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M. López-Caniego

66 papers hereh-index 409.1k citations166 works total

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

author position
  • first author4
  • middle author32
  • last author1

Across the 37 of 66 papers where every author was matched, so the position is known.

fields
  • astro-ph.CO34
  • astro-ph.GA11
  • astro-ph.IM9
  • astro-ph8
  • astro-ph.HE4
same name
  • M. López-Caniego — 43 papers, h 43
  • M. López-Caniego — 33 papers
  • M. López-Caniego — 5 papers, h 4
  • M. López-Caniego — 2 papers, h 2
  • M. López-Caniego — 1 paper, h 0

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
20042023
most citedPlanck 2018 results. V. CMB power spectra and likelihoods

1.1k citations · 9.2k across the 66 of their papers we have counts for

collaborators
Showing 2005Show all

3 papers · 1 filter

astro-ph2005

Some comments on the note "Some comments on the paper 'Filter design for the detection of compact sources based on the Neyman-Pearson detector' by M. Lopez-Caniego et. al (2005, MNRAS 359, 993)" by R. Vio and P. Andreani (astroph/0509394)

M. Lopez-Caniego, D. Herranz, R. B. Barreiro +1

In this note we stress the necessity of a careful check of the arguments used by Vio & Andreani (2005) (VA hereinafter) to criticise the superior performance of the biparametric sc…

astro-ph2005★ 26 cited

Filter design for the detection of compact sources based on the Neyman-Pearson detector

M. Lopez-Caniego, D. Herranz, R. B. Barreiro +1

This paper considers the problem of compact source detection on a Gaussian background in 1D. Two aspects of this problem are considered: the design of the detector and the filterin…

astro-ph2005★ 11 cited

A Bayesian approach to filter design: detection of compact sources

M. Lopez-Caniego, D. Herranz, R. B. Barreiro +1

We consider filters for the detection and extraction of compact sources on a background. We make a one-dimensional treatment (though a generalization to two or more dimensions is p…

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