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Abstract:

<jats:p><jats:italic>Context.N</jats:italic>-body simulations are computationally expensive and machine learning (ML) based emulation techniques have thus emerged as a way to increase their speed. Surrogate models are indeed fast, however, they are limited in terms of their trustworthiness due to potentially substantial emulation errors that current approaches are not equipped to correct.</jats:p> <jats:p><jats:italic>Aims.</jats:italic> To alleviate this problem, we have introduced COmoving Computer Acceleration (COCA), a hybrid framework interfacing ML algorithm with an <jats:italic>N</jats:italic>-body simulator. The correct physical equations of motion are solved in an emulated frame of reference, so that any emulation error is corrected by design. Thus, we are able to find a solution for the perturbation of particle trajectories around the ML solution. This approach is computationally cheaper than obtaining the full solution and it is guaranteed to converge to the truth as the number of force evaluations is increased.</jats:p> <jats:p><jats:italic>Methods.</jats:italic> Even though it is applicable to any ML algorithm and <jats:italic>N</jats:italic>-body simulator, we assessed this approach in the particular case of particle-mesh (PM) cosmological simulations in a frame of reference predicted by a convolutional neural network. In such cases, the time dependence is encoded as an additional input parameter to the network.</jats:p> <jats:p><jats:italic>Results.</jats:italic> We find that COCA efficiently reduces emulation errors in particle trajectories, requiring far fewer force evaluations than running the corresponding simulation without ML. As a consequence, we were able to obtain accurate final density and velocity fields for a reduced computational budget. We demonstrate that this method exhibits robustness when applied to examples outside the range of the training data. When compared to the direct emulation of the Lagrangian displacement field using the same training resources, COCA’s ability to correct emulation errors results in more accurate predictions.</jats:p> <jats:p><jats:italic>Conclusions.</jats:italic> Therefore, COCA makes <jats:italic>N</jats:italic>-body simulations cheaper by skipping unnecessary force evaluations, while still solving the correct equations of motion and correcting for emulation errors made by ML.</jats:p>

Abstract:

Most stripped-envelope supernova progenitors are thought to be formed through binary interaction, losing hydrogen and/or helium from their outer layers. Ultrastripped supernovae are an emerging class of transient that are expected to be produced through envelope stripping by a neutron star companion. However, relatively few examples are known, and the outcomes of such systems can be diverse and are poorly understood at present. Here we present spectroscopic observations and high-cadence, multiband photometry of SN 2023zaw, a rapidly evolving supernova with a low ejecta mass. SN 2023zaw was discovered in a nearby spiral galaxy at D = 39.7 Mpc. It has significant Milky Way extinction, E(B − V)MW = 0.21, and significant (but uncertain) host extinction. Bayesian evidence comparison reveals that nickel is not the only power source and that an additional energy source is required to explain our observations. Our models suggest that an ejecta mass of Mej ∼ 0.07 M⊙ and a synthesised nickel mass of MNi ∼ 0.007 M⊙ are required to explain the observations. We find that additional heating from a central engine, or interaction with circumstellar material, can power the early light curve.