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Galaxy major mergers are indicated as one of the principal pathways to trigger active galactic nuclei (AGN). We present the first statistical analysis of the major merger and AGN connection in the Euclid Deep Fields, and showcase the statistical power of the Euclid data. We constructed a stellar-mass-complete (M★ > 109.8 M⊙) sample of galaxies from the quick data release (Euclid Quick Release Q1 2025) in the redshift range z = 0.5–2. We selected AGN using X-ray detections, optical spectroscopy, and mid-infrared (MIR) colours, and by processing IE observations with an image decomposition algorithm. We used convolutional neural networks trained on cosmological hydrodynamic simulations to classify galaxies as mergers and non-mergers. We found a larger fraction of AGN in mergers compared to the non-merger controls for all AGN selections, with AGN excess factors ranging from two to six. The largest excess we observed was in the MIR AGN. Likewise, a generally larger merger fraction (fmerg) was seen in active galaxies than in the non-active controls, with the excess depending on the AGN selection method. Furthermore, we analysed fmerg as a function of the AGN bolometric luminosity (Lbol) and the contribution of the point-source component to the total galaxy light in the IE-band (fPSF) as a proxy for the relative AGN contribution fraction. We uncovered a rising fmerg, with an increasing fPSF up to fPSF ≃ 0.55, after which we observed a decreasing trend. In the range fPSF = 0.3–0.7, mergers appear to be the dominant AGN fuelling mechanism. We then derived the point-source luminosity (LPSF) and showed that fmerg monotonically increases as a function of LPSF at z < 0.9, with fmerg ≥ 50% for LPSF ≃ 2 × 1043 erg s−1. Similarly, at 0.9 ≤ z ≤ 2, fmerg rises as a function of LPSF, though mergers do not dominate until LPSF ≃ 1045 erg s−1. For the X-ray and spectroscopically detected AGN, we derived the bolometric luminosity, Lbol, which has a positive correlation with fmerg for X-ray AGN, while there is a less pronounced trend for spectroscopically selected AGN due to the smaller sample size. At Lbol > 1045 erg s−1, AGN mostly reside in mergers. We conclude that mergers are most strongly associated with the most powerful and dust-obscured AGN, which are typically linked to a fast-growing phase of the supermassive black hole, while other mechanisms, such as secular processes, might be the trigger of less luminous and dominant AGN.
La Marca, A., Wang, L., Margalef-Bentabol, B., Gabarra, L., Toba, Y., Mezcua, M., et al. (2026). Euclid Quick Data Release (Q1): XVII. First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei. ASTRONOMY & ASTROPHYSICS, 711, A17 [10.1051/0004-6361/202554579].
Euclid Quick Data Release (Q1): XVII. First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei
La Marca A.
;Wang L.;Margalef-Bentabol B.;Gabarra L.;Toba Y.;Mezcua M.;Rodriguez-Gomez V.;Ricci F.;Fotopoulou S.;Matamoro Zatarain T.;Allevato V.;La Franca F.;Shankar F.;Bisigello L.;Stevens G.;Siudek M.;Roster W.;Salvato M.;Tortora C.;Spinoglio L.;Man A. W. S.;Knapen J. H.;Baes M.;O'Ryan D.;Aghanim N.;Altieri B.;Amara A.;Andreon S.;Auricchio N.;Aussel H.;Baccigalupi C.;Baldi M.;Bardelli S.;Battaglia P.;Biviano A.;Bonchi A.;Branchini E.;Brescia M.;Brinchmann J.;Camera S.;Canas-Herrera G.;Capobianco V.;Carbone C.;Carretero J.;Castellano M.;Castignani G.;Cavuoti S.;Chambers K. C.;Cimatti A.;Colodro-Conde C.;Congedo G.;Conselice C. J.;Conversi L.;Copin Y.;Costille A.;Courbin F.;Courtois H. M.;Cropper M.;Da Silva A.;Degaudenzi H.;De Lucia G.;Di Giorgio A. M.;Dolding C.;Dole H.;Dubath F.;Duncan C. A. J.;Dupac X.;Ealet A.;Escoffier S.;Fabricius M.;Farina M.;Farinelli R.;Faustini F.;Ferriol S.;Finelli F.;Frailis M.;Franceschi E.;Galeotta S.;George K.;Gillis B.;Giocoli C.;Gomez-Alvarez P.;Gracia-Carpio J.;Granett B. R.;Grazian A.;Grupp F.;Guzzo L.;Gwyn S.;Haugan S. V. H.;Holmes W.;Hook I. M.;Hormuth F.;Hornstrup A.;Hudelot P.;Jahnke K.;Jhabvala M.;Joachimi B.;Keihanen E.;Kermiche S.;Kiessling A.;Kubik B.;Kummel M.;Kunz M.;Kurki-Suonio H.;Le Boulc'h Q.;Le Brun A. M. C.;Le Mignant D.;Ligori S.;Lilje P. B.;Lindholm V.;Lloro I.;Mainetti G.;Maino D.;Maiorano E.;Mansutti O.;Marcin S.;Marggraf O.;Martinelli M.;Martinet N.;Marulli F.;Massey R.;Maurogordato S.;Medinaceli E.;Mei S.;Melchior M.;Mellier Y.;Meneghetti M.;Merlin E.;Meylan G.;Mora A.;Moresco M.;Moscardini L.;Nakajima R.;Neissner C.;Niemi S. -M.;Nightingale J. W.;Padilla C.;Paltani S.;Pasian F.;Pedersen K.;Percival W. J.;Pettorino V.;Pires S.;Polenta G.;Poncet M.;Popa L. A.;Pozzetti L.;Raison F.;Rebolo R.;Renzi A.;Rhodes J.;Riccio G.;Romelli E.;Roncarelli M.;Rusholme B.;Saglia R.;Sakr Z.;Sapone D.;Sartoris B.;Schewtschenko J. A.;Schneider P.;Schrabback T.;Scodeggio M.;Secroun A.;Seidel G.;Seiffert M.;Serrano S.;Simon P.;Sirignano C.;Sirri G.;Stanco L.;Steinwagner J.;Tallada-Crespi P.;Taylor A. N.;Teplitz H. I.;Tereno I.;Tessore N.;Toft S.;Toledo-Moreo R.;Torradeflot F.;Tutusaus I.;Valenziano L.;Valiviita J.;Vassallo T.;Verdoes Kleijn G.;Veropalumbo A.;Wang Y.;Weller J.;Zacchei A.;Zamorani G.;Zerbi F. M.;Zinchenko I. A.;Zucca E.;Ballardini M.;Bolzonella M.;Bozzo E.;Burigana C.;Cabanac R.;Cappi A.;Di Ferdinando D.;Escartin Vigo J. A.;Huertas-Company M.;Martin-Fleitas J.;Matthew S.;Mauri N.;Metcalf R. B.;Pezzotta A.;Pontinen M.;Porciani C.;Risso I.;Scottez V.;Sereno M.;Tenti M.;Viel M.;Wiesmann M.;Akrami Y.;Alvi S.;Andika I. T.;Anselmi S.;Archidiacono M.;Atrio-Barandela F.;Benoist C.;Benson K.;Bertacca D.;Bethermin M.;Blanchard A.;Blot L.;Bohringer H.;Borgani S.;Brown M. L.;Bruton S.;Calabro A.;Camacho Quevedo B.;Caro F.;Carvalho C. S.;Castro T.;Cogato F.;Conseil S.;Contini T.;Cooray A. R.;Cucciati O.;Davini S.;De Paolis F.;Desprez G.;Diaz-Sanchez A.;Diaz J. J.;Di Domizio S.;Diego J. M.;Duc P. -A.;Enia A.;Fang Y.;Ferrari A. G.;Finoguenov A.;Fontana A.;Fontanot F.;Franco A.;Ganga K.;Garcia-Bellido J.;Gasparetto T.;Gautard V.;Gaztanaga E.;Giacomini F.;Gianotti F.;Gozaliasl G.;Guidi M.;Gutierrez C. M.;Hall A.;Hartley W. G.;Hernandez-Monteagudo C.;Hildebrandt H.;Hjorth J.;Kajava J. J. E.;Kang Y.;Kansal V.;Karagiannis D.;Kiiveri K.;Kirkpatrick C. C.;Kruk S.;Le Graet J.;Legrand L.;Lembo M.;Lepori F.;Leroy G.;Lesci G. F.;Lesgourgues J.;Leuzzi L.;Liaudat T. I.;Loureiro A.;Macias-Perez J.;Maggio G.;Magliocchetti M.;Magnier E. A.;Mannucci F.;Maoli R.;Martins C. J. A. P.;Maurin L.;Miluzio M.;Monaco P.;Moretti C.;Morgante G.;Naidoo K.;Navarro-Alsina A.;Nesseris S.;Passalacqua F.;Paterson K.;Patrizii L.;Pisani A.;Potter D.;Quai S.;Radovich M.;Rocci P. -F.;Sacquegna S.;Sahlen M.;Sanders D. B.;Sarpa E.;Scarlata C.;Schaye J.;Schneider A.;Sciotti D.;Sellentin E.;Smith L. C.;Stanford S. A.;Tanidis K.;Testera G.;Teyssier R.;Tosi S.;Troja A.;Tucci M.;Valieri C.;Venhola A.;Vergani D.;Verza G.;Vielzeuf P.;Walton N. A.;Soubrie E.;Scott D.
2026-01-01
Abstract
Galaxy major mergers are indicated as one of the principal pathways to trigger active galactic nuclei (AGN). We present the first statistical analysis of the major merger and AGN connection in the Euclid Deep Fields, and showcase the statistical power of the Euclid data. We constructed a stellar-mass-complete (M★ > 109.8 M⊙) sample of galaxies from the quick data release (Euclid Quick Release Q1 2025) in the redshift range z = 0.5–2. We selected AGN using X-ray detections, optical spectroscopy, and mid-infrared (MIR) colours, and by processing IE observations with an image decomposition algorithm. We used convolutional neural networks trained on cosmological hydrodynamic simulations to classify galaxies as mergers and non-mergers. We found a larger fraction of AGN in mergers compared to the non-merger controls for all AGN selections, with AGN excess factors ranging from two to six. The largest excess we observed was in the MIR AGN. Likewise, a generally larger merger fraction (fmerg) was seen in active galaxies than in the non-active controls, with the excess depending on the AGN selection method. Furthermore, we analysed fmerg as a function of the AGN bolometric luminosity (Lbol) and the contribution of the point-source component to the total galaxy light in the IE-band (fPSF) as a proxy for the relative AGN contribution fraction. We uncovered a rising fmerg, with an increasing fPSF up to fPSF ≃ 0.55, after which we observed a decreasing trend. In the range fPSF = 0.3–0.7, mergers appear to be the dominant AGN fuelling mechanism. We then derived the point-source luminosity (LPSF) and showed that fmerg monotonically increases as a function of LPSF at z < 0.9, with fmerg ≥ 50% for LPSF ≃ 2 × 1043 erg s−1. Similarly, at 0.9 ≤ z ≤ 2, fmerg rises as a function of LPSF, though mergers do not dominate until LPSF ≃ 1045 erg s−1. For the X-ray and spectroscopically detected AGN, we derived the bolometric luminosity, Lbol, which has a positive correlation with fmerg for X-ray AGN, while there is a less pronounced trend for spectroscopically selected AGN due to the smaller sample size. At Lbol > 1045 erg s−1, AGN mostly reside in mergers. We conclude that mergers are most strongly associated with the most powerful and dust-obscured AGN, which are typically linked to a fast-growing phase of the supermassive black hole, while other mechanisms, such as secular processes, might be the trigger of less luminous and dominant AGN.
La Marca, A., Wang, L., Margalef-Bentabol, B., Gabarra, L., Toba, Y., Mezcua, M., et al. (2026). Euclid Quick Data Release (Q1): XVII. First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei. ASTRONOMY & ASTROPHYSICS, 711, A17 [10.1051/0004-6361/202554579].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/553736
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
