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Active galactic nuclei (AGN) are an important phase in galaxy evolution. However, they can be difficult to identify due to their varied observational signatures. Furthermore, to understand the impact of an AGN on its host galaxy, it is important to quantify the strength of the AGN with respect to the host galaxy. We developed a deep learning (DL) model to identify AGN in imaging data by deriving the contribution of the central point source. The model was trained with Euclidised mock galaxy images in which we artificially injected different levels of AGN, in the form of varying contributions of the point spread function (PSF). Our DL-based method can precisely and accurately recover the injected AGN contribution fraction, fPSF, with a mean difference between the predicted and true fPSF of −0.0078 and an overall root mean square error of 0.051. With this new method, we move beyond the simplistic AGN versus non-AGN classification and are able to precisely quantify the AGN contribution and study galaxy evolution across a continuous spectrum of AGN activity. We applied our method to a stellar-mass-limited sample (with M* ≥ 109.8 M⊙ and 0.5 ≤ z ≤ 2.0) selected from the first Euclid quick data release (Q1) and, using a threshold of fPSF > 0.2, we identified 61 432 ± 70 AGN over 63.1 deg2 (9.8 ± 0.1% of our sample). We compared these DL-selected AGN with AGN selected in the X-ray, mid-infrared (MIR), and via optical spectroscopy and investigated their overlapping fractions depending on different thresholds on the PSF contribution. We find that the overlap increases with increasing X-ray or bolometric AGN luminosity. We observe a positive correlation between the luminosity in the IE filter of the AGN and the host galaxy stellar mass, suggesting that supermassive black holes (SMBHs) generally grow faster (in absolute terms, i.e. the luminosity of the PSF component is larger) in more massive galaxies. Moreover, the mean relative contribution of the AGN is higher in the quiescent galaxy population than in the star-forming population. In terms of absolute power, starburst galaxies, as well as the most massive galaxies (across the star-formation main sequence), tend to host the most luminous AGN, indicating concomitant assembly of the SMBH and the host galaxy.
Margalef-Bentabol, B., Wang, L., La Marca, A., Rodriguez-Gomez, V., Humphrey, A., Fotopoulou, S., et al. (2026). Euclid Quick Data Release (Q1): XVIII. First Euclid statistical study of the active galactic nuclei contribution fraction. ASTRONOMY & ASTROPHYSICS, 711, 1-21 [10.1051/0004-6361/202554583].
Euclid Quick Data Release (Q1): XVIII. First Euclid statistical study of the active galactic nuclei contribution fraction
B. Margalef-Bentabol;L. Wang;A. La Marca;V. Rodriguez-Gomez;A. Humphrey;S. Fotopoulou;F. Ricci;Y. Toba;G. Stevens;M. Mezcua;W. Roster;J. H. Knapen;M. Salvato;M. Siudek;F. Shankar;T. Matamoro Zatarain;L. Spinoglio;P. Dayal;J. Petley;R. Kondapally;N. Aghanim;B. Altieri;A. Amara;S. Andreon;N. Auricchio;H. Aussel;C. Baccigalupi;M. Baldi;A. Balestra;S. Bardelli;P. Battaglia;A. Biviano;A. Bonchi;D. Bonino;E. Branchini;M. Brescia;J. Brinchmann;S. Camera;G. Cañas-Herrera;V. Capobianco;C. Carbone;J. Carretero;S. Casas;M. Castellano;G. Castignani;S. Cavuoti;K. C. Chambers;A. Cimatti;C. Colodro-Conde;G. Congedo;C. J. Conselice;L. Conversi;Y. Copin;A. Costille;F. Courbin;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;A. Ealet;S. Escoffier;M. Farina;R. Farinelli;F. Faustini;S. Ferriol;F. Finelli;M. Frailis;E. Franceschi;S. Galeotta;K. George;B. Gillis;C. Giocoli;P. Gómez-Alvarez;J. Gracia-Carpio;B. R. Granett;A. Grazian;F. Grupp;L. Guzzo;S. Gwyn;S. V. H. Haugan;W. Holmes;I. M. Hook;F. Hormuth;A. Hornstrup;P. Hudelot;K. Jahnke;M. Jhabvala;E. Keihänen;S. Kermiche;A. Kiessling;B. Kubik;M. Kümmel;M. Kunz;H. Kurki-Suonio;Q. Le Boulc'h;A. M. C. Le Brun;D. Le Mignant;P. Liebing;S. Ligori;P. B. Lilje;V. Lindholm;I. Lloro;G. Mainetti;D. Maino;E. Maiorano;O. Mansutti;S. Marcin;O. Marggraf;M. Martinelli;N. Martinet;F. Marulli;R. Massey;S. Maurogordato;E. Medinaceli;S. Mei;M. Melchior;Y. Mellier;M. Meneghetti;E. Merlin;G. Meylan;A. Mora;M. Moresco;L. Moscardini;R. Nakajima;C. Neissner;S. -M. Niemi;J. W. Nightingale;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;R. Rebolo;A. Renzi;J. Rhodes;G. Riccio;E. Romelli;M. Roncarelli;H. J. A. Rottgering;B. Rusholme;R. Saglia;Z. Sakr;D. Sapone;B. Sartoris;J. A. Schewtschenko;P. Schneider;T. Schrabback;A. Secroun;G. Seidel;M. Seiffert;S. Serrano;P. Simon;C. Sirignano;G. Sirri;L. Stanco;J. Steinwagner;P. Tallada-Crespí;A. N. Taylor;I. Tereno;S. Toft;R. Toledo-Moreo;F. Torradeflot;I. Tutusaus;L. Valenziano;J. Valiviita;T. Vassallo;G. Verdoes Kleijn;A. Veropalumbo;Y. Wang;J. Weller;A. Zacchei;G. Zamorani;F. M. Zerbi;I. A. Zinchenko;E. Zucca;V. Allevato;M. Ballardini;M. Bolzonella;E. Bozzo;C. Burigana;R. Cabanac;A. Cappi;D. Di Ferdinando;J. A. Escartin Vigo;L. Gabarra;J. Martín-Fleitas;S. Matthew;N. Mauri;R. B. Metcalf;A. Pezzotta;M. Pöntinen;C. Porciani;I. Risso;V. Scottez;M. Sereno;M. Tenti;M. Viel;M. Wiesmann;Y. Akrami;I. T. Andika;S. Anselmi;M. Archidiacono;F. Atrio-Barandela;C. Benoist;K. Benson;D. Bertacca;M. Bethermin;L. Bisigello;A. Blanchard;L. Blot;H. Böhringer;S. Borgani;M. L. Brown;S. Bruton;A. Calabro;B. Camacho Quevedo;F. Caro;C. S. Carvalho;T. Castro;F. Cogato;A. R. Cooray;O. Cucciati;S. Davini;F. De Paolis;G. Desprez;A. Díaz-Sánchez;J. J. Diaz;S. Di Domizio;J. M. Diego;P. -A. Duc;A. Enia;Y. Fang;A. G. Ferrari;P. G. Ferreira;A. Finoguenov;A. Fontana;A. Franco;K. Ganga;J. García-Bellido;T. Gasparetto;V. Gautard;R. Gavazzi;E. Gaztanaga;F. Giacomini;F. Gianotti;G. Gozaliasl;M. Guidi;C. M. Gutierrez;A. Hall;W. G. Hartley;S. Hemmati;C. Hernández-Monteagudo;H. Hildebrandt;J. Hjorth;J. J. E. Kajava;Y. Kang;V. Kansal;D. Karagiannis;K. Kiiveri;C. C. Kirkpatrick;S. Kruk;J. Le Graet;L. Legrand;M. Lembo;F. Lepori;G. Leroy;G. F. Lesci;J. Lesgourgues;L. Leuzzi;T. I. Liaudat;S. J. Liu;A. Loureiro;J. Macias-Perez;G. Maggio;M. Magliocchetti;F. Mannucci;R. Maoli;C. J. A. P. Martins;L. Maurin;M. Miluzio;P. Monaco;C. Moretti;G. Morgante;C. Murray;K. Naidoo;A. Navarro-Alsina;S. Nesseris;F. Passalacqua;K. Paterson;L. Patrizii;A. Pisani;D. Potter;S. Quai;M. Radovich;P. -F. Rocci;G. Rodighiero;S. Sacquegna;M. Sahlén;D. B. Sanders;E. Sarpa;C. Scarlata;A. Schneider;D. Sciotti;E. Sellentin;L. C. Smith;K. Tanidis;G. Testera;R. Teyssier;S. Tosi;A. Troja;M. Tucci;C. Valieri;A. Venhola;D. Vergani;G. Verza;P. Vielzeuf;N. A. Walton;D. Scott
2026
Abstract
Active galactic nuclei (AGN) are an important phase in galaxy evolution. However, they can be difficult to identify due to their varied observational signatures. Furthermore, to understand the impact of an AGN on its host galaxy, it is important to quantify the strength of the AGN with respect to the host galaxy. We developed a deep learning (DL) model to identify AGN in imaging data by deriving the contribution of the central point source. The model was trained with Euclidised mock galaxy images in which we artificially injected different levels of AGN, in the form of varying contributions of the point spread function (PSF). Our DL-based method can precisely and accurately recover the injected AGN contribution fraction, fPSF, with a mean difference between the predicted and true fPSF of −0.0078 and an overall root mean square error of 0.051. With this new method, we move beyond the simplistic AGN versus non-AGN classification and are able to precisely quantify the AGN contribution and study galaxy evolution across a continuous spectrum of AGN activity. We applied our method to a stellar-mass-limited sample (with M* ≥ 109.8 M⊙ and 0.5 ≤ z ≤ 2.0) selected from the first Euclid quick data release (Q1) and, using a threshold of fPSF > 0.2, we identified 61 432 ± 70 AGN over 63.1 deg2 (9.8 ± 0.1% of our sample). We compared these DL-selected AGN with AGN selected in the X-ray, mid-infrared (MIR), and via optical spectroscopy and investigated their overlapping fractions depending on different thresholds on the PSF contribution. We find that the overlap increases with increasing X-ray or bolometric AGN luminosity. We observe a positive correlation between the luminosity in the IE filter of the AGN and the host galaxy stellar mass, suggesting that supermassive black holes (SMBHs) generally grow faster (in absolute terms, i.e. the luminosity of the PSF component is larger) in more massive galaxies. Moreover, the mean relative contribution of the AGN is higher in the quiescent galaxy population than in the star-forming population. In terms of absolute power, starburst galaxies, as well as the most massive galaxies (across the star-formation main sequence), tend to host the most luminous AGN, indicating concomitant assembly of the SMBH and the host galaxy.
Margalef-Bentabol, B., Wang, L., La Marca, A., Rodriguez-Gomez, V., Humphrey, A., Fotopoulou, S., et al. (2026). Euclid Quick Data Release (Q1): XVIII. First Euclid statistical study of the active galactic nuclei contribution fraction. ASTRONOMY & ASTROPHYSICS, 711, 1-21 [10.1051/0004-6361/202554583].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1078691
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