Daily Papers

Blue large-amplitude pulsators (BLAPs) are a recently discovered group of hot stars pulsating in radial modes. Their origin needs to be explained, and several scenarios for their formation have already been proposed. We investigate whether BLAPs can originate as the product of a merger of two low-mass white dwarfs (WDs) and estimate how many BLAPs can be formed in this evolutionary channel. We used the MESA code to model the merger of three different double extremely low-mass (DELM) WDs and the subsequent evolution of the merger product. We also performed a population synthesis of Galactic DELM WDs using the COSMIC code. We find that BLAPs can be formed from DELM WDs provided that the total mass of the system ranges between 0.32 and 0.7 M_odot. BLAPs born in this scenario either do not have any thermonuclear fusion at all or show off-centre He burning. The final product evolves to hot subdwarfs and eventually finishes its evolution either as a cooling He WD or a hybrid He/CO WD. The merger products become BLAPs only a few thousand years after coalescence, and it takes them 20 to 70 thousand years to pass the BLAP region. We found the instability of the fundamental radial mode to be in fair agreement with observations, but we also observed instability of the radial first overtone. From the population synthesis, we found that up to a few hundred BLAPs born in this scenario can exist at present in the Galaxy. Given the estimated number of BLAPs formed in the studied DELM WD merger scenario, there is a good chance to observe BLAPs that originated through this scenario. Since strong magnetic fields can be generated during mergers, this scenario could lead to the formation of magnetic BLAPs. This fits well with the discovery of two likely magnetic BLAPs whose pulsations can be explained in terms of the oblique rotator model.

Hybrid storage systems (HSS) integrate multiple storage devices with diverse characteristics to deliver high performance and capacity at low cost. The performance of an HSS highly depends on the effectiveness of two key policies: (1) the data-placement policy, which determines the best-fit storage device for incoming data, and (2) the data-migration policy, which dynamically rearranges stored data (i.e., prefetches hot data and evicts cold data) across the devices to sustain high HSS performance. Prior works optimize either data placement or data migration in isolation, which leads to suboptimal HSS performance. Unfortunately, no prior work tries to optimize both policies together. Our goal is to design a holistic data-management technique that optimizes both data-placement and data-migration policies to fully exploit the potential of an HSS, and thus significantly improve system performance. We propose Harmonia, a multi-agent reinforcement learning (RL)-based data-management technique that employs two lightweight autonomous RL agents, a data-placement agent and a data-migration agent, that adapt their policies for the current workload and HSS configuration while coordinating with each other to improve overall HSS performance. We evaluate Harmonia on real HSS configurations with up to four heterogeneous storage devices and seventeen data-intensive workloads. On performance-optimized (cost-optimized) HSS with two storage devices, Harmonia outperforms the best-performing prior approach by 49.5% (31.7%) on average. On an HSS with three (four) devices, Harmonia outperforms the best-performing prior work by 37.0% (42.0%) on average. Harmonia's performance benefits come with low latency (240ns for inference) and storage overheads (206 KiB in DRAM for both RL agents combined). We will open-source Harmonia's implementation to aid future research on HSS.