AI Institute: Planning: Physics of the Future

人工智能研究所：规划：未来的物理学

• 批准号: 2020295
• 负责人: Scott Dodelson
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020295&HistoricalAwards=false
• 关键词: AI; Institute; Planning; Physics; Future

Physics applications of Artificial Intelligence (AI) have led to some of the most exciting recent breakthroughs, from astrophysics to regulatory genomics and cellular imaging. Scientists at Carnegie-Mellon University (CMU) in Machine Learning, Statistics, and other departments actively collaborate with colleagues in the Department of Physics because of the opportunity for each field to spur development in the other. This award will allow planning of a joint Physics/AI Institute that will bring cutting edge methods from AI into a broad range of physics areas, propagate successful methods from one field of Physics to another, and facilitate back-transfer from the data-rich sub-fields of physics to AI development. This planning phase focuses on areas where CMU scientists are already leaders, with existing strong collaborations, and where rapid advances are being made: astrophysics, subatomic physics, and biophysics. AI has obvious benefits to society in general, so this project includes education, public outreach and promotion of diversity, empowering a wide range of audiences to use AI on a broad array of data.Applying AI will lead to significant advances in the areas of dark energy and galaxy formation; new ways of extracting information about the Higgs bosons and anomalies in gluon physics; and enhanced understanding of biological networks and predictions for cancerous tissues. Benefits in the other direction are clear as well: physics provides complex use cases and profound problems that motivate AI researchers to advance foundational AI. Planning activities include weekly interdisciplinary, interactive seminars; a visitor program; topical conferences; planning conferences; a graduate student summer program; postdoc mentoring; and an extensive outreach program. Help in planning to scale up to the Institute level will come from partners at universities, national laboratories, and corporations, and by employing a consultant to survey and quantitatively assess the success of different programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）的物理应用已经导致了一些最令人兴奋的最新突破，从天体物理学到调控基因组学和细胞成像。 卡内基梅隆大学（CMU）机器学习，统计学和其他部门的科学家积极与物理系的同事合作，因为每个领域都有机会刺激其他领域的发展。 该奖项将允许规划一个联合物理/人工智能研究所，将人工智能的尖端方法引入广泛的物理领域，将成功的方法从一个物理领域传播到另一个领域，并促进从数据丰富的物理子领域到人工智能开发的反向转移。 这个规划阶段的重点是CMU科学家已经是领导者的领域，现有的强大合作，以及正在取得快速进展的领域：天体物理学，亚原子物理学和生物物理学。 人工智能对整个社会都有明显的好处，因此该项目包括教育、公众宣传和促进多样性，使广大受众能够在广泛的数据上使用人工智能。应用人工智能将导致暗能量和星系形成领域的重大进步;提取希格斯玻色子和胶子物理异常信息的新方法;并增强了对生物网络的理解和对癌组织的预测。 另一个方向的好处也很明显：物理学提供了复杂的用例和深刻的问题，激励人工智能研究人员推进基础人工智能。 规划活动包括每周跨学科，互动研讨会;访客计划;专题会议;规划会议;研究生暑期课程;博士后指导;和广泛的推广计划。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mapping variations of redshift distributions with probability integral transforms

• DOI: 10.1093/mnras/stac3585
• 发表时间: 2022-10
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: J. Myles;D. Gruen;A. Amon;A. Alarcon;J. DeRose;S. Everett;S. Dodelson;G. Bernstein;A. Campos;I. Harrison;N. MacCrann;J. McCullough;M. Raveri;C. S'anchez;M. Troxel;Biao Yin;T. Abbott;S. Allam;O. Alves;F. Andrade-Oliveira;E. Bertin;D. Brooks;D. Burke;A. Rosell;M. Kind;J. Carretero;R. Cawthon;M. Costanzi;L. Costa;M. Pereira;S. Desai;P. Doel;I. Ferrero;B. Flaugher;J. Frieman;J. Garc'ia-Bellido;M. Gatti;D. Gerdes;R. Gruendl;J. Gschwend;G. Gutiérrez;W. Hartley;S. Hinton;D. Hollowood;K. Honscheid;D. James;K. Kuehn;O. Lahav;Peter Melchior;J. Mena-Fern'andez;F. Menanteau;R. Miquel;J. Mohr;A. Palmese;F. Paz-Chinch'on;A. Pieres;A. A. P. Malag'on-A.;J. Prat;M. Rodríguez-Monroy;E. Sanchez;V. Scarpine;I. Sevilla-Noarbe;M. Smith;E. Suchyta;M. Swanson;G. Tarlé;D. Tucker;M. Vincenzi;N. Weaverdyck

Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing

• DOI: 10.1103/physrevd.105.023520
• 发表时间: 2022-01-13
• 期刊: PHYSICAL REVIEW D
• 影响因子: 5
• 作者: Abbott, T. M. C.;Aguena, M.;Zuntz, J.
• 通讯作者: Zuntz, J.

Dark Energy Survey Year 3 results: Cosmology with moments of weak lensing mass maps

• DOI: 10.1103/physrevd.106.083509
• 发表时间: 2021-10
• 期刊: Physical Review D
• 影响因子: 5
• 作者: M.Gatti;B. Jain;C. Chang;M. Raveri;D. Zurcher;L. Secco;L. Whiteway;N. Jeffrey;C. Doux

Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations

暗能量调查第 3 年结果：使用离散实现排名对红移分布不确定性进行边缘化

• DOI: 10.1093/mnras/stac147
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Cordero, Juan P;Harrison, Ian;Rollins, Richard P;Bernstein, G M;Bridle, S L;Alarcon, A;Alves, O;Amon, A;Andrade-Oliveira, F;Camacho, H
• 通讯作者: Camacho, H

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . I. Construction of CMB lensing maps and modeling choices

• DOI: 10.1103/physrevd.107.023529
• 发表时间: 2022-03
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Y. Omori;E. Baxter;C. Chang;O. Friedrich;A. Alarcon;O. Alves;A. Amon;F. Andrade-Oliveira;K. Bechtol;M. Becker;G. Bernstein;J. Blazek;L. Bleem;H. Camacho;A. Campos;A. Rosell;M. Kind;R. Cawthon;R. Chen;A. Choi;J. Cordero;T. Crawford;M. Crocce;C. Davis;J. DeRose;S. Dodelson;C. Doux;A. Drlica-Wagner;K. Eckert;T. Eifler;F. Elsner;J. Elvin-Poole;S. Everett;X. Fang;A. Fert'e;P. Fosalba;M. Gatti;G. Giannini;D. Gruen;R. Gruendl;I. Harrison;K. Herner;H. Huang;E. Huff;D. Huterer;M. Jarvis;E. Krause;N. Kuropatkin;P. Léget;P. Lemos;A. Liddle;N. MacCrann;J. McCullough;J. Muir;J. Myles;A. Navarro-Alsina;S. Pandey;Y. Park;A. Porredon;J. Prat;M. Raveri;R. Rollins;A. Roodman;R. Rosenfeld;A. Ross;E. Rykoff;C. S'anchez;J. Sánchez;L. Secco;I. Sevilla-Noarbe;E. Sheldon;T. Shin;M. Troxel;I. Tutusaus;T. Varga;N. Weaverdyck;Risa Wechsler;W. L. K. Wu;B. Yanny;Biao Yin;Y. Zhang;J. Zuntz;T. Abbott;M. Aguena;S. Allam;J. Annis;D. Bacon;B. Benson;E. Bertin;S. Bocquet;D. Brooks;D. Burke;J. Carlstrom;J. Carretero;C. Chang;R. Chown;M. Costanzi;L. Costa;A. Crites;M. Pereira;T. Haan;J. Vicente;S. Desai;H. Diehl;M. Dobbs;P. Doel;W. Everett;I. Ferrero;B. Flaugher;D. Friedel;J. Frieman;J. Garc'ia-Bellido;E. Gaztañaga;E. George;T. Giannantonio;N. Halverson;S. Hinton;G. Holder;D. Hollowood;W. Holzapfel;K. Honscheid;J. Hrubeš;D. James;L. Knox;K. Kuehn;O. Lahav;A. Lee;M. Lima;D. Luong-Van;M. March;J. McMahon;Peter Melchior;F. Menanteau;S. Meyer;R. Miquel;L. Mocanu;J. Mohr;R. Morgan;T. Natoli;S. Padin;A. Palmese;F. Paz-Chinch'on;A. Pieres;A. P. Malag'on;C. Pryke;C. Reichardt;A. Romer;J. Ruhl;E. Sanchez;K. Schaffer;M. Schubnell;S. Serrano;E. Shirokoff;M. Smith;Z. Staniszewski;A. Stark;E. Suchyta;G. Tarlé;D. Thomas;C. To;J. Vieira;J. Weller;R. Williamson