相关论文: A representation learning approach to probe for dy…

Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a…

The cosmic microwave background power spectra are a primary window into the early universe. However, achieving interpretable, likelihood-compatible compression and fast inference under weak model assumptions remains challenging. We propose…

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,…

Dark energy (DE) models with many free parameters are often considered excessive, as constraining all parameters poses a significant challenge. While such models offer greater flexibility to probe the DE sector in more detail. With 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…

We study the evidence for dark energy (DE) evolution at low redshift, using baryonic acoustic oscillations (BAOs) from the DESI Early Data Release, Pantheon+ Type Ia supernovae (SNe-Ia), and redshift space distortions (RSDs) to constrain…

The phenomenological parametrizations of dark-energy (DE) equation of state can be very helpful, since they allow for the investigation of its cosmological behavior despite the fact that its underlying theory is unknown. However, although…

In this paper, we study several models and parameterizations of dynamical dark energy (DE) that have been studied already in the past, in conjunction with the recently proposed model $w$XCDM, the running vacuum model (RVM) with and without…

Baryon Acoustic Oscillation (BAO) provides a powerful tool to measure cosmic expansion and consequently the nature of the Dark Energy (DE). Recent precise BAO measurements by Dark Energy Spectroscopic Instrument data release 1 (DESI DR1),…

I present a Variational Autoencoder (VAE) trained on collider physics data (specifically boosted $W$ jets), with reconstruction error given by an approximation to the Earth Movers Distance (EMD) between input and output jets. This VAE…

We consider an Early Dark Energy (EDE) cosmological model, and perform an analysis which takes into account both background and perturbation effects via the parameters $c^{2}_{\rm eff}$ and $c^{2}_{\rm vis}$, representing effective sound…

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing…

One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous studies, which increase the information bottleneck during training, tend to lose…

We introduce a modified form of the Phenomenologically Emergent Dark Energy (PEDE) model by showing a very elegant approach. The model is named as Modified Emergent Dark Energy (MEDE) to distinguish from PEDE model and it includes…

We consider a general dark energy (DE) model parametrized by its equation-of-state (EoS), featuring three free parameters: $w_0$ (the present-day value of the DE EoS), $w_{\beta}$ (quantifying the dynamical nature of the DE EoS), and…

We present a Dark Energy (DE) model with a sound derivation as a natural extension of the Standard Model of particle physics with no free parameters and an excellent fit with current cosmological data improving by 21% the $\Lambda$CDM fit…

Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the representations learned by VAE are less competitive than…

Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn…

Constructing a compressed latent space through a variational autoencoder (VAE) is the key for efficient 3D diffusion models. This paper introduces COD-VAE that encodes 3D shapes into a COmpact set of 1D latent vectors without sacrificing…

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are…