Related papers: Intrinsic Dimension Estimation for Radio Galaxy Zo…

The effects of dust on infrared emission vary among galaxies of different morphological types. We investigated integrated spectral energy distributions in infrared and submillimeter emissions from the Large Magellanic Cloud based on the…

Understanding the origin of fast radio bursts (FRB's) is a central unsolved problem in astrophysics that is severely hampered by their poorly determined distance scale. Determining the redshift distribution of FRB's appears to require…

We present a method to simultaneously infer the interstellar extinction parameters $A_0$ and $R_0$, stellar effective temperature $T_{\rm eff}$, and distance modulus $\mu$ in a Bayesian framework. Using multi-band photometry from SDSS and…

Giant radio sources (GRSs) are the single largest astrophysical objects known in the universe that have grown to megaparsec scales ($\ge$ 0.7 Mpc). GRSs are much rarer compared with normal-sized radio galaxies. Still, the reason for the…

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose…

While deep neural networks (NN) significantly advance image compressed sensing (CS) by improving reconstruction quality, the necessity of training current CS NNs from scratch constrains their effectiveness and hampers rapid deployment.…

Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in large-scale surveys…

We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo 2…

We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The…

Non-thermal properties of galaxy clusters have been studied with detailed and deep radio images in comparison with X-ray data. While much progress has been made, most of the studied clusters are at a relatively low redshift (z < 0.3). We…

Mixed noise, composed of white Gaussian noise (WGN) and impulsive noise (IN), appears in numerous communication scenarios and can severely degrade system performance. In this paper, we optimize the transmitted constellation under mixed…

Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA) methods still suffer from finding a balance between learning feature information at the pixel level of the image and capturing high-level feature information and the…

Generative models that maximize model likelihood have gained traction in many practical settings. Among them, perturbation based approaches underpin many strong likelihood estimation models, yet they often face slow convergence and limited…

We discuss spectral energy distributions, photometric redshifts, redshift distributions, luminosity functions, source-counts and the far infrared to optical luminosity ratio for sources in the SWIRE Legacy Survey. The spectral energy…

We use the Expanded Very Large Array to image radio continuum emission from local luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) in 1 GHz windows centered at 4.7, 7.2, 29, and 36 GHz. This allows us to probe the integrated…

Panchromatic analysis of galaxy spectral energy distributions, spanning from the ultraviolet to the far-infrared, probes not only the stellar population but also the properties of interstellar dust through its extinction and long-wavelength…

Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences…

We complement our previous analysis of a sample of z~1-2 luminous and ultra-luminous infrared galaxies ((U)LIRGs), by adding deep VLA radio observations at 1.4 GHz to a large data-set from the far-UV to the sub-mm, including Spitzer and…

We compare observed far infra-red/sub-millimetre (FIR/sub-mm) galaxy spectral energy distributions (SEDs) of massive galaxies ($M_{\star}\gtrsim10^{10}$ $h^{-1}$M$_{\odot}$) derived through a stacking analysis with predictions from a new…