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S. Schuldt

34 papers hereh-index 12385 citations42 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author22
  • last author1

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • astro-ph.CO15
  • astro-ph.GA12
  • astro-ph.IM5
  • astro-ph.HE1
  • astro-ph.SR1
same name
  • S. Schuldt — 26 papers, h 5
  • S. Schuldt — 16 papers, h 3
  • S. Schuldt — 4 papers, h 13

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedHOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks

78 citations · 682 across the 30 of their papers we have counts for

collaborators
Showing 2023Show all

3 papers · 1 filter

astro-ph.CO2023

Augmenting the power of time-delay cosmography in lens galaxy clusters by probing their member galaxies I. Type Ia supernovae

A. Acebron, S. Schuldt, C. Grillo +9

We present a simple and promising new method to measure the expansion rate and the geometry of the universe that combines observations related to the time delays between the multip…

astro-ph.GA2023★ 1 cited

The missing quasar image in the gravitationally lensed quasar HE0230−2130: Implications for the cored lens mass distribution and dark satellites

S. Ertl, S. Schuldt, S. H. Suyu +3

Strongly lensed systems with peculiar configurations allow us to probe the local properties of the deflecting lens mass while simultaneously testing general profile assumptions. Th…

astro-ph.GA2023★ 13 cited

HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys

R. Canameras, S. Schuldt, Y. Shu +10

While supervised neural networks have become state of the art for identifying the rare strong gravitational lenses from large imaging data sets, their selection remains significant…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.