{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/26026"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/26026","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Model-based spectral inference in noisy physical time series: applications in laser linewidth estimation and precision magnetometry","abstract":"This dissertation introduces and applies model-based inference techniques for spectral analysis of noisy physical time series. Two distinct experimental settings – narrow-linewidth semiconductor lasers and spin-precession-based magnetometry – pose inverse problems that traditional methods struggle with due to low signal-to- noise ratios, nonstationary dynamics, and convolutional measurement effects. Spectral estimation is crucial in precision metrology, laser diagnostics, and fun- damental physics experiments. Often, the signal of interest isn’t directly observed but inferred through a noisy, nonlinear measurement process. Challenges arise from ill-posed inversion problems, temporally correlated noise, and limited statistical res- olution, particularly in low-SNR regimes. Standard approaches frequently overlook physical structure and fail to propagate uncertainty properly, motivating inference methods that integrate domain-specific knowledge, parametric signal models, and well-defined statistical behavior under experimental constraints. Three studies form this work’s core. The first presents a parametric Wiener filter- ing framework using a power spectrum equalization (PSE) criterion to deconvolve laser frequency noise spectra from delayed self-heterodyne (DSH) measurements, addressing spectral nulls and measurement noise. The second applies Bayesian in- ference to the laser system, deriving a likelihood function for the observed spectrum and estimating FN-PSD parameters via Markov Chain Monte Carlo. The third focuses on frequency tracking in free spin precession (FSP) signals from 3He mag- netometry, employing an extended Kalman smoothing approach with Expectation – Maximization-based automatic tuning of model parameters. These methods illustrate how incorporating physical system knowledge into sta- tistical inference enhances the accuracy and robustness of spectral estimation in low-SNR and dynamically filtered regimes. Validation includes synthetic and ex- perimental datasets, emphasizing reproducibility, uncertainty quantification, and computational efficiency. The techniques extend beyond the two systems studied.","abstract_html":"This dissertation introduces and applies model-based inference techniques for spectral analysis of noisy physical time series. Two distinct experimental settings – narrow-linewidth semiconductor lasers and spin-precession-based magnetometry – pose inverse problems that traditional methods struggle with due to low signal-to- noise ratios, nonstationary dynamics, and convolutional measurement effects. Spectral estimation is crucial in precision metrology, laser diagnostics, and fun- damental physics experiments. Often, the signal of interest isn’t directly observed but inferred through a noisy, nonlinear measurement process. Challenges arise from ill-posed inversion problems, temporally correlated noise, and limited statistical res- olution, particularly in low-SNR regimes. Standard approaches frequently overlook physical structure and fail to propagate uncertainty properly, motivating inference methods that integrate domain-specific knowledge, parametric signal models, and well-defined statistical behavior under experimental constraints. Three studies form this work’s core. The first presents a parametric Wiener filter- ing framework using a power spectrum equalization (PSE) criterion to deconvolve laser frequency noise spectra from delayed self-heterodyne (DSH) measurements, addressing spectral nulls and measurement noise. The second applies Bayesian in- ference to the laser system, deriving a likelihood function for the observed spectrum and estimating FN-PSD parameters via Markov Chain Monte Carlo. The third focuses on frequency tracking in free spin precession (FSP) signals from 3He mag- netometry, employing an extended Kalman smoothing approach with Expectation – Maximization-based automatic tuning of model parameters. These methods illustrate how incorporating physical system knowledge into sta- tistical inference enhances the accuracy and robustness of spectral estimation in low-SNR and dynamically filtered regimes. Validation includes synthetic and ex- perimental datasets, emphasizing reproducibility, uncertainty quantification, and computational efficiency. The techniques extend beyond the two systems studied.","abstract_has_math":false,"creators":["Mertenskötter, Lutz"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Knorr, Andreas"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-27T21:28:31Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by-nc/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-24854"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-24854","href":"https://doi.org/10.14279/depositonce-24854","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/26026","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Knorr, Andreas"]},{"key":"dc:creator","label":"Author","values":["Mertenskötter, Lutz"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-03-31T12:42:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-03-31T12:42:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/26026","https://doi.org/10.14279/depositonce-24854"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation introduces and applies model-based inference techniques for spectral analysis of noisy physical time series. Two distinct experimental settings – narrow-linewidth semiconductor lasers and spin-precession-based magnetometry – pose inverse problems that traditional methods struggle with due to low signal-to- noise ratios, nonstationary dynamics, and convolutional measurement effects. Spectral estimation is crucial in precision metrology, laser diagnostics, and fun- damental physics experiments. Often, the signal of interest isn’t directly observed but inferred through a noisy, nonlinear measurement process. Challenges arise from ill-posed inversion problems, temporally correlated noise, and limited statistical res- olution, particularly in low-SNR regimes. Standard approaches frequently overlook physical structure and fail to propagate uncertainty properly, motivating inference methods that integrate domain-specific knowledge, parametric signal models, and well-defined statistical behavior under experimental constraints. Three studies form this work’s core. The first presents a parametric Wiener filter- ing framework using a power spectrum equalization (PSE) criterion to deconvolve laser frequency noise spectra from delayed self-heterodyne (DSH) measurements, addressing spectral nulls and measurement noise. The second applies Bayesian in- ference to the laser system, deriving a likelihood function for the observed spectrum and estimating FN-PSD parameters via Markov Chain Monte Carlo. The third focuses on frequency tracking in free spin precession (FSP) signals from 3He mag- netometry, employing an extended Kalman smoothing approach with Expectation – Maximization-based automatic tuning of model parameters. These methods illustrate how incorporating physical system knowledge into sta- tistical inference enhances the accuracy and robustness of spectral estimation in low-SNR and dynamically filtered regimes. Validation includes synthetic and ex- perimental datasets, emphasizing reproducibility, uncertainty quantification, and computational efficiency. The techniques extend beyond the two systems studied.","Diese Dissertation führt modellbasierte Inferenztechniken zur Spektralanalyse verrauschter physikalischer Zeitreihen ein und wendet sie an. Zwei unterschiedliche experimentelle Kontexte – Halbleiterlaser mit schmaler Linienbreite und Spinpräzession basierende Magnetometrie – stellen inverse Probleme dar, bei denen herkömmliche Methoden aufgrund niedriger Signal-Rausch-Verhältnisse, nichtstationärer Dynamik und Faltungseffekten bei der Messung an ihre Grenzen stoßen. Spektrale Iferenz ist entscheidend in der Präzisionsmetrologie, Lasercharacterisierung und in Experimenten der fundamentalen Physik. Häufig wird das Signal von Intresse nicht direkt beobachtet, sondern muss durch einen verrauschten, nichtlinearen Messprozess inderiert werden. Herausforderungen ergeben sich aus schlecht gestellten inversen Problemen, zeitlich korreliertem Rauschen und begrenzter statistischer Auflösung, insbesondere in Bereichen mit niedrigem SNR. Standardverfahren übersehen oft physikalische Strukturen und propagieren Unsicherheiten nicht korrekt, was Inferenzmethoden erforderlich macht, die domänenspezifisches Wissen, parametrische Signalmodelle und wohldefiniertes statistisches Verhalten unter experimentellen Randbedingungen integrieren. Drei Studien bilden den Kern dieser Arbeit. Die erste stellt einen parametrischen Wiener-Filter-Ansatz unter Verwendung eines Power Spectrum Equalization (PSE) Kriteriums vor, um Laserspektren des Frequenzrauschens aus verzögerten Selbstheterodynen- Messungen (DSH) zu dekonvolvieren und dabei spektrale Nullen und Messrauschen zu berücksichtigen. Die zweite wendet Bayes’sche Inferenz auf das Lasersystem an, leitet eine Likelihood-Funktion für das beobachtete Spektrum her und schätzt FN-PSD-Parameter mittelsMarkov-Chain-Monte-Carlo-Verfahren. Die dritte befasst sich mit Frequenzschätzung in freien Spinpräzessionssignalen (FSP) der 3He-Magnetometrie und verwendet dabei einen erweiterten Kalman-Smoother mit Expectation-Maximization-basierter automatischer Optimiertung der Modellparameter. Diese Methoden veranschaulichen, wie die Einbeziehung von Modellwissen in statistische Inferenz die Genauigkeit und Robustheit der Spektralschätzung in Bereichen mit niedrigem SNR und dynamisch gefilterten Systemen verbessert. Die Validierung erfolgt anhand synthetischer und experimenteller Datensätze mit Schwerpunkt auf Reproduzierbarkeit, Unsicherheitsquantifizierung und Recheneffizienz. Die Techniken sind über die beiden untersuchten Systeme hinaus anwendbar."]},{"key":"dc:title","label":"Title","values":["Model-based spectral inference in noisy physical time series: applications in laser linewidth estimation and precision magnetometry"]}]}],"canonical_facts":{"dc:contributor.advisor":["Knorr, Andreas"],"dc:creator":["Mertenskötter, Lutz"],"dc:date.accessioned":["2026-03-31T12:42:15Z"],"dc:date.available":["2026-03-31T12:42:15Z"],"dc:date.issued":["2026"],"dc:description.abstract":["This dissertation introduces and applies model-based inference techniques for spectral analysis of noisy physical time series. Two distinct experimental settings – narrow-linewidth semiconductor lasers and spin-precession-based magnetometry – pose inverse problems that traditional methods struggle with due to low signal-to- noise ratios, nonstationary dynamics, and convolutional measurement effects. Spectral estimation is crucial in precision metrology, laser diagnostics, and fun- damental physics experiments. Often, the signal of interest isn’t directly observed but inferred through a noisy, nonlinear measurement process. Challenges arise from ill-posed inversion problems, temporally correlated noise, and limited statistical res- olution, particularly in low-SNR regimes. Standard approaches frequently overlook physical structure and fail to propagate uncertainty properly, motivating inference methods that integrate domain-specific knowledge, parametric signal models, and well-defined statistical behavior under experimental constraints. Three studies form this work’s core. The first presents a parametric Wiener filter- ing framework using a power spectrum equalization (PSE) criterion to deconvolve laser frequency noise spectra from delayed self-heterodyne (DSH) measurements, addressing spectral nulls and measurement noise. The second applies Bayesian in- ference to the laser system, deriving a likelihood function for the observed spectrum and estimating FN-PSD parameters via Markov Chain Monte Carlo. The third focuses on frequency tracking in free spin precession (FSP) signals from 3He mag- netometry, employing an extended Kalman smoothing approach with Expectation – Maximization-based automatic tuning of model parameters. These methods illustrate how incorporating physical system knowledge into sta- tistical inference enhances the accuracy and robustness of spectral estimation in low-SNR and dynamically filtered regimes. Validation includes synthetic and ex- perimental datasets, emphasizing reproducibility, uncertainty quantification, and computational efficiency. The techniques extend beyond the two systems studied.","Diese Dissertation führt modellbasierte Inferenztechniken zur Spektralanalyse verrauschter physikalischer Zeitreihen ein und wendet sie an. Zwei unterschiedliche experimentelle Kontexte – Halbleiterlaser mit schmaler Linienbreite und Spinpräzession basierende Magnetometrie – stellen inverse Probleme dar, bei denen herkömmliche Methoden aufgrund niedriger Signal-Rausch-Verhältnisse, nichtstationärer Dynamik und Faltungseffekten bei der Messung an ihre Grenzen stoßen. Spektrale Iferenz ist entscheidend in der Präzisionsmetrologie, Lasercharacterisierung und in Experimenten der fundamentalen Physik. Häufig wird das Signal von Intresse nicht direkt beobachtet, sondern muss durch einen verrauschten, nichtlinearen Messprozess inderiert werden. Herausforderungen ergeben sich aus schlecht gestellten inversen Problemen, zeitlich korreliertem Rauschen und begrenzter statistischer Auflösung, insbesondere in Bereichen mit niedrigem SNR. Standardverfahren übersehen oft physikalische Strukturen und propagieren Unsicherheiten nicht korrekt, was Inferenzmethoden erforderlich macht, die domänenspezifisches Wissen, parametrische Signalmodelle und wohldefiniertes statistisches Verhalten unter experimentellen Randbedingungen integrieren. Drei Studien bilden den Kern dieser Arbeit. Die erste stellt einen parametrischen Wiener-Filter-Ansatz unter Verwendung eines Power Spectrum Equalization (PSE) Kriteriums vor, um Laserspektren des Frequenzrauschens aus verzögerten Selbstheterodynen- Messungen (DSH) zu dekonvolvieren und dabei spektrale Nullen und Messrauschen zu berücksichtigen. Die zweite wendet Bayes’sche Inferenz auf das Lasersystem an, leitet eine Likelihood-Funktion für das beobachtete Spektrum her und schätzt FN-PSD-Parameter mittelsMarkov-Chain-Monte-Carlo-Verfahren. Die dritte befasst sich mit Frequenzschätzung in freien Spinpräzessionssignalen (FSP) der 3He-Magnetometrie und verwendet dabei einen erweiterten Kalman-Smoother mit Expectation-Maximization-basierter automatischer Optimiertung der Modellparameter. Diese Methoden veranschaulichen, wie die Einbeziehung von Modellwissen in statistische Inferenz die Genauigkeit und Robustheit der Spektralschätzung in Bereichen mit niedrigem SNR und dynamisch gefilterten Systemen verbessert. Die Validierung erfolgt anhand synthetischer und experimenteller Datensätze mit Schwerpunkt auf Reproduzierbarkeit, Unsicherheitsquantifizierung und Recheneffizienz. Die Techniken sind über die beiden untersuchten Systeme hinaus anwendbar."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/26026","https://doi.org/10.14279/depositonce-24854"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc/4.0/"],"dc:title":["Model-based spectral inference in noisy physical time series: applications in laser linewidth estimation and precision magnetometry"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:31Z"}