(11468) Shantanunaidu orbit determination test

Let’s start by importing the necessary libraries

import grss
prop = grss.prop
fit = grss.fit

We’ll then retrieve the cometary state of the asteroid (from JPL SBDB) plus any nongravitational accelerations acting on it.

import numpy as np
np.set_printoptions(precision=40, linewidth=np.inf)

body_id = '11468'
init_sol, init_cov, nongrav_info = fit.get_sbdb_info(body_id)
de_kernel = 440

Next, we’ll retrieve the observations from different sources (MPC, JPL, Gaia Data Releases) and prepare them for the orbit determination process.

add_gaia_obs = True
optical_obs_file = None
t_min_tdb = None
t_max_tdb = None
debias_lowres = True
deweight = True
eliminate = False
num_obs_per_night = 4
verbose = True
obs_df = fit.get_optical_obs(body_id, optical_obs_file, t_min_tdb, t_max_tdb, debias_lowres, deweight, eliminate, num_obs_per_night, verbose)
obs_df = fit.add_radar_obs(obs_df, t_min_tdb, t_max_tdb, verbose)
if add_gaia_obs:
    gaia_dr = 'gaiafpr'
    obs_df = fit.add_gaia_obs(obs_df, t_min_tdb, t_max_tdb, gaia_dr, verbose)

Read in 1755 observations from the MPC.
        WARNING: At least one unknown observation mode in the data.
        WARNING: At least one deprecated star catalog in the data.
        Filtered to 1755 observations that satisfy the time range and accepted observatory constraints.
Applying Eggl et al. (2020) debiasing scheme to the observations.
        Unknown star catalog: GSC
        Unknown star catalog: UNK
        No debiasing needed for 731 observations.
        Debiased 984 observations.
        No bias information for 40 observations.
Applying Vereš et al. (2017) weighting scheme to the observations.
        Using 1556 CCD observations with station-specific weight rules.
Applying sqrt(N/4) deweighting scheme.
        Deweighted 484 observations.
Read in 308 Gaia observations from gaiafpr
        Filtered to 308 observations that satisfy the time range constraints.

All we need to do now is initialize the OD simulation and run the filter.

n_iter_max = 10
fit_sim = fit.FitSimulation(init_sol, obs_df, init_cov, n_iter_max=n_iter_max, de_kernel=de_kernel, nongrav_info=nongrav_info)

fit_sim.filter_lsq()

Iteration               Unweighted RMS          Weighted RMS            Chi-squared             Reduced Chi-squared
1                        0.430                   0.567                   1328.394                        0.322
2                        0.430                   0.567                   1328.290                        0.322
Converged without rejecting outliers. Starting outlier rejection now...
3                        0.396                   0.546                   1228.714                        0.299
4                        0.396                   0.546                   1228.666                        0.299
Converged after rejecting outliers. Rejected 6 out of 2063 optical observations.

Let’s print some summary statistics and plot some results.

fit_sim.print_summary()

Summary of the orbit fit calculations after postfit pass:
==============================================================
RMS unweighted: 0.39615156178420524
RMS weighted: 0.5456979091410805
chi-squared: 1228.6658943769469
reduced chi-squared: 0.2990910161579715
square root of reduced chi-squared: 0.5468921430757362
--------------------------------------------------------------
Solution Time: MJD 57705.000 TDB = 2016-11-13 00:00:00.000 TDB
Solution Observation Arc: 16011.87 days (43.84 years)
--------------------------------------------------------------
Fitted Variable         Initial Value                   Uncertainty                     Fitted Value                    Uncertainty                     Change                          Change (sigma)
e                       1.66657894909e-01               2.25803501501e-09               1.66657895249e-01               2.27082797542e-09               +3.40328487614e-10              +0.150
q                       2.56494181058e+00               2.93186689373e-09               2.56494181055e+00               2.94264435411e-09               -2.73483458102e-11              -0.009
tp                      5.75112207046e+04               4.22648850546e-06               5.75112207052e+04               4.24849047875e-06               +5.80948835704e-07              +0.137
om                      2.06375221086e+02               6.74062921881e-06               2.06375221066e+02               6.76577424404e-06               -2.00732301892e-08              -0.003
w                       1.53989698777e+02               6.81235669305e-06               1.53989698932e+02               6.83803263417e-06               +1.55106931743e-07              +0.023
i                       7.88982675264e-01               8.49038260981e-08               7.88982680757e-01               8.50447328290e-08               +5.49360601454e-09              +0.065

fit_sim.plot_summary(auto_close=True)

fit_sim.iters[-1].plot_iteration_summary(title='Postfit Residuals', auto_close=True)

mean_0 = np.array(list(init_sol.values())[1:])
cov_0 = init_cov
mean_f = np.array(list(fit_sim.x_nom.values()))
cov_f = fit_sim.covariance

maha_dist_f, maha_dist_0, bhattacharya, bhatt_coeff = fit.get_similarity_stats(mean_0, cov_0, mean_f, cov_f)
print(f'Mahalonobis distance between JPL and GRSS solution: {maha_dist_f:0.2f}')
print(f'Mahalonobis distance between GRSS and JPL solution: {maha_dist_0:0.2f}')
print(f'Bhattacharya distance between JPL and GRSS solution: {bhattacharya:0.4f}')
print(f'Bhattacharya coefficient between JPL and GRSS solution: {bhatt_coeff:0.4f}')

Mahalonobis distance between JPL and GRSS solution: 0.19
Mahalonobis distance between GRSS and JPL solution: 0.19
Bhattacharya distance between JPL and GRSS solution: 0.0000
Bhattacharya coefficient between JPL and GRSS solution: 1.0000

Finally, we’ll make sure the GRSS solution is statistically consistent with the JPL SBDB solution

assert maha_dist_f < 5.0
assert maha_dist_0 < 5.0
assert bhattacharya < 0.10
assert bhatt_coeff > 0.90