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This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The…

Low luminosity active galactic nuclei (LLAGN) probe accretion physics in the low Eddington regime and can provide additional clues about galaxy evolution. AGN variability is ubiquitous and thus provides a reliable tool for finding AGN. We…

The NSF-DOE Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), beginning full operations in late 2025, will dramatically transform solar system science by vastly expanding discoveries and providing detailed characterization…

We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…

In this work, we study the AGN radio detection effectiveness in the major deep extragalactic surveys, considering different AGN obscuration levels, redshift, and AGN bolometric luminosities. We particularly focus on comparing their radio…

The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture…

Astrophysics of Galaxies · Physics 2025-12-22 Xiaolei Yin , Guanwen Fang , Shiying Lu , Zesen Lin , Yao Dai , Chichun Zhou

We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including…

Instrumentation and Methods for Astrophysics · Physics 2009-11-04 Kirk Borne

The distinction between stars and galaxies is a fundamental problem in the field of celestial classification. This issue has become challenging for these ongoing and upcoming digital surveys, which will produce terabytes and even petabytes…

Instrumentation and Methods for Astrophysics · Physics 2026-04-14 Zhuoming Han , Tianmeng Zhang , Chao Liu , Chenxiaoji Ling

Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy groups and clusters, yet no multiclass classifier simultaneously separates them from straight…

Instrumentation and Methods for Astrophysics · Physics 2026-05-28 M. S. Hossain , M. S. H. Shahal , K. M. B. Asad , P. Saikia , A. Khan , F. Akter , A. Ali , M. A. Amin , D. P. Guha , M. O. B. Jihad , A. Momen , S. Sen , A. K. M. M. Rahman

We explore spectroscopic and photometric methods for identifying high-redshift galaxies containing an Active Galactic Nucleus (AGN) with JWST observations. After demonstrating the limitations of standard optical methods, which appear…

Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts…

Astrophysics of Galaxies · Physics 2025-09-24 L. Ulivi , F. Mannucci , M. Scialpi , C. Marconcini , G. Cresci , A. Marconi , A. Feltre , M. Ginolfi , F. Ricci , D. Sluse , F. Belfiore , E. Bertola , C. Bracci , E. Cataldi , M. Ceci , Q. D'Amato , I. Lamperti , R. B. Metcalf , B. Moreschini , M. Perna , G. Tozzi , G. Venturi , M. V. Zanchettin , Y. Fu , M. Huertas-Company , N. E. P. Lines , M. Mezcua , M. Pöntinen , K. Rojas , V. Scottez , M. Siudek , H. Teimoorinia , I. T. Andika , J. A. Acevedo Barroso , B. Clément , F. Courbin , R. Gavazzi , L. R. Ecker , B. C. Nagam , R. Pearce-Casey , S. Schuldt , S. H. Vincken , D. Stern , A. Chakraborty , S. Andreon , N. Auricchio , C. Baccigalupi , M. Baldi , A. Balestra , S. Bardelli , A. Biviano , E. Branchini , M. Brescia , S. Camera , G. Cañas-Herrera , V. Capobianco , C. Carbone , J. Carretero , M. Castellano , G. Castignani , S. Cavuoti , A. Cimatti , C. Colodro-Conde , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin , H. M. Courtois , M. Cropper , A. Da Silva , H. Degaudenzi , G. De Lucia , A. M. Di Giorgio , C. Dolding , H. Dole , F. Dubath , C. A. J. Duncan , X. Dupac , S. Dusini , S. Escoffier , M. Fabricius , M. Farina , R. Farinelli , F. Faustini , S. Ferriol , F. Finelli , M. Frailis , E. Franceschi , M. Fumana , S. Galeotta , K. George , B. Gillis , C. Giocoli , J. Gracia-Carpio , A. Grazian , F. Grupp , S. V. H. Haugan , W. Holmes , I. M. Hook , F. Hormuth , A. Hornstrup , K. Jahnke , M. Jhabvala , B. Joachimi , E. Keihänen , S. Kermiche , A. Kiessling , B. Kubik , M. Kümmel , M. Kunz , H. Kurki-Suonio , A. M. C. Le Brun , S. Ligori , P. B. Lilje , V. Lindholm , I. Lloro , G. Mainetti , D. Maino , E. Maiorano , O. Mansutti , O. Marggraf , M. Martinelli , N. Martinet , F. Marulli , R. J. Massey , E. Medinaceli , S. Mei , M. Melchior , Y. Mellier , M. Meneghetti , E. Merlin , G. Meylan , A. Mora , M. Moresco , L. Moscardini , R. Nakajima , C. Neissner , R. C. Nichol , S. -M. Niemi , C. Padilla , S. Paltani , F. Pasian , K. Pedersen , W. J. Percival , V. Pettorino , S. Pires , G. Polenta , M. Poncet , L. A. Popa , L. Pozzetti , F. Raison , A. Renzi , J. Rhodes , G. Riccio , E. Romelli , M. Roncarelli , R. Saglia , Z. Sakr , D. Sapone , B. Sartoris , M. Schirmer , P. Schneider , T. Schrabback , A. Secroun , G. Seidel , S. Serrano , P. Simon , C. Sirignano , G. Sirri , L. Stanco , J. Steinwagner , P. Tallada-Crespí , D. Tavagnacco , A. N. Taylor , I. Tereno , N. Tessore , S. Toft , R. Toledo-Moreo , F. Torradeflot , I. Tutusaus , E. A. Valentijn , L. Valenziano , J. Valiviita , T. Vassallo , G. Verdoes Kleijn , A. Veropalumbo , Y. Wang , J. Weller , G. Zamorani , F. M. Zerbi , E. Zucca , A. A. Nucita

We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools. We show that the redshift, $z$, bolometric luminosity, $L_{\rm Bol}$, central mass of the supermassive black hole (SMBH), $M_{\rm BH}$,…

Astrophysics of Galaxies · Physics 2024-05-17 Sarah Mechbal , Markus Ackermann , Marek Kowalski

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to…

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference…

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…

Astrophysics · Physics 2008-11-26 Nicholas M. Ball , Robert J. Brunner , Adam D. Myers , David Tcheng

Variability is a main property of active galactic nuclei (AGN) and it was adopted as a selection criterion using multi epoch surveys conducted for the detection of supernovae (SNe). We have used two SN datasets. First we selected the AXAF…

Astrophysics of Galaxies · Physics 2010-03-03 K. Boutsia , B. Leibundgut , D. Trevese , F. Vagnetti

Understanding the co-evolution of supermassive black holes (SMBHs) and their host systems requires a comprehensive census of active galactic nuclei (AGN) behavior across a wide range of redshift, luminosity, obscuration level and galaxy…

Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys…

Astrophysics of Galaxies · Physics 2026-02-18 Berta Margalef-Bentabol , Lingyu Wang , Antonio La Marca , Vicente Rodriguez-Gomez

We report on optical searches in the error circles of 93 ROSAT survey sources located at low galactic latitudes (|b| < 20 deg). These sources were extracted from the ROSAT Galactic Plane Survey using various selection criteria on hardness…

Landslide susceptibility prediction has always been an important and challenging content. However, there are some uncertain problems to be solved in susceptibility modeling, such as the error of landslide samples and the complex nonlinear…

Machine Learning · Computer Science 2023-10-10 Li Zhu , Lekai Liu , Changshi Yu
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