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Deep learning for computational chemistry

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arXiv 2017年

作者： Goh, Garrett B. Hodas, Nathan O. Vishnu, Abhinav High Performance Computing Group Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Data Science and Analytics Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational chemistry

Chemception: A deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models

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arXiv 2017年

作者： Goh, Garrett B. Siegel, Charles Vishnu, Abhinav Hodas, Nathan O. Baker, Nathan High Performance Computing Group Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Data Science and Analytics Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States

In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

HPDedup: A hybrid prioritized data deduplication mechanism for primary storage in the cloud

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arXiv 2017年

作者： Wu, Huijun Wang, Chen Fu, Yinjin Sakr, Sherif Zhu, Liming Lu, Kai Data61 CSIRO University of New South Wales Australia PLA University of Science and Technology China Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server & Storage Technology College of Computer National University of Defense Technology Changsha China

—Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Most existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written into disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to weak temporal locality from disks. The hybrid deduplication mechanism significantly reduces the amount of redundant data written to the storage system while maintaining inline data writing performance. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline

关键词： Efficiency

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

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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

Euclid preparation. Simulations and nonlinearities beyond ΛCDM. 2. Results from non-standard simulations

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arXiv 2024年

作者： Rácz, G. Breton, M.-A. Fiorini, B. Le Brun, A.M.C. Winther, H.-A. Sakr, Z. Pizzuti, L. Ragagnin, A. Gayoux, T. Altamura, E. Carella, E. Pardede, K. Verza, G. Koyama, K. Baldi, M. Pourtsidou, A. Vernizzi, F. Adame, A.G. Adamek, J. Avila, S. Carbone, C. Despali, G. Giocoli, C. Hernández-Aguayo, C. Hassani, F. Kunz, M. Li, B. Rasera, Y. Yepes, G. Gonzalez-Perez, V. Corasaniti, P.-S. García-Bellido, J. Hamaus, N. Kiessling, A. Marinucci, M. Moretti, C. Mota, D.F. Piga, L. Pisani, A. Szapudi, I. Tallada-Crespí, P. Aghanim, N. Andreon, S. Baccigalupi, C. Bardelli, S. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Cardone, V.F. Carretero, J. Casas, S. Castellano, M. Castignani, G. 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. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Gillis, B. Gómez-Alvarez, P. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Ilić, S. Jahnke, K. Jhabvala, M. Joachimi, B. Keihänen, E. Kermiche, S. Kilbinger, M. Kitching, T. Kubik, B. Kurki-Suonio, H. Lilje, P.B. Lindholm, V. Lloro, I. Mainetti, G. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Meylan, G. Moresco, M. Moscardini, L. 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. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Salvignol, J.-C. Sánchez, A.G. Sapone, D. Sartoris, B. Schirmer, M. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics University of Helsinki P.O. Box 64 00014 Finland Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés Barcelona08193 Spain Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190 France Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom School of Physics and Astronomy Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern Oslo0315 Norway 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 Dipartimento di Fisica "G. Occhialini" Università degli Studi di Milano Bicocca Piazza della Scienza 3 Milano20126 Italy 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 Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16

The Euclid mission will measure cosmological parameters with unprecedented precision. To distinguish between cosmological models, it is essential to generate realistic mock observables from cosmological simulations that were run in both the standard Λ-cold-dark-matter (ΛCDM) paradigm and in many non-standard models beyond ΛCDM. We present the scientific results from a suite of cosmological N-body simulations using non-standard models including dynamical dark energy, k-essence, interacting dark energy, modified gravity, massive neutrinos, and primordial non-Gaussianities. We investigate how these models affect the large-scale-structure formation and evolution in addition to providing synthetic observables that can be used to test and constrain these models with Euclid data. We developed a custom pipeline based on the Rockstar halo finder and the nbodykit large-scale structure toolkit to analyse the particle output of non-standard simulations and generate mock observables such as halo and void catalogues, mass density fields, and power spectra in a consistent way. We compare these observables with those from the standard ΛCDM model and quantify the deviations. We find that non-standard cosmological models can leave significant imprints on the synthetic observables that we have generated. Our results demonstrate that non-standard cosmological N-body simulations provide valuable insights into the physics of dark energy and dark matter, which is essential to maximising the scientific return of Euclid. © 2024, CC BY.

关键词： Dark energy

Euclid preparation L. Calibration of the halo linear bias in Λ(ν)CDM cosmologies

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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

Euclid preparation. 3-dimensional galaxy clustering in configuration space Part I: 2-point correlation function estimation

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arXiv 2025年

作者： de la Torre, S. Marulli, F. Keihänen, E. Viitanen, A. Viel, M. Veropalumbo, A. Branchini, E. Tavagnacco, D. Rizzo, F. Valiviita, J. Lindholm, V. Allevato, V. Parimbelli, G. Sarpa, E. Z., Ghaffari A., Amara S., Andreon N., Auricchio C., Baccigalupi M., Baldi S., Bardelli A., Basset D., Bonino M., Brescia J., Brinchmann A., Caillat S., Camera V., Capobianco C., Carbone J., Carretero S., Casas F.J., Castander M., Castellano G., Castignani S., Cavuoti A., Cimatti C., Colodro-Conde G., Congedo C.J., Conselice L., Conversi Y., Copin F., Courbin H.M., Courtois M., Crocce A., Da Silva H., Degaudenzi G., De Lucia A.M., Di Giorgio J., Dinis F., Dubath C.A.J., Duncan X., Dupac S., Dusini M., Farina S., Farrens F., Faustini S., Ferriol N., Fourmanoit M., Frailis E., Franceschi P., Franzetti M., Fumana S., Galeotta K., George W., Gillard B., Gillis C., Giocoli P., Gómez-Alvarez B.R., Granett A., Grazian F., Grupp L., Guzzo S.V.H., Haugan W., Holmes F., Hormuth A., Hornstrup S., Ilić K., Jahnke M., Jhabvala B., Joachimi S., Kermiche A., Kiessling M., Kilbinger B., Kubik M., Kunz H., Kurki-Suonio S., Ligori P.B., Lilje I., Lloro G., Mainetti D., Maino E., Maiorano O., Mansutti O., Marggraf K., Markovic M., Martinelli N., Martinet R., Massey S., Maurogordato E., Medinaceli S., Mei M., MELMhior Y., Mellier M., Meneghetti E., Merlin G., Meylan M., Moresco B., Morin L., Moscardini E., Munari C., Neissner S.-M., Niemi C., Padilla S., Paltani F., Pasian K., Pedersen W.J., Percival V., Pettorino S., Pires G., Polenta M., Poncet L., Pozzetti F., Raison A., Renzi J., Rhodes G., Riccio E., Romelli M., Roncarelli E., Rossetti R., Saglia Z., Sakr A.G., Sánchez D., Sapone B., Sartoris P., Schneider T., Schrabback M., Scodeggio A., Secroun E., Sefusatti G., Seidel M., Seiffert S., Serrano C., Sirignano G., Sirri L., Stanco J., Steinwagner C., Surace P., Tallada-Crespí A.N., Taylor I., Tereno R., Toledo-Moreo Aix-Marseille Université CNRS CNES LAM Marseille France Dipartimento di Fisica e Astronomia Augusto Righi Alma Mater Studiorum Università di Bologna via Piero 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 Department of Physics Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 00014 Finland INAF-Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio Catone00078 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università di Genova Via Dodecaneso 33 Genova16146 Italy Department of Physics University of Helsinki P.O. Box 64 00014 Finland Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 Helsinki Finland INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain School of Mathematics and Physics University of Surrey Guild-ford SurreyGU2 7XH United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy Centre National d’Etudes Spatiales Centre spatial de Toulouse 18 avenue Edouard Belin Toulouse31401Cedex 9 France Pino

The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20–30 million Hα-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. These methods are crucial for handling the massive volume of data efficiently. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. Those have been performed by using various mock galaxy catalogues to ensure that it meets the stringent accuracy requirement of the Euclid mission. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission’s t

关键词： Dark energy

Euclid preparation. XXXIX. The effect of baryons on the Halo Mass Function

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arXiv 2023年

作者： Castro, T. Borgani, S. Costanzi, M. Dakin, J. Dolag, K. Fumagalli, A. Ragagnin, A. Saro, A. Le Brun, A.M.C. Aghanim, N. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Cropper, M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Dubath, F. Duncan, C.A.J. Dupac, X. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Gillis, B. Giocoli, C. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Jahnke, K. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. 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. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. 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. Zacchei, A. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Bozzo, E. Cerna, C. Colodro-Conde, C. Di Ferdinando, D. Mauri, N. Neissner, C. Sakr, Z. Scottez, V. Tenti, M. Viel, M. Wiesmann, M. Akrami, Y. Anselmi, S. Baccigalupi, C. Ballardini, M. Borlaff, A.S. Bruton, S. Burigana, C. Cabanac, R. Cappi, A. Carvalho, C.S. 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 Dipartimento di Fisica - Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190 France Université Paris-Saclay CNRS Institut d'astrophysique spatiale Orsay91405 France Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 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 Via Cinthia 6 Napoli80126 Italy Instit

The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are calibrated using non-radiative cosmological hydrodynamical simulations, and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable 1% relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model arising from baryonic corrections to cluster masses are subdominant when compared to the precision with which mass-observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

Active learning of uniformly accurate inter-atomic potentials for materials simulation

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arXiv 2018年

作者： Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy