Several studies showed that modeling the radio source anisotropic beaming pattern can reveal a wealth of physical information about the planetary or exoplanetary magnetospheres that produce these emissions.

Earth and Planetary Astrophysics

This means that the nuisance marginalized inference task involves learning $n$ interesting parameters from $n$ "nuisance hardened" data summaries, regardless of the presence or number of additional nuisance parameters to be marginalized over.

Cosmology and Nongalactic Astrophysics

Computing the mass of planetary envelopes and the critical mass beyond which planets accrete gas in a runaway fashion is important when studying planet formation, in particular for planets up to the Neptune mass range.

Earth and Planetary Astrophysics

The predictive variance of the model takes into account both the variance due to data density and photometric noise.

Instrumentation and Methods for Astrophysics I.2.6

Boltzmann codes are used extensively by several groups for constraining cosmological parameters with Cosmic Microwave Background and Large Scale Structure data.

Cosmology and Nongalactic Astrophysics

Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (\textsc{delfi}), which learns a parameterized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence.

Cosmology and Nongalactic Astrophysics

In order to simulate the telescope beam effect, a Gaussian smoothing is applied on the plane perpendicular to the line of sight.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies

Interpreting and modelling astronomical catalogues requires an understanding of the catalogues' completeness or selection function: objects of what properties had a chance to end up in the catalogue.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

We present near-field radio holography measurements of the Simons Observatory Large Aperture Telescope Receiver optics.

Instrumentation and Methods for Astrophysics

We present a deep neural network Real/Bogus classifier that improves classification performance in the Tomo-e Gozen transient survey by handling label errors in the training data.

Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena