Euclid preparation. LXXXII. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle

We introduce (M_/M_⊙) ∼ 9 due to the flux-limited nature of Euclid spectroscopic samples, where spectra below the detection threshold lack reliable redshift measurements, preventing effective stacking. The star formation rate–stellar mass relation of the parent sample is recovered reliably only in the deep survey for log_ a versatile spectral stacking pipeline developed for the Euclid mission's NISP spectroscopic surveys, aimed at extracting faint emission lines and spectral features from large galaxy samples in the Wide and Deep Surveys. Designed for computational efficiency and flexible configuration, supports the processing of extensive datasets critical to Euclid's non-cosmological science goals. We validated the pipeline using simulated spectra processed to match Euclid's expected final data quality. Stacking enables robust recovery of key emission lines, including and ∋i, below individual detection limits. However, the measurement of galaxy properties such as star formation rate, dust attenuation, and gas-phase metallicity are biased at stellar mass below log_ 10 10 (M_/M_⊙) ≳ 10, whereas the metallicity–mass relation is recovered more accurately over a wider mass range. These limitations are caused by the increased fraction of redshift measurement errors at lower masses and fluxes. We examined the impact of residual redshift contaminants that arises from mis-identified emission lines and noise spikes, on stacked spectra. Even after stringent quality selections, low-level contamination ($<6%$) has minimal impact on line fluxes due to the systematically weaker emission of contaminants. A percentile-based analysis of stacked spectra provides a sensitive diagnostic for detecting contamination via coherent spurious features at characteristic wavelengths. While our simulations include most instrumental effects, real Euclid data will require a further refinement of contamination mitigation strategies.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-12-19
DOI
https://doi.org/10.1051/0004-6361/202557329/pdf
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
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article

Euclid preparation. LXXXII. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle

Diego Jm, R.J. Massey, A.R. Cooray, I.M Hook et al.
Springer Link (Chiba Institute of Technology)
Galaxies: Formation, Evolution, Phenomena
article

Euclid preparation. LXXXII. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle

Diego Jm, R.J. Massey, A.R. Cooray, I.M Hook, B.R. Granett, I.T Andika, H.I Teplitz, J.E. Kajava, G.F Lesci, P.B. Lilje, S.J. Liu, P. Simon, E. Maiorano, H. Hildebrandt, T. Vassallo, L. Valenziano, X. Lopez Lopez, M. Tenti, E. Branchini, F. Dubath, J.-L. Starck, S.V.H. Haugan, L. Gabarra, W.J. Percival, D. Di Ferdinando, S. Anselmi, M. Frailis, D. Potter, S. Bardelli, N. Fourmanoit, A. Calabro, S. de la Torre, G. Verza, S. Cavuoti, L. Maurin, E. Daddi, A.M.C. Le Brun, O. Marggraf, A. Díaz-Sánchez, Y. Q. Fang, J. Lesgourgues, S. Mei, C. Porciani, M. Seiffert, S. Serrano, M. Hirschmann, E. Medinaceli, B. Gillis, H. Degaudenzi, S. Camera, S. Bruton, S.-M. Niemi, J. Martín-Fleitas, A.G. Ferrari, K. George, S. Ferriol, A. Blanchard, V. Allevato, H. Dole, S. Pires, Z. Sakr, F. Courbin, L. Legrand, A. Fontana, N. Martinet, I. Risso, P. Hudelot, S. Davini, G. Morgante, J. Weller, G. Congedo, K. Tanidis, M. Scodeggio, A. Cimatti, A. Veropalumbo, R. Farinelli, S. Hemmati, J. García-Bellido, C.J. Conselice, E. Lusso, F. Finelli, K. Jahnke, S. Quai, D. Vergani, C. Giocoli, S. Andreon, A. Renzi, J. Gracia-Carpio, E. Romelli, A. Enia, F. Cogato, R. Toledo-Moreo, L. Pozzetti, S. Nadathur, M. Talia, F. Shankar, A. Pisani, A. Mora, C. Scarlata, E. Zucca, G. Meylan, Y. N. Wang, G. Gozaliasl, W. Gillard, M. Sahlén, G. Castignani, H.J. Dickinson, M. Meneghetti, G. Seidel, M. Miluzio, B. Sartoris, G. Verdoes Kleijn, A. Troja, V. Le Brun, F. Torradeflot, N.A. Walton, S. Marcin, C. C. Kirkpatrick, M. Farina, L. Moscardini, J. Valiviita, D. Sciotti, H.M. Courtois, F. Pasian, Z. P. Mao, M.L. Brown, F. Raison, E. Sellentin, M. Castellano, C. Tao, S. Matthew, M. Kunz, F. Giacomini, J. Rhodes, M. Wiesmann, P.-F. Rocci, X. Dupac, A.N. Taylor, D. Tavagnacco, L. Leuzzi, F. Lepori, I. Tutusaus, M. Huertas-Company, C. Baccigalupi, V. Scottez, A. Hornstrup, C. Mancini, M. Melchior, A. Pezzotta, S. Escoffier, A. Secroun, M. Magliocchetti, R. Saglia, K. Pedersen, C. Carbone, E. Sefusatti, F. Marulli, G. Polenta, F. Fontanot, B. Kubik, B. Camacho Quevedo, V. Pettorino, I. Lloro, K. Naidoo, E. Keihänen, M. Bethermin, K. Paterson, A. Hall, S. Kruk, S. Dusini, D. Sapone, A. Loureiro, F. Mannucci, E. Merlin, J. Steinwagner, G. Maggio, L.A. Popa, R. Cabanac, B. Joachimi, G. Sirri, A. Cappi, A. Ealet, E. Rossetti, K. Kiiveri, C.M. Gutierrez, M. Lembo, D. Bertacca, F. Faustini, S. Galeotta, L.C. Smith, D.B. Sanders, C. Neissner, J. Hjorth, Y. Kang, L. Blot, V. Kansal, P. Vielzeuf, A. Da Silva, F. Caro, S. Toft, D. Maino, S. Ligori, M. Radovich, Y. Copin, C. Colodro-Conde, R. Teyssier, T.I. Liaudat, M. Tucci, V. Gautard, H. Kurki-Suonio, A. Finoguenov, S. Paltani, C. Burigana, D. Karagiannis, F. Passalacqua, P. Schneider, O. Cucciati, E. Gaztanaga, G. Rodighiero, F. Gianotti, J. Macias-Perez, M. Viel, A. Franco, C. Moretti, G. Leroy, P. Monaco, L. Patrizii, E. Sarpa, J.A. Escartin Vigo, G. De Lucia, J. J. Diaz, V. Capobianco, T. Contini, G. Desprez, S. Borgani, O. Mansutti, T. Gasparetto, A.G. Sánchez, A. Amara, G. Cañas-Herrera, M. Guidi, M. Siudek, G. Riccio, M. Poncet, J. Carretero, M. Martinelli, F. Grupp, A. Venhola, E. Bozzo, M. Sereno, M. Ballardini, M. Jhabvala, D. Vibert, A. Schneider, A. Biviano, I.A. Zinchenko, M. Moresco, C. Padilla, E. Franceschi, K.C. Chambers, R.B. Metcalf, M. Pöntinen, M. Archidiacono, V. Lindholm, L. Conversi, S. Kermiche, A. Kiessling, S. Nesseris, S. Casas, R. Maoli, S. Sacquegna, I. Tereno, P. Bergamini, N. Mauri, L. L. Wang, M. Bolzonella, W. Holmes, Y. Akrami, M. Brescia, L. Guzzo, G. Testera, P. Cassata, M. Baldi, C.J.A.P. Martins, J.G. Sorce, S. Di Domizio, C. Valieri, R. Rebolo, M. Douspis, M. Kümmel, S. Tosi, C. Sirignano, A. Navarro-Alsina, Y. Mellier, M. Roncarelli, G. Zamorani, S. Conseil, F. Atrio-Barandela, A. Grazian, K. Ganga, C.S. Carvalho, L. Bisigello, T. Schrabback, N. Aghanim, N. Auricchio, P. Tallada-Crespí, L. Stanco, C. Hernández-Monteagudo, T. Castro, G. Mainetti, F. Hormuth, J. Brinchmann
article en

Abstract

We introduce (M_/M_⊙) ∼ 9 due to the flux-limited nature of Euclid spectroscopic samples, where spectra below the detection threshold lack reliable redshift measurements, preventing effective stacking. The star formation rate–stellar mass relation of the parent sample is recovered reliably only in the deep survey for log_ a versatile spectral stacking pipeline developed for the Euclid mission's NISP spectroscopic surveys, aimed at extracting faint emission lines and spectral features from large galaxy samples in the Wide and Deep Surveys. Designed for computational efficiency and flexible configuration, supports the processing of extensive datasets critical to Euclid's non-cosmological science goals. We validated the pipeline using simulated spectra processed to match Euclid's expected final data quality. Stacking enables robust recovery of key emission lines, including and ∋i, below individual detection limits. However, the measurement of galaxy properties such as star formation rate, dust attenuation, and gas-phase metallicity are biased at stellar mass below log_ 10 10 (M_/M_⊙) ≳ 10, whereas the metallicity–mass relation is recovered more accurately over a wider mass range. These limitations are caused by the increased fraction of redshift measurement errors at lower masses and fluxes. We examined the impact of residual redshift contaminants that arises from mis-identified emission lines and noise spikes, on stacked spectra. Even after stringent quality selections, low-level contamination ($<6%$) has minimal impact on line fluxes due to the systematically weaker emission of contaminants. A percentile-based analysis of stacked spectra provides a sensitive diagnostic for detecting contamination via coherent spurious features at characteristic wavelengths. While our simulations include most instrumental effects, real Euclid data will require a further refinement of contamination mitigation strategies.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 27%
Galaxies: Formation, Evolution, Phenomena
DOISourceOpenAlex
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