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arXiv 2024年

作者： Reshniak, Viktor Wang, Xiaoping Zhang, Guannan Liu, Siyan Yin, Junqi Data Analysis and Machine Learning Group Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Single Crystal Diffraction Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Computational Earth Sciences Group Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States Analytics and AI Methods at Scale Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37830 United States

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected separately and saved as a descriptive entry in a database enabling unprecedented accuracy of the collected experimental data. Nevertheless, the common data processing pipeline still involves the binning of data to perform analysis and feature extraction. For weak reflections, improper binning leads to sparse histograms with low signal-to-noise ratios, rendering them uninformative. In this study, we propose the Bayesian approach for the identification of Bragg peaks in TOF diffraction data. The method is capable of adaptively handling the varying sampling rates found in different regions of the reciprocal space. Unlike histogram fitting methods, our approach focuses on estimating the true neutron flux function. We accomplish this by employing a profile fitting algorithm based on the event-level likelihood, along with a multiresolution histogram-level prior. By using this approach, we ensure that there is no loss of information due to data reduction in strong reflections and that the search space is appropriately restricted for weak reflections. To demonstrate the effectiveness of our proposed model, we apply it to real experimental data collected at the TOPAZ single crystal diffractometer at SNS. Copyright © 2024, The Authors. All rights reserved.

关键词： Diffraction

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Feature extraction for hyperspectral imagery: The evolution from shallow to deep

arXiv

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arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

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Neural networks and their application in forecasting problems

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk F F Pashchenko Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

The report describes popular machine learning methods and applications of neural networks. It reveals methods of training neural networks and offers a method of forecasting based on neural networks for modelling financial time series. Neural networks have recently gained in popularity among scholars. In particular, neural networks are widely used in the field of visualization and image recognition. But the practical significance of neural networks does not end there, they also find usage in such areas as forecasting, classification, clustering and modelling. This success can mainly be attributed to the remarkable property of models based on neural networks – they can «see» non-linear connections in contrast to many models, which for the most part have linear connections only. Currently, the use of neural networks is developing in the following directions: Stock market and macroeconomic forecasting (Neuro XL, OptimuStock, StocksNeural); Speech recognition and man-machine dialogue (Siri, Alexa, Cortana, Alice); Imitation of intellectual activity (weak AI in Siri, Alexa, Cortana, Alice); Improving low-quality and noisy information (DeepImagePrior). The advantages of using neural networks include: versatility. simplicity. Neural networks are able to model dependencies also in cases when there is a large number of variables.

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Forecasting the dynamics of financial time series based on neural networks

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk N M Abdikeev A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

Forecasting is one of the high-demand data mining problems, but also a very difficult one. The difficulties of forecasting are associated with insufficient quality and quantity of input data, the changes in the environment where the process takes place, and the impact of subjective factors. A forecast always implies some margin of error, which depends on the forecast model used and the completeness of the input data. Methods based on neural networks are the most relevant and highly-demanded techniques today. Neural networks are great for finding accurate solutions in an environment characterized by complex or fragmented information. In the field of finance and economics, the values of time series parameters can be more accurately modelled using neural analysis methods. Artificial neural networks have more common and flexible functional forms than statistical methods. They can generalize information and provide a qualitative forecast under conditions of uncertainty and crisis. The article proposes a forecasting model based on a neural network that can predict the price of a financial asset in a well-defined time interval. Ten technical indicators are used as input signals, and the closing price of the next period is used as an output signal.

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Ensemble forecasting method

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

The purpose of this article is to analyze the time series based on aggregate forecasting methods. Forecasting time series comprises an important scientific and technical task which is relevant in various sectors of economy and production. The main objective of any forecasting process is to get the ability to assess trends in changes to a particular factor. To predict the behaviour of future processes, a qualitative analysis of data is required, which provides the basis for the forecast. It is also important to choose the appropriate forecasting method. The forecast obtained may not be always correct, so it is crucial to determine how accurately it is built. In the present paper, characteristics of time series are provided. The main mathematical methods of time series forecasting are analyzed and their classification is presented. Criteria for the accuracy and reliability of forecasting models are defined. The accuracy of the forecast is an important criterion for evaluating the forecasting method. The aggregate ensemble forecast has proven to be an effective method for enhancing the accuracy of forecasting. The article provides an example of constructing an aggregate ensemble forecast. The types of nonlinear models are described and several methods are selected for constructing the aggregate ensemble forecast. The practical application of exponential smoothing is considered, the MA (moving average) model and the AR (autoregressive) model are constructed.

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The re-markable 21-cm power spectrum I: Probing the Hi distribution in the post-reionization era using marked statistics

arXiv

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arXiv 2024年

作者： Kamran, Mohd Sahlén, Martin Sarkar, Debanjan Majumdar, Suman Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Department of Physics Trottier Space Institute McGill University QCH3A 2T8 Canada Ciela-Montreal Institute for Astrophysical Data Analysis and Machine Learning QCH2V 0B3 Canada Department of Physics Ben-Gurion University of the Negev Be'er Sheva84105 Israel Department of Astronomy Astrophysics & Space Engineering Indian Institute of Technology Indore Indore453552 India Department of Physics Blackett Laboratory Imperial College LondonSW7 2AZ United Kingdom

The neutral hydrogen (Hi) power spectrum, measured from intensity fluctuations in the 21-cm background, offers insights into the large-scale structures (LSS) of our Universe in the post-reionization era (redshift z −1) we expectedly find that the Hi evolves slowly at early times, but more rapidly at late times. This evolution is not well-captured by the power spectrum of the standard (unmarked) Hi field. We also study how the evolution of the Hi field depends on the chosen smoothing scale for the mark, and how this affects the marked power spectrum. We conclude that the information about the Hi content at low and intermediate densities is important for a correct and consistent analysis of Hi content and evolution based on the 21-cm background. The marked power spectrum can thus provide a less biased statistic for parameter constraints than the normal power spectrum. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

arXiv

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arXiv 2023年

作者： Stone, Connor J. Courteau, Stéphane Cuillandre, Jean-Charles Hezaveh, Yashar Perreault-Levasseur, Laurence Arora, Nikhil Department of Physics Université de Montréal MontréalQC Canada Mila - Québec Artificial Intelligence Institute MontréalQC Canada Ciela - Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Engineering Physics & Astronomy Queen’s University KingstonON Canada AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-YvetteF-91191 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates New York University Abu Dhabi United Arab Emirates

We present ASTROPHOT, a fast, powerful, and user-friendly Python based astronomical image photometry solver. ASTROPHOT incorporates automatic differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PYTORCH. Everything: ASTROPHOT can fit models for sky, stars, galaxies, PSFs, and more in a principled X2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: ASTROPHOT can optimize forward models on CPU or GPU;across images that are large, multiband, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg-Marquardt, Gradient descent, and No-U-Turn MCMC sampling. With an object-oriented user interface, ASTROPHOT makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the ASTROPHOT code and compares it to other popular astronomical image modeling software. ASTROPHOT is open-source, fully Python based, and freely accessible here: https://***/Autostronomy/AstroPhot © 2023, CC BY.

关键词： Python

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A machine learning framework to generate star cluster realisations

arXiv

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arXiv 2024年

作者： Prodan, George P. Pasquato, Mario Iorio, Giuliano Ballone, Alessandro Torniamenti, Stefano Niccolò Di Carlo, Ugo Mapelli, Michela Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 3 Padova35122 Italy IASF Milano via Alfonso Corti 12 Milano Italy Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada INFN–Padova Via Marzolo 8 Padova35131 Italy INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 Padova Italy Institut für Theoretische Astrophysik ZAH Universität Heidelberg Albert-Ueberle-Str. 2 Heidelberg69120 Germany SISSA - Scuola Internazionale Superiore di Studi Avanzati via Bonomea 365 TriesteI-34136 Italy Faculty of Sciences University of Craiova A.I. Cuza 13 Craiova200585 Romania

Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star formation, which ultimately determine the features of the stars, including positions, velocities, and masses. To mitigate this issue, we introduce a new method for sampling physically realistic initial conditions from a limited set of simulations using Gaussian processes. Results. We evaluated the resulting sets of initial conditions based on whether they meet tests for physical realism. We find that direct sampling based on the learned distribution of the star features fails to reproduce binary systems. Consequently, we show that physics-informed sampling algorithms solve this issue, as they are capable of generating realisations closer to reality. © 2024, CC BY.

关键词： Stars

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Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

arXiv

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arXiv 2023年

作者： Legin, Ronan Ho, Matthew Lemos, Pablo Perreault-Levasseur, Laurence Ho, Shirley Hezaveh, Yashar Wandelt, Benjamin Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Sorbonne Université CNRS UMR 7095 Institut d’Astrophysique de Paris 98 bis bd Arago Paris75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Sorbonne Université Institut Lagrange de Paris 98 bis boulevard Arago Paris75014 France

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods. © 2023, CC BY.

关键词： Sampling

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The Cosmic Microwave Background and H0

arXiv

引用

arXiv 2023年

作者： Lemos, Pablo Shah, Paul Mila - Quebec Artificial Intelligence Institute Montréal 6666 Rue Saint-Urbain QCH2S 3H1 Canada Department of Physics Université de Montréal Montréal 1375 Avenue Thérèse-Lavoie-Roux QCH2V 0B3 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

The cosmic microwave background (CMB) offers a unique window into the earlyuniverse, providing insights into cosmological parameters like the Hubbleconstant. Recent precise measurements of the CMB by experiments like Planckseem to point to a lower value for the Hubble constant compared to some othermeasurements like those from Type Ia supernovae. This discrepancy, known as theHubble tension, currently lacks a definitive explanation. In this chapter, weprovide an overview of how the Hubble constant is determined from detailedmeasurements of the CMB power spectrum. We explain the physics underlying keyfeatures of the CMB spectrum and their connection to cosmological *** then examine the consistency of Planck\'s Hubble constant determination, bothinternally within the data itself and externally with other astrophysicalprobes. While largely consistent, some anomalies like the lensing amplitudeparameter $A_L$ remain unresolved. We also explore various theoreticalextensions to the standard ${\\Lambda}$CDM cosmological model and assess theirpotential to resolve the Hubble tension. No clear resolution emerges,indicating significant tensions remain between early and late universe probeswithin simple extensions to ${\\Lambda}$CDM. Upcoming CMB experiments promiseimproved precision and should provide further insights into this cosmicconundrum. A coherent picture bridging measurements across cosmic time remainsan open challenge at the forefront of modern cosmology. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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