TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys
Context. There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms such as the trend filtering algorithm (TFA) use simultaneously-observed stars to measure and...
| Autores: | ; ; |
|---|---|
| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/208020 |
| Acceso en línea: | https://hdl.handle.net/2445/208020 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Fotometria Planetes Estels Photometry Planets Stars |
| _version_ | 1878734475080237056 |
|---|---|
| author | Ser Badia, Daniel del |
| author2 | Fors Aldrich, Octavi Núñez de Murga, Jorge, 1955- |
| author2_role | author author |
| author_browse | Fors Aldrich, Octavi Núñez de Murga, Jorge, 1955- Ser Badia, Daniel del |
| author_facet | Ser Badia, Daniel del Fors Aldrich, Octavi Núñez de Murga, Jorge, 1955- |
| author_role | author |
| collection | Recercat. Dipósit de la Recerca de Catalunya |
| description | Context. There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms such as the trend filtering algorithm (TFA) use simultaneously-observed stars to measure and remove systematic effects, and binning is used to reduce high-frequency random noise. Aims: We present TFAW, a wavelet-based modified version of TFA. First, TFAW aims to increase the periodic signal detection and second, to return a detrended and denoised signal without modifying its intrinsic characteristics. Methods: We modified TFA's frequency analysis step adding a stationary wavelet transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet-based filter was added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the underlying noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW's application to simulated multiperiodic signals. Results: TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ̃2.5× for low S/R light curves. For simulated transits, the transit detection rate improves by a factor ̃2 - 5× in the low-S/R regime compared to TFA. TFAW signal approximation performs up to a factor ̃2× better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ̃40% better compared to TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (by approximately ten times) credibility intervals for simulated transits. TFAW is also able to improve the characterization of multiperiodic signals. We present a newly-discovered variable star from TFRM. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_6aa7b9ebb28690a4e941a8cc4948c310 |
| institution | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| instname_str | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:recercat.cat:2445/208020 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher | EDP Sciences |
| publisherStr | EDP Sciences |
| reponame_str | Recercat. Dipósit de la Recerca de Catalunya |
| spelling | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys Ser Badia, Daniel del Fors Aldrich, Octavi Núñez de Murga, Jorge, 1955- Fotometria Planetes Estels Photometry Planets Stars Context. There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms such as the trend filtering algorithm (TFA) use simultaneously-observed stars to measure and remove systematic effects, and binning is used to reduce high-frequency random noise. Aims: We present TFAW, a wavelet-based modified version of TFA. First, TFAW aims to increase the periodic signal detection and second, to return a detrended and denoised signal without modifying its intrinsic characteristics. Methods: We modified TFA's frequency analysis step adding a stationary wavelet transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet-based filter was added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the underlying noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW's application to simulated multiperiodic signals. Results: TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ̃2.5× for low S/R light curves. For simulated transits, the transit detection rate improves by a factor ̃2 - 5× in the low-S/R regime compared to TFA. TFAW signal approximation performs up to a factor ̃2× better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ̃40% better compared to TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (by approximately ten times) credibility intervals for simulated transits. TFAW is also able to improve the characterization of multiperiodic signals. We present a newly-discovered variable star from TFRM. EDP Sciences https://hdl.handle.net/2445/208020 |
| spellingShingle | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys Ser Badia, Daniel del Fotometria Planetes Estels Photometry Planets Stars |
| status_str | publishedVersion |
| title | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| title_full | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| title_fullStr | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| title_full_unstemmed | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| title_short | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| title_sort | TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys |
| topic | Fotometria Planetes Estels Photometry Planets Stars |
| topic_facet | Fotometria Planetes Estels Photometry Planets Stars |
| url | https://hdl.handle.net/2445/208020 |