Pith. sign in

REVIEW

Object classification with Convolutional Neural Networks: from KiDS to Euclid

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.01613 v1 pith:K5FVTXZ4 submitted 2024-03-03 astro-ph.IM

G.A. Verdoes Kleijn , C.A. Marocico , Y. Mzayek , M. Pöntinen , M. Granvik , O. Williams , J.T.A. de Jong , T. Saifollahi
show 6 more authors
L. Wang B. Margalef-Bentabol A. La Marca B. Chowdhary Nagam L.V.E. Koopmans E.A. Valentijn
This is my paper · ORCID
classification astro-ph.IM
keywords astronomersconvolutionaleuclidkidsnetworksneuralsurveyallows
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

Discussion (0). Continue with ORCID to comment.

Pith tools