ner_tags: O O O O O O O O O O O O O O O B-Model O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model I-Model B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O B-Citation I-Citation I-Citation O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O B-Model O O O O O B-Model O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset I-Dataset O O O O O O O O O O O B-Model I-Model O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
tokens: defined by the effective beam at the given frequency. The algorithm starts optimization at the MHW2 source location, assuming initially a circular Gaussian source with the FHWM of the PSF. Optimization of all six parameters converges in a reasonable number of iterations, usually less than 1000. Appendix B.2: Error estimation The downhill simplex method does not provide any information on the uncertainties in the optimized parameters. We estimate errors using a Markov Chain Monte Carlo (MCMC) method to sample the posterior distribution. In our case, the burn-in time can be very small since to start the chain the simplex-optimized value of the parameter from the previous step can be used, and it is usually very close to the peak of the distribution. The length of the MCMC chain is set to 1000 samples by default. To obtain the proper sampling in the MCMC chain it is necessary to define the optimal step size in the parameter space. This determination is complex since there are more than 100 000 sources in the catalogue to process; it would be impossible to adjust every MCMC chain, since it would take an excessive amount of time to try several values of the sampling step using an iterative approach. Hence we decided to use an analytical approximation for the errors of most of the Gaussian profile parameters, following Hagen Dereniak (2008) . We used an analytical approximation for the separable 2D Gaussian profile model; the errors are derived for five parameters; namely position coordinates, flux density, and two non-equal width values. The calculated errors were used to find the value of the step size in the parameter space to start the MCMC sampler, which delivered the error on the tilt angle. This combined technique allowed us to avoid most of the problems related to undersampled or oversampled MCMC chains. Appendix B.3: Validation of the method using model data The results of the Gaussian fit were compared with the MHW2 positions and flux densities for a test set of sources with known parameters. The PR2 100 GHz map with injected sources and cold cores was used to check the 2D Gaussian fitting algorithm. The cold cores were modelled as 2D Gaussian profiles with uniformly distributed random orientation angles. The major FWHM was in the range from 4.́5 to 19, and the ellipticity parameter, , varied from 1 (circular source) to 7 (highly elliptical source). All sources were uniformly distributed across the sky. Plots comparing the flux density values for MHW2 and Gaussian fitting for this test set are shown in Figs. B.1 B.4 . Both methods give almost the same point source flux-density values (Figs. B.1 and B.2 ), but MHW2 gives less scatter for the fainter point sources. For the cold cores, Gaussian fitting works better and gives much less bias, especially for the bright sources (Figs. B.3 and B.4 ). The visible digitization of the flux densities reflects the fact that only integer values were used for the flux densities of injected sources. The