Hintze Centre for Astrophysical Surveys

Abstract:

Context. At late stages, massive stars experience strong mass-loss rates, losing their external layers and thus producing a dense H-rich circumstellar medium (CSM). After the explosion of a massive star, the collision and continued interaction of the supernova (SN) ejecta with the CSM power the SN light curve through the conversion of kinetic energy into radiation. When the interaction is strong, the light curve shows a broad peak and high luminosity that lasts for several months. For these SNe, the spectral evolution is also slower compared to non-interacting SNe. Notably, energetic shocks between the ejecta and the CSM create the ideal conditions for particle acceleration and the production of high-energy (HE) neutrinos above 1 TeV. Aims. We study four strongly interacting Type IIn SNe, 2021acya, 2021adxl, 2022qml, and 2022wed, in order to highlight their peculiar characteristics, derive the kinetic energy of their explosion and the characteristics of the CSM, infer clues on the possible progenitors and their environment, and relate them to the production of HE neutrinos. Methods. We analysed spectro-photometric data of a sample of interacting SNe to determine their common characteristics and derive the physical properties (radii and masses) of the CSM and the ejecta kinetic energies and compare them to HE neutrino production models. Results. The SNe analysed in this sample exploded in dwarf star-forming galaxies, and they are consistent with energetic explosions and strong interaction with the surrounding CSM. For SNe 2021acya and 2022wed, we find high CSM masses and mass-loss rates, linking them to very massive progenitors. For SN 2021adxl, the spectral analysis and less extreme CSM mass suggest a stripped-envelope massive star as a possible progenitor. SN 2022qml is marginally consistent with being a Type Ia thermonuclear explosion embedded in a dense CSM. The mass-loss rates for all the SNe are consistent with the expulsion of several solar masses of material during eruptive episodes in the last few decades before the explosion. Finally, we find that the SNe in our sample are marginally consistent with HE neutrino production

Abstract:

In this work, we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters (‘feature sets’) as applied to two anomaly detection techniques in the astronomaly package and analyse our performance by comparison with citizen science labels on the same data set. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best-performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other data sets, and as a real-time transient detection system for upcoming large surveys.

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>