91̽»¨

Abstract:

Despite being operational for only a short time, the Einstein Probe mission, with its large field of view and rapid localization capabilities, has already significantly advanced the study of rapid variability in the soft X-ray sky. We report the discovery of luminous and variable radio emission from the Einstein Probe fast X-ray transient EP240414a, the second such source with a radio counterpart. The radio emission at 3 GHz peaks at ∼30 days postexplosion and with a spectral luminosity ∼2 × 1030 erg s−1 Hz−1, similar to what is seen from long gamma-ray bursts, and distinct from other extragalactic transients including supernovae and tidal disruption events, although we cannot completely rule out emission from engine driven stellar explosions, e.g., the fast blue optical transients. An equipartition analysis of our radio data reveals that an outflow with at least a moderate bulk Lorentz factor (Γ ≳ 1.6) with a minimum energy of ∼1048 erg is required to explain our observations. The apparent lack of a reported gamma-ray counterpart to EP240414a could suggest that an off-axis or choked jet could be responsible for the radio emission, although a low-luminosity gamma-ray burst may have gone undetected. Our observations are consistent with the hypothesis that a significant fraction of extragalactic fast X-ray transients are associated with the deaths of massive stars.

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>