Understanding the Optical Lightcurves of LEO Spacecraft: The Application of Machine Learning Techniques

Abstract

This report is officially from a 6 month period and is a duplicate of the previous years report. During this period the paper shown here was undergoing assessment by the Advances in Space Research Journal. This was a traumatic experience as the journal spent many months unable to get anyone to assess the paper. After 9 months (summer 2023) we received two referees reports but we were not happy with them as it was clear these referees were not expert in machine learning techniques and we felt the reports were not useful. Given that we consider this work somewhat ground breaking we really wanted data scientist experts to critically examine the manuscript. Consequently, in September 2023 we removed the manuscript from Advances in Space Science and resubmitted it to the RAS Techniques and Instruments (RASTI) Journal. While this is a normal space research journal it benefits from having access to real experts in these techniques. In addition the editors from RASTI have expressed interest in increasing the SDA content of their journal. Anyway in Dec 2023 we received 2 referees reports from RASTI which were clearly from experts. They are extremely supportive of the paper as it stands and have suggested an additional small piece of work. They requested an examination of MMT9 database for any debris lightcurves and to see how they score in our framework. While we have agreed to this Im in two minds as to its usefulness. This is because the MMT9 system is composed of a number of small aperture telescopes and only the largest debris (ie satellite sized) will be detectable. It stands to reason that these will follow the same rules as the other platforms. The exception to this is satellites that have become debris due to damage. In essence we have already been looking at this as we have been examining within the current framework evolutionary changes in platform design.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 2024
Accession Number
AD1230843

Entities

People

  • Billy Shrive
  • Don Pollacco
  • Paul Chote

Organizations

  • University of Warwick

Tags

Fields of Study

  • Computer science

Readers

  • Space Exploration and Orbital Mechanics.
  • Technical Research and Report Writing.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Space
  • Space - Space Objects