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Using a simplified two-stage ice sheet model, we explore the potential of statistical data assimilation methods to improve predictions of glacier melt, which has significant implications for reducing uncertainty in projections of sea level rise. Through twin experiments utilizing artificial data, we find that the ensemble Kalman filter improves simulations of glacier evolution initialized with incorrect initial conditions and parameters, providing us with better predictions of future glacier melt. We explore the number of observations necessary to produce an accurate model run. We also explore optimal observation assimilation schemes, and determine that deviations from the true glacier response that stem from having few data points in the pre-satellite era can be corrected with modern observation data. Our results show that statistical data assimilation methods have great potential to improve complex glacier models using real-world observations. 

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization.