Arrival Directions of Cosmic Rays above 32 EeV from Phase One of the Pierre Auger Observatory

Arrival Directions of Cosmic Rays above 32 EeV from Phase One of the Pierre Auger Observatory P. Abreu 1 , M. Aglietta 2,3 , J. M. Albury 4 , I. Allekotte 5 , K. Almeida Cheminant 6 , A. Almela 7,8 , J. Alvarez-Muñiz 9 , R. Alves Batista 10 , J. Ammerman Yebra 9 , G. A. Anastasi 2,3 , L. Anchordoqui 11 , B. Andrada 7 , S. Andringa 1 , C. Aramo 12 , P. R. Araújo Ferreira 13 , E. Arnone 3,14 , J. C. Arteaga Velázquez 15 , H. Asorey 7 , P. Assis 1 , G. Avila 16 , E. Avocone 17,18 , A. M. Badescu 19 , A. Bakalova 20 , A. Balaceanu 21 , F. Barbato 18,22 , J. A. Bellido 4,23 , C. Berat 24 , M. E. Bertaina 3,14 , G. Bhatta 6 ,P.L.Biermann 94 ,V.Binet 25 ,K.Bismark 7,26 ,T.Bister 13 ,J.Biteau 27 ,J.Blazek 20 ,C.Bleve 24 ,J.Blümer 28 , M. Boháčová 20 , D. Boncioli 17,18 ,C.Bonifazi 29,30 , L. Bonneau Arbeletche 31 , N. Borodai 6 ,A.M.Botti 7 ,J.Brack 96 , T. Bretz 13 ,P.G.BrichettoOrchera 7 ,F.L.Briechle 13 , P. Buchholz 32 ,A.Bueno 33 , S. Buitink 34 ,M.Buscemi 35 , M. Büsken 7,26 , K. S. Caballero-Mora 36 ,L.Caccianiga 37,38 ,F.Canfora 10,39 , I. Caracas 40 ,R.Caruso 35,41 , A. Castellina 2,3 ,F.Catalani 42 , G. Cataldi 43 ,L.Cazon 9 ,M.Cerda 44 ,J.A.Chinellato 31 , J. Chudoba 20 ,L.Chytka 45 ,R.W.Clay 4 ,A.C.CobosCerutti 46 , R. Colalillo 12,47 ,A.Coleman 48 ,M.R.Coluccia 43 ,R.Conceição 1 ,A.Condorelli 18,22 ,G.Consolati 38,49 ,F.Contreras 16 , F. Convenga 28 ,D.CorreiadosSantos 50 ,C.E.Covault 51 ,S.Dasso 52,53 ,K.Daumiller 28 ,B.R.Dawson 4 ,J.A.Day 4 , R. M. de Almeida 50 ,J.deJesús 7,28 ,S.J.deJong 10,39 ,J.R.T.deMelloNeto 30,54 ,I.DeMitri 18,22 ,J.deOliveira 55 , D. de Oliveira Franco 31 ,F.dePalma 56,43 ,V.deSouza 57 ,E.DeVito 43,56 ,A.DelPopolo 35,41 ,M.delRío 16 ,O.Deligny 58 , L. Deval 7,28 , A. di Matteo 3 ,M.Dobre 21 ,C.Dobrigkeit 31 ,J.C.D’Olivo 59 ,L.M.DominguesMendes 1 ,R.C.dosAnjos 60 , M. T. Dova 61 ,J.Ebr 20 ,R.Engel 26,28 ,I.Epicoco 43,56 ,M.Erdmann 13 ,C.O.Escobar 93 , A. Etchegoyen 7,8 , H. Falcke 10,39,62 , J. Farmer 63 ,G.Farrar 64 ,A.C.Fauth 31 , N. Fazzini 93 , F. Feldbusch 65 ,F.Fenu 3,14 ,B.Fick 66 ,J.M.Figueira 7 ,A.Filipčič 67,68 , T. Fitoussi 28 ,T.Fodran 10 ,T.Fujii 63,97 ,A.Fuster 7,8 ,C.Galea 10 ,C.Galelli 37,38 ,B.García 46 ,H.Gemmeke 65 , F. Gesualdi 7,28 ,A.Gherghel-Lascu 21 ,P.L.Ghia 58 , U. Giaccari 10 , M. Giammarchi 38 ,J.Glombitza 13 ,F.Gobbi 44 ,F.Gollan 7 , G. Golup 5 ,M.GómezBerisso 5 ,P.F.GómezVitale 16 ,J.P.Gongora 16 ,J.M.González 5 ,N.González 69 ,I.Goos 5,28 ,D.Góra 6 , A. Gorgi 2,3 ,M.Gottowik 40 ,T.D.Grubb 4 ,F.Guarino 12,47 ,G.P.Guedes 70 ,E.Guido 3,14 ,S.Hahn 7,28 ,P.Hamal 20 , M. R. Hampel 7 ,P.Hansen 61 ,D.Harari 5 ,V.M.Harvey 4 ,A.Haungs 28 , T. Hebbeker 13 ,D.Heck 28 ,G.C.Hill 4 ,C.Hojvat 93 , J. R. Hörandel 10,39 ,P.Horvath 45 , M. Hrabovský 45 ,T.Huege 28,34 ,A.Insolia 35,41 ,P.G.Isar 71 , P. Janecek 20 , J. A. Johnsen 72 ,J.Jurysek 20 ,A.Kääpä 40 ,K.H.Kampert 40 , B. Keilhauer 28 , A. Khakurdikar 10 ,V.V.KizakkeCovilakam 7,28 , H. O. Klages 28 ,M.Kleifges 65 , J. Kleinfeller 44 ,F.Knapp 26 , N. Kunka 65 ,B.L.Lago 73 , N. Langner 13 ,M.A.LeiguideOliveira 74 , V. Lenok 28 , A. Letessier-Selvon 75 , I. Lhenry-Yvon 58 ,D.LoPresti 35,41 ,L.Lopes 1 ,R.López 76 ,L.Lu 77 ,Q.Luce 26 , J. P. Lundquist 68 ,A.MachadoPayeras 31 ,G.Mancarella 56,43 ,D.Mandat 20 ,B.C.Manning 4 ,J.Manshanden 78 ,P.Mantsch 93 , S. Marafico 58 ,F.M.Mariani 37,38 ,A.G.Mariazzi 61 ,I.C.Mariş 69 ,G.Marsella 35,79 ,D.Martello 43,56 ,S.Martinelli 7,28 , O. Martínez Bravo 76 ,M.Mastrodicasa 17,18 ,H.J.Mathes 28 ,J.Matthews 98 ,G.Matthiae 80,81 ,E.Mayotte 40,72 ,S.Mayotte 72 , P. O. Mazur 93 ,G.Medina-Tanco 59 ,D.Melo 7 ,A.Menshikov 65 ,S.Michal 45 , M. I. Micheletti 25 ,L.Miramonti 37,38 , S. Mollerach 5 , F. Montanet 24 ,L.Morejon 40 ,C.Morello 2,3 ,M.Mostafá 82 ,A.L.Müller 20 ,M.A.Muller 31 ,K.Mulrey 10,39 , R. Mussa 3 ,M.Muzio 64 ,W.M.Namasaka 40 ,A.Nasr-Esfahani 40 ,L.Nellen 59 ,G.Nicora 83 , M. Niculescu-Oglinzanu 21 , M. Niechciol 32 , D. Nitz 66 ,I.Norwood 66 ,D.Nosek 84 , V. Novotny 84 ,L.Nožka 45 ,ANucita 43,56 ,L.A.Núñez 85 , C. Oliveira 57 ,M.Palatka 20 ,J.Pallotta 83 , P. Papenbreer 40 , G. Parente 9 ,A.Parra 76 ,J.Pawlowsky 40 ,M.Pech 20 ,J.Pȩkala 6 , R. Pelayo 86 ,J.Peña-Rodriguez 85 ,E.E.PereiraMartins 7,26 ,J.PerezArmand 87 ,C.PérezBertolli 7,28 ,L.Perrone 43,56 , S. Petrera 18,22 ,C.Petrucci 17,18 ,T.Pierog 28 ,M.Pimenta 1 , V. Pirronello 35,41 , M. Platino 7 ,B.Pont 10 , M. Pothast 10,39 , P. Privitera 63 ,M.Prouza 20 ,A.Puyleart 66 ,S.Querchfeld 40 ,J.Rautenberg 40 , D. Ravignani 7 , M. Reininghaus 7,28 ,J.Ridky 20 , F. Riehn 1 ,M.Risse 32 ,V.Rizi 17,18 , W. Rodrigues de Carvalho 10 ,J.RodriguezRojo 16 ,M.J.Roncoroni 7 ,S.Rossoni 78 , M. Roth 28 ,E.Roulet 5 ,A.C.Rovero 52 , P. Ruehl 32 ,A.Saftoiu 21 , M. Saharan 10 ,F.Salamida 17,18 ,H.Salazar 76 , G. Salina 81 ,J.D.SanabriaGomez 85 ,F.Sánchez 7 ,E.M.Santos 87 ,E.Santos 20 ,F.Sarazin 72 ,R.Sarmento 1 ,R.Sato 16 , P. Savina 77 ,C.M.Schäfer 28 ,V.Scherini 43,56 ,H.Schieler 28 ,M.Schimassek 7,26 ,M.Schimp 40 ,F.Schlüter 7,28 , D. Schmidt 26 ,O.Scholten 34 ,H.Schoorlemmer 10,39 , P. Schovánek 20 ,F.G.Schröder 28,48 ,J.Schulte 13 ,T.Schulz 28 , S. J. Sciutto 61 , M. Scornavacche 7,28 ,A.Segreto 35,88 ,S.Sehgal 40 ,R.C.Shellard 89 ,G.Sigl 78 ,G.Silli 7,28 ,O.Sima 21,99 ,R.Smau 21 , R. Šmída 63 ,P.Sommers 82 ,J.F.Soriano 11 ,R.Squartini 44 , M. Stadelmaier 20 ,D.Stanca 21 ,S.Stanič 68 ,J.Stasielak 6 , P. Stassi 24 ,M.Straub 13 ,A.Streich 7,26 ,M.Suárez-Durán 69 ,T.Sudholz 4 ,T.Suomijärvi 27 ,A.D.Supanitsky 7 ,Z.Szadkowski 90 , A. Tapia 91 ,C.Taricco 14,3 ,C.Timmermans 39,10 , O. Tkachenko 28 ,P.Tobiska 20 ,C.J.ToderoPeixoto 42 ,B.Tomé 1 , Z. Torrès 24 ,A.Travaini 44 ,P.Travnicek 20 ,C.Trimarelli 17,18 ,M.Tueros 61 ,R.Ulrich 28 ,M.Unger 28 ,L.Vaclavek 45 , M. Vacula 45 , J. F. Valdés Galicia 59 ,L.Valore 12,47 ,E.Varela 76 , A. Vásquez-Ramírez 85 ,D.Veberič 28 ,C.Ventura 54 , I. D. Vergara Quispe 61 ,V.Verzi 81 ,J.Vicha 20 ,J.Vink 92 , S. Vorobiov 68 ,H.Wahlberg 61 , C. Watanabe 30 ,A.A.Watson 95 , A. Weindl 28 ,L.Wiencke 72 ,H.Wilczyński 6 , D. Wittkowski 40 , B. Wundheiler 7 , A. Yushkov 20 , O. Zapparrata 69 ,E.Zas 9 , D. Zavrtanik 67,68 ,M.Zavrtanik 67,68 , and L. Zehrer 68 ThePierreAugerCollaboration The Astrophysical Journal, 935:170 (24pp), 2022 August 20 https://doi.org/10.3847/1538-4357/ac7d4e © 2022. The Author(s). Published by the American Astronomical Society. 1 1 Laboratório de Instrumentação e Física Experimental de Partículas—LIP and Instituto Superior Técnico—IST, Universidade de Lisboa—UL, Lisboa, Portugal 2 Osservatorio Astrofisico di Torino (INAF), Torino, Italy 3 INFN, Sezione di Torino, Torino, Italy 4 University of Adelaide, Adelaide, S.A., Australia 5 Centro Atómico Bariloche and Instituto Balseiro (CNEA-UNCuyo-CONICET), San Carlos de Bariloche, Argentina 6 Institute of Nuclear Physics PAN, Krakow, Poland 7 Instituto de Tecnologías en Detección y Astropartículas (CNEA, CONICET, UNSAM), Buenos Aires, Argentina 8 Universidad Tecnológica Nacional—Facultad Regional Buenos Aires, Buenos Aires, Argentina 9 Instituto Galego de Física de Altas Enerxías (IGFAE), Universidade de Santiago de Compostela, Santiago de Compostela, Spain 10 IMAPP, Radboud University Nijmegen, Nijmegen, The Netherlands 11 Department of Physics and Astronomy, Lehman College, City University of New York, Bronx, NY, USA 12 INFN, Sezione di Napoli, Napoli, Italy 13 RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 14 Università Torino, Dipartimento di Fisica, Torino, Italy 15 Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, México 16 Observatorio Pierre Auger and Comisión Nacional de Energía Atómica, Malargüe, Argentina 17 Università dell’Aquila, Dipartimento di Scienze Fisiche e Chimiche, L’Aquila, Italy 18 INFN Laboratori Nazionali del Gran Sasso, Assergi (L’Aquila), Italy 19 University Politehnica of Bucharest, Bucharest, Romania 20 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 21 “Horia Hulubei”National Institute for Physics and Nuclear Engineering, Bucharest-Magurele, Romania 22 Gran Sasso Science Institute, L’Aquila, Italy 23 Universidad Nacional de San Agustin de Arequipa, Facultad de Ciencias Naturales y Formales, Arequipa, Peru 24 Univ. Grenoble Alpes, CNRS, Grenoble Institute of Engineering Univ., Grenoble Alpes, LPSC-IN2P3, 38000 Grenoble, France 25 Instituto de Física de Rosario (IFIR)—CONICET/U.N.R. and Facultad de Ciencias Bioquímicas y Farmacéuticas U.N.R., Rosario, Argentina 26 Karlsruhe Institute of Technology (KIT), Institute for Experimental Particle Physics, Karlsruhe, Germany 27 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France 28 Karlsruhe Institute of Technology (KIT), Institute for Astroparticle Physics, Karlsruhe, Germany; [email protected] 29 International Center of Advanced Studies and Instituto de Ciencias Físicas, ECyT-UNSAM and CONICET, Campus Miguelete—San Martín, Buenos Aires, Argentina 30 Universidade Federal do Rio de Janeiro, Instituto de Física, Rio de Janeiro, RJ, Brazil 31 Universidade Estadual de Campinas, IFGW, Campinas, SP, Brazil 32 Universität Siegen, Department Physik—Experimentelle Teilchenphysik, Siegen, Germany 33 Universidad de Granada and C.A.F.P.E., Granada, Spain 34 Vrije Universiteit Brussels, Brussels, Belgium 35 INFN, Sezione di Catania, Catania, Italy 36 Universidad Autónoma de Chiapas, Tuxtla Gutiérrez, Chiapas, México 37 Università di Milano, Dipartimento di Fisica, Milano, Italy 38 INFN, Sezione di Milano, Milano, Italy 39 Nationaal Instituut voor Kernfysica en Hoge Energie Fysica (NIKHEF), Science Park, Amsterdam, The Netherlands 40 Bergische Universität Wuppertal, Department of Physics, Wuppertal, Germany 41 Università di Catania, Dipartimento di Fisica e Astronomia “Ettore Majorana“, Catania, Italy 42 Universidade de São Paulo, Escola de Engenharia de Lorena, Lorena, SP, Brazil 43 INFN, Sezione di Lecce, Lecce, Italy 44 Observatorio Pierre Auger, Malargüe, Argentina 45 Palacky University, RCPTM, Olomouc, Czech Republic 46 Instituto de Tecnologías en Detección y Astropartículas (CNEA, CONICET, UNSAM), and Universidad Tecnológica Nacional—Facultad Regional Mendoza (CONICET/CNEA), Mendoza, Argentina 47 Università di Napoli “Federico II”, Dipartimento di Fisica “Ettore Pancini”, Napoli, Italy 48 University of Delaware, Department of Physics and Astronomy, Bartol Research Institute, Newark, DE, USA 49 Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali, Milano, Italy 50 Universidade Federal Fluminense, EEIMVR, Volta Redonda, RJ, Brazil 51 Case Western Reserve University, Cleveland, OH, USA 52 Instituto de Astronomía y Física del Espacio (IAFE, CONICET-UBA), Buenos Aires, Argentina 53 Departamento de Física and Departamento de Ciencias de la Atmósfera y los Océanos, FCEyN, Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina 54 Universidade Federal do Rio de Janeiro (UFRJ), Observatório do Valongo, Rio de Janeiro, RJ, Brazil 55 Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro (IFRJ), Brazil 56 Università del Salento, Dipartimento di Matematica e Fisica “E. De Giorgi”, Lecce, Italy 57 Universidade de São Paulo, Instituto de Física de São Carlos, São Carlos, SP, Brazil 58 CNRS/IN2P3, IJCLab, Université Paris-Saclay, Orsay, France 59 Universidad Nacional Autónoma de México, México, D.F., México 60 Universidade Federal do Paraná, Setor Palotina, Palotina, Brazil 61 IFLP, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 62 Stichting Astronomisch Onderzoek in Nederland (ASTRON), Dwingeloo, The Netherlands 63 University of Chicago, Enrico Fermi Institute, Chicago, IL, USA 64 New York University, New York, NY, USA 65 Karlsruhe Institute of Technology (KIT), Institut für Prozessdatenverarbeitung und Elektronik, Karlsruhe, Germany 66 Michigan Technological University, Houghton, MI, USA 67 Experimental Particle Physics Department, J. Stefan Institute, Ljubljana, Slovenia 68 Center for Astrophysics and Cosmology (CAC), University of Nova Gorica, Nova Gorica, Slovenia 69 Université Libre de Bruxelles (ULB), Brussels, Belgium 70 Universidade Estadual de Feira de Santana, Feira de Santana, Brazil 71 Institute of Space Science, Bucharest-Magurele, Romania 72 Colorado School of Mines, Golden, CO, USA 73 Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, Nova Friburgo, Brazil 74 Universidade Federal do ABC, Santo André, SP, Brazil 75 Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), Sorbonne Université, Université de Paris, CNRS-IN2P3, Paris, France 2 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. 76 Benemérita Universidad Autónoma de Puebla, Puebla, México 77 University of Wisconsin-Madison, Department of Physics and WIPAC, Madison, WI, USA 78 Universität Hamburg, II. Institut für Theoretische Physik, Hamburg, Germany 79 Università di Palermo, Dipartimento di Fisica e Chimica ”E. Segrè”, Palermo, Italy 80 Università di Roma “Tor Vergata,”Dipartimento di Fisica, Roma, Italy 81 INFN, Sezione di Roma “Tor Vergata", Roma, Italy 82 Pennsylvania State University, University Park, PA, USA 83 Centro de Investigaciones en Láseres y Aplicaciones, CITEDEF and CONICET, Villa Martelli, Argentina 84 Charles University, Faculty of Mathematics and Physics, Institute of Particle and Nuclear Physics, Prague, Czech Republic 85 Universidad Industrial de Santander, Bucaramanga, Colombia 86 Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas del Instituto Politécnico Nacional (UPIITA-IPN), México, D.F., México 87 Universidade de São Paulo, Instituto de Física, São Paulo, SP, Brazil 88 Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo (INAF), Palermo, Italy 89 Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, RJ, Brazil 90 University of Łódź, Faculty of High-Energy Astrophysics, Łódź, Poland 91 Universidad de Medellín, Medellín, Colombia 92 Universiteit van Amsterdam, Faculty of Science, Amsterdam, The Netherlands Received 2022 May 3; revised 2022 June 27; accepted 2022 June 28; published 2022 August 24 Abstract A promising energy range to look for angular correlations between cosmic rays of extragalactic origin and their sources is at the highest energies, above a few tens of EeV (1 EeV ≡10 18 eV). Despite the flux of these particles being extremely low, the area of ∼3000 km 2 covered at the Pierre Auger Observatory, and the 17 yr data-taking period of the Phase 1 of its operations, have enabled us to measure the arrival directions of more than 2600 ultrahigh-energy cosmic rays above 32 EeV. We publish this data set, the largest available at such energies from an integrated exposure of 122,000 km 2 sr yr, and search it for anisotropies over the 3.4πsteradians covered with the Observatory. Evidence for a deviation in excess of isotropy at intermediate angular scales, with ∼15°Gaussian spread or ∼25°top-hat radius, is obtained at the 4σsignificance level for cosmic-ray energies above ∼40 EeV. Unified Astronomy Thesaurus concepts: Ultra-high-energy cosmic radiation (1733);Cosmic ray astronomy (324); Clustering (1908);Active galaxies (17);Starburst galaxies (1570) Supporting material: figure sets, machine-readable tables 1. Introduction Cosmic rays are observed up to the astounding energies of more than 10 20 eV, making them the most energetic particles known in the universe. However, the origin of these particles remains elusive. The search for the sources of ultra-high-energy cosmic rays (UHECRs), at energies above a few EeV (1 EeV ≡10 18 eV), is challenging since they are almost all charged particles and thus deflected by the magnetic fields permeating the interstellar, intra-halo, and intergalactic media (see, e.g., Alves Batista et al. 2019, for an overview). These magnetic fields are difficult to study and their modeling is far from being complete. However, above a few tens of EeV, the deflections could be small enough for cosmic rays to retain some directional information on the position of their sources, at least for nuclei with a sufficiently small charge (e.g., Erdmann et al. 2016; Farrar & Sutherland 2019). The cosmological volume within which UHECR sources should be sought is fortunately limited. Cosmic rays at EeV energies can interact with the photon backgrounds populating intergalactic space, through the so-called Greisen–Zatsepin– Kuzmin (GZK)effect (Greisen 1966; Zatsepin & Kuz’min 1966). In particular, protons are expected to undergo photopion production and nuclei photodissociation interactions. The mean free path for energy losses depends on the cosmic-ray mass and energy. At 100 EeV, the loss length is of the order of 200–300 Mpc for protons and iron and 3–6 Mpc for intermediate nuclei such as helium and nitrogen (Allard 2012; see also Figure 6 from Addazi et al. 2022 for a recent overview). Such short distances mean that the sources of the highestenergy cosmic rays must be in the local universe. The recent detection by the Pierre Auger Collaboration of a dipolar anisotropy in the arrival directions of UHECRs with energies above 8 EeV is evidence that the majority of UHECR sources are not in the Milky Way (Pierre Auger Collaboration2017a). The direction of the dipole points ∼120°away from the Galactic center and is instead consistent at the 2σlevel with the local distribution of stellar mass (2MASS Redshift Survey; Huchra et al. 2012), after accounting for the deflections expected in the Galactic magnetic field (Jansson & Farrar 2012). Even without relying on magnetic deflections, the case for a density of UHECR sources following local extragalactic structures is further strengthened by the consistency at the 1σconfidence level (C.L.)between the directions of the UHECR anti-dipole and of the Local Void at equatorial coordinates (α,δ)=(294°,15°)or Galactic coordinates (l,b)=(51°,−3°)(Biteau 2021). Combined with the growth of the dipole amplitude with energy expected from the shrinking horizon out to which extragalactic sources remain visible (Pierre Auger Collaboration 2018a), the properties of the large-scale anisotropy discovered by the Pierre Auger Collaboration provide a 93 Fermi National Accelerator Laboratory, Fermilab, Batavia, IL, USA. 94 Max-Planck-Institut für Radioastronomie, Bonn, Germany. 95 School of Physics and Astronomy, University of Leeds, Leeds, UK. 96 Colorado State University, Fort Collins, CO, USA. 97 now at Hakubi Center for Advanced Research and Graduate School of Science, Kyoto University, Kyoto, Japan. 98 Louisiana State University, Baton Rouge, LA, USA. 99 also at University of Bucharest, Physics Department, Bucharest, Romania. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s)and the title of the work, journal citation and DOI. 3 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. growing body of evidence against a Galactic origin of these cosmic rays. Which (classes of)extragalactic sources host UHECR accelerators nonetheless remains an open question. In this article, we update previous searches for anisotropies at the highest energies (Pierre Auger Collaboration 2015a,2018b) with an unprecedentedly large data set. In particular, we exploit the entire Phase 1 of the Pierre Auger Observatory, i.e., the phase preceding the AugerPrime upgrade (Pierre Auger Collaboration2016a). Important progress has been made on estimating the mass distribution of UHECRs using only the surface detector of the Observatory with its full duty cycle (see, e.g., Pierre Auger Collaboration 2016b,2017b; Ave et al. 2017; Pierre Auger Collaboration 2021a,2021b). However, the proposed methods are still not ready to be employed in arrival-direction studies, e.g., by selecting only candidate light nuclei which would be less deflected by magnetic fields, should such a subsample exist in the data set. In the following, we then consider, as in previous works, only the energy and arrival direction of each event recorded with the Pierre Auger Observatory over 17 yr of operation. The data set includes more than 2600 events with energies E32 EeV and zenith angles up to 80°, as described in Section 2. The release of this data set complements the publication of the arrival directions of events at energies between 4 and 8 EeV and above 8 EeV made available by the Pierre Auger Collaboration (2017a). 100 The choice of an energy threshold at 32 EeV for the present release anticipates upcoming publications focused on lower energy bins, namely 8–16 EeV and 16–32 EeV, as investigated by, e.g., the Pierre Auger Collaboration (2018a) and Pierre Auger Collaboration (2020a), where ∼1500 and ∼2000 events were studied above 32 EeV, respectively. In Section 3, we describe a first set of analyses that are not based on specific source models, i.e., a blind search for excesses in the sky, an autocorrelation study, and the search for correlations with the Galactic and supergalactic planes as well as the Galactic center. Section 4is devoted to the comparison of UHECR arrival directions with the expected flux pattern from specific classes of galaxies traced by their electromagnetic emission, from radio wavelengths to gamma-rays. Finally, Section 5is devoted to a more in-depth study of the Centaurus region, which has intrigued the UHECR community since the early days of the Pierre Auger Observatory (Pierre Auger Collaboration 2007). To encourage further studies of the Phase 1 high-energy data set, this article is accompanied by supplementary materials. These include the data set itself in Appendix Aand the dedicated analysis software in Appendix B. Appendix C describes the catalogs of galaxies used here. 2. Data Set The Pierre Auger Observatory (Pierre Auger Collaboration2015b)is located in Argentina near the town of Malargüe. Stable data acquisition began on 2004 January 1. The Observatory is composed of a surface detector (SD)made of 1660 water-Cherenkov stations distributed on a triangular grid overlooked by a fluorescence detector (FD). The FD consists of 27 telescopes at four locations on the perimeter of the SD array. Here, we analyse the events with reconstructed energies larger than 32 EeV recorded with the SD array from 2004 January 1 to 2020 December 31. The SD is used to sample secondary particles in air showers and has full efficiency above 4 EeV with ∼100% duty cycle. Events recorded with the SD are reconstructed differently based on their arrival direction in local coordinates: events with zenith angles, θ,lessthan60°are called vertical events, while events arriving with zenith angles from 60°to 80°are called inclined events. Vertical events are included when the SD station with the largest signal is surrounded by at least four active stations. This a priori condition is complemented by the a posteriori requirement that the reconstructed core of the shower falls within an elementary isosceles triangle of active stations. These requirements ensure that the footprint of the shower is well-contained within the array, with ample data for an accurate reconstruction (Pierre Auger Collaboration 2010a). Inclined events, on the other hand, are selected if the station closest to the reconstructed core position is surrounded by at least five active stations. Note that other analyses performed by the Pierre Auger Collaboration at lower energies may use a tighter selection. For example, the UHECR spectrum of Pierre Auger Collaboration (2020b)is measured by requiring that all six active stations around the one withthehighestsignalareactive.Weareabletousearelaxed selection as the high-energy events included here all have large footprints, with an average of 17.7 triggered stations. We inspected each event and verified that the reconstruction was robust even with inactive stations in the core region. With respect to previous analyses, the identification of active stations that were not triggered has been improved to ensure a better selection. This was done through an a posteriori check of the consistency of the signal distribution at the ground: if a station is not triggered in a region of the array where the signal is more than twice that of the full trigger efficiency, which occurs for 11 events in the data set, the station is classified as non-active at the moment of the event (Pierre Auger Collaboration 2010a). The selection results in 2040 events with θ<60°and 595 with θ60°above 32 EeV. 101 The exposure can be computed in a geometrical way since we are operating above the energy threshold for full efficiency for both data samples (3 EeV for vertical and 4 EeV for inclined). The geometrical exposure for the selection and time span considered is 95,700 km 2 sr yr for the vertical sample and 26,300 km 2 sr yr for the inclined data set. The reconstruction procedure for vertical events is described in detail in Pierre Auger Collaboration (2020c). The arrival direction is determined by fitting a spherical model to the arrival times of particles comprising the shower front. For inclined events, the reconstruction procedure is described in Pierre Auger Collaboration (2014). The arrival direction is, in this case, obtained by fitting the arrival times with a front which takes into account muon propagation from its production point. For both data sets, the angular resolution, defined as the 68% containment radius, is better than 1°at all energies considered here. The energy estimate is based on different observables for the two samples. The signal at a reference distance of 1000 m from the shower core, S(1000), is used for the vertical sample. The inclined reconstruction uses as an estimator N 19 , which represents the muon content of the shower with respect to a 100 https://www.auger.org/document-centre-public/download/78-data/ 4642-arrival-directions-8eev-science-2017 101 To avoid border effects at the zenith angle separating the inclined and vertical selections, we identified events in the 60°<θ<62°region that are well-reconstructed with the vertical procedure but not included in the inclined data set and, vice-versa, events in the 58°<θ<60°region that are wellreconstructed with the inclined procedure but not included in the vertical data set. We found one event in the former case and none in the latter. 4 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. reference simulated proton shower with energy E=10 19 eV. For both samples, a correction is applied to take into account the absorption that showers undergo at different zenith angles. This correction is performed through a data-driven procedure called constant intensity cut, which is described in Pierre Auger Collaboration (2020b). The constant intensity cut method is used to convert S(1000)and N 19 for each shower to the value they would have if the same shower had arrived from a reference zenith angle of 38°and 68°for vertical and inclined events, respectively. The corrected energy estimators, S 38 and N 68 , are then calibrated using hybrid events, i.e., events observed with both the FD and the SD. Since the FD analysis enables a quasi-calorimetric measurement of the shower energy, the calibration procedure results in a reliable energy estimation for the whole SD data set without using air-shower simulations. The systematic uncertainty in the energy calibration is ∼14% while the energy resolution for the SD at the energies considered here is ∼7% (Pierre Auger Collaboration 2014,2020d). We checked the consistency between the vertical and inclined data sets by comparing the ratio of number of events in the two samples, N incl /N vert =0.292 ±0.014, and the value expected from the ratio of geometrical exposures, accounting for the finite energy resolution of each data stream, 0.278 c c 32 EeV 32 EeV incl incl vert vert () () = w w  . In the latter ratio, ωis the geometrical exposure for each data set, which does not depend on energy, and c(32 EeV)accounts for the net spillover of events from low to higher energies (see the unfolding procedure described in Pierre Auger Collaboration 2020b). The ratios are in agreement at the 1σC.L., showing that the vertical and inclined samples can be used together. To keep the analysis as data-driven as possible, we use the ratio of events observed above 32 EeV as the expected exposure ratio when constructing simulated data sets above any energy threshold. It should be noted that at the highest energies probed here, E80 EeV, a deficit of inclined events is observed at a significance level of 2.5σ. A further discussion of this deficit, which does not affect the results presented below, is provided in Appendix Atogether with the information on how to access the data. 3. Search for Overdensities and Correlation with Structures An earlier wide-ranging search with the Observatory for small- and intermediate-scale anisotropy was reported by Pierre Auger Collaboration (2015a). Searches for localized excesses in top-hat windows of angular radius Ψacross the entire field of view of the Observatory, or around the Galactic center, Centaurus A, and candidate host galaxies identified in multiwavelength surveys, were performed by comparing the expected and observed numbers of events within the window. Similar analyses were performed along the Galactic and supergalactic planes, by counting the number of events within an angle Ψfrom these structures, and an autocorrelation study exploited the number of pairs of events separated by less than Ψ. The analyses were repeated above energy thresholds ranging from 40 to 80 EeV. An additional scan on the maximum distance of the sources was performed for analyses against catalogs of candidate host galaxies. Both scans in energy threshold and maximum distance were motivated by the limited horizon from which UHECRs can reach Earth, although the determination of its observational value remains hindered by uncertainties on UHECR composition. In this Section, we update the results presented by Pierre Auger Collaboration (2015a), with the exception of the search for correlations with catalogs, which is performed in Section 4. 3.1. Search for Localized Excesses The first analysis is a blind search for excesses over the fraction of the sky covered with the Observatory. The number of UHECRs detected in circular windows on the sky (N obs )is compared to that expected, in the same window, from an isotropic distribution of events ( N exp). This search is performed over the entire field of view, which covers about 85% of the sky. The search windows are centered on a HEALPix grid (HEALPix v3.70; Górski et al. 2005),defined by the parameter nSide =64, which sets the size of the pixels to be of the order of the angular resolution of the Observatory. Events are counted within search windows of radius Ψ, ranging from 1°to 30°in 1°steps. Similarly, the search is performed by selecting events above energy thresholds, E th , ranging from 32 to 80 EeV in 1 EeV steps. For each window and energy threshold, we estimate the binomial probability of obtaining by chance N obs or more events from an isotropic distribution of data. The computation of N exp is performed by simulating events with coordinates distributed according to the sum of the vertical and inclined exposures, weighted in proportion to the observed number of events at energies above 32 EeV (see Section 2). For each realization of the simulated data set, the number of events is of the same size as observed across the field of view. Simulated events follow the same energy distribution as the observed events. Performing the analysis on simulated isotropic data sets allows us to take into account the trial factors for having tested different directions, radii and energy thresholds. We consider the post-trial probability as the fraction of these simulations with an equal or lower local pvalue than the best one obtained with the observed data set. We also compute the local Li–Ma significance (equation (17) in Li & Ma 1983)for each point in the sky, where the ON- region is centered on each point of the HEALPix grid and the OFF-region is defined as the remainder of the field of view. The local significance map is displayed in Galactic coordinates in Figure 1. The most significant excess, with 5.4σlocal significance, is found above an energy threshold of 41 EeV within a top-hat window of 24°radius centered on equatorial coordinates (α,δ)=(196°.3, −46°.6), which corresponds to Galactic coordinates (l,b)=(305°.4, 16°.2). At this position of the parameter space, 153 events are observed when 97.7 are expected from isotropy. The local p-value in this position is 3.7 ×10 −8 , resulting in a post-trial p-value of 3%. 3.2. Autocorrelation Another model-independent approach to assess the clustering of events is the search for autocorrelation, i.e., counting pairs of events separated by a given angular distance. This approach is particularly effective if the events form multiple clusters on similar angular scales in different directions in the sky. Following Pierre Auger Collaboration (2015a), we count the number of event pairs, N obs , above energy thresholds ranging from 32 to 80 EeV, that are separated by less than an angle Ψ ranging from 1°to 30°in steps of 0°.25 up to 5°and of 1° above. We compute the expected number of pairs, N exp,by analysing simulated isotropic event sets of the same size as the 5 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. observed data set. For each Ψand E th , we consider the local pvalue as the fraction of simulated data sets, f(E th ,Ψ), for which N N exp obs . The values of fare shown in Figure 2(a)and the best results are shown in Table 1. 3.3. Correlations with Structures The most constrained analysis performed in this Section is the search for correlations with local astrophysical structures. Although a Galactic origin of UHECRs at energies above 8 EeV is disfavored by the large-scale anisotropy discovered by the Collaboration, we test as targets the Galactic plane and the Galactic center in addition to the supergalactic plane, for consistency with Pierre Auger Collaboration (2015a). The search is performed in a similar way as the study described in Section 3.2, with N obs being the number of events observed within an angle Ψfrom the chosen structure. In practice, for the Galactic and supergalactic planes, we count events with an absolute value of latitude smaller than Ψin the respective coordinate system. The results are shown in Figure 2and in Table 1. The lowest p-values are found for Ψ20°above energy thresholds near ∼40 and ∼60 EeV. No significant departure from isotropy is observed in these searches, as in Pierre Auger Collaboration (2015a). 4. Likelihood Analysis with Catalogs of Candidate Host Galaxies In Pierre Auger Collaboration (2015a), we presented the results of cross-correlation studies with three flux-limited catalogs: the 2MASS Redshift Survey of near-infrared galaxies (Huchra et al. 2012), the Swift-BAT 70-month catalog of active galactic nuclei (AGNs)observed in hard X-rays (Baumgartner et al. 2013), and the catalog of radio-emitting galaxies from van Velzen et al. (2012). Such cross-correlation analyses inherently assume all galaxies under investigation to have an equal weight (standard-candle approach)and do not easily account for the inverse-square law of the UHECR flux, nor for its attenuation resulting from energy losses induced by propagation. These limitations were addressed by Pierre Auger Collaboration (2018b) through a likelihood-ratio test that expanded upon the maximumlikelihood test presented by Pierre Auger Collaboration (2010b). We also tested two additional catalogs based on gamma-ray observations from Fermi-LAT. The full-sky gamma-ray survey of Fermi-LAT has shown star-forming galaxies and jetted AGNs to be the main contributors to the extragalactic gamma-ray background at GeV energies, although their relative contributions remains uncertain (see, e.g., Ajello et al. 2015;Rothetal.2021). 4.1. From Catalogs to UHECR Sky Models We first explore correlations with the large-scale distribution of matter using the Two Micron All-Sky Survey (2MASS; Skrutskie et al. 2006). The expected UHECR flux in this scenario is traced by K-band observations at 2.16 mm, i.e., we assume an UHECR luminosity proportional to stellar mass. We limit the study to galaxies up to a K-band magnitude of 11.75 mag, which corresponds to the flux limit over more than 90% of the 2MASS Redshift Survey. We verified through the HyperLEDA 102 database (Makarov et al. 2014)that all the selected objects are galaxies and we kept in the sample AGN hosts, noting though that their near-infrared emission may be contaminated by non-thermal emission. A second sample consists of galaxies with a high star formation rate, broadly denoted here as starburst galaxies. Lunardini et al. (2019)selected local galaxies with a farinfrared flux at 60 mm larger than 60 Jy from the IRAS all-sky survey (Sanders et al. 2003)and with a radio flux at 1.4 GHz larger than 20 mJy from the NVSS (Condon et al. 1998)and Parkes surveys (Calabretta et al. 2014)in the Northern and Southern hemispheres, respectively. The authors also imposed a far-infrared to radio flux ratio larger than 30, which removes galaxies dominated by jetted AGN emission. We further select galaxies with a far-infrared to radio flux ratio smaller than 1000, which excludes dwarf galaxies with negligible radio emission. The latter criterion removes the Large and Small Magellanic Clouds from the sample of starburst galaxies in Lunardini et al. (2019), as these are clear outliers of the fluxratio distribution. Although the IRAS survey can safely be considered as flux limited over the entire sky for fluxes larger than 60 Jy, the subtraction of the Galactic foreground is more demanding in studies of extended radio sources down to 20 mJy. Following their reanalysis of the Southern radio sky, Lunardini et al. (2019)excluded areas close the Galactic plane, which contain in particular the bright Circinus galaxy at latitude l=−3°.8. The latter galaxy satisfies the abovementioned selection criteria and we add it to the sample using its radio flux tabulated in the Parkes catalog (Wright & Otrupcek 1996). The radio flux of galaxies in the sample is used as a tracer for UHECR emission, effectively assuming an UHECR luminosity proportional to star-forming activity. The third sample encompasses AGNs observed in hard X-rays with Swift-BAT, as tabulated in their 105 month catalog (Oh et al. 2018). We select hard X-ray sources with a 14–195 keV flux larger than 8.4 ×10 −12 erg cm −2 s −1 , which corresponds to the Swift-BAT flux limit over more than 90% of the sky. We retain objects labeled as jetted AGN, Seyfert galaxies, or other AGNs with or without jets. We adopt with this catalog the hard X-ray flux as a tracer of the UHECR flux, effectively assuming that the UHECR luminosity is driven by accretion onto supermassive black holes. We note though that the Figure 1. Local Li–Ma significance map at energies above 41 EeV and within a top-hat search angle of Ψ=24°in Galactic coordinates. The supergalactic plane is shown as a gray line. The significance is not evaluated in windows whose centers lie outside of the field of view of the Observatory, as indicated by the white area. 102 http://leda.univ-lyon1.fr/ 6 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. X-ray flux of the subsample of radio-loud AGN, in particular that of blazars, is expected to be dominated by jet emission. Finally, the fourth sample comprises γ-ray selected AGN from the Fermi-LAT 3FHL catalog (Fermi-LAT Collaboration 2017). We select radio galaxies and blazars with an integral flux between 10 GeV and 1 TeV larger than 3.3 ×10 −11 cm −2 s −1 . Above this value, the 3FHL catalog is flux limited over 90% of the sky (97% for Galactic latitudes |b|>5°). 103 The γ-ray flux is used as an UHECR proxy, effectively assuming an UHECR luminosity proportional to the inner jet activity. The bands adopted to trace UHECR emission are affected by little absorption in the host galaxy and along the line of sight but UHECRs suffer increasing energy losses and photodissociation with increasing travel time. Robust estimates of the luminosity distances of host galaxies are needed to account for the attenuation of their relative UHECR flux above a given energy threshold. Putative sources within a few tens of Mpc may in particular have a substantial impact on UHECR anisotropies while their host galaxies are not in the Hubble flow, which would make their spectroscopic redshift a biased distance estimate. We crossmatched all four catalogs with the HyperLEDA database and adopted the best distance estimate (modbest field)and associated uncertainty, which account for peculiar motion and exploit cosmic-distance-ladder estimates whenever available. Galaxies within 250 Mpc are retained in the sample and we exclude those located in the Local Group through a cut at 1 Mpc. Nearby galaxies would otherwise dominate sky models aimed at tracing UHECR emission on larger scales. A smaller horizon at 130 Mpc is considered for starburst galaxies, following the selection of Lunardini et al. (2019). We note that few (if any)star-forming galaxies within 130–250 Mpc are expected to pass the radio and far-infrared flux selection. All 26 jetted AGNs and 44 starburst galaxies in our sample are included in HyperLEDA. The apparent total K- band magnitude available in HyperLEDA (Kt field)enables a straightforward selection of 44,113 2MASS galaxies. We identified 23 Swift-BAT AGN, among 523 host galaxies, without a tabulated HyperLEDA distance that nonetheless show compatible redshift estimates (|Δz|<0.002)in NED 104 and SIMBAD. 105 The distances of these 23 galaxies are based on their NED spectroscopic redshifts (corrected for Local Group infall to the Virgo cluster), as tabulated in Appendix C. As in Pierre Auger Collaboration (2018b), the UHECR flux expected from each host galaxy is increasingly attenuated with Figure 2. Local p-value as a function of search angle, Ψ, and threshold energy, E th . Panels (a),(b),(c), and (d)display the results of the autocorrelation study, supergalactic-plane, Galactic-center, and Galactic-plane searches, respectively. The most significant excess identified in each analysis is indicated with a white cross. 103 Estimated from the data in Figure 4 of Fermi-LAT Collaboration (2017), where the flux limit is provided for a source of photon index Γ=2.5 detected with TS =25. Data in the figure are courtesy of the Fermi-LAT Collaboration. 104 doi:10.26132/NED1 105 http://simbad.u-strasbg.fr 7 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. increasing luminosity distance, d L , following the best-fit model of the spectrum and composition data acquired at the Pierre Auger Observatory (Pierre Auger Collaboration 2017c; first minimum obtained with the EPOS-LHC hadronic interaction model). The attenuation weights, a(d L ), are marginalized over distance uncertainty for the three catalogs with fewer than 1,000 galaxies, with little impact on the final sky models. For the sake of computational intensity, no marginalization over distance uncertainty is performed for the fourth sample, made of more than 44,000 near-infrared galaxies, with negligible impact on the final results. All four sky models represent significant improvements with respect to those studied by Pierre Auger Collaboration (2018b) from an astronomical point of view. From a quantitative perspective, the improvement in sky coverage and depth of the surveys yield an increase in jetted AGNs from 17 to 26 objects, in starburst galaxies from 23 to 44, in all AGNs from 330 to 523, and in near-infrared galaxies from 41,129 to 44,113. The estimations of distance uncertainties also provide a qualitative improvement with respect to the study presented by Pierre Auger Collaboration (2018b). It should be noted though that the results are barely affected by such improvements (see Section 4.3), suggesting that our previous analysis already accounted for sufficiently complete surveys from an astroparticle point of view. We further evaluated in Pierre Auger Collaboration (2015a) possible correlations with the catalog of van Velzen et al. (2012). The latter compiles observations at 1.4 GHz and 843 MHz of extended radio sources down to a flux limit corresponding to the flux of Centaurus A placed at 200 Mpc. Accounting for attenuation, the resulting sky model is entirely dominated by the nearby Centaurus A (distance of 3.68 ±0.05 Mpc)and can thus be considered as redundant with the flux pattern obtained with the Swift-BAT model (see Appendix C). We thus limit the present study to the four sky models obtained from the near-infrared emission of galaxies (2MASS), radio emission from starburst galaxies, X-rays from AGNs (Swift-BAT), and γ-rays from jetted AGNs (Fermi-LAT). 4.2. Likelihood-ratio Analysis As in Pierre Auger Collaboration (2018b), the correlation of UHECR arrival directions with the flux pattern expected from the catalogs is evaluated against isotropy using a likelihoodratio analysis. The model as a function of direction uis computed in equal-area bins on the sphere using HEALPix v3.70 with the parameter nSide=64, as in Section 3.1. The null hypothesis under investigation, H 0 , is that of an isotropic flux distribution. Accounting for the directional exposure of the array, ω(u), the isotropic model for the UHECR count density reads uu u n,1 H ii 0() () () () w w = å which is normalized so that the sum over the HEALPix pixels indexed over iand of direction u i is equal to one. The alternative hypothesis, H 1 , in which H 0 is nested, is considered as the sum of the isotropic component and a component derived from the tested catalog. The amplitude of the latter component is the variable signal fraction, α. The isotropic remainder accounts for faint or distant galaxies not included in the catalogs or for a heavy nuclear component deflected away on large angular scales. The model for the UHECR count density under H 1 reads uu u u nn s s 1; ;,2 i HH jj ij j 10 () ( ) () () () ()aa=- ´ +´ åQ åå Q where the index jruns over the galaxies in the catalog. The contribution to the UHECR flux from each galaxy, s j (u;Θ),is modeled as a von Mises–Fisher distribution centered on the direction of the galaxy with smearing angle Θ. The amplitude of its contribution is proportional to the electromagnetic flux of the galaxy, f j , accounting for attenuation as a function of luminosity distance, a(d j ), so that ⎜⎟ ⎛ ⎝⎞ ⎠ uu uu sad;exp 21 cos .3 jjj j () () () · () ()wfQ= ´ ´ -Q The von Mises–Fisher distribution is maximum in the direction of the galaxy of interest, u j , effectively leaving aside coherent deflections which remain under-constrained by current models of the Galactic magnetic field (Erdmann et al. 2016). The smearing angle Θ, equivalent to a 2D Gaussian extent in the small-angle limit, is assumed to be the same for all galaxies in a given catalog. This parameter accounts for the average angular dispersion in intervening magnetic fields. As a note, normalization of the von Mises–Fisher distribution in Equation (3)is omitted, as it is the same for every galaxy and because the overall anisotropic component is normalized in the same way on the celestial sphere (see Equation (2)). The likelihood-ratio test between the nested models H 0 and H 1 defines the test statistic (TS)as  T S2ln 10 ()=, where the likelihood scores of the null and alternative hypothesis, 0 and  1, are obtained as the product over the events of the models n H0and n H1, respectively. The evaluation of the TS is performed by grouping events by HEALPix bins. With an observed event count k i in the direction u i , the TS is evaluated Table 1 Results of the Search for Autocorrelation and Correlation with Astrophysical Structures Search E th [EeV]Angle, Ψ[deg]N obs N exp Local p-value, fmin Post-trial p-value Autocorrelation 62 3.75 93 66.4 2.5 ×10 −3 0.24 Supergalactic plane 44 20 394 349.1 1.8 ×10 −3 0.13 Galactic plane 58 20 151 129.8 1.4 ×10 −2 0.44 Galactic center 63 18 17 10.1 2.6 ×10 −2 0.57 Note. The energy threshold, E th , and the search angle, Ψ, which minimize the local p-value, are based on the number of observed and expected events/pairs. The posttrial p-value accounts for the scan in energy threshold and search angle, Ψ. 8 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. as u u kn n TS 2 ln . 4 i i Hi Hi 1 0 () () () å =´ TS is maximized as a function of the two free parameters in the analysis (the search radius, Θ, and the signal fraction, α) above successive energy thresholds. Maximization can be achieved by scanning the 2D parameter space by steps of 0.2% in signal fraction and 0.2°in search radius. This approach provides an accurate estimate that is independent from any specific maximization algorithm. Alternatively, maximization with the Minuit package provides a fast estimate for simulated data sets, with an accuracy of the TS better than 0.1 units for event counts larger than 100. Above a fixed energy threshold, the TS is observed through Monte-Carlo simulations to follow a χ 2 distribution with two degrees of freedom under the null hypothesis (Wilks 1938). The 1 and 2σC.L. on the best-fit parameters are set by isoTS contours differing from the maximum TS value by 2.3 and 6.2 units, respectively. The scan in energy threshold is accounted for, as in Section 3, by estimating the post-trial p-value through isotropic Monte-Carlo simulations. The post-trial p-value, which accounts for the energy scan, differs from the local p-value expected from Wilks’theorem by a penalty factor that is well-approximated by a linear function of TS: pen =1+ (0.30 ±0.01)×TS. This empirical penalty factor is estimated from simulated isotropic data sets analyzed against each catalog and the uncertainty on the linear coefficient is estimated from the variance across the four tested catalogs. The penalty factor reaches a value of ∼10 for TS =30. 4.3. Results The radii and signal fractions maximizing the TS above fixed energy thresholds ranging in 32–80 EeV are displayed in Figure 3 for the four catalogs. The TS follows a double hump structure as a function of energy, with a first peak at energies above ∼40 EeV and a second peak at energies above ∼60 EeV. The latter peak corresponds to the maximum signal fraction for all catalogs, rangingin11%–19%. Lower signal fractions ranging in 6%–16% are inferred from the global TS maximum, at energies above ∼40 EeV. As shown in the upper axis in Figure 3, the four times larger number of events in the first peak (1,387 above 40 EeV versus 331 above 60 EeV)yields a more significant deviation from isotropy above 40 EeV. The amplitude of variations of the best-fit parameters as a function of energy threshold can be evaluated against the statistical uncertainties on these parameters, as shown in Figure 4. As the search is performed above successive energy thresholds in steps of 1 EeV, successive energy bins have a nonnegligible overlap. For reference, we estimate that there is a total of five to six independent energy bins, by identifying the successive reference energy thresholds above which the number of events is less that half that above a previous reference energy. Such a procedure suggests reference energy thresholds at E32, 40, 50, 60, 70, and 80 EeV, with boundaries distant by more than Elog 0.06 10 D =, which corresponds to an energy resolution of ±7% relevant in the range covered here (Pierre Auger Collaboration 2020b). As illustrated by the set of figures above energy thresholds ranging in 32–80 EeV (see online material attached to Figure 4), the reconstructed parameters do not show significant variations with energy. For the sake of completeness, we provide the best-fit parameters and maximum TS obtained above energy thresholds corresponding to the global maximum at E40 EeV, in the upper part of Table 2, as well as those obtained above the secondary maximum identified at E60 EeV, in the lower part of the same table. The most significant departure from isotropy is identified for all four catalogs at energy thresholds in the range 38–40 EeV, with post-trial p-values of 8.3 ×10 −4 , 7.9 ×10 −4 , 4.2 ×10 −4 , and 3.2 ×10 −5 for jetted AGNs traced by their γ-ray emission, galaxies traced by their near-infrared emission, all AGNs traced by their X-ray emission, and starburst galaxies traced by their radio emission, respectively. As in Pierre Auger Collaboration (2018b), we do not penalize for the test of the four catalogs, which all provide similar UHECR flux patterns. As a note, the infrared sample of galaxies contains a large fraction (more than 75%)of each of the three other catalogs and only jetted AGN and starburst catalogs can be considered as strictly distinct galaxy samples. Figure 3. TS (top), signal fraction (center), and Fisher search radius (bottom) maximizing the deviation from isotropy as a function of energy threshold. The results obtained with each of the four catalogs are displayed with varying colors and line styles, as labeled in the figure. The uncertainties on the parameters, which are correlated above successive energy thresholds, are not displayed for the sake of readability. 9 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. Utilities, and Visuals. A brief description of each folder follows. 1. The Data folder contains the file of the data set used in all of the analyses, named AugerApJS2022_Yr_J- D_UTC_Th_Ph_RA_Dec_E_Expo.dat. Additionally, the folder contains a C++ script named exposure. cc, which computes the directional exposure of the Observatory for both vertical and inclined events, which is integrated over the duration of the acquisition period; the exposure script produces two files, exposure. root and exposure.fits, which contain the exposure as a function of decl. in TF1-root format and in healpixmap-fits form (RING scheme, Galactic coordinates), respectively. The Time_exposure file provides the evolution of exposure with time, as displayed in the upper axis of Figure 7. The DataPath.h contains the declaration of the data set file to be used by all the analyses for easy user intervention. 2. The Utilities folder contains files with auxiliary classes and functions used by all other parts of the code, in particular coordinate-conversion utilities and HEAL- Pix map manipulation. 3. The Targeted_Blind folder contains the code for the targeted (Sections 5and 3.3), blind (Section 3.1), and autocorrelation (Section 3.2)analyses. The first folder level contains: (a)Dedicated utilities (utils.h and utils.cc); (b)Three subfolders: Blind,Targeted, and Autocorrelation containing the respective analyses. Each of the three subfolders contains a script performing the computation of the local p-value, 1_pvalue.cc, and a script penalizing for the search over the parameter space, 2_pena- lization.cc. The results of the Blind code are stored in . txt files for easy readout, while the results of the Autocorrelation code are stored as a .root file. In the Targeted folder, 1_pvalue.cc produces the outputs fig5.root,fig2gc.root,fig2gp.root, and fig2sgp.root, which contain the local p-value in bins of energy threshold and search radius for the Centaurus region, Galactic center, Galactic plane, and supergalactic plane analyses, respectively; the .root files in the Autocorrelation and Targeted folders also contain copies of the support histograms used to calculate the local p-value which are used by the penalization algorithm. The script 2_pena- lization.cc produces the post-trial p-value in single-value text form, as well as the file fig6tmp.txt, which stores the pre-trial p-values as a function of energy threshold penalized only for the scan in angle (see Figure 6). 4. The Catalog_Based folder contains the code for the likelihood-ratio analysis. The first folder level comprises: (a)Dedicated utilities (utils.h and utils.cc); (b)The folder Catalogs, which contains the raw catalogs of galaxies in the subfolder Multiwavelength, as described in Appendix C. The raw catalog files are input, above a fixed energy threshold, to the script propagate.cc, in conjunction with the Auger composition model contained in the file AnaCRP3.root, to produce the attenuated models used in the analysis. These attenuated models can be produced above all energy thresholds by running the script propagate.py, with outputs stored in the subfolder ModelsUHECR. The latter is organized as different folders for each catalog; (c)The analysis routines: 1_pre_trial.cc, which produces the results stored in the files fig3.root, showing the TS, signal fraction, and search radius as a Figure 9. Schematic view of the code. 16 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. function of the threshold energy, and fig4.root, showing the TS as a function of the signal fraction and search radius with 68% C.L. contours for each catalog; 2_penalization.cc produces the posttrial p-values. 5. The Visuals folder contains scripts that produce the figures shown in the paper. The python script skymaps. py produces the sky maps in Hammer–Aitoff view: the Li–Ma significance map, fig1.pdf, the flux maps above successive energy threshold stored in fig8.pdf and the model maps stored in fig10.pdf. The script show_figures.py produces fig2.pdf,fig3. pdf,fig4.pdf, and fig5.pdf from their respective root files. The script evolution.cc produces fig6. pdf, a plot of the pre-trial p-values from the Centaurusregion analysis and likelihood-ratio analysis against starburst galaxies as a function of the threshold energy; it also produces fig7.pdf, a plot of the evolution of TS of the starburst analysis and of the excess in the Centaurus region as a function of the exposure accumulated at the Observatory. Appendix C Catalogs The best-fit sky models above 40 EeV obtained with the four catalogs described in Section 4.1 are shown in Figure 10. These sky maps do not include any isotropic component and display only the flux expected from galaxies included in the catalogs, which is smeared on the best-fit Fisher angular scale above 40 EeV obtained with each catalog. A further top-hat smoothing on an angular scale of Ψ=25°is performed for the sake of comparison with Figure 8. The models shown in Figure 10 are based on the UHECR flux expected from each galaxy in proportion to its electromagnetic flux. The multiwavelength information on the galaxies is made available in the Multiwavelength subfolder of the catalog-based study, as described in Appendix B, and is available online at https://doi.org/ 10.5281/zenodo.6504276. The Multiwavelength folder contains one file per catalog, with tabulated values detailed in Tables 4,5,6, and 7. The first column of each of these tables provides the name of the source as referenced by the authors of the source catalog. The second column provides a counterpart name that is consistent across all four catalogs. The third column provides the type of galaxy, extracted either from the source catalog or from the HyperLEDA database. The fourth and fifth columns provide the equatorial coordinates of the galaxy. The sixth and seventh columns display the distance modulus and associated uncertainty extracted from the modbest entry of the HyperLEDA database. The eighth and ninth columns display the corresponding luminosity distance in Mpc as well as the relative uncertainty on this quantity. The electromagnetic flux of each galaxy is provided in column 10, except in Table 4where the K-band magnitude is provided. Whenever available, the uncertainty on the quantity provided in column 10 is shown in column 11. Finally, a flag is provided in the last columns of Tables 5,6, and 7. This flag indicates whether the galaxy was also included in the main samples studied by Pierre Auger Collaboration (2018b)(Y), in one of Figure 10. Best-fit UHECR source models above 40 EeV with a top-hat smoothing radius of Ψ=25°in Galactic coordinates. The supergalactic plane is shown as a gray line. Prominent sources in each of the catalogs are marked with gray circles. 17 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. Table 4 Galaxies (2MASS(K<11.75)×HyperLEDA) PGC Counterpart Object Type R.A. Decl. (m−M)σ(m−M)d L σ(d L )/d L K t σ(K t ) °°mag mag Mpc mag mag 29128 NGC3109 G 150.78 −26.16 25.56 0.02 1.29 0.007 9.57 0.40 29653 PGC029653 G 152.75 −4.69 25.59 0.03 1.31 0.013 11.31 0.56 28913 UGC05373 G 150.00 5.33 25.79 0.01 1.44 0.006 10.76 0.23 100169 PGC100169 G 31.52 69.00 26.15 0.20 1.70 0.092 9.69 0.24 67908 IC5152 G 330.67 −51.30 26.46 0.03 1.96 0.012 9.05 0.36 3238 NGC0300 G 13.72 −37.68 26.53 0.02 2.03 0.007 6.58 0.36 1014 NGC0055 G 3.72 −39.20 26.62 0.01 2.11 0.006 6.34 0.18 9140 PGC009140 G 36.18 −73.51 26.63 0.07 2.12 0.032 10.83 0.10 13115 UGC02773 G 53.03 47.79 26.69 0.20 2.18 0.092 9.80 0.10 39573 IC3104 G 184.69 −79.73 26.86 0.02 2.36 0.007 9.24 0.14 60849 IC4662 G 266.79 −64.64 27.03 0.01 2.55 0.006 9.45 0.21 47495 UGC08508 G 202.68 54.91 27.07 0.02 2.60 0.011 11.51 0.10 40904 UGC07577 G 186.92 43.50 27.08 0.02 2.60 0.011 10.45 0.20 54392 ESO274-001 G 228.56 −46.81 27.24 0.06 2.80 0.026 8.30 0.39 51472 UGC09240 G 216.18 44.53 27.25 0.02 2.82 0.008 10.89 0.13 39023 NGC4190 G 183.44 36.63 27.26 0.04 2.83 0.020 11.40 0.77 14241 PGC014241 G 59.96 67.14 27.37 0.03 2.98 0.012 8.24 0.16 4126 NGC0404 G 17.36 35.72 27.37 0.02 2.98 0.007 7.53 0.02 39225 NGC4214 G 183.91 36.33 27.37 0.01 2.98 0.002 8.09 0.21 38881 NGC4163 G 183.04 36.17 27.38 0.02 2.99 0.007 10.92 0.08 15488 NGC1560 G 68.20 71.88 27.38 0.10 2.99 0.046 9.07 0.22 49050 ESO383-087 G 207.32 −36.06 27.52 0.02 3.19 0.007 9.91 0.14 15439 PGC015439 G 68.01 63.62 27.53 0.05 3.20 0.024 10.97 0.17 21396 NGC2403 G 114.21 65.60 27.53 0.01 3.20 0.004 6.24 0.14 47762 NGC5206 G 203.43 −48.15 27.53 0.01 3.21 0.005 8.39 0.25 LLLLLLLLLLL 127001 PGC127001 G 67.39 −61.25 36.99 0.07 249.7 0.030 11.72 0.18 Note. 44,113 entries within 250 Mpc, 17,143 entries at d L <100 Mpc, and 39,563 at d L <200 Mpc. The full data set is available in the same format at https://doi. org/10.5281/zenodo.6504276 and in machine-readable format in the online article. (This table is available in machine-readable form.) 18 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. Table 5 Starburst Galaxies (Lunardini+’19) Lunardi Name Counterpart Host Type R.A. Decl. (m−M)σ(m−M)d L σ(d L )/d L Φ(1.4 GHz)σ(Φ) flag: in Pierre Auger Collaboration (2018b)? °°mag mag Mpc Jy Jy (No/Yes/Xcheck) NGC0055 NGC0055 SBm 3.72 −39.20 26.62 0.01 2.11 0.005 0.37 N/AN NGC1569 NGC1569 IB 67.70 64.85 27.53 0.05 3.21 0.023 0.40 N/AX NGC2403 NGC2403 SABc 114.21 65.60 27.53 0.01 3.21 0.005 0.39 N/AX IC342 IC342 SABc 56.70 68.10 27.68 0.03 3.44 0.014 2.25 N/AY NGC4945 NGC4945 Sbc 196.37 −49.47 27.70 0.02 3.47 0.009 6.60 N/AY NGC3034 (M82) M82 S? 148.97 69.68 27.79 0.01 3.61 0.005 7.29 N/AY NGC0253 NGC253 SABc 11.89 −25.29 27.84 0.02 3.70 0.009 6.00 N/AY N/A Circinus Sb 213.29 −65.34 28.12 0.36 4.21 0.166 1.50 N/AY NGC5236 (M83) M83 Sc 204.25 −29.87 28.45 0.02 4.90 0.009 2.44 N/AY Maffei2 Maffei2 Sbc 40.48 59.60 28.79 0.12 5.73 0.055 1.01 N/AX NGC6946 NGC6946 SABc 308.72 60.15 29.14 0.05 6.73 0.023 1.40 N/AY NGC4631 NGC4631 SBcd 190.53 32.54 29.33 0.02 7.35 0.009 1.12 N/AY NGC5194 (M51) M51 SABb 202.48 47.20 29.67 0.02 8.59 0.009 1.31 N/AY NGC5055 (M63) NGC5055 Sbc 198.96 42.03 29.78 0.01 9.04 0.005 0.35 N/AY NGC2903 NGC2903 Sbc 143.04 21.50 29.85 0.11 9.33 0.051 0.44 N/AY NGC891 NGC891 Sb 35.64 42.35 29.94 1.72 9.73 0.792 0.70 N/AY NGC1068 NGC1068 Sb 40.66 0.00 30.12 0.34 10.6 0.157 4.85 N/AY NGC3628 NGC3628 SBb 170.07 13.59 30.21 0.34 11.0 0.157 0.47 N/AY NGC4818 NGC4818 SABa 194.20 −8.53 30.27 0.33 11.3 0.152 0.45 N/AN NGC3627 NGC3627 Sb 170.06 12.99 30.30 0.04 11.5 0.018 0.46 N/AY NGC1808 NGC1808 Sa 76.93 −37.51 30.45 0.36 12.3 0.166 0.50 N/AX NGC4303 M61 Sbc 185.48 4.47 30.45 0.10 12.3 0.046 0.44 N/AX NGC3521 NGC3521 SABb 166.45 −0.04 30.47 0.29 12.4 0.134 0.35 N/AN NGC0660 NGC660 Sa 25.76 13.65 30.50 1.31 12.6 0.603 0.37 N/AY NGC4254 NGC4254 Sc 184.71 14.42 30.77 1.13 14.3 0.520 0.37 N/AN LLLLLLLLLLLL NGC6240 NGC6240 S0-a 253.26 2.40 35.18 0.15 108.6 0.069 0.65 N/AY Note. 44 entries within 250 Mpc, 43 entries at d L <100 Mpc, and 44 at d L <200 Mpc. The full data set is available in the same format at https://doi.org/10.5281/ zenodo.6504276 and in machine-readable format in the online article. (This table is available in machine-readable form.) 19 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. Table 6 Jetted and Non-jetted AGNs (Swift-BAT 105 Months) BAT 105 Name Counterpart AGN Type R.A. Decl. (m−M)σ(m−M)d L σ(d L )/d L Φ(14 −195 keV)σ(Φ)flag: ref. (m−M) °°mag mag Mpc 10 −12 erg cm −2 s −1 10 −12 erg cm −2 s −1 (HyperLEDA/NED) J1305.4-4928 NGC4945 Sy2 196.37 −49.47 27.70 0.02 3.47 0.009 282.1 N/AH J0955.5+6907 M81 Sy1.9 148.94 69.06 27.78 0.01 3.60 0.005 20.3 N/AH J1325.4-4301 CenA BeamedAGN 201.37 −43.02 27.83 0.03 3.68 0.014 1346.3 N/AH J1412.9-6522 Circinus Sy2 213.29 −65.34 28.12 0.36 4.21 0.166 273.2 N/AH J1210.5+3924 NGC4151 Sy1.5 182.64 39.41 28.39 1.65 4.76 0.760 618.9 N/AH J1202.5+3332 NGC4395 Sy2 186.45 33.53 28.39 0.01 4.76 0.005 27.5 N/AH J0420.0-5457 NGC1566 Sy1.5 64.96 −54.94 29.13 1.16 6.70 0.534 19.5 N/AH J1219.4+4720 M106 Sy1.9 184.75 47.29 29.41 0.01 7.62 0.005 23.0 N/AH J1329.9+4719 M51 Sy2 202.48 47.20 29.67 0.02 8.59 0.009 13.3 N/AH J0242.6+0000 NGC1068 Sy1.9 40.66 0.00 30.12 0.34 10.6 0.157 37.9 N/AH J1717.1-6249 NGC6300 Sy2 259.25 −62.83 30.15 0.09 10.7 0.041 96.4 N/AH J1203.0+4433 NGC4051 Sy1.5 180.78 44.52 30.28 0.35 11.4 0.161 42.5 N/AH J1652.0-5915B NGC6221 Sy2 253.18 −59.23 30.34 0.62 11.7 0.286 22.4 N/AH J1209.4+4340 NGC4138 Sy2 182.35 43.70 30.70 0.25 13.8 0.115 24.4 N/AH J1157.8+5529 NGC3998 Sy1.9 179.46 55.44 30.73 0.19 14.0 0.087 13.2 N/AH J2235.9-2602 NGC7314 Sy1.9 338.95 −26.05 31.03 0.25 16.1 0.115 57.4 N/AH J1432.8-4412 NGC5643 Sy2 218.19 −44.15 31.03 1.00 16.1 0.461 16.8 N/AH J1001.7+5543 NGC3079 Sy2 150.46 55.67 31.16 0.32 17.1 0.147 36.7 N/AH J1341.9+3537 NGC5273 Sy1.5 205.47 35.66 31.16 0.12 17.1 0.055 16.0 N/AH J1207.8+4311 NGC4117 Sy2 181.95 43.12 31.18 0.94 17.2 0.433 12.9 N/AH J0333.6-3607 NGC1365 Sy2 53.39 −36.14 31.19 0.02 17.3 0.009 63.5 N/AH J0241.3-0816 NGC1052 BeamedAGN 40.29 −8.24 31.22 0.11 17.5 0.051 31.4 N/AH J1132.7+5301 NGC3718 Sy1.9 173.22 53.02 31.25 0.89 17.8 0.410 12.2 N/AH J1206.2+5243 NGC4102 Sy2 181.59 52.71 31.29 0.25 18.1 0.115 32.1 N/AH J2318.4-4223 NGC7582 Sy2 349.60 −42.37 31.41 0.10 19.1 0.046 82.3 N/AH LLLLLLLLLLLL J0534.8-6026 2MASXJ05343093-6016153 Sy1 83.70 −60.27 36.98 0.06 248.9 0.028 10.7 N/AH Note. 523 entries within 250 Mpc, 201 entries at d L <100 Mpc, and 458 at d L <200 Mpc. The full data set is available in the same format at https://doi.org/10.5281/zenodo.6504276 and in machine-readable format in the online article. (This table is available in machine-readable form.) 20 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. Table 7 Jetted AGNs (Fermi-LAT 3FHL) 3FHL Name Counterpart Jetted AGN Type R.A. Decl. (m−M)σ(m−M)d L σ(d L )/d L Φ(0.01 −1 TeV)σ(Φ)flag: in Pierre Auger Collaboration (2018b)? °°mag mag Mpc 10 −10 cm −2 s −1 10 −10 cm −2 s −1 (No/Yes) J1325.5-4300 CenA RDG 201.37 −43.02 27.83 0.03 3.68 0.014 1.54 0.25 Y J1230.8+1223 M87 RDG 187.71 12.39 31.12 0.06 16.7 0.028 0.98 0.20 Y J0322.6-3712e FornaxA RDG 50.67 −37.21 31.55 0.03 20.4 0.014 0.48 0.16 N J1346.2-6026 CenB RDG 206.70 −60.41 33.71 0.29 55.2 0.134 0.64 0.18 N J0319.8+4130 NGC1275 RDG 49.95 41.51 34.46 0.08 78.0 0.037 14.17 0.67 Y J0316.6+4120 IC310 RDG 49.18 41.32 34.60 0.19 83.2 0.087 0.43 0.13 Y J0153.5+7115 TXS0149+710 BCU 28.36 71.25 35.07 0.15 103.3 0.069 0.44 0.12 Y J0308.4+0408 NGC1218 RDG 47.11 4.11 35.48 0.13 124.7 0.060 0.54 0.16 N J1104.4+3812 Mkn421 BLL 166.10 38.21 35.63 0.12 133.7 0.055 59.35 1.38 Y J1653.8+3945 Mkn501 BLL 253.47 39.76 35.91 0.10 152.1 0.046 19.17 0.76 Y J0131.1+5546 TXS0128+554 BCU 22.81 55.75 36.06 0.10 162.9 0.046 0.33 0.12 N J1543.6+0452 CGCG050-083 BCU 235.89 4.87 36.26 0.09 178.6 0.041 0.69 0.17 N J0223.0-1119 1RXSJ022314.6-111741 BLL 35.81 −11.29 36.31 0.09 182.8 0.041 0.40 0.13 N J2347.0+5142 1ES2344+514 BLL 356.76 51.69 36.47 0.08 196.8 0.037 3.32 0.31 Y J0816.4-1311 PMNJ0816-1311 BLL 124.11 −13.20 36.51 0.08 200.4 0.037 2.71 0.33 N J1136.5+7009 Mkn180 BLL 174.11 70.16 36.54 0.08 203.2 0.037 1.74 0.21 Y J1959.9+6508 1ES1959+650 BLL 299.97 65.16 36.63 0.08 211.8 0.037 8.43 0.46 Y J1647.6+4950 SBS1646+499 BLL 251.90 49.83 36.64 0.08 212.8 0.037 0.48 0.12 N J1517.6-2422 APLibrae BLL 229.42 −24.37 36.68 0.07 216.8 0.032 3.76 0.37 Y J0214.5+5145 TXS0210+515 BLL 33.55 51.77 36.70 0.11 218.8 0.051 0.42 0.12 Y J1806.8+6950 3C371 BLL 271.71 69.82 36.77 0.07 225.9 0.032 1.30 0.18 N J1353.0-4413 PKS1349-439 BLL 208.24 −44.21 36.79 0.07 228.0 0.032 0.33 0.12 N J0200.1-4109 1RXSJ020021.0-410936 BLL 30.09 −41.16 36.85 0.07 234.4 0.032 0.51 0.14 N J0627.1-3528 PKS0625-35 BLL 96.78 −35.49 36.89 0.07 238.8 0.032 1.81 0.26 Y J2039.4+5219 1ES2037+521 BLL 309.85 52.33 36.89 0.07 238.8 0.032 0.58 0.15 N J0523.0-3627 PKS0521-36 BLL 80.76 −36.46 36.91 0.07 241.0 0.032 1.17 0.21 N Note. 26 entries within 250 Mpc, 6 entries at d L <100 Mpc, and 14 at d L <200 Mpc. The full data set is available in the same format at https://doi.org/10.5281/zenodo.6504276 and in machine-readable format in the online article. (This table is available in machine-readable form.) 21 The Astrophysical Journal, 935:170 (24pp), 2022 August 20 Abreu et al. the cross-check samples (X), or not included in earlier versions of these catalogs (N). The flag column of Table 6indicates the origin of the redshift estimate, either from HyperLEDA or from NED for the 23 X-ray AGNs that are not listed in HyperLEDA. ORCID iDs P. Abreu https://orcid.org/0000-0002-9973-7314 I. Allekotte https://orcid.org/0000-0002-9624-3044 K. Almeida Cheminant https://orcid.org/0000-0001- 6352-5339 A. Almela https://orcid.org/0000-0003-1233-2670 J. Alvarez-Muñiz https://orcid.org/0000-0002-2367-0803 R. Alves Batista https://orcid.org/0000-0003-2656-064X J. Ammerman Yebra https://orcid.org/0000-0002- 2059-6206 G. A. Anastasi https://orcid.org/0000-0003-0824-5283 L. Anchordoqui https://orcid.org/0000-0003-1463-7136 S. Andringa https://orcid.org/0000-0002-6397-9207 P. R. Araújo Ferreira https://orcid.org/0000-0002- 0502-3602 E. Arnone https://orcid.org/0000-0001-6740-5051 H. Asorey https://orcid.org/0000-0002-4559-8785 P. Assis https://orcid.org/0000-0001-7765-3606 E. Avocone https://orcid.org/0000-0003-3757-2718 A. Bakalova https://orcid.org/0000-0003-1001-4484 F. Barbato https://orcid.org/0000-0003-0751-6731 J. A. Bellido https://orcid.org/0000-0002-0833-9194 C. Berat https://orcid.org/0000-0001-8294-6294 M. E. Bertaina https://orcid.org/0000-0003-1069-1397 G. Bhatta https://orcid.org/0000-0002-0705-6619 P. L. Biermann https://orcid.org/0000-0003-3948-6143 K. Bismark https://orcid.org/0000-0001-8637-7041 J. Biteau https://orcid.org/0000-0002-4202-8939 J. Blazek https://orcid.org/0000-0002-5870-8947 J. Blümer https://orcid.org/0000-0001-7715-9608 D. Boncioli https://orcid.org/0000-0003-1186-9353 C. Bonifazi https://orcid.org/0000-0002-3087-3609 L. Bonneau Arbeletche https://orcid.org/0000-0003- 4544-9808 N. Borodai https://orcid.org/0000-0003-1864-937X J. Brack https://orcid.org/0000-0003-4096-2063 A. Bueno https://orcid.org/0000-0002-7439-4247 M. Buscemi https://orcid.org/0000-0003-2123-5434 M. Büsken https://orcid.org/0000-0002-2174-5779 K. S. Caballero-Mora https://orcid.org/0000-0002- 4042-3855 L. Caccianiga https://orcid.org/0000-0002-2245-5108 R. Caruso https://orcid.org/0000-0003-1622-8731 A. Castellina https://orcid.org/0000-0002-0045-2467 F. Catalani https://orcid.org/0000-0001-9332-1476 G. Cataldi https://orcid.org/0000-0001-8066-7718 L. Cazon https://orcid.org/0000-0001-6748-8395 J. Chudoba https://orcid.org/0000-0002-6425-2579 R. W. Clay https://orcid.org/0000-0002-9040-9648 A. C. Cobos Cerutti https://orcid.org/0000-0002-0652-5460 R. Colalillo https://orcid.org/0000-0002-4179-9352 A. Coleman https://orcid.org/0000-0003-1510-1712 M. R. Coluccia https://orcid.org/0000-0002-3023-0566 R. Conceição https://orcid.org/0000-0003-4945-5340 G. Consolati https://orcid.org/0000-0003-3614-245X S. Dasso https://orcid.org/0000-0002-7680-4721 K. Daumiller https://orcid.org/0000-0002-0527-4823 B. R. Dawson https://orcid.org/0000-0002-4271-3055 S. J. de Jong https://orcid.org/0000-0002-3120-3367 J. R. T. de Mello Neto https://orcid.org/0000-0002- 3234-6634 I. De Mitri https://orcid.org/0000-0002-8665-1730 F. de Palma https://orcid.org/0000-0001-5898-2834 V. de Souza https://orcid.org/0000-0003-0865-233X A. Del Popolo https://orcid.org/0000-0002-9057-0239 O. Deligny https://orcid.org/0000-0001-6863-6572 A. di Matteo https://orcid.org/0000-0002-8260-1867 C. Dobrigkeit https://orcid.org/0000-0002-1236-0789 L. M. Domingues Mendes https://orcid.org/0000-0003- 0651-9404 J. Ebr https://orcid.org/0000-0001-8807-6162 R. Engel https://orcid.org/0000-0003-2924-8889 M. Erdmann https://orcid.org/0000-0002-1653-1303 A. Etchegoyen https://orcid.org/0000-0001-6989-2404 H. Falcke https://orcid.org/0000-0002-2526-6724 G. Farrar https://orcid.org/0000-0003-2417-5975 A. C. Fauth https://orcid.org/0000-0001-7239-0288 J. M. Figueira https://orcid.org/0000-0002-6768-5214 T. Fitoussi https://orcid.org/0000-0001-9473-9356 T. Fujii https://orcid.org/0000-0003-2401-504X A. Fuster https://orcid.org/0000-0002-6575-7958 C. Galea https://orcid.org/0000-0002-3075-6605 C. Galelli https://orcid.org/0000-0002-7372-9703 B. García https://orcid.org/0000-0003-0919-2734 F. Gesualdi https://orcid.org/0000-0002-4080-7840 P. L. Ghia https://orcid.org/0000-0003-1101-4857 J. Glombitza https://orcid.org/0000-0001-9683-4568 F. Gollan https://orcid.org/0000-0002-0495-2768 G. Golup https://orcid.org/0000-0001-9471-8811 J. M. González https://orcid.org/0000-0003-3003-5063 N. González https://orcid.org/0000-0001-6566-2222 D. Góra https://orcid.org/0000-0002-4853-5974 A. Gorgi https://orcid.org/0000-0002-8085-2304 F. 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