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arXiv 2024年

作者： Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to "clean" non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations;alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade. © 2024, CC BY.

关键词： Background noise

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Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

arXiv

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arXiv 2023年

作者： Payot, Nicolas Pasquato, Mario Trani, Alessandro Alberto Hezaveh, Yashar Perreault-Levasseur, Laurence Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Niels Bohr Institute Copenhagen Denmark Research Center for the Early Universe School of Science The University of Tokyo Tokyo Japan Okinawa Institute of Science and Technology Okinawa Japan

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem’s decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology. © 2023, CC BY.

关键词： Fractals

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Autoencoder-based Ultrasonic NDT of Adhesive Bonds

Autoencoder-based Ultrasonic NDT of Adhesive Bonds

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IEEE SENSORS

作者： Ivan Kraljevski Frank Duckhorn Martin Barth Constanze Tschoepe Frank Schubert Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Ultrasonic Sensors and Methods Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with *** models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added *** resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.

关键词： Visualization Adhesives machine learning Probability Acoustics Sensors Reliability

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learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

arXiv

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arXiv 2023年

作者： Payot, Nicolas Lemos, Pablo Perreault-Levasseur, Laurence Cuesta-Lazaro, Carolina Modi, Chirag Hezaveh, Yashar Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Center for Computational Astrophysics Flatiron Institute NY United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA United States Center for Astrophysics Harvard & Smithsonian CambridgeMA United States Center for Computational Mathematics Flatiron Institute NY United States

Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities. © 2023, CC BY.

关键词： Cosmology

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Overview of Modern Forecasting Methods and Models

Overview of Modern Forecasting Methods and Models

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Vera Ivanyuk Anatoliy Tsvirkun Anna Sunchalina Tatiana Goroshnikova Andrey Sunchalin Galina Zholobova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of the Development of Large-Scale Systems V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Faculty of International Economic Relations Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods.

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Neural networks and their application in forecasting problems 23

Neural networks and their application in forecasting problem...

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23rd International Conference on Soft Computing and Measurement, SCM 2020

作者： Ivanyuk, V.A. Pashchenko, F.F. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 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. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Forecasting

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Ensemble forecasting method 23

Ensemble forecasting method

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23rd International Conference on Soft Computing and Measurement, SCM 2020

作者： Ivanyuk, V.A. Tsvirkun, A.D. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 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. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Aggregates

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Forecasting the dynamics of financial time series based on neural networks 23

Forecasting the dynamics of financial time series based on n...

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23rd International Conference on Soft Computing and Measurement, SCM 2020

作者： Ivanyuk, V.A. Abdikeev, N.M. Tsvirkun, A.D. Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 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. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Neural networks

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The Intrinsic Flattening of Extragalactic Stellar Disks

arXiv

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arXiv 2024年

作者： Favaro, Jeremy Courteau, Stephane Comerón, Sébastien Stone, Connor Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada Departamento de Astrofísica Universidad de La Laguna Tenerife La Laguna38200 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna38205 Spain 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

Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, c/a, where c and a are respectively the vertical and long radial axes of the disk measured at suitable stellar densities. The ratio c/a is a necessary quantity in the assessment of galaxy inclinations, three-dimensional structural reconstructions, total masses, as well as a constraint to galaxy formation models. 3.6 µm maps of 133 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G) and its early-type galaxy extension are used to revisit the assessment of c/a free from dust extinction and away from the influence of a stellar bulge. We present a simple definition of c/a and explore trends with other galactic physical parameters: total stellar mass, concentration index, total H i mass, mass of the central mass concentration, circular velocity, model-dependent scales, as well as Hubble type. Other than a dependence on early/late Hubble types, and a related trend with light concentration, no other parameters were found to correlate with the intrinsic flattening of spiral galaxies. The latter is mostly constant with 〈c/a〉 = 0.124 ±0.001 (stat) ±0.033 (intrinsic/systematic) and greater for earlier types. © 2024, CC BY.

关键词： Galaxies

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Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data

arXiv

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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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