Early Recognition of Cometary Activity in Sky Survey Data

The recognition of cometary activity is a crucial step in the analysis of small bodies discovered by modern sky surveys. Detecting activity at the earliest stages of a newly discovered object can guide subsequent observations and help distinguish active comets from apparently inactive nuclei. Despite its practical importance, objective statistical criteria for detecting weak cometary activity in trailed survey images remain valuable. We present a bootstrap-calibrated matched-template framework for the statistical detection of cometary comae. The method employs a library of synthetic coma models based on elongated power-law surface-brightness profiles, convolved with a point-spread function (PSF) and a model of the object’s apparent motion. The observed image is compared with these templates using a matched-template fitting procedure. We evaluate the detection performance through idealized simulations in which synthetic trailed-nucleus images are combined with spatially uncorrelated Gaussian noise. A parametric bootstrap under the null hypothesis of an inactive nucleus provides an empirical distribution of the detection statistic and a threshold corresponding to a user-selected false-alarm probability under the adopted model and noise assumptions. The simulations quantify the sensitivity to coma amplitude and morphology, while illustrating the limitations of parameter recovery in the presence of morphological degeneracies. We also apply the method to a publicly available Rubin Observatory image of the interstellar comet 3I/ATLAS. This case study demonstrates the application of the method to an observed image, but does not constitute a validation across realistic survey backgrounds or noise conditions. The framework provides a quantitative criterion for assessing evidence of cometary activity and, in non-detections, can support the derivation of upper limits under the adopted assumptions. Its application to individual survey observations requires appropriate characterization of the relevant image properties and calibration of the null model.