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Folder containing the progress made during the JAE ICU fellowship at the IAA-CSIC. During these 5 months, I am deepening my knowledge of high-energy transient events in our universe. My progress can be followed in this notebook. Starting with prueba folder, this was my first Python programming experience, later with Synchrotron Code fodler I learned the basics of synchrotron emission following NRAO book and Pacholzyck.

Once the basics were established, I started to analyse GRS 1915+105, a well known superluminal source which can be described by a Van Deer Lan model. All the synchrotron functions were implemented from scratch, along with the relevant documentation, and functions related to the specific model were also implemented. With all this, a fit was made using scipy functions with a result consistent with those in the bibliography. In any case, the previous analysis considered each outbreak independently, but the importance of overlap was thought to be important, so I developed code to implement multiple outbreak fits simultaneously. The relevance of the overlap was then analysed in Figure

The data can also be used to extract information about the power law dependence of the flux on frequency. I performed another fit and extracted the prediction of the power law exponent and with it the predicted curve of Flux-Freq with the corresponding confidence interval.

Next, an analysis of the supernova SN1993J was carried out. Starting from the previously developed synchrotron functions and assuming a modified Standard Interaction Model (SIM), where we have a strong interaction between the supernova and the interacting medium. This interaction leads to a self-similar expanding shell structure, where in this modified scenario the synchrotron emission comes from the shell formed shortly after the outburst. The code corresponding to this new model was also developed from scratch. Observational data could not be successfully fitted to the model, and several consistency tests were carried out checking the validity of the code. As an expample the result plot for 1.2cm wavelenght is shown below

Finally, the implementation of a low energy cut-off for the relativistic electron population started, following the assumptions of the bibliography. Problems were found in the implementation of the numerical integration, and eventually the analysis of this source was dropped. This was due to the learning purpose of this well known source, during the depuration of the code and the analysis of the model I improved several skills and parts of the fitting code. I learnt about numerical integration in Python, special functions, optimising code and comparing different least squares fitting methods within scipy.

Using previous knowledge of the synchrotron process, we analysed TDE jets from AT2019dsg. We performed a least-squares fit using only my own developed functions, which are all included in this repository. At this point I learnt about Bayesian fitting methods, analysed the UltraNest and Dynesty packages, and finally chose UltraNest for it's larger documentation towards starters. Using the least-squares results as input for the prior transformation of the Bayesian fit, we performed a Bayesian fit on the data, obtaining similar results to those in the bibliography and contrasting our procedure.

Finally, all these tools and skills lead to the analysis of the TDE AT2019azh. This source is analysed in the bibliography with a rather complex model, and we thought that it could also be described by a simple synchrotron emission and self-absorption like AT2019dsg. Starting with a least-squares fit, the data fitted well to our proposed model, and a power-law dependence between magnetic field and radius was also postulated based on the results of the fit. Nowadays we are focusing on checking the validity of this power law dependence and comparing the bibliography with our model. All this will be done using the UltraNest model comparison module and could lead to a possible future publication.