相关论文: Detecting Galaxy Tidal Features Using Self-Supervi…

Retinal vessel segmentation based on deep learning requires a lot of manual labeled data. That is time-consuming, laborious and professional. What is worse, the acquisition of abundant fundus images is difficult. These problems are more…

Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze…

Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a…

We present a new morphological indicator designed for automated recognition of galaxies with faint asymmetric tidal features suggestive of an ongoing or past merger. We use this new indicator, together with preexisting diagnostics of galaxy…

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations,…

Deep observations of faint surface brightness stellar tidal streams in external galaxies with LSST are addressed in this White Paper contribution. We propose using the Wide--Fast--Deep survey that contains several nearby galaxies (at…

Long tails and streams of stars are the most noticeable upshots of galaxy collisions. Their origin as gravitational, tidal, disturbances has however been recognized only less than fifty years ago and more than ten years after their first…

In the past decade, surveys of the stellar component of the Galaxy have revealed a number of streams from tidally disrupted dwarf galaxies and globular clusters. Simulations of hierarchical structure formation in LCDM cosmologies predict…

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…

One of the generic predictions of modern cosmological models is that large galaxies should have experienced many mergers with smaller galaxies at some point in their past. Debris from such encounters will leave spatially distinct…

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong…

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers…

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…

The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…

Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a…

Upcoming deep imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will be confronted with challenges that come with increased depth. One of the leading systematic errors in deep surveys is the blending of…

Amateur small equipment has demonstrated to be competitive tools to obtain ultra-deep imaging of the outskirts of nearby massive galaxies and to survey vast areas of the sky with unprecedented depth. Over the last decade, amateur data have…

Here we present a statistical study on tidal features around massive early-type galaxies (ETGs). Utilizing the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), we measure the flux fraction of tidal features ($f_{\rm…

We introduce the Galaxy Replacement Technique (GRT) that allows us to model tidal stripping of galaxies with very high-mass (m$_{\rm{star}}=5.4\times10^4$~M$_\odot$/h) and high-spatial resolution (10 pc/h), in a fully cosmological context,…

We study galaxy pair samples selected from the Sloan Digital Sky Survey (SDSS-DR7) and we perform an analysis of minor and major mergers with the aim of investigating the dependence of galaxy properties on interactions. We build a galaxy…