## 3 Results
### Data Quantity and Out-of-Time Test Set Evaluation  
Once the model is trained, questions arise regarding (1) the amount of data required for the neural network to converge and achieve good performance. Additionally, it is interesting to understand (2) the network behavior when applied to data outside the training period. To address the first question, thirteen experiments were conducted within the data period spanning 2019 to 2020, characterized by solar minima to minimize the impact of solar X/gamma-rays and flares. Table 4 displays results for training periods of 4, 7, 12 and 24 months, using 75% of the dataset for training (30% of which was further reserved as a validation set) and 25% for testing. To facilitate convergence, a dropout of 0.002 was chosen, and the learning rate for the 24-month dataset was reduced by a factor of 10 compared to [9]. The performance metrics, both MAE and MeAE, show no significant improvement with an increase in the dataset size. The performance remains consistently good and comparable beyond a 4-month training period. For reference, one month of data comprises approximately 500,000 datapoints. To address the second question, two group of experiments were conducted on data covering the period of 2019 and 2020. Experiments # 6 to # 9 reserve a \"future\" period as a test set with respect to the train set, while # 10 to #13 display the performance of an NN trained on data \"in the middle\" with respect to the train set. In particular, experiment # 6 reserves as the test set July 2020 to January 2021, and # 10 displays the performance of an NN trained on data excluding January 2020 to July 2020 (6 months or 25% of the dataset). Training results align with previous experiments, as expected since the data are an extension of the previous dataset. However, applying the model to an out-of-time period results in slightly poorer performance compared to the in-time sets, with an MAE increase of approximately 2.5 counts/s and a MeAE increase of 1.0 counts/s. Considering that the average background rate is around 350 counts/s (as shown in Figure 2), this corresponds to a relative percentage error increase of about 0.7% and 0.3%, respectively. This indicates  
Figure 2: Quantile regression for estimating the count rates of detector n7 in energy range r1 over a three-hour period on 20 April 2019. The red line represents the \(\tau=0.5\) (median) estimate, while the blue shaded region denotes the range between the upper value at \(\tau=0.9\) and the lower value at \(\tau=0.1\). The spike observed at 23:30 corresponds to GRB190420981, whereas the one at about 22:30 is not present in the Fermi GBM catalog but is detected by [9] and further discussed. that while the NN did not perfectly generalize to a period outside the training set, it still yielded comparable results. Experiments # 7, # 8, # 9 are run over an increasing size of the train set but tested on the same 2 months of data, Nov20-Jan21. We can see that the MAE is even higher than that of # 6, and a closer examination of the MAE reveals a more pronounced impact on the \(r0\) range, which is most susceptible to solar activity. This suggests that including a temporal feature to account for the varying effects of solar activity on the detectors would be beneficial over longer time periods. The number of solar flares, thus a proxy for solar activity, can be seen in Figure 3. Finally, experiments # 11, # 12, # 13 are the equivalent of the previous experiments but tested on a test set \"in the middle\", Jan20-Mar20, which shows slightly better performance on the test set and is not influenced by the train set size. It is important to note that the actual purpose of the NN is not to predict the future; thus, changes in architecture, such as RNN, may be beneficial for more accurate predictions. The NN serves as a tool to interpolate expected count rates for chosen periods, demonstrating robustness against unexpected increases in count rates, such as those caused by solar flares or GRBs.