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We present a comprehensive, configurable open-source software framework for estimating the rate of electromagnetic detection of kilonovae (KNe) associated with gravitational wave detections of binary neutron star (BNS) mergers. We simulate the current LIGO-Virgo-KAGRA (LVK) observing run (O4) using current sensitivity and uptime values as well as using predicted sensitivites for the next observing run (O5). We find the number of discoverable kilonovae during LVK O4 to be ${ 1}_{- 1}^{+ 4}$ or ${ 2 }_{- 2 }^{+ 3 }$, (at 90 per cent confidence) depending on the distribution of NS masses in coalescing binaries, with the number increasing by an order of magnitude during O5 to ${ 19 }_{- 11 }^{+ 24 }$. Regardless of mass model, we predict at most five detectable KNe (at 95 per cent confidence) in O4. We also produce optical and near-infrared light curves that correspond to the physical properties of each merging system. We have collated important information for allocating observing resources for search and follow-up observations, including distributions of peak magnitudes in several broad-bands and time-scales for which specific facilities can detect each KN. The framework is easily adaptable, and new simulations can quickly be produced in response to updated information such as refined merger rates and NS mass distributions. Finally, we compare our suite of simulations to the thus-far completed portion of O4 (as of 2023, October 14), finding a median number of discoverable KNe of 0 and a 95 percentile upper limit of 2, consistent with no detections so far in O4.

We present environmental analyses for 13 KPNO International Spectroscopic Survey Green Pea (GP) galaxies. These galaxies were discovered via their strong [Oiii] emission in the redshift range 0.29 <z< 0.42, and they are undergoing a major burst of star formation. A primary goal of this study is to understand what role the environment plays in driving the current star formation activity. By studying the environments around these extreme star-forming galaxies, we can learn more about what triggers their star formation processes and how they fit into the narrative of galaxy evolution. Using the Hydra multifiber spectrograph on the WIYN 3.5 m telescope, we mapped out the galaxy distribution around each of the GPs (out to ∼15 Mpc at the redshifts of the targets). Using three density analysis methodologies chosen for their compatibility with the geometry of our redshift survey, we categorized the galaxian densities of the GPs into different density regimes. We find that the GPs in our sample tend to be located in low-density environments. We find no correlation between the density and the SFRs seen in the GPs. We conclude that the environments the GPs are found in are likely not the driving factor behind their extreme star formation activity.

Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open‐source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi‐Messenger Astrophysics project. The collaboration addressed the difficulties of open‐source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user‐group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use‐case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.