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Following #79, once the mathematical framework is built (ideally in such a way that it can be generically extended with more hypotheses, so that work can be conducted on this issue in parallel as well), construct the codebase to perform these matches, from a primary catalogue (LSST) to a secondary dataset (all of our determined ancillary catalogues, #14, in turn), for all primary objects. Should create an output of the relative probabilities of which object, if any, in the secondary dataset is the missed counterpart and why the object failed to be matched (or why there are no counterparts in this secondary dataset).
The text was updated successfully, but these errors were encountered:
Onoddil
changed the title
T3.3.2.2: Implement algorithms for missing match calculations
T3.3.2.2: Implement Algorithms For Missing Match cCalculations
May 21, 2024
Onoddil
changed the title
T3.3.2.2: Implement Algorithms For Missing Match cCalculations
T3.3.2.2: Implement Algorithms For Missing Match Calculations
May 21, 2024
Onoddil
changed the title
T3.3.2.2: Implement Algorithms For Missing Match Calculations
T3.3.2.2: Missing Match Calculations Codebase
May 21, 2024
Following #79, once the mathematical framework is built (ideally in such a way that it can be generically extended with more hypotheses, so that work can be conducted on this issue in parallel as well), construct the codebase to perform these matches, from a primary catalogue (LSST) to a secondary dataset (all of our determined ancillary catalogues, #14, in turn), for all primary objects. Should create an output of the relative probabilities of which object, if any, in the secondary dataset is the missed counterpart and why the object failed to be matched (or why there are no counterparts in this secondary dataset).
The text was updated successfully, but these errors were encountered: