it with the Tracking and Navigation Service Data Files (TNF) to keep up with the evolution of the radio science system. Most data processing software (e.g., orbit determination software) cannot use them directly, thus limiting the utilization of these data. As such, the vast majority of historical closed-loop radio science data have not yet been processed with modern software and with our improved understanding of the solar system. The preprocessing tool presented in this paper makes it possible to revisit such historical data using modern techniques and software to conduct crucial radio science experiments.