相关论文: Towards constraining warm dark matter with stellar…
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference…
Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the…
In cold dark matter cosmological models, structures form and grow by merging of smaller units. Numerical simulations have shown that such merging is incomplete; the inner cores of halos survive and orbit as "subhalos" within their hosts.…
The free streaming length of dark matter particles determines the abundance of structure on sub-galactic scales. We present a statistical technique, amendable to any parameterization of subhalo density profile and mass function, to probe…
Weak gravitational lensing is one of the few direct methods to map the dark-matter distribution on large scales in the Universe, and to estimate cosmological parameters. We study a Bayesian inference problem where the data covariance…
Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from…
Stellar tidal streams are sensitive tracers of the properties of the gravitational potential in which they orbit and detailed observations of their density structure can be used to place stringent constraints on fluctuations in the…
Star formation is a multi-scale problem, and only global simulations that account for the connection from the molecular cloud scale gas flow to the accreting protostar can reflect the observed complexity of protostellar systems.…
One of the most promising tracers of the Galactic potential in the halo region are stellar streams. However, individual stream fits can be limited by systematic biases. To study these individual stream systematics, we fit streams in Milky…
Cold dark matter subhalos are expected to populate galaxies in numbers. If dark matter self-annihilates, these objects turn into prime targets for indirect searches, in particular with gamma-ray telescopes. Incidentally, the Fermi-LAT…
Examining the properties of subhalos with strong gravitational lensing images can shed light on the nature of dark matter. From upcoming large-scale surveys, we expect to discover orders of magnitude more strong lens systems that can be…
Approximate Bayesian Computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by…
Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems. As simulators become more advanced, the analytical tractability of the…
Analyzes of next-generation galaxy data require accurate treatment of systematic effects such as the bias between observed galaxies and the underlying matter density field. However, proposed models of the phenomenon are either numerically…
The positions and velocities of stellar streams have been used to constrain the mass and shape of the Milky Way's dark matter halo. Several extragalactic streams have already been detected, though it has remained unclear what can be…
The accuracy of the measurements of some astrophysical dynamical systems allows to constrain the existence of incredibly small gravitational perturbations. In particular, the internal Solar System dynamics (planets, Earth-Moon) opens up the…
We provide new constraints on the connection between galaxies in the local universe, identified by the Sloan Digital Sky Survey (SDSS), and dark matter halos and their constituent substructures in the $\Lambda$CDM model using WMAP7…
In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this context, performing Bayesian inference can be challenging.…
With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even…
We present a new method for determining the Galactic gravitational potential based on forward modeling of tidal stellar streams. We use this method to test the performance of smooth and static analytic potentials in representing realistic…