We now analyze different nongravitational perturbation models, whose results are summarized in Table 5. Besides the orbital fit statistics, to quan- tify the predictive power of the models Table 5 shows the σ shift applied to the pre-encounter solution to fit the post-encounter data. As described by Yeomans and Chodas (1989), the first refinement is to introduce an offset Δ T of the activity relative to perihelion, which is where the Marsden et al. (1973) nongravitational perturbations peak. This asym- metry can be obtained by replacing g ( r ( t )) with g ( r ( t + Δ T )) in Eq. (1). For C/2013 A1 the inclusion of Δ T does not result in any significant improve- ment: χ 2 is essentially unchanged, the Δ T estimate from the orbital fit is 1 . 6 ± 2 . 8 d, which is consistent with Δ T = 0, and we still have a 10 σ shift from pre-encounter best-fit solution to post-encounter best-fit solution. A higher fidelity model is the Rotating Jet Model (RJM), which is fully described in Chesley and Yeomans (2005) and we briefly recall here. The RJM considers the action of discrete jets on a rotating nucleus. Given a jet, we compute the mean acceleration averaged over a comet rotation. The acceleration depends on the orientation of the spin pole, e.g., defined by RA and DEC, the jet’s thrust angle η , i.e., the colatitude of the location of the jet, and the strength A$_{J}$ of the jet. The model can be extended to include a diurnal lag angle θ and thus account for diurnal lag in the jet’s activity. All these parameters can be estimated as part of the orbital fit to the available astrometry. Note that the RJM does not depend on the rotation period and, unless θ = 0 $^{◦}$, is insensitive to the sense of rotation. 24