Conveners
Mercredi matin: Mercredi matin
- Valérie Gautard (CEA-Irfu)
The 2021 edition of the School of Statistics SOS2021 was held online for the first time (postponed from May 2020 in Carry-le-Rouet) from 18 to 29 January 2021. The school targets PHD students, post-docs and senior scientists wishing to strengthen their knowledge or discover new methods in statistical analysis applied in particle and astroparticle physics and cosmology.
The programme covers...
I will present a first investigation of the suitability and performance of IPUs in deep learning applications in cosmology.
As upcoming photometric galaxy surveys will produce an unprecedented amount of observational data, more and more people turn to deep learning for fast and accurate data processing. In this work I tested typical examples of tasks that will be required to process and...
The neutrino telescopes KM3NeT search for cosmic neutrinos from distant
astrophysical sources such as supernovae, gamma ray bursters or
colliding stars flaring blazars. Once the events are received, they are
rapidly reconstructed online. The online events must be classified to
identify signal neutrinos from atmospheric muon background events.
Dedicated applications will then analyse...
The localization of radioactive sources provides mandatory information for the monitoring and the diagnostic of radiological scenes and it still constitutes a critical challenge. Gamma-ray imaging is performed through coded mask aperture imaging when the energy of the photons is sufficiently low to insure photoelectric interactions into the mask. Then, classically, a deconvolution algorithm is...
Currently, dynamic aperture calculations of high-energy hadron colliders are
generated through computer simulation, which is both a resource-heavy and
time-costly process.
The aim of this research is to use a reservoir computing machine learning
model in order to achieve a faster extrapolation of dynamic aperture values. In
order to achieve these results, a recurrent echo-state network...
The next generation of astronomical surveys will completely change the discovery process in astronomy. Faced with millions of possible new sources per night, serendipitous discoveries will not occur. At the same time, given the significant improvement in detection efficiency it is also reasonable to expect that unforeseen astrophysical sources will be detected. However, if we do not have tools...
The Cherenkov Telecope Array (CTA) is the future of ground-based gamma astronomy and will be composed of tens of telescopes divided in two arrays in both hemispheres.
GammaLearn is a project started in 2017 to develop innovative analysis for CTA event reconstruction based on deep learning.
Here we present a status report of the project, the network architecture developed for event...
We present a novel methodology to address ill-posed inverse problems, by providing a description of the posterior distribution instead of a point estimate solution. Our approach combines Neural Score Matching for learning a prior distribution from physical simulations, and an Annealed Hamiltonian Monte-Carlo technique to sample the full high-dimensional posterior of our problem.
In the...
Weak gravitational lensing is one of the most promising tools of cosmology to constrain models and probe the evolution of dark-matter structures. Yet, the current analysis techniques are only able to exploit the 2-pt statistics of the lensing signal, ignoring a large fraction of the cosmological information contained in the non-Gaussian part of the signal. Exactly how much information is lost,...
Telescope images are corrupted with blur and noise. Generally, blur is represented by a convolution with a Point Spread Function and noise is modelled as Additive Gaussian Noise. Restoring galaxy images from the observations is an inverse problem that is ill-posed and specifically ill-conditioned. The majority of the standard reconstruction methods minimise the Mean Square Error to...
