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arXiv 2024年

作者： Castro, T. Fumagalli, A. Angulo, R.E. Bocquet, S. Borgani, S. Costanzi, M. Dakin, J. Dolag, K. Monaco, P. Saro, A. Sefusatti, E. Aghanim, N. Amendola, L. Andreon, S. Baccigalupi, C. Baldi, M. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Caillat, A. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Costille, A. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. De Lucia, G. Di Giorgio, A.M. Douspis, M. Dupac, X. Dusini, S. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Ilić, S. Jahnke, K. Jhabvala, M. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kubik, B. Kunz, M. Kurki-Suonio, H. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Melchior, M. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moscardini, L. Munari, E. Niemi, S.-M. 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. Renzi, A. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sakr, Z. Salvignol, J.-C. Sánchez, A.G. Sapone, D. Sartoris, B. Schirmer, M. Secroun, A. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Steinwagner, J. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valenziano, L. Vassallo, T. Kleijn, G. Verdoes Wang, Y. Weller, J. Zacchei, A. Zamorani, G. Zucca, E. Biviano, A. Bolzonella, M. Bozzo, E. Burigana, C. Calabrese, M. Di Ferdinando, D. Vigo, J.A. Escartin Finelli, F. Gracia-Carpio, J. Matthew, S. Mauri, N. Pezzotta, A. INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany Paseo Manuel de Lardizabal 4 Guipuzkoa Donostia-San Sebastián20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48013 Spain Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany Dipartimento di Fisica - Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany Pino Torinese10025 Italy Dipartimento di Fisica Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy INFN section of Naples Vi

The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric galaxy cluster survey. Central to this endeavor is the accurate calibration of the mass- and redshift-dependent halo bias (HB), which is the focus of this paper. We enhance the precision of HB predictions, which is crucial for deriving cosmological constraints from the clustering of galaxy clusters. Our study is based on the peak-background split (PBS) model linked to the halo mass function (HMF), and extends it with a parametric correction to precisely align with results from an extended set of N-body simulations carried out with the OpenGADGET3 code. Employing simulations with fixed and paired initial conditions, we meticulously analyze the matter-halo cross-spectrum and model its covariance using a large number of mock catalogs generated with Lagrangian Perturbation Theory simulations with the PINOCCHIO code. This ensures a comprehensive understanding of the uncertainties in our HB calibration. Our findings indicate that the calibrated HB model is remarkably resilient against changes in cosmological parameters including those involving massive neutrinos. The robustness and adaptability of our calibrated HB model provide an important contribution to the cosmological exploitation of the cluster surveys to be provided by the Euclid mission. This study highlights the necessity of continuously refining the calibration of cosmological tools like the HB to match the advancing quality of observational data. As we project the impact of our calibrated model on cosmological constraints, we find that, given the sensitivity of the Euclid survey, a miscalibration of the HB could introduce biases in cluster cosmology analyses. Our work fills this critical gap, ensuring the HB calibration matches the expected precision of the Euclid survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Maximum principle based algorithms for deep learning

arXiv

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arXiv 2017年

作者： Li, Qianxiao Chen, Long Tai, Cheng Weinan, E. Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way Connexis North138632 Singapore Beijing Institute of Big Data Research Peking University Beijing100080 China Princeton University PrincetonNJ08544 United States

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on at landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables. 68T05, 49D, 49M Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum principle

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The impact of line-of-sight projections on the covariance between galaxy cluster multi-wavelength observable properties – insights from hydrodynamic simulations

arXiv

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arXiv 2024年

作者： Ragagnin, A. Saro, A. Andreon, S. Biviano, A. Dolag, K. Ettori, S. Giocoli, C. Le Brun, A.M.C. Mamon, G.A. Maughan, B.J. Meneghetti, M. Moscardini, L. Pacaud, F. Pratt, G.W. Sereno, M. Borgani, S. Calura, F. Castignani, G. De Petris, M. Eckert, D. Lesci, G.F. Macias-Perez, J. Maturi, M. Amara, A. Auricchio, N. Baccigalupi, C. Baldi, M. Bardelli Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Cavuoti, S. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. De Lucia, G. Dinis, J. Dubath, F. Dupac, X. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Fumana, M. George, K. Gillis, B. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hook, I. Hormuth, F. Hornstrup, A. Jahnke, K. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kubik, B. Kümmel, M. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Mei, S. Mellier, Y. Meylan, G. Moresco, M. Munari, E. Neissner, C. Niemi, S.-M. Nightingale, J.W. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Pettorino, V. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Rossetti, E. Saglia, R. Sakr, Z. Sánchez, A.G. Sapone, D. Sartoris, B. Scaramella, R. Schneider, P. Schrabback, T. Secroun, A. Sefusatti, E. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Steinwagner, J. Tallada-Crespí, P. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valenziano, L. Vassallo, T. Kleijn, G. Verdoes Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zucca, E. Bolzonella, M. Boucaud, A. Bozzo, E. Burigana, C. Calabrese, M. Di Ferdinando Vigo, J.A. Escartin Farinelli, R. INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Dipartimento di Fisica - Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany INFN-Bologna Via Irnerio 46 Bologna40126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bologna Via Irnerio 46 Bologna40126 Italy Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190 France Institut d’Astrophysique de Paris 98bis Boulevard Arago Paris75014 France Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Universität Bonn Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 2 Roma00185 Italy Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Switzerland Univ. Grenoble Alpes CNRS Grenoble INP LPSC-IN2P3 53 Avenue des Martyrs Grenob

Context. Cluster cosmology can benefit from combining multi-wavelength studies, which themselves can benefit from a characterisation of the correlation coefficients between different mass-observable relations. Copyrig... 详细信息

关键词： Galaxies

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Euclid preparation. Sensitivity to neutrino parameters

arXiv

引用

arXiv 2024年

作者： Archidiacono, M. Lesgourgues, J. Casas, S. Pamuk, S. Schöneberg, N. Sakr, Z. Parimbelli, G. Schneider, A. Hervas Peters, F. Pace, F. Sabarish, V.M. Costanzi, M. Camera, S. Carbone, C. Clesse, S. Frusciante, N. Fumagalli, A. Monaco, P. Scott, D. Viel, M. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Galeotta, S. Gillis, B. Giocoli, C. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hoekstra, H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. McCracken, H.J. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. 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. Saglia, R. Sapone, D. Sartoris, B. Scaramella, R. Schirmer, M. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Boucaud, A. Bozzo, E. Burigana, C. Calabrese, M. Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN-Sezione di Milano Via Celoria 16 Milano20133 Italy RWTH Aachen University Aachen52056 Germany Martí i Franquès 1 Barcelona08028 Spain Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-Yvette91191 France Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy INAF-Osservatorio Astrofisico di Torino Via Osservatorio 20 TO Pino Torinese10025 Italy Hamburger Sternwarte University of Hamburg Gojenbergsweg 112 Hamburg21029 Germany Dipartimento di Fisica Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Service de Physique Théorique CP225 Boulevard du Triophe Bruxelles1050 Belgium Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy Department of Physics and Astronomy University of British Colu

Context. The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. Aims. We present forecasts from the combination of the Euclid photometric galaxy surveys (weak lensing, galaxy clustering and their cross-correlation) and its spectroscopic redshift survey on the sensitivity to cosmological parameters including the summed neutrino mass ∑ mν and the effective number of relativistic species Neff in the standard ΛCDM scenario and in a scenario with dynamical dark energy (w0waCDM). Methods. We compare the accuracy of different algorithms predicting the nonlinear matter power spectrum for such models. We then validate several pipelines for Fisher matrix and Markov Chain Monte Carlo (MCMC) forecasts, using different theory codes, algorithms for numerical derivatives, and assumptions concerning the non-linear cut-off scale. Results. The Euclid primary probes alone will reach a sensitivity of σ(∑ mν = 60 meV) = 56 meV in the ΛCDM+∑ mν model, whereas the combination with cosmic microwave background (CMB) data from Planck is expected to achieve σ(∑ mν) = 23 meV and raise the evidence for a non-zero neutrino mass to at least the 2.6 σ level. This can be pushed to a 4 σ detection if future CMB data from LiteBIRD and CMB Stage-IV are included. In combination with Planck, Euclid will also deliver tight constraints on ∆Neff ν+Neff model, or ∆Neff eff remains stable, while that to ∑ mν degrades at most by a factor two. Conclusions. This work illustrates the complementarity between the Euclid spectroscopic and imaging/photometric surveys and between Euclid and CMB constraints. Euclid will have a great potential for measuring the neutrino mass and excluding well-motivated scenarios with additional relativistic particles. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

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Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-ΛCDM models

arXiv

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arXiv 2023年

作者： Bose, B. Carrilho, P. Marinucci, M. Moretti, C. Pietroni, M. Carella, E. Piga, L. Wright, B.S. Vernizzi, F. Carbone, C. Casas, S. D’Amico, G. Frusciante, N. Koyama, K. Pace, F. Pourtsidou, A. Baldi, M. de la Bella, L.F. Fiorini, B. Giocoli, C. Lombriser, L. Aghanim, N. Amara, A. Andreon, S. Auricchio, N. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Costille, A. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Galeotta, S. Garilli, B. Gillis, B. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Meneghetti, M. Meylan, G. Moresco, M. Moscardini, L. Mota, D.F. Munari, E. Niemi, S.-M. 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. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Starck, J.-L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Bozzo, E. Burigana, C. Colodro-Conde, C. Di Ferdinando, D. Graciá-Carpio, J. Mauri, N. Neissner, C. Sakr, Z. Scottez, V. Tenti, M. Viel, M. Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Viale delle Scienze 7/A Parma43124 Italy Technion Israel Institute of Technology Israel SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Gruppo Collegato di Parma Viale delle Scienze 7/A Parma43124 Italy Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy School of Physics and Astronomy Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Institut de Physique Théorique CEA CNRS Université Paris-Saclay Gif-sur-YvetteCedex 91191 France RWTH Aachen University Aachen52056 Germany Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy Pino Torinese10025 Italy Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bologna Via Irnerio 46 Bologna40126 Italy Université de Genève Département de Ph

Context. The Euclid space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the ΛCDM model. Aims. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali–Gabadadze–Porrati model, the Hu–Sawicki f(R) model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein–de Sitter universe;we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos;we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya–Nishimishi–Saito © 2023, CC BY.

关键词： Dark energy

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Maximum principle based algorithms for deep learning

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2017年第1期18卷

作者： Qianxiao Li Long Chen Cheng Tai E. Weinan Institute of High Performance Computing Agency for Science Technology and Research Singapore Peking University Beijing China Beijing Institute of Big Data Research and Peking University Beijing China Princeton University Princeton NJ and Beijing Institute of Big Data Research and Peking University Beijing China

关键词： deep learning method of successive approximations optimal control pontryagin's maximum principle

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Towards a metadata-driven multi-community research data management service 8

Towards a metadata-driven multi-community research data mana...

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8th International Workshop on Science Gateways, IWSG 2016

作者： Grunzke, Richard Nagel, Wolfgang E. Hartmann, Volker Jejkal, Thomas Prabhune, Ajinkya Stotzka, Rainer Hoffmann, Alexander Herres-Pawlis, Sonja Deicke, Aline Schrade, Torsten Herold, Hendrik Meinel, Gotthard Center for Information Services and High Performance Computing Technische Universität Dresden Dresden Germany Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany Institut für Anorganische Chemie Rheinisch-Westfälische Technische Hochschule Aachen Aachen Germany Digitale Akademie Akademie der Wissenschaften und Literatur Mainz Mainz Germany Monitoring of Settlement and Open Space Development Institute of Ecological and Regional Development Dresden Germany

Nowadays, the daily work of many research communities is characterized by an increasing amount and complexity of data. This makes it increasingly difficult to manage, access and utilize to ultimately gain scientific insights based on it. At the same time, domain scientists want to focus on their science instead of IT. The solution is research data management in order to store data in a structured way to enable easy discovery for future reference. An integral part is the use of metadata. With it, data becomes accessible by its content instead of only its name and location. The use of metadata shall be as automatic and seamless as possible in order to foster a high usability. Here we present the architecture and initial steps of the MASi project with its aim to build a comprehensive research data management service. First, it extends the existing KIT data Manager framework by a generic programming interface and by a generic graphical web interface. Advanced additional features includes the integration of provenance metadata and persistent identifiers. The MASi service aims at being easily adaptable for arbitrary communities with limited effort. The requirements for the initial use cases within geography, chemistry and digital humanities are elucidated. The MASi research data management service is currently being built up to satisfy these complex and varying requirements in an efficient way.

关键词： Metadata

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big data Research in Italy： A Perspective

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Engineering 2016年第2期2卷 163-170页

作者： Sonia Bergamaschi Emanuele Carlini Michelangelo Ceci Barbara Furletti Fosca Giannotti Donato Malerba Mario Mezzanzanica Anna Monreale Gabriella Pasi Dino Pedreschi Raffele Perego Salvatore Ruggieri Department of Engneering "Enzo Ferrari " University of Modena and Reggio Emilia Modena 41125 Italy High Performance Computing Laboratory Institute of Information Science and Technologies of the Italian National Research Council (ISTI-CNR) Pisa 56124 Italy Department of Computer Science University ofBari Aldo Moro Bari 70125 Italy Knowledge Discovery and Data Mining Laboratory ISTI-CNR Pisa 56127 Italy Big Data Laboratory National Interuniversity Consortium for Informatics Rome 00185 Italy Department of Statistics and Quantitative Methods University of Milano-Bicocca Milan 20126 Italy Department of Computer Science Systems and Communications University of Milano-Bicocca Milan 20126 Italy Department of Computer 5denee University of Pisa Pisa 56127 Italy

The aim of this article is to synthetically describe the research projects that a selection of Italian univer- sities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.

关键词： big data Smart cities EnergyJob offersPrivacy

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Active Learning for Name Entity Recognition with External Knowledge

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ACM Transactions on Asian and Low-Resource Language Information processing 1000年

作者： Ying Ma Yu Zhang Arun Kumar Sangaiah Ming Yan Guoqi Li Tian Wang Harbin Institute of Technology Xiamen University of Technology China Xiamen University of Technology China National Yunlin University of Science and Technology China Institute of High Performance Computing Agency for Science Technology and Research Singapore Institute of Automation China Academy of Sciences China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College (UIC) China

Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language models, some research works formulate the NER task into the machine reading comprehension form (MRC-form) to enhance their model generalization capability of commonsense knowledge. However, this transformation still faces the data-hungry issue with limited training data for the specific NER tasks. To address the low-resource issue in NER, we introduce a method named active multi-task-based NER (AMT-NER), which is a two-stage multi-task active learning training model. Specifically, A multi-task learning module is first introduced into AMT-NER to improve its representation capability in low-resource NER tasks. Then, a two-stage training strategy is proposed to optimize AMT-NER multi-task learning. An associated task of Natural Language Inference (NLI) is also employed to enhance its commonsense knowledge further. More importantly, AMT-NER introduces an active learning module, uncertainty selective, to actively filter training data to help the NER model learn efficiently. Besides, we also find different external supportive data under different pipelines improves model performance differently in the NER tasks. Extensive experiments are performed to show the superiority of our method, which also proves our findings that the introduction of external knowledge is significant and effective in the MRC-form NER tasks.

关键词： Named Entity Recognition Machine Reading Comprehension Multi-task Learning Active Learning

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