Evidence map›Paper›PMID 42647647›Full record

ArticleScience advances2026

Parameter estimation from amplitude collapse in correlated matter-wave interference.

Daniel Derr, Dominik Pfeiffer, Ludwig Lind, Gerhard Birkl, Enno Giese

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In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel DerrTechnische Universität Darmstadt, Fachbereich Physik, Institut für Angewandte Physik, Schloßgartenstraße 7, 64289 Darmstadt, Germany.ORCID 0000-0002-8690-3897
Dominik PfeifferTechnische Universität Darmstadt, Fachbereich Physik, Institut für Angewandte Physik, Schloßgartenstraße 7, 64289 Darmstadt, Germany.ORCID 0000-0003-4530-3251
Ludwig LindTechnische Universität Darmstadt, Fachbereich Physik, Institut für Angewandte Physik, Schloßgartenstraße 7, 64289 Darmstadt, Germany.ORCID 0009-0000-3354-511X
Gerhard BirklTechnische Universität Darmstadt, Fachbereich Physik, Institut für Angewandte Physik, Schloßgartenstraße 7, 64289 Darmstadt, Germany.ORCID 0000-0002-4137-9227
Enno GieseTechnische Universität Darmstadt, Fachbereich Physik, Institut für Angewandte Physik, Schloßgartenstraße 7, 64289 Darmstadt, Germany.ORCID 0000-0002-1126-6352

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Operating matter-wave interferometers as quantum detectors for fundamental physics or inertial sensors with unprecedented accuracies relies on noise rejection, often implemented by correlating multiple sensors. They can be spatially separated (gradiometry or gravitational-wave detection) or consist of different internal states (magnetometry or quantum clock interferometry), with a signal-amplitude modulation serving as a signature of a differential phase. In this work, we introduce parameter estimation from amplitude collapse (PEAC) by applying statistical inference techniques for different magnetically sensitive substates of an atom interferometer. We demonstrate that PEAC provides higher trueness, resulting in a substantially reduced bias compared to standard methods for perfectly correlated signals, while achieving competitive precision near, but not at, vanishing amplitudes. This indicates that vanishing signals do not constitute the most favorable working point for high-accuracy sensing, relevant to quantum clock interferometry. PEAC presents a generally applicable complementary evaluation method for correlated interferometers without phase stability, increasing the overall accuracy and enabling applications beyond atom-based interferometry.

Identifiers

PMID42647647
PMCPMC13510596

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.