Investigation on the Navigation Anomaly of Pioneer 10 and 11 probes

The present work consists of the investigation of the navigation of Pioneer 10 and 11 probes becoming known as the “Pioneer Anomaly”: the trajectories followed by the spacecrafts did not match the ones retrieved with standard navigation software. Mismatching appeared as a linear drift in the Doppler data received by the spacecrafts, which has been ascribed to a constant sunward acceleration of about 8.5×10-10 m/s2. The study presented hereafter tries to find a convincing explanation to this discrepancy. The research is based on the analysis of Doppler tracking data through the ODP (Orbit Determination Program), developed by NASA/JPL. The method can be summarized as: seek for any kind of physics affecting the dynamics of the spacecraft or the propagation of radiometric data, which may have not been properly taken into account previously, and check whether or not these might rule out the anomaly. A major effort has been put to build a thermal model of the spacecrafts for predicting the force due to anisotropic thermal radiation, since this is a model not natively included in the ODP. Tracking data encompassing more than twenty years of Pioneer 10 interplanetary cruise, plus twelve years of Pioneer 11 have been analyzed in light of the results of the thermal model. Different strategies of orbit determination have been implemented, including single arc, multi arc and stochastic filters, and their performance compared. Orbital solutions have been obtained without the needing of any acceleration other than the thermal recoil one indicating it as the responsible for the observed linear drift in the Doppler residuals. As a further support to this we checked that inclusion of additional constant acceleration as does not improve the quality of orbital solutions. All the tests performed lead to the conclusion that no anomalous acceleration is acting on Pioneers spacecrafts.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.