Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

Abstract: We present the nonrelativistic limit of the stellar structure equations of Ricci-based gravities, a family of metric-affine theories whose Lagrangian is built via contractions of the metric with the Ricci tensor of an a priori independent connection. We find that this limit is characterized by four parameters that arise in the expansion of several geometric quantities in powers of the stress-energy tensor of the matter fields. We discuss the relevance of this result for the phenomenology of nonrelativistic stars, such as main-sequence stars as well as several substellar objects.

Abstract: Neutron destruction reactions of the cosmic gamma-ray emitter Al-26 are of importance to determine the amount of Al-26 ejected into our galaxy by supernova explosions and for Al-26 production in asymptotic giant branch stars. We performed a new measurement of the Al-26(n, alpha) reaction up to 160-keV neutron energy at the neutron time-of-flight facilities n_TOF at CERN and GELINA at EC-JRC. We provide strengths for ten resonances, six of them for the first time. We use our data to calculate astrophysical reactivities for stellar temperatures up to 0.7 GK. Our results resolve a discrepancy between the two previous direct measurements of this reaction, and indicate higher stellar destruction rates than the most recently recommended reactivity.