PyWavelets/pywt: PyWavelets 1.1.1

Lee, Gregory R.; Gommers, Ralf; Wohlfahrt, Kai J.; Wasilewski, Filip; O’Leary, Aaron; Nahrstaedt, Holger; Hurtado, David Ménendez; Sauvé, A.; Arildsen, Thomas; Oliveira, Helder Cesar Rodigues de; Pelt, Daniël M.; Agrawal, Ankit; SylvainLan,; Pelletier, M.; Brett, Matthew; Yu, Frank H.; Choudhary, Saket; Tricoli, Daniele; Craig, Luke M; Ravindranathan, Lokesh; Dan, Jonathan; jakirkham,; Antonello, Jacopo; Laszuk, Dawid; Goertzen, Daniel; Goldberg, Corey; Reczey, Balint; Smith, A W (Sam); asnt,

doi:10.5281/zenodo.3510098

Cited by 7 publications

(2 citation statements)

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“…Here, ψ(t) is the so-called mother wavelet function, which is translated by parameter t and scaled by the scale parameter s. The scale s can be regarded as a period or inverse frequency. We choose a mother wavelet function of the real Morlet form [42] ψ(t) = e − t 2 2 cos(5t).…”

Section: A Training Data Generationmentioning

confidence: 99%

Learning crystal field parameters using convolutional neural networks

Berthusen,

Sizyuk,

Scheurer

et al. 2020

Preprint

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We present a deep machine learning algorithm to extract crystal field (CF) Stevens parameters from thermodynamic data of rare-earth magnetic materials. The algorithm employs a twodimensional convolutional neural network (CNN) that is trained on magnetization, magnetic susceptibility and specific heat data that is calculated theoretically within the single-ion approximation and further processed using a standard wavelet transformation. We apply the method to crystal fields of cubic, hexagonal and tetragonal symmetry and for both integer and half-integer total angular momentum values J of the ground state multiplet. We evaluate its performance on both theoretically generated synthetic and previously published experimental data on PrAgSb2 and PrMg2Cu9, and find that it can reliably and accurately extract the CF parameters for all site symmetries and values of J considered. This demonstrates that CNNs provide an unbiased approach to extracting CF parameters that avoids tedious multi-parameter fitting procedures.

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Section: A Training Data Generationmentioning

confidence: 99%

Learning crystal field parameters using convolutional neural networks

Berthusen,

Sizyuk,

Scheurer

et al. 2020

Preprint

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show abstract

“…We also thank Philippe Gris for the updated requirements for well-measured Type Ia SNe. (Lee et al 2019a(Lee et al , 2019b, scikit-learn (Pedregosa et al 2011), SciPy (Virtanen et al 2020), seaborn (Waskom et al 2020), snmachine (Lochner et al 2016;Alves et al 2022).…”

mentioning

confidence: 99%

Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification

Alves

Peiris

Lochner

et al. 2023

ApJS

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The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST.

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Quantifying the role of Darrieus–Landau instability in turbulent premixed flame speed determination at various burner sizes

Wang

Cheng

et al. 2021

Physics of Fluids

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To probe the impact of Darrieus–Landau (DL) instability on turbulent premixed flame propagation at various burner sizes, methane–air premixed flames from five Bunsen-type burners with different nozzle diameters (4 mm, 6 mm, 8 mm, 10 mm, and 12 mm) were investigated at Reynolds numbers ranging from 1000 to 8500. The flame curvatures used to identify DL instability were determined using Mie scatter images captured by a particle image velocimetry system. The flame speed was further derived by applying an asymmetric hypothesis to the images. The energy-frequency spectrum of the inflow disturbance was determined using a hot-wire anemometry system, and specific wavelet transform analysis was performed to investigate the dependence of DL instability on the proportion of effective disturbances (Ped) and quantify the role of DL instability in determining the turbulent flame speed. The results showed that the burner diameter had an obvious effect on the presence of DL instability and its role in flame propagation. The ability of DL instability to enhance the flame curvature skewness and the turbulent flame speed was closely related to Ped. Ped increased when the burner diameter increased from 6 mm to 12 mm, thus enhancing the DL instability. Changing the burner diameter also affected the interplay between DL instability and turbulence. The above interactions and their effects on the flame speed during the change of inflow disturbances could be formulated by Ped. Finally, a Ped-based correlation was proposed to describe the dependence of the turbulent flame speed on the burner size.

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PyWavelets/pywt: PyWavelets 1.1.1

Cited by 7 publications

References 0 publications

Learning crystal field parameters using convolutional neural networks

Learning crystal field parameters using convolutional neural networks

Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification

Quantifying the role of Darrieus–Landau instability in turbulent premixed flame speed determination at various burner sizes

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