Related papers: Variational autoencoder inverse mapper for extract…
Generalized Parton Distributions (GPDs) are multidimensonal structure functions that encode the information about the internal structure of hadrons. Using privileged channels such as Deeply Virtual Compton Scattering (DVCS) or Timelike…
We analyze the hard scattering amplitude of gravitational form factors (GFFs) of hadrons within QCD factorization at large momentum transfers, focusing on their conformal field theory (CFT) description. These form factors are key to…
The factorization of the amplitude for the deeply virtual Compton scattering (DVCS) process gamma*(Q) gamma -> gamma gamma at high Q2 is demonstrated in two distinct kinematical domains, allowing to define the photon generalized parton…
A likelihood analysis of the observables in deeply virtual exclusive meson production off a proton target is presented. We consider the unpolarized process for which the largest amount of data with all the kinematic dependences are…
In data-driven drug discovery, designing molecular descriptors is a very important task. Deep generative models such as variational autoencoders (VAEs) offer a potential solution by designing descriptors as probabilistic latent vectors…
The ability to extract generative parameters from high-dimensional fields of data in an unsupervised manner is a highly desirable yet unrealized goal in computational physics. This work explores the use of variational autoencoders (VAEs)…
By assuming factorization of the GPD under the deconvolution integral for the hand-bag diagram, we develop a method of solving this integral beyond the cross-over line. As examples we use explicit models of deeply virtual Compton scattering…
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however,…
We revisit the phenomenology of the deep exclusive electroproduction of a lepton pair, i.e. double deeply virtual Compton scattering (DDVCS), in view of new experiments planned in the near future. The importance of DDVCS in the…
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and…
Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux…
Flux inversion is the process by which sources and sinks of a gas are identified from observations of gas mole fraction. The inversion often involves running a Lagrangian particle dispersion model (LPDM) to generate sensitivities between…
We assess the impact of future measurements of deeply virtual Compton scattering (DVCS) off protons using the planned detector at the Electron-Ion Collider in China (EicC), proposed as an upgrade to the High Intensity heavy-ion Accelerator…
In this paper, we explore the use of a variational autoencoder (VAE), a deep generative model, to compress and generate images of dark matter density fields from $\Lambda$CDM like cosmological simulations. The VAE learns a compact,…
Disentangled representation learning aims to represent the underlying generative factors of a dataset in a latent representation independently of one another. In our work, we propose a discrete variational autoencoder (VAE) based model…
The Fourier transform of the deeply virtual Compton scattering amplitude (DVCS) with respect to the skewness parameter \zeta= Q^2/ 2 p.q can be used to provide an image of the target hadron in the boost-invariant variable \sigma, the…
We utilize the DVCS asymmetry measurements of the HERMES collaboration for access to Compton form factors in the deeply virtual regime and to generalized parton distributions. In particular, the (almost) complete measurement of DVCS…
Combining dispersion and operator product expansion techniques, we derive the conformal partial wave decomposition of the virtual Compton scattering amplitude in terms of complex conformal spin to twist-two accuracy. The perturbation theory…
We present our current progress in the holographic computation of the scattering amplitude for Deeply Virtual Compton Scattering (DVCS) processe, as a function of the Mandelstam invariant $t$. We show that it is possible to describe…
Embracing the deep learning techniques for representation learning in clustering research has attracted broad attention in recent years, yielding a newly developed clustering paradigm, viz. the deep clustering (DC). Typically, the DC models…