The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

One of the most robust features of the solar magnetic cycle is that the stronger cycles rise faster than the weaker ones. This is popularly known as the Waldmeier Effect, which is known for more than 80 years. This fundamental feature of the solar cycle has not only practical implications, e,g., in predicting the solar cycle, but also implications in understanding the solar dynamo. Here we ask the question whether the Waldmeier Effect exists in other Sun-like stars. To answer this question, we analyze the Ca II H & K S-index from Mount Wilson Observatory for 21 Sun-like G-K stars. We specifically check two aspects of Waldmeier Effect, namely, WE1: the anti-correlation between the rise times and the peaks and WE2: the positive correlation between rise rates and amplitudes. We show that except HD 16160, HD 81809, HD 155886 and HD 161239, all stars considered in the analysis show WE2. While WE1 is found to be present only in some of the stars studied. Further, the WE1 correlation is weaker than the WE2. Both WE1 and WE2 exist in the solar S-index as well. Similar to the solar cycles, the magnetic cycles of many stars are asymmetric about their maxima. The existence of the Waldmeier Effect and asymmetric cycles in Sun-like stars suggests that the dynamo mechanism which operates in the Sun is also operating in other stars.

No abstract

The field of gravitational wave astronomy has made remarkable progress in recent years, with 90 successful detections by Advanced LIGO and Advanced Virgo in three observing runs. The use of deep learning to analyze gravitational wave data is an active area of research with the potential to improve our ability to detect and study these signals. However, the inherent black-box nature of deep learning models poses challenges in interpreting their predictions. To address this, we applied gradient-weighted class activation mapping technique to visualize our 4-class classification model trained on signals from binary black hole mergers, neutron star-black hole mergers, binary neutron star mergers, and noise. The visualization allows us to gain insight into which part of the strain was most influential in the model's predictions. The visualized maps indicated that as the signal duration increased, the model prioritized data before the merger time.

No abstract