相关论文: Galaxies in the zone of avoidance: Misclassificati…
We present the first results of a blind HI survey for galaxies in the southern Zone of Avoidance with a multibeam receiver on the Parkes telescope. This survey is eventually expected to catalog several thousand galaxies within Galactic…
We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multi-band digital surveys. In particular, we applied the MLPQNA (Multi Layer Perceptron…
The detection of clusters of galaxies in large surveys plays an important part in extragalactic astronomy, and particularly in cosmology, since cluster counts can give strong constraints on cosmological parameters. X-ray imaging is in…
Recent ISO-data has allowed for the first time observationally based estimates for source confusion in mid-infrared surveys. We use the extragalactic source counts from ISOCAM in conjunction with K-band counts to predict the confusion due…
Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real…
Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, is essential for constraining star formation theories. However, it is a difficult task to…
The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning…
Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other…
In the absence of the two emission lines H$\alpha$ and [NII] (6584\AA) in a BPT diagram, we show that other spectral information is sufficiently informative to distinguish AGN galaxies from star-forming galaxies. We use pattern recognition…
The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and…
We address the problem of morphological classification of galaxies from the Galaxy Zoo DECaLS dataset using classical machine learning techniques. Our approach employs a dimensionality reduction method followed by a classical classifier to…
We study morphology and luminosity segregation of galaxies in loose groups. We analyze the two catalogs of groups which have been identified in the Nearby Optical Galaxy (NOG) sample, by means of hierarchical and percolation…
[abridged] New near-infrared surveys, using the HST, offer an unprecedented opportunity to study rest-frame optical galaxy morphologies at z>1 and to calibrate automated morphological parameters that will play a key role in classifying…
We conducted the MeerKAT Vela Supercluster survey, named Vela$-$HI, to bridge the gap between the Vela SARAO MeerKAT Galactic Plane Survey (Vela$-$SMGPS, $-2^{\circ} \leq b \leq 1^{\circ}$), and optical and near-infrared spectroscopic…
Low-mass objects are ubiquitous in our Galaxy. Their low temperature provides them with complex atmospheres characterised by the presence of strong molecular absorption bands which, together with their faintness, have made their accurate…
We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…
We present an X-ray analysis of fourteen nearby (z < 0.044) AGN in low mass galaxies (M_* <= 5*10^9 Msun) selected based on their optical variability (Baldassare et al. 2020). Comparing and contrasting different AGN selection techniques in…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…
The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…