We consider the optimization of the observing strategy (cadence, exposure time, and filter choice) using medium-size (2-m-class) optical telescopes in the follow-up of kilonovae localized with arcminute accuracy to be able to distinguish among various kilonova models and viewing angles. To develop an efficient observation plan, we made use of the synthetic light curves obtained with the Monte Carlo radiative transfer code POlarization Spectral Synthesis In Supernovae for different kilonova models and as a function of different viewing angles and distances. By adding the appropriate photon counting noise to the synthetic light curves, we analysed four alternative sequences having the same total time exposure of 8 h, with different time windows (0.5, 1, 2, and 4 h), each with i, r, and u filters, to determine the observing sequence that maximizes the chance of a correct identification of the model parameters. We suggest to avoid u filter and to avoid the use of colour curves. We also found that, if the error on distance is ≤2 per cent, 0.5, 1, and 2-h time window sequences are equivalent, so we suggest to use 2-h one, because it has 1-d cadence, so it can be easily realized. When the distance of the source is unknown, 0.5-h time window sequence is preferable.
Optimising the observation of optical kilonovae with medium size telescopes
Bulla, M;Guidorzi, C;
2023
Abstract
We consider the optimization of the observing strategy (cadence, exposure time, and filter choice) using medium-size (2-m-class) optical telescopes in the follow-up of kilonovae localized with arcminute accuracy to be able to distinguish among various kilonova models and viewing angles. To develop an efficient observation plan, we made use of the synthetic light curves obtained with the Monte Carlo radiative transfer code POlarization Spectral Synthesis In Supernovae for different kilonova models and as a function of different viewing angles and distances. By adding the appropriate photon counting noise to the synthetic light curves, we analysed four alternative sequences having the same total time exposure of 8 h, with different time windows (0.5, 1, 2, and 4 h), each with i, r, and u filters, to determine the observing sequence that maximizes the chance of a correct identification of the model parameters. We suggest to avoid u filter and to avoid the use of colour curves. We also found that, if the error on distance is ≤2 per cent, 0.5, 1, and 2-h time window sequences are equivalent, so we suggest to use 2-h one, because it has 1-d cadence, so it can be easily realized. When the distance of the source is unknown, 0.5-h time window sequence is preferable.File | Dimensione | Formato | |
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