Related papers: Intrinsic Dimension Estimation for Radio Galaxy Zo…

The success of denoising diffusion models raises important questions regarding their generalisation behaviour, particularly in high-dimensional settings. Notably, it has been shown that when training and sampling are performed perfectly,…

We present an investigation of the relationships between the radio properties of a giant radio galaxy MRC B0319-454 and the surrounding galaxy distribution with the aim of examining the influence of intergalactic gas and gravity associated…

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data…

This paper investigates the score-based diffusion models for density estimation when the target density admits a factorizable low-dimensional nonparametric structure. To be specific, we show that when the log density admits a $d^*$-way…

The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transfer learning to classify the…

We investigate the approximation efficiency of score functions by deep neural networks in diffusion-based generative modeling. While existing approximation theories utilize the smoothness of score functions, they suffer from the curse of…

(Abridged) The spectral energy distributions of a well-defined sample of 54 RGB stars are constructed, and fitted with the dust radiative transfer model DUSTY. The central stars are modeled by MARCS model atmospheres. In a first step, the…

Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively generating its SID conditioned on the user's history. However,…

We study the effects of dust grain size on the spectral energy distribution (SED) of spherical circumstellar envelopes. Based on the self-similarity relations of dusty SEDs derived by Ivezic & Elitzur (1997), we expect an approximate…

We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We carry out a classification of galaxy images from the…

Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current…

Context: Fast radio bursts are transient radio pulses of extragalactic origin. Their dispersion measure is indicative of the baryon content in the ionized intergalactic medium between the source and the observer. However, inference using…

We present detailed spectral energy distribution (SED) modeling of 14 local ultraluminous infrared galaxies (ULIRGs) with outstanding photometric data from the literature covering the ultraviolet--infrared (FIR) and radio bands ($\sim$50…

Aims. We aim to understand the nature of the diffuse radio emission surrounding the massive galaxy cluster PSZ2 G083.29-31.03, at z=0.412, already known to host a radio halo. Our investigation was triggered by Radio U-Net, a novel machine…

In machine learning, the performance of a classifier depends on both the classifier model and the separability/complexity of datasets. To quantitatively measure the separability of datasets, we create an intrinsic measure -- the…

We combine Herschel-PACS data from the PEP program with Spitzer 24 um and 16 um photometry and ultra deep IRS mid-infrared spectra, to measure the mid- to far-infrared spectral energy distribution (SED) of 0.7<z<2.5 normal star forming…

We report the discovery of a gravitationally lensed hyperluminous infrared galaxy (L_IR~10^13 L_sun) with strong radio emission (L_1.4GHz~10^25 W/Hz) at z=2.553. The source was identified in the citizen science project SpaceWarps through…

We study the high latitude (|b|>10) diffuse gamma-ray emission in the Galaxy in light of the recently published data from the Fermi collaboration at energies between 100 MeV and 100 GeV. The unprecedented accuracy in these measurements…

By combining the data of the Two Micron All Sky Survey, the \textit{Wide Field Infrared Survey Explorer} and the \textit{AKARI} satellite, we study the infrared colour properties of a sample of 2712 nearby radio-luminous galaxies (RLGs).…

Score-based diffusion modeling is a generative machine learning algorithm that can be used to sample from complex distributions. They achieve this by learning a score function, i.e., the gradient of the log-probability density of the data,…