%0 Generic %1 adamek2022euclid %A Adamek, J. %A Angulo, R. E. %A Arnold, C. %A Baldi, M. %A Biagetti, M. %A Bose, B. %A Carbone, C. %A Castro, T. %A Dakin, J. %A Dolag, K. %A Elbers, W. %A Fidler, C. %A Giocoli, C. %A Hannestad, S. %A Hassani, F. %A Hernández-Aguayo, C. %A Koyama, K. %A Li, B. %A Mauland, R. %A Monaco, P. %A Moretti, C. %A Mota, D. F. %A Partmann, C. %A Parimbelli, G. %A Potter, D. %A Schneider, A. %A Schulz, S. %A Smith, R. E. %A Springel, V. %A Stadel, J. %A Tram, T. %A Viel, M. %A Villaescusa-Navarro, F. %A Winther, H. A. %A Wright, B. S. %A Zennaro, M. %A Aghanim, N. %A Amendola, L. %A Auricchio, N. %A Bonino, D. %A Branchini, E. %A Brescia, M. %A Camera, S. %A Capobianco, V. %A Cardone, V. F. %A Carretero, J. %A Castander, F. J. %A Castellano, M. %A Cavuoti, S. %A Cimatti, A. %A Cledassou, R. %A Congedo, G. %A Conversi, L. %A Copin, Y. %A Da Silva, A. %A Degaudenzi, H. %A Douspis, M. %A Dubath, F. %A Duncan, C. A. J. %A Dupac, X. %A Dusini, S. %A Farrens, S. %A Ferriol, S. %A Fosalba, P. %A Frailis, M. %A Franceschi, E. %A Galeotta, S. %A Garilli, B. %A Gillard, W. %A Gillis, B. %A Grazian, A. %A Haugan, S. V. %A Holmes, W. %A Hornstrup, A. %A Jahnke, K. %A Kermiche, S. %A Kiessling, A. %A Kilbinger, M. %A Kitching, T. %A Kunz, M. %A Kurki-Suonio, H. %A Lilje, P. B. %A Lloro, I. %A Mansutti, O. %A Marggraf, O. %A Marulli, F. %A Massey, R. %A Medinaceli, E. %A Meneghetti, M. %A Meylan, G. %A Moresco, M. %A Moscardini, L. %A Munari, E. %A Niemi, S. M. %A Padilla, C. %A Paltani, S. %A Pasian, F. %A Pedersen, K. %A Percival, W. J. %A Pettorino, V. %A Polenta, G. %A Poncet, M. %A Popa, L. A. %A Raison, F. %A Rebolo, R. %A Renzi, A. %A Rhodes, J. %A Riccio, G. %A Romelli, E. %A Roncarelli, M. %A Saglia, R. %A Sapone, D. %A Sartoris, B. %A Schneider, P. %A Schrabback, T. %A Secroun, A. %A Seidel, G. %A Sirignano, C. %A Sirri, G. %A Stanco, L. %A Starck, J. L. %A Tallada-Crespí, P. %A Taylor, A. N. %A Tereno, I. %A Toledo-Moreo, R. %A Torradeflot, F. %A Tutusaus, I. %A Valenziano, L. %A Vassallo, T. %A Wang, Y. %A Weller, J. %A Zacchei, A. %A Zamorani, G. %A Zoubian, J. %A Fabbian, G. %A Scottez, V. %D 2022 %K cosmology hep-ph important lss neutrinos review %T Euclid: Modelling massive neutrinos in cosmology -- a code comparison %U http://arxiv.org/abs/2211.12457 %X The measurement of the absolute neutrino mass scale from cosmological large-scale clustering data is one of the key science goals of the Euclid mission. Such a measurement relies on precise modelling of the impact of neutrinos on structure formation, which can be studied with $N$-body simulations. Here we present the results from a major code comparison effort to establish the maturity and reliability of numerical methods for treating massive neutrinos. The comparison includes eleven full $N$-body implementations (not all of them independent), two $N$-body schemes with approximate time integration, and four additional codes that directly predict or emulate the matter power spectrum. Using a common set of initial data we quantify the relative agreement on the nonlinear power spectrum of cold dark matter and baryons and, for the $N$-body codes, also the relative agreement on the bispectrum, halo mass function, and halo bias. We find that the different numerical implementations produce fully consistent results. We can therefore be confident that we can model the impact of massive neutrinos at the sub-percent level in the most common summary statistics. We also provide a code validation pipeline for future reference.

@misc{adamek2022euclid, abstract = {The measurement of the absolute neutrino mass scale from cosmological large-scale clustering data is one of the key science goals of the Euclid mission. Such a measurement relies on precise modelling of the impact of neutrinos on structure formation, which can be studied with $N$-body simulations. Here we present the results from a major code comparison effort to establish the maturity and reliability of numerical methods for treating massive neutrinos. The comparison includes eleven full $N$-body implementations (not all of them independent), two $N$-body schemes with approximate time integration, and four additional codes that directly predict or emulate the matter power spectrum. Using a common set of initial data we quantify the relative agreement on the nonlinear power spectrum of cold dark matter and baryons and, for the $N$-body codes, also the relative agreement on the bispectrum, halo mass function, and halo bias. We find that the different numerical implementations produce fully consistent results. We can therefore be confident that we can model the impact of massive neutrinos at the sub-percent level in the most common summary statistics. We also provide a code validation pipeline for future reference.}, added-at = {2023-01-05T14:35:10.000+0100}, author = {Adamek, J. and Angulo, R. E. and Arnold, C. and Baldi, M. and Biagetti, M. and Bose, B. and Carbone, C. and Castro, T. and Dakin, J. and Dolag, K. and Elbers, W. and Fidler, C. and Giocoli, C. and Hannestad, S. and Hassani, F. and Hernández-Aguayo, C. and Koyama, K. and Li, B. and Mauland, R. and Monaco, P. and Moretti, C. and Mota, D. F. and Partmann, C. and Parimbelli, G. and Potter, D. and Schneider, A. and Schulz, S. and Smith, R. E. and Springel, V. and Stadel, J. and Tram, T. and Viel, M. and Villaescusa-Navarro, F. and Winther, H. A. and Wright, B. S. and Zennaro, M. and Aghanim, N. and Amendola, L. and Auricchio, N. and Bonino, D. and Branchini, E. and Brescia, M. and Camera, S. and Capobianco, V. and Cardone, V. F. and Carretero, J. and Castander, F. J. and Castellano, M. and Cavuoti, S. and Cimatti, A. and Cledassou, R. and Congedo, G. and Conversi, L. and Copin, Y. and Da Silva, A. and Degaudenzi, H. and Douspis, M. and Dubath, F. and Duncan, C. A. J. and Dupac, X. and Dusini, S. and Farrens, S. and Ferriol, S. and Fosalba, P. and Frailis, M. and Franceschi, E. and Galeotta, S. and Garilli, B. and Gillard, W. and Gillis, B. and Grazian, A. and Haugan, S. V. and Holmes, W. and Hornstrup, A. and Jahnke, K. and Kermiche, S. and Kiessling, A. and Kilbinger, M. and Kitching, T. and Kunz, M. and Kurki-Suonio, H. and Lilje, P. B. and Lloro, I. and Mansutti, O. and Marggraf, O. and Marulli, F. and Massey, R. and Medinaceli, E. and Meneghetti, M. and Meylan, G. and Moresco, M. and Moscardini, L. and Munari, E. and Niemi, S. M. and Padilla, C. and Paltani, S. and Pasian, F. and Pedersen, K. and Percival, W. J. and Pettorino, V. and Polenta, G. and Poncet, M. and Popa, L. A. and Raison, F. and Rebolo, R. and Renzi, A. and Rhodes, J. and Riccio, G. and Romelli, E. and Roncarelli, M. and Saglia, R. and Sapone, D. and Sartoris, B. and Schneider, P. and Schrabback, T. and Secroun, A. and Seidel, G. and Sirignano, C. and Sirri, G. and Stanco, L. and Starck, J. L. and Tallada-Crespí, P. and Taylor, A. N. and Tereno, I. and Toledo-Moreo, R. and Torradeflot, F. and Tutusaus, I. and Valenziano, L. and Vassallo, T. and Wang, Y. and Weller, J. and Zacchei, A. and Zamorani, G. and Zoubian, J. and Fabbian, G. and Scottez, V.}, biburl = {https://www.bibsonomy.org/bibtex/242e9a03e9721fa6a299ebe3c6debf07f/intfxdx}, description = {Euclid: Modelling massive neutrinos in cosmology -- a code comparison}, interhash = {56893e0bbe08d275ee6a6962ecdb96ed}, intrahash = {42e9a03e9721fa6a299ebe3c6debf07f}, keywords = {cosmology hep-ph important lss neutrinos review}, note = {cite arxiv:2211.12457Comment: 43 pages, 17 figures, 2 tables; published on behalf of the Euclid Consortium; data available at https://doi.org/10.5281/zenodo.7297976}, timestamp = {2023-01-05T14:35:10.000+0100}, title = {Euclid: Modelling massive neutrinos in cosmology -- a code comparison}, url = {http://arxiv.org/abs/2211.12457}, year = 2022 }