DM-54923: Allow AP association to fall back to second-best match#358
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ebellm
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Jun 25, 2026
| obj_dec = dia_objects["dec"].to_numpy()[obj_idx] | ||
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| # Wrap RA difference to (-180, 180]. | ||
| dra = ((src_ra - obj_ra) + 180.0) % 360.0 - 180.0 |
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what's the reason to do this offset? doesn't it have the same boundary problems, just in a different location? I think we need dra to be correct even at ra=180
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I'll try to make the comment more clear. This adds 180, does the mod 360, then subtracts the 180 offset back off. This makes sure that the difference is never greater than +/-180.
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Of course! Sorry, wasn't thinking carefully enough
| var_ra = np.maximum(var_ra, sigma_floor_sq_deg) | ||
| var_dec = np.maximum(var_dec, sigma_floor_sq_deg) | ||
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| chi2 = dra*dra/var_ra + ddec*ddec/var_dec |
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| chi2 = dra*dra/var_ra + ddec*ddec/var_dec | |
| chi2 = dra**2./var_ra + ddec**2./var_dec |
| if (self._has_position_errors(dia_sources) | ||
| and self._has_position_errors(dia_objects)): | ||
| scores = self._position_nll(dia_sources, dia_objects, src_idx, obj_idx) | ||
| # ``max_dist`` is the sole association gate: every candidate |
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I think ultimately we want to replace this with actual probabilistic association (a la NWAY) rather than using a hard max_dist cut. But no objections for now.
…s are present The associator previously ranked candidate matches by chord distance only and accepted any pair within a fixed 1" cap, ignoring per-source and per-object positional uncertainties entirely. When the uncertainty columns are missing or contain no usable values, the score reverts to the kd-tree chord distance (the previous behavior).
The previous matcher considered only each DIASource's single nearest DIAObject. When two sources competed for the same object, the loser would be unassociated and triggered a new DIAObject even if a viable second-best candidate was nearby.
…sociations This avoids the case where a further diaObject with worse coordinate errors has a lower chi-squared than a nearby diaObject with tight errors.
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