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We present a catalog of 9017 X-ray sources identified in Chandra observations of a 2 by 0.8 degree field around the Galactic center. We increase the number of known X-ray sources in the region by a factor of 2.5. The catalog incorporates…

With the rapid advancement of electronic information technology, the number and variety of unknown radiation sources have increased significantly. Some of these sources share common characteristics, which offers the potential to effectively…

信号处理 · 电气工程与系统科学 2025-10-01 Haobo Geng , Yaoyao Li , Weiping Tong , Youwei Meng , Houpu Xiao , Yicong Liu

We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains…

密码学与安全 · 计算机科学 2023-08-10 Yizheng Chen , Zhoujie Ding , Lamya Alowain , Xinyun Chen , David Wagner

A well characterised detection pipeline is an important ingredient for X-ray cluster surveys. We present the final development of the XXL Survey pipeline. The pipeline optimally uses X-ray information by combining many overlapping…

天体物理仪器与方法 · 物理学 2018-11-21 L. Faccioli , F. Pacaud , J. -L. Sauvageot , M. Pierre , L. Chiappetti , N. Clerc , R. Gastaud , E. Koulouridis , A. M. C. Le Brun , A. Valotti

Using the Chandra Advanced CCD Imaging Spectrometer Imaging array (ACIS-I), we have carried out a deep hard X-ray observation of the Galactic plane region at (l,b) ~ (28.5, 0.0), where no discrete X-ray source had been reported previously.…

Hyper-luminous X-ray sources (HLXs; L_X>10^41 erg s^-1) are off-nuclear X-ray sources in galaxies and strong candidates for intermediate-mass black holes (IMBHs). We have constructed a sample of 169 HLX candidates by combining X-ray…

星系天体物理 · 物理学 2019-09-25 R. Scott Barrows , Mar Mezcua , Julia M. Comerford

We employ X-ray stacking techniques to examine the contribution from X-ray undetected, mid-infrared-selected sources to the unresolved, hard (6-8 keV) cosmic X-ray background (CXB). We use the publicly available, 24 micron Spitzer Space…

天体物理学 · 物理学 2009-11-13 A. T. Steffen , W. N. Brandt , D. M. Alexander , S. C. Gallagher , B. D. Lehmer

The Survey Science Centre of the XMM-Newton satellite released the first incremental version of the 2XMM catalogue in August 2008 . With more than 220,000 X-ray sources, the 2XMMi was at that time the largest catalogue of X-ray sources ever…

高能天体物理现象 · 物理学 2015-05-20 F. -X. Pineau , C. Motch , F. Carrera , R. Della Ceca , S. Derriere , L. Michel , A. Schwope , M. G. Watson

We present the results of an homogeneous X-ray analysis for 82 nearby LINERs selected from the catalogue of Carrillo et al. (1999). All sources have available Chandra (68 sources) and/or XMM-Newton (55 sources) observations. This is the…

宇宙学与河外天体物理 · 物理学 2015-05-13 O. Gonzalez-Martin , J. Masegosa , I. Marquez , M. Guainazzi , E. Jimenez-Bailon

The Third Catalog of Hard Fermi Large Area Telescope Sources (3FHL) reports the detection of 1556 objects at E > 10 GeV. However, 177 sources remain unassociated and 23 are associated with a ROSAT X-ray detection of unknown origin. Pointed…

高能天体物理现象 · 物理学 2022-12-07 S. Joffre , R. Silver , M. Rajagopal , M. Ajello , N. Torres-Albà , A. Pizzetti , S. Marchesi , A. Kaur

We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health records observations, and (ii) quantify the source causal effects…

机器学习 · 计算机科学 2025-11-03 Marco Barbero-Mota , Eric V. Strobl , John M. Still , William W. Stead , Thomas A. Lasko

We have devised a predominantly Naive Bayes method to classify the optical/IR matches to X-ray sources detected by Chandra in the Cygnus OB2 association into foreground, member, and background objects. We employ a variety of X-ray, optical,…

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…

计算工程、金融与科学 · 计算机科学 2015-05-29 Isadora Nun , Karim Pichara , Pavlos Protopapas , Dae-Won Kim

We present X-ray point-source catalogs for a deep 400 ks Chandra ACIS-I exposure of the SSA22 field. The observations are centred on a z = 3.09 protocluster, which is populated by Lyman break galaxies (LBGs), Lyalpha emitters (LAEs), and…

宇宙学与河外天体物理 · 物理学 2015-05-13 B. D. Lehmer , D. M. Alexander , S. C. Chapman , Ian Smail , F. E. Bauer , W. N. Brandt , J. E. Geach , Y. Matsuda , J. R. Mullaney , A. M. Swinbank

We present an analysis of the X-ray properties of a sample of solar- and late-type field stars identified in the Chandra Cosmic Evolution Survey (COSMOS), a deep (160ks) and wide (0.9 deg2) extragalactic survey. The sample of 60 sources was…

太阳与恒星天体物理 · 物理学 2015-05-20 Nicholas J. Wright , Jeremy J. Drake , Francesca Civano

Chandra observations show the importance of the X-ray band for studying the evolution of galaxies. Binary X-ray sources are an easily detectable tracer of the stellar population. Chandra studies of these populations are giving us insights…

天体物理学 · 物理学 2007-05-23 G. Fabbiano

The Chandra Multiwavength Plane (ChaMPlane) Survey of the galactic plane incorporates serendipitous sources from selected Chandra pointings in or near the galactic plane (b < 12deg; >20 ksec; lack of bright diffuse or point sources) to…

We present a catalog of cross-correlated radio, infrared and X-ray sources using a very restrictive selection criteria with an IDL-based code developed by us. The significance of the observed coincidences was evaluated through Monte Carlo…

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources,…

天体物理仪器与方法 · 物理学 2023-03-15 Euclid Collaboration , A. Humphrey , L. Bisigello , P. A. C. Cunha , M. Bolzonella , S. Fotopoulou , K. Caputi , C. Tortora , G. Zamorani , P. Papaderos , D. Vergani , J. Brinchmann , M. Moresco , A. Amara , N. Auricchio , M. Baldi , R. Bender , D. Bonino , E. Branchini , M. Brescia , S. Camera , V. Capobianco , C. Carbone , J. Carretero , F. J. Castander , M. Castellano , S. Cavuoti , A. Cimatti , R. Cledassou , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin , L. Corcione , F. Courbin , M. Cropper , A. Da Silva , H. Degaudenzi , M. Douspis , F. Dubath , C. A. J. Duncan , X. Dupac , S. Dusini , S. Farrens , S. Ferriol , M. Frailis , E. Franceschi , M. Fumana , P. Gomez-Alvarez , S. Galeotta , B. Garilli , W. Gillard , B. Gillis , C. Giocoli , A. Grazian , F. Grupp , L. Guzzo , S. V. H. Haugan , W. Holmes , F. Hormuth , K. Jahnke , M. Kummel , S. Kermiche , A. Kiessling , M. Kilbinger , T. Kitching , R. Kohley , M. Kunz , H. Kurki-Suonio , S. Ligori , P. B. Lilje , I. Lloro , E. Maiorano , O. Mansutti , O. Marggraf , K. Markovic , F. Marulli , R. Massey , S. Maurogordato , H. J. McCracken , E. Medinaceli , M. Melchior , M. Meneghetti , E. Merlin , G. Meylan , L. Moscardini , E. Munari , R. Nakajima , S. M. Niemi , J. Nightingale , C. Padilla , S. Paltani , F. Pasian , K. Pedersen , V. Pettorino , S. Pires , M. Poncet , L. Popa , L. Pozzetti , F. Raison , A. Renzi , J. Rhodes , G. Riccio , E. Romelli , M. Roncarelli , E. Rossetti , R. Saglia , D. Sapone , B. Sartoris , R. Scaramella , P. Schneider , M. Scodeggio , A. Secroun , G. Seidel , C. Sirignano , G. Sirri , L. Stanco , P. Tallada-Crespi , D. Tavagnacco , A. N. Taylor , I. Tereno , R. Toledo-Moreo , F. Torradeflot , I. Tutusaus , L. Valenziano , T. Vassallo , Y. Wang , J. Weller , A. Zacchei , J. Zoubian , S. Andreon , S. Bardelli , A. Boucaud , R. Farinelli , J. Gracia-Carpio , D. Maino , N. Mauri , S. Mei , N. Morisset , F. Sureau , M. Tenti , A. Tramacere , E. Zucca , C. Baccigalupi , A. Balaguera-Antolinez , A. Biviano , A. Blanchard , S. Borgani , E. Bozzo , C. Burigana , R. Cabanac , A. Cappi , C. S. Carvalho , S. Casas , G. Castignani , C. Colodro-Conde , A. R. Cooray , J. Coupon , H. M. Courtois , O. Cucciati , S. Davini , G. De Lucia , H. Dole , J. A. Escartin , S. Escoffier , M. Fabricius , M. Farina , F. Finelli , K. Ganga , J. Garcia-Bellido , K. George , F. Giacomini , G. Gozaliasl , I. Hook , M. Huertas-Company , B. Joachimi , V. Kansal , A. Kashlinsky , E. Keihanen , C. C. Kirkpatrick , V. Lindholm , G. Mainetti , R. Maoli , S. Marcin , M. Martinelli , N. Martinet , M. Maturi , R. B. Metcalf , G. Morgante , A. A. Nucita , L. Patrizii , A. Peel , J. E. Pollack , V. Popa , C. Porciani , D. Potter , P. Reimberg , A. G. Sanchez , M. Schirmer , M. Schultheis , V. Scottez , E. Sefusatti , J. Stadel , R. Teyssier , C. Valieri , J. Valiviita , M. Viel , F. Calura , H. Hildebrandt

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach),…

机器学习 · 计算机科学 2024-11-18 Elaine Cecília Gatto , Felipe Nakano Kenji , Jesse Read , Mauri Ferrandin , Ricardo Cerri , Celine Vens