Prediction of a single Gaussian shape of spectral line measured with low-dispersion spectrometer by using machine learning

F. Kin; T. Nakano; N. Oyama; A. Terakado; T. Wakatsuki; E. Narita

doi:10.1063/5.0039781

Prediction of a single Gaussian shape of spectral line measured with low-dispersion spectrometer by using machine learning

Review of Scientific Instruments ◽

10.1063/5.0039781 ◽

2021 ◽

Vol 92 (5) ◽

pp. 053505

Author(s):

F. Kin ◽

T. Nakano ◽

N. Oyama ◽

A. Terakado ◽

T. Wakatsuki ◽

...

Keyword(s):

Machine Learning ◽

Spectral Line ◽

Gaussian Shape ◽

Low Dispersion

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Automatic Source Classification in Digitised First Byurakan Survey

Proceedings of the International Astronomical Union ◽

10.1017/s1743921317000266 ◽

2016 ◽

Vol 12 (S325) ◽

pp. 186-190

Author(s):

Martin Topinka ◽

Areg Mickaelian ◽

Roberto Nesci ◽

Corinne Rossi

Keyword(s):

Machine Learning ◽

Gamma Ray ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Late Type ◽

Byurakan Survey ◽

Gamma Ray Burst ◽

First Byurakan Survey ◽

Astrophysical Objects ◽

Low Dispersion

AbstractThe Digitised First Byurakan Survey (DFBS) provides low dispersion optical spectra for about 24 million sources. A two-step machine learning algorithm based on similarities to predefined templates is applied to select different classes of rare objects in the dataset automatically, for example late type stars, quasars and white dwarves. Identifying outliers from the groups of common astrophysical objects may lead to discovery of rare objects, such as gamma-ray burst afterglows.

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Stark spectral line broadening modeling by machine learning algorithms

Neural Computing and Applications ◽

10.1007/s00521-021-06763-4 ◽

2022 ◽

Author(s):

Irinel Tapalaga ◽

Ivan Traparić ◽

Nora Trklja Boca ◽

Jagoš Purić ◽

Ivan P. Dojčinović

Keyword(s):

Machine Learning ◽

Spectral Line ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Line Broadening ◽

Spectral Line Broadening

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Novae in the Magellanic Clouds

International Astronomical Union Colloquium ◽

10.1017/s0252921100073334 ◽

1979 ◽

Vol 46 ◽

pp. 96-101

Author(s):

J.A. Graham

Keyword(s):

Large Magellanic Cloud ◽

Magellanic Cloud ◽

Magellanic Clouds ◽

University Of Michigan ◽

The Past ◽

Spectroscopic Information ◽

Objective Prism ◽

The University ◽

Small Cloud ◽

Low Dispersion

During the past several years, a systematic search for novae in the Magellanic Clouds has been carried out at Cerro Tololo Inter-American Observatory. The Curtis Schmidt telescope, on loan to CTIO from the University of Michigan is used to obtain plates every two weeks during the observing season. An objective prism is used on the telescope. This provides additional low-dispersion spectroscopic information when a nova is discovered. The plates cover an area of 5°x5°. One plate is sufficient to cover the Small Magellanic Cloud and four are taken of the Large Magellanic Cloud with an overlap so that the central bar is included on each plate. The methods used in the search have been described by Graham and Araya (1971). In the CTIO survey, 8 novae have been discovered in the Large Cloud but none in the Small Cloud. The survey was not carried out in 1974 or 1976. During 1974, one nova was discovered in the Small Cloud by MacConnell and Sanduleak (1974).

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On the Inhomogeneity of Chemical Composition over the Surface of 21 Per

International Astronomical Union Colloquium ◽

10.1017/s0252921100080659 ◽

1976 ◽

Vol 32 ◽

pp. 343-349

Author(s):

Yu.V. Glagolevsky ◽

K.I. Kozlova ◽

V.S. Lebedev ◽

N.S. Polosukhina

Keyword(s):

Chemical Composition ◽

Spectral Line ◽

Variable Star ◽

Radial Velocities ◽

Crimean Astrophysical Observatory ◽

Line Intensities ◽

Magnetic Variable

SummaryThe magnetic variable star 21 Per has been studied from 4 and 8 Å/mm spectra obtained with the 2.6 - meter reflector of the Crimean Astrophysical Observatory. Spectral line intensities (Wλ) and radial velocities (Vr) have been measured.

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