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In this paper we present the updated empirical radio surface-brightness-to-diameter (Σ-D) relation for Galactic supernova remnants (SNRs) calibrated using 110 SNRs with reliable distances. We apply orthogonal fitting procedure and kernel density smoothing in Σ − D plane and compare the results with the latest theoretical Σ − D relations derived from simulations of radio evolution of SNRs. We argue that the best agreement between the empirical and simulated Σ − D relations is achieved if the mixed-morphology SNRs and SNRs of both, low brightness and small diameter, are filtered out from the calibration sample. The distances to 5 newly discovered remnants and 27 new candidates for shell SNRs are estimated from our full and filtered calibration samples.

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In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim }20\%$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.

We present detection of 67 H ii regions and two optical supernova remnant (SNR) candidates in the nearby irregular galaxy NGC 2366. The SNR candidates were detected by applying [S ii]/Hα ratio criterion to observations made with the 2-m RCC telescope at Rozhen National Astronomical Observatory in Bulgaria. In this paper we report coordinates, diameters, Hα and [S ii] fluxes for detected objects across the two fields of view in NGC 2366 galaxy. Using archival XMM-Newton observations we suggest possible X-ray counterparts of two optical SNR candidates. Also, we discard classification of two previous radio SNR candidates in this galaxy, since they appear to be background galaxies.

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amortized Neural Posterior Estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with MCMC methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2x10^6 spectra (corresponding to a 5-parameter SED model) with NPE. We show that SBI -- with its combination of fast and amortized posterior estimations -- is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modeling with Bayesian MCMC methods (e.g., 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9-1.2x10^5 galaxy spectra via SBI/SNPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

We present the detection of 16 optical supernova remnant (SNR) candidates in the nearby spiral galaxy IC342. The candidates were detected by applying [S ii]/Hα ratio criterion on observations made with the 2 m RCC telescope at Rozhen National Astronomical Observatory in Bulgaria. In this paper, we report the coordinates, diameters, Hα and [S ii] fluxes for 16 SNRs detected in two fields of view in the IC342 galaxy. Also, we estimate that the contamination of total Hα flux from SNRs in the observed portion of IC342 is 1.4%. This would represent the fractional error when the star formation rate (SFR) for this galaxy is derived from the total galaxy's Hα emission.