Related papers: RTModel: a platform for real-time modeling and mas…
Characterization of microlensing planets requires modeling of observed light curves including many parameters. Studying the dependency of the pattern of light curves on the lensing parameters and the correlations between the parameters is…
Context. The ability to automatically select scientifically-important transient events from an alert stream of many such events, and to conduct follow-up observations in response, will become increasingly important in astronomy. With…
Light curves serve as a valuable source of information on stellar formation and evolution. With the rapid advancement of machine learning techniques, it can be effectively processed to extract astronomical patterns and information. In this…
Microlensing has a unique advantage for detecting dark objects in the Milky Way, such as free-floating planets, neutron stars, and stellar-mass black holes. Most microlensing surveys focus on the Galactic bulge, where higher stellar density…
Microlensing events provide a unique capacity to study the stellar remnant population of the Galaxy. Optical microlensing suffers from a near complete degeneracy between the mass, the velocity and the distance. However, a subpopulation of…
In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, `supervised' paradigm for the application of machine learning involves training a model…
Astrometric microlensing will offer in the next future a new channel for investigating the nature of both lenses and sources involved in a gravitational microlensing event. The effect, corresponding to the shift of the position of the…
Galactic microlensing datasets now comprise in excess of $10^4$ events, and with the advent of next generation microlensing surveys that may be undertaken with facilities such as the Rubin Observatory (formerly LSST) and Roman Space…
Mixture models combine multiple components into a single probability density function. They are a natural statistical model for many situations in astronomy, such as surveys containing multiple types of objects, cluster analysis in various…
We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four…
Gravitational lensing directly measures mass density fluctuations along the lines of sight to very distant objects. No assumptions need to be made concerning bias, the ratio of fluctuations in galaxy density to mass density. Hence, lensing…
Recent studies have demonstrated that detailed monitoring of gravitational microlensing events can reveal the presence of planets orbiting the microlensed source stars. With the potential of probing planets in the Galactic Bulge and…
Massive stars at cosmological distances can be individually detected during transient microlensing events, when gravitational lensing magnifications may exceed ~1000. Nine such sources were identified in JWST NIRCam imaging of a single…
Microlensing of extragalactic sources, in particular the probability of significant amplifications, is a potentially powerful probe of the abundance of compact objects outside the halo of the Milky Way. Accurate experimental constraints…
Gravitational microlensing of planetary-mass objects (or "nanolensing", as it has been termed) can be used to probe the distribution of mass in a galaxy that is acting as a gravitational lens. Microlensing and nanolensing light curve…
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypotheses in astrophysics and other areas. We use a neural…
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data…
A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension of the Latent Class Analysis model, which captures…
The gravitational microlensing technique allows the discovery of exoplanets around stars distributed in the disk of the galaxy towards the bulge. However, the alignment of two stars that led to the discovery is unique over the timescale of…
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape…