Manisha Shrestha (University of Arizona/Steward Observatory)
Extended shock breakout and early circumstellar interaction in very nearby SN 2024ggi

The progenitors of most hydrogen-rich supernovae are red supergiants. However, the mass loss in the final years before a red supergiant’s supernova explosion is poorly understood and difficult to observe directly. Interestingly, the circumstellar material (CSM) created by this mass loss can have a major impact on the early spectra and light curves of supernovae. Hence, observing young supernovae with high cadence can shed light on mass-loss history and other progenitors and supernovae. Here we present early and high-cadence photometric and spectroscopic observations of SN 2024ggi (~7Mpc), one of the closest supernovae in a decade. SN 2024ggi is a Type II supernova with strong flash spectroscopy features and a peculiar early light curve. The densely sampled color evolution shows a strong blueward evolution over the first few days while flash features are present in the spectra. After a few days, the SN behaves as a normal type II SN. This blueward evolution could be a sign of CSM interaction. Our high and low-resolution spectra clearly show high-ionization flash features. Comparison of observed spectra with radiative transfer models shows that the pre-explosion mass loss of SN 2024ggi is in the range of 10^-3 to 10^-2 solar mass per year. This mass loss is similar to the value observed for the nearby SN 2023ixf. However, the flash features in SN 2024ggi disappear faster than in the case of SN 2023ixf pointing to the extent of the CSM of SN 2024ggi being smaller. SN 2024ggi demonstrates the need for a sample of well-observed SNe which can be used to constrain their progenitor properties.

Jayashree Behara (KSU)
Implementing Stochastic Corrections into Galaxy Evolution Models
This talk explores a simple and flexible formalism for integrating stochastic corrections into galaxy evolution models to capture the short-timescale variability often missing in traditional approaches. I will begin by explaining the method including validation of this approach on a neural network model trained on hydrodynamic simulations from IllustrisTNG (https://arxiv.org/pdf/2409.16548). Following this, I will demonstrate its application to dark matter-only simulations (Uchuu and TNG-dark) and address the challenges related to the absence of baryonic effects and simulation resolution  (https://arxiv.org/pdf/2409.16079). This approach provides a powerful tool for more realistic galaxy formation histories and refined mock galaxy catalogs across different simulation environments.