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Periodic Astrometric Signal Recovery through Convolutional Autoencoders

Overview of attention for chapter in “Intelligent Astrophysics”
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
3 Mendeley
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Chapter title
Periodic Astrometric Signal Recovery through Convolutional Autoencoders
Book title
Intelligent Astrophysics
Published in
arXiv, April 2021
DOI 10.1007/978-3-030-65867-0_8
Book ISBNs
978-3-03-065866-3, 978-3-03-065867-0
Authors

Michele Delli Veneri, Louis Desdoigts, Morgan A. Schmitz, Alberto Krone-Martins, Emille E. O. Ishida, Peter Tuthill, Rafael S. de Souza, Richard Scalzo, Massimo Brescia, Giuseppe Longo, Antonio Picariello, Veneri, Michele Delli, Desdoigts, Louis, Schmitz, Morgan A., Krone-Martins, Alberto, Ishida, Emille E. O., Tuthill, Peter, Souza, Rafael S. de, Scalzo, Richard, Brescia, Massimo, Longo, Giuseppe, Picariello, Antonio

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Arts and Humanities 1 33%
Physics and Astronomy 1 33%
Unknown 1 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2020.
All research outputs
#4,163,973
of 23,217,343 outputs
Outputs from arXiv
#88,264
of 956,704 outputs
Outputs of similar age
#94,297
of 401,766 outputs
Outputs of similar age from arXiv
#3,092
of 33,956 outputs
Altmetric has tracked 23,217,343 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 956,704 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 401,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 33,956 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.