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Conditional density estimation (CDE) is a fundamental task in machine learning that aims to model the full conditional law $\mathbb{P}(\mathbf{y} \mid \mathbf{x})$, beyond mere point prediction (e.g., mean, mode). A core challenge is…
We study the deeply virtual Compton scattering cross-section in twist-two generalized parton distribution (GPD) approximation, and show that different choices of light-cone vectors and gauges for the final photon polarization will lead to…
We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a…
Recent advances in deep learning have shown their ability to learn strong feature representations for images. The task of image clustering naturally requires good feature representations to capture the distribution of the data and…
Measurements of deeply virtual Compton scattering (DVCS) made at HERMES using 27.6 GeV $e^\pm$ beams and various internal polarized or unpolarized gaseous targets are discussed. Results are reported on the transverse target-spin asymmetry…
Deeply Virtual Compton Scattering represents the best experimental channel through which to understand Generalised Parton Distributions. The HERMES experiment measured the most diverse set of DVCS results of any experiment; this talk…
Double Deeply Virtual Compton Scattering (DDVCS) is a promising channel for Generalized Parton Distribution (GPD) studies as it is a generalization of the Deeply Virtual Compton Scattering (DVCS) and Timelike Compton Scattering (TCS)…
Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), have the potential to meet this demand by building latent…
We study the double deeply virtual Compton scattering (DDVCS) process off the nucleon, through the scattering of a spacelike virtual photon with large virtuality resulting in the production of a timelike virtual photon, decaying into an e^+…
The parameterization of the nucleon structure through Generalized Parton Distributions (GPDs) shed a new light on the nucleon internal dynamics. For its direct interpretation, Deeply Virtual Compton Scattering (DVCS) is the golden channel…
Generalized parton distributions provide a unifying framework for the interpretation of exclusive reactions at high $Q^2$. The most promising reaction for the investigation of these distributions is the hard production of photons using…
Deeply virtual Compton scattering (DVCS) allows one to probe Generalized Parton Distributions (GPDs) describing the 3D structure of the nucleon. We report the first measurement of the DVCS beam-spin asymmetry using the CLAS12 spectrometer…
Using twist 3 contributions and the relations, which emanate from the QCD equations of motion, we derive the gauge invariant amplitude for the deeply virtual Compton scattering (DVCS) off hadron with an arbitrary spin. We emphasize that our…
The Generalized Parton Distributions (GPD) have drawn a lot of interest from the theoretical community since 1997, but also from the experimental community and especially at Jefferson Lab. First, the results for Deeply Virtual Compton…
Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known analytical form for the posterior and likelihood expectation,…
Variational autoencoders (VAEs) and other generative methods have garnered growing interest not just for their generative properties but also for the ability to dis-entangle a low-dimensional latent variable space. However, few existing…
Measuring DVCS on a neutron target is a necessary step to deepen our understanding of the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). The combination of neutron and proton targets allows to perform a flavor…
Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of…
We investigate the hard scatterings of hadronic matrix elements corresponding to hadronic gravitational form factors (GFFs) of the pion and proton using QCD factorization, applying conformal field theory (CFT) tools. These GFFs are key to…
Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded within high-dimensional data, these latent representations…