[{"publisher":"EDP Sciences","article_type":"original","quality_controlled":"1","publication_status":"published","date_created":"2022-07-18T12:21:32Z","article_processing_charge":"No","title":"ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods","intvolume":"       647","_id":"11608","scopus_import":"1","author":[{"full_name":"Breton, S. N.","last_name":"Breton","first_name":"S. N."},{"full_name":"Santos, A. R. G.","last_name":"Santos","first_name":"A. R. G."},{"full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","last_name":"Bugnet","first_name":"Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"last_name":"Mathur","first_name":"S.","full_name":"Mathur, S."},{"full_name":"García, R. A.","first_name":"R. A.","last_name":"García"},{"full_name":"Pallé, P. L.","first_name":"P. L.","last_name":"Pallé"}],"acknowledgement":"We thank Suzanne Aigrain and Joe Llama for providing us with the simulated data used in Aigrain et al. (2015). S. N. B., L. B. and R. A. G. acknowledge the support from PLATO and GOLF CNES grants. A. R. G. S. acknowledges the support from NASA under grant NNX17AF27G. S. M. acknowledges the support from the Spanish Ministry of Science and Innovation with the Ramon y Cajal fellowship number RYC-2015-17697. P. L. P. and S. M. acknowledge support from the Spanish Ministry of Science and Innovation with the grant number PID2019-107187GB-I00. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. Software: Python (Van Rossum & Drake 2009), numpy (Oliphant 2006), pandas (The pandas development team 2020; McKinney 2010), matplotlib (Hunter 2007), scikit-learn (Pedregosa et al. 2011). The source code used to obtain the present results can be found at: https://gitlab.com/sybreton/pushkin ; https://gitlab.com/sybreton/ml_surface_rotation_paper .","volume":647,"extern":"1","arxiv":1,"doi":"10.1051/0004-6361/202039947","day":"19","abstract":[{"lang":"eng","text":"In order to understand stellar evolution, it is crucial to efficiently determine stellar surface rotation periods. Indeed, while they are of great importance in stellar models, angular momentum transport processes inside stars are still poorly understood today. Surface rotation, which is linked to the age of the star, is one of the constraints needed to improve the way those processes are modelled. Statistics of the surface rotation periods for a large sample of stars of different spectral types are thus necessary. An efficient tool to automatically determine reliable rotation periods is needed when dealing with large samples of stellar photometric datasets. The objective of this work is to develop such a tool. For this purpose, machine learning classifiers constitute relevant bases to build our new methodology. Random forest learning abilities are exploited to automate the extraction of rotation periods in Kepler light curves. Rotation periods and complementary parameters are obtained via three different methods: a wavelet analysis, the autocorrelation function of the light curve, and the composite spectrum. We trained three different classifiers: one to detect if rotational modulations are present in the light curve, one to flag close binary or classical pulsators candidates that can bias our rotation period determination, and finally one classifier to provide the final rotation period. We tested our machine learning pipeline on 23 431 stars of the Kepler K and M dwarf reference rotation catalogue for which 60% of the stars have been visually inspected. For the sample of 21 707 stars where all the input parameters are provided to the algorithm, 94.2% of them are correctly classified (as rotating or not). Among the stars that have a rotation period in the reference catalogue, the machine learning provides a period that agrees within 10% of the reference value for 95.3% of the stars. Moreover, the yield of correct rotation periods is raised to 99.5% after visually inspecting 25.2% of the stars. Over the two main analysis steps, rotation classification and period selection, the pipeline yields a global agreement with the reference values of 92.1% and 96.9% before and after visual inspection. Random forest classifiers are efficient tools to determine reliable rotation periods in large samples of stars. The methodology presented here could be easily adapted to extract surface rotation periods for stars with different spectral types or observed by other instruments such as K2, TESS or by PLATO in the near future."}],"date_updated":"2022-08-22T08:47:47Z","year":"2021","citation":{"short":"S.N. Breton, A.R.G. Santos, L.A. Bugnet, S. Mathur, R.A. García, P.L. Pallé, Astronomy &#38; Astrophysics 647 (2021).","mla":"Breton, S. N., et al. “ROOSTER: A Machine-Learning Analysis Tool for Kepler Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>, vol. 647, A125, EDP Sciences, 2021, doi:<a href=\"https://doi.org/10.1051/0004-6361/202039947\">10.1051/0004-6361/202039947</a>.","ista":"Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. 2021. ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy &#38; Astrophysics. 647, A125.","apa":"Breton, S. N., Santos, A. R. G., Bugnet, L. A., Mathur, S., García, R. A., &#38; Pallé, P. L. (2021). ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. <i>Astronomy &#38; Astrophysics</i>. EDP Sciences. <a href=\"https://doi.org/10.1051/0004-6361/202039947\">https://doi.org/10.1051/0004-6361/202039947</a>","ama":"Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. <i>Astronomy &#38; Astrophysics</i>. 2021;647. doi:<a href=\"https://doi.org/10.1051/0004-6361/202039947\">10.1051/0004-6361/202039947</a>","chicago":"Breton, S. N., A. R. G. Santos, Lisa Annabelle Bugnet, S. Mathur, R. A. García, and P. L. Pallé. “ROOSTER: A Machine-Learning Analysis Tool for Kepler Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>. EDP Sciences, 2021. <a href=\"https://doi.org/10.1051/0004-6361/202039947\">https://doi.org/10.1051/0004-6361/202039947</a>.","ieee":"S. N. Breton, A. R. G. Santos, L. A. Bugnet, S. Mathur, R. A. García, and P. L. Pallé, “ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods,” <i>Astronomy &#38; Astrophysics</i>, vol. 647. EDP Sciences, 2021."},"external_id":{"arxiv":["2101.10152"]},"language":[{"iso":"eng"}],"keyword":["Space and Planetary Science","Astronomy and Astrophysics","methods: data analysis / stars: solar-type / stars: activity / stars: rotation / starspots"],"oa_version":"Preprint","month":"03","article_number":"A125","publication":"Astronomy & Astrophysics","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2101.10152"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","publication_identifier":{"eissn":["1432-0746"],"issn":["0004-6361"]},"oa":1,"date_published":"2021-03-19T00:00:00Z","type":"journal_article"},{"oa_version":"Preprint","month":"06","publication":"Monthly Notices of the Royal Astronomical Society","keyword":["Space and Planetary Science","Astronomy and Astrophysics","asteroseismology","methods: data analysis","techniques: image processing","stars: oscillations","stars: statistics"],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0035-8711"],"eissn":["1365-2966"]},"oa":1,"type":"journal_article","date_published":"2019-06-01T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1903.00115"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2022-07-18T14:26:03Z","article_processing_charge":"No","publication_status":"published","intvolume":"       485","title":"A search for red giant solar-like oscillations in all Kepler data","scopus_import":"1","_id":"11615","issue":"4","author":[{"first_name":"Marc","last_name":"Hon","full_name":"Hon, Marc"},{"first_name":"Dennis","last_name":"Stello","full_name":"Stello, Dennis"},{"full_name":"García, Rafael A","last_name":"García","first_name":"Rafael A"},{"last_name":"Mathur","first_name":"Savita","full_name":"Mathur, Savita"},{"first_name":"Sanjib","last_name":"Sharma","full_name":"Sharma, Sanjib"},{"full_name":"Colman, Isabel L","last_name":"Colman","first_name":"Isabel L"},{"last_name":"Bugnet","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","id":"d9edb345-f866-11ec-9b37-d119b5234501"}],"publisher":"Oxford University Press","article_type":"original","quality_controlled":"1","page":"5616-5630","day":"01","doi":"10.1093/mnras/stz622","arxiv":1,"abstract":[{"lang":"eng","text":"The recently published Kepler mission Data Release 25 (DR25) reported on ∼197 000 targets observed during the mission. Despite this, no wide search for red giants showing solar-like oscillations have been made across all stars observed in Kepler’s long-cadence mode. In this work, we perform this task using custom apertures on the Kepler pixel files and detect oscillations in 21 914 stars, representing the largest sample of solar-like oscillating stars to date. We measure their frequency at maximum power, νmax, down to νmax≃4μHz and obtain log (g) estimates with a typical uncertainty below 0.05 dex, which is superior to typical measurements from spectroscopy. Additionally, the νmax distribution of our detections show good agreement with results from a simulated model of the Milky Way, with a ratio of observed to predicted stars of 0.992 for stars with 10<νmax<270μHz. Among our red giant detections, we find 909 to be dwarf/subgiant stars whose flux signal is polluted by a neighbouring giant as a result of using larger photometric apertures than those used by the NASA Kepler science processing pipeline. We further find that only 293 of the polluting giants are known Kepler targets. The remainder comprises over 600 newly identified oscillating red giants, with many expected to belong to the Galactic halo, serendipitously falling within the Kepler pixel files of targeted stars."}],"year":"2019","citation":{"ista":"Hon M, Stello D, García RA, Mathur S, Sharma S, Colman IL, Bugnet LA. 2019. A search for red giant solar-like oscillations in all Kepler data. Monthly Notices of the Royal Astronomical Society. 485(4), 5616–5630.","short":"M. Hon, D. Stello, R.A. García, S. Mathur, S. Sharma, I.L. Colman, L.A. Bugnet, Monthly Notices of the Royal Astronomical Society 485 (2019) 5616–5630.","mla":"Hon, Marc, et al. “A Search for Red Giant Solar-like Oscillations in All Kepler Data.” <i>Monthly Notices of the Royal Astronomical Society</i>, vol. 485, no. 4, Oxford University Press, 2019, pp. 5616–30, doi:<a href=\"https://doi.org/10.1093/mnras/stz622\">10.1093/mnras/stz622</a>.","ieee":"M. Hon <i>et al.</i>, “A search for red giant solar-like oscillations in all Kepler data,” <i>Monthly Notices of the Royal Astronomical Society</i>, vol. 485, no. 4. Oxford University Press, pp. 5616–5630, 2019.","chicago":"Hon, Marc, Dennis Stello, Rafael A García, Savita Mathur, Sanjib Sharma, Isabel L Colman, and Lisa Annabelle Bugnet. “A Search for Red Giant Solar-like Oscillations in All Kepler Data.” <i>Monthly Notices of the Royal Astronomical Society</i>. Oxford University Press, 2019. <a href=\"https://doi.org/10.1093/mnras/stz622\">https://doi.org/10.1093/mnras/stz622</a>.","ama":"Hon M, Stello D, García RA, et al. A search for red giant solar-like oscillations in all Kepler data. <i>Monthly Notices of the Royal Astronomical Society</i>. 2019;485(4):5616-5630. doi:<a href=\"https://doi.org/10.1093/mnras/stz622\">10.1093/mnras/stz622</a>","apa":"Hon, M., Stello, D., García, R. A., Mathur, S., Sharma, S., Colman, I. L., &#38; Bugnet, L. A. (2019). A search for red giant solar-like oscillations in all Kepler data. <i>Monthly Notices of the Royal Astronomical Society</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/mnras/stz622\">https://doi.org/10.1093/mnras/stz622</a>"},"date_updated":"2022-08-22T07:35:19Z","external_id":{"arxiv":["1903.00115"]},"volume":485,"acknowledgement":"Funding for this Discovery mission is provided by NASA’s Science mission Directorate. We thank the entire Kepler team without whom this investigation would not be possible. DS is the recipient of an Australian Research Council Future Fellowship (project number FT1400147). RAG acknowledges the support from CNES. SM acknowledges support from NASA grant NNX15AF13G, NSF grant AST-1411685, and the Ramon y Cajal fellowship number RYC-2015-17697. ILC acknowledges scholarship support from the University of Sydney. We would like to thank Nicholas Barbara and Timothy Bedding for providing us with a list of variable stars that helped to validate a number of detections in this study. We also thank the group at the University of Sydney for fruitful discussions. Finally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.","extern":"1"},{"keyword":["Space and Planetary Science","Astronomy and Astrophysics","methods: data analysis","stars: activity","stars: low-mass","stars: rotation","starspots","techniques: photometric"],"language":[{"iso":"eng"}],"publication":"The Astrophysical Journal Supplement Series","article_number":"21","month":"09","oa_version":"Preprint","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://arxiv.org/abs/1908.05222","open_access":"1"}],"type":"journal_article","date_published":"2019-09-19T00:00:00Z","oa":1,"publication_identifier":{"issn":["0067-0049"]},"quality_controlled":"1","article_type":"original","publisher":"IOP Publishing","issue":"1","author":[{"full_name":"Santos, A. R. G.","first_name":"A. R. G.","last_name":"Santos"},{"full_name":"García, R. A.","last_name":"García","first_name":"R. A."},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"id":"d9edb345-f866-11ec-9b37-d119b5234501","last_name":"Bugnet","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000"},{"last_name":"van Saders","first_name":"J. L.","full_name":"van Saders, J. L."},{"first_name":"T. S.","last_name":"Metcalfe","full_name":"Metcalfe, T. S."},{"full_name":"Simonian, G. V. A.","first_name":"G. V. A.","last_name":"Simonian"},{"first_name":"M. H.","last_name":"Pinsonneault","full_name":"Pinsonneault, M. H."}],"scopus_import":"1","_id":"11623","intvolume":"       244","title":"Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars","article_processing_charge":"No","date_created":"2022-07-19T09:21:58Z","publication_status":"published","extern":"1","volume":244,"acknowledgement":"The authors thank Róbert Szabó Paul G. Beck, Katrien Kolenberg, and Isabel L. Colman for helping on the classification of stars. This paper includes data collected by the Kepler mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the Kepler mission is provided by the National Aeronautics and Space Administration (NASA) Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. A.R.G.S. acknowledges the support from NASA under grant NNX17AF27G. R.A.G. and L.B. acknowledge the support from PLATO and GOLF CNES grants. S.M. acknowledges the support from the Ramon y Cajal fellowship number RYC-2015-17697. T.S.M. acknowledges support from a Visiting Fellowship at the Max Planck Institute for Solar System Research. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.\r\n\r\nSoftware: KADACS (García et al. 2011), NumPy (van der Walt et al. 2011), SciPy (Jones et al. 2001), Matplotlib (Hunter 2007).\r\n\r\nFacilities: MAST - , Kepler Eclipsing Binary Catalog - , Exoplanet Archive. -","external_id":{"arxiv":["1908.05222"]},"citation":{"ista":"Santos ARG, García RA, Mathur S, Bugnet LA, van Saders JL, Metcalfe TS, Simonian GVA, Pinsonneault MH. 2019. Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars. The Astrophysical Journal Supplement Series. 244(1), 21.","mla":"Santos, A. R. G., et al. “Surface Rotation and Photometric Activity for Kepler Targets. I. M and K Main-Sequence Stars.” <i>The Astrophysical Journal Supplement Series</i>, vol. 244, no. 1, 21, IOP Publishing, 2019, doi:<a href=\"https://doi.org/10.3847/1538-4365/ab3b56\">10.3847/1538-4365/ab3b56</a>.","short":"A.R.G. Santos, R.A. García, S. Mathur, L.A. Bugnet, J.L. van Saders, T.S. Metcalfe, G.V.A. Simonian, M.H. Pinsonneault, The Astrophysical Journal Supplement Series 244 (2019).","ieee":"A. R. G. Santos <i>et al.</i>, “Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars,” <i>The Astrophysical Journal Supplement Series</i>, vol. 244, no. 1. IOP Publishing, 2019.","chicago":"Santos, A. R. G., R. A. García, S. Mathur, Lisa Annabelle Bugnet, J. L. van Saders, T. S. Metcalfe, G. V. A. Simonian, and M. H. Pinsonneault. “Surface Rotation and Photometric Activity for Kepler Targets. I. M and K Main-Sequence Stars.” <i>The Astrophysical Journal Supplement Series</i>. IOP Publishing, 2019. <a href=\"https://doi.org/10.3847/1538-4365/ab3b56\">https://doi.org/10.3847/1538-4365/ab3b56</a>.","apa":"Santos, A. R. G., García, R. A., Mathur, S., Bugnet, L. A., van Saders, J. L., Metcalfe, T. S., … Pinsonneault, M. H. (2019). Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars. <i>The Astrophysical Journal Supplement Series</i>. IOP Publishing. <a href=\"https://doi.org/10.3847/1538-4365/ab3b56\">https://doi.org/10.3847/1538-4365/ab3b56</a>","ama":"Santos ARG, García RA, Mathur S, et al. Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars. <i>The Astrophysical Journal Supplement Series</i>. 2019;244(1). doi:<a href=\"https://doi.org/10.3847/1538-4365/ab3b56\">10.3847/1538-4365/ab3b56</a>"},"year":"2019","date_updated":"2022-08-22T08:10:38Z","abstract":[{"text":"Brightness variations due to dark spots on the stellar surface encode information about stellar surface rotation and magnetic activity. In this work, we analyze the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M and K in order to measure their surface rotation and photometric activity level. Rotation-period estimates are obtained by the combination of a wavelet analysis and autocorrelation function of the light curves. Reliable rotation estimates are determined by comparing the results from the different rotation diagnostics and four data sets. We also measure the photometric activity proxy Sph using the amplitude of the flux variations on an appropriate timescale. We report rotation periods and photometric activity proxies for about 60% of the sample, including 4431 targets for which McQuillan et al. did not report a rotation period. For the common targets with rotation estimates in this study and in McQuillan et al., our rotation periods agree within 99%. In this work, we also identify potential polluters, such as misclassified red giants and classical pulsator candidates. Within the parameter range we study, there is a mild tendency for hotter stars to have shorter rotation periods. The photometric activity proxy spans a wider range of values with increasing effective temperature. The rotation period and photometric activity proxy are also related, with Sph being larger for fast rotators. Similar to McQuillan et al., we find a bimodal distribution of rotation periods.","lang":"eng"}],"day":"19","arxiv":1,"doi":"10.3847/1538-4365/ab3b56"},{"keyword":["asteroseismology - methods","data analysis - thecniques","machine learning - stars","oscillations"],"language":[{"iso":"eng"}],"author":[{"full_name":"Saux, A. Le","first_name":"A. Le","last_name":"Saux"},{"id":"d9edb345-f866-11ec-9b37-d119b5234501","orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","first_name":"Lisa Annabelle","last_name":"Bugnet"},{"full_name":"Mathur, S.","first_name":"S.","last_name":"Mathur"},{"full_name":"Breton, S. N.","last_name":"Breton","first_name":"S. N."},{"full_name":"Garcia, R. A.","first_name":"R. A.","last_name":"Garcia"}],"publication":"arXiv","_id":"11630","article_number":"1906.09611","month":"06","title":"Automatic classification of K2 pulsating stars using machine learning techniques","date_created":"2022-07-21T06:57:10Z","article_processing_charge":"No","oa_version":"Preprint","publication_status":"submitted","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","extern":"1","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1906.09611","open_access":"1"}],"external_id":{"arxiv":["1906.09611"]},"type":"preprint","date_published":"2019-06-23T00:00:00Z","year":"2019","citation":{"chicago":"Saux, A. Le, Lisa Annabelle Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>.","ieee":"A. L. Saux, L. A. Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia, “Automatic classification of K2 pulsating stars using machine learning techniques,” <i>arXiv</i>. .","apa":"Saux, A. L., Bugnet, L. A., Mathur, S., Breton, S. N., &#38; Garcia, R. A. (n.d.). Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>","ama":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>","ista":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.","short":"A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.).","mla":"Saux, A. Le, et al. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, 1906.09611, doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>."},"date_updated":"2022-08-22T08:20:29Z","oa":1,"abstract":[{"text":"The second mission of NASA’s Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars.","lang":"eng"}],"day":"23","arxiv":1,"doi":"10.48550/arXiv.1906.09611"},{"doi":"10.15479/AT:ISTA:6473","degree_awarded":"PhD","day":"23","abstract":[{"lang":"eng","text":"Single cells are constantly interacting with their environment and each other, more importantly, the accurate perception of environmental cues is crucial for growth, survival, and reproduction. This communication between cells and their environment can be formalized in mathematical terms and be quantified as the information flow between them, as prescribed by information theory. \r\nThe recent availability of real–time dynamical patterns of signaling molecules in single cells has allowed us to identify encoding about the identity of the environment in the time–series. However, efficient estimation of the information transmitted by these signals has been a data–analysis challenge due to the high dimensionality of the trajectories and the limited number of samples. In the first part of this thesis, we develop and evaluate decoding–based estimation methods to lower bound the mutual information and derive model–based precise information estimates for biological reaction networks governed by the chemical master equation. This is followed by applying the decoding-based methods to study the intracellular representation of extracellular changes in budding yeast, by observing the transient dynamics of nuclear translocation of 10 transcription factors in response to 3 stress conditions. Additionally, we apply these estimators to previously published data on ERK and Ca2+ signaling and yeast stress response. We argue that this single cell decoding-based measure of information provides an unbiased, quantitative and interpretable measure for the fidelity of biological signaling processes. \r\nFinally, in the last section, we deal with gene regulation which is primarily controlled by transcription factors (TFs) that bind to the DNA to activate gene expression. The possibility that non-cognate TFs activate transcription diminishes the accuracy of regulation with potentially disastrous effects for the cell. This ’crosstalk’ acts as a previously unexplored source of noise in biochemical networks and puts a strong constraint on their performance. To mitigate erroneous initiation we propose an out of equilibrium scheme that implements kinetic proofreading. We show that such architectures are favored  over their equilibrium counterparts for complex organisms despite introducing noise in gene expression. "}],"date_updated":"2025-05-28T11:57:00Z","year":"2019","citation":{"short":"S.A. Cepeda Humerez, Estimating Information Flow in Single Cells, Institute of Science and Technology Austria, 2019.","mla":"Cepeda Humerez, Sarah A. <i>Estimating Information Flow in Single Cells</i>. Institute of Science and Technology Austria, 2019, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6473\">10.15479/AT:ISTA:6473</a>.","ista":"Cepeda Humerez SA. 2019. Estimating information flow in single cells. Institute of Science and Technology Austria.","ama":"Cepeda Humerez SA. Estimating information flow in single cells. 2019. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6473\">10.15479/AT:ISTA:6473</a>","apa":"Cepeda Humerez, S. A. (2019). <i>Estimating information flow in single cells</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:6473\">https://doi.org/10.15479/AT:ISTA:6473</a>","ieee":"S. A. Cepeda Humerez, “Estimating information flow in single cells,” Institute of Science and Technology Austria, 2019.","chicago":"Cepeda Humerez, Sarah A. “Estimating Information Flow in Single Cells.” Institute of Science and Technology Austria, 2019. <a href=\"https://doi.org/10.15479/AT:ISTA:6473\">https://doi.org/10.15479/AT:ISTA:6473</a>."},"ddc":["004"],"publication_status":"published","department":[{"_id":"GaTk"}],"article_processing_charge":"No","date_created":"2019-05-21T00:11:23Z","title":"Estimating information flow in single cells","alternative_title":["ISTA Thesis"],"_id":"6473","author":[{"id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","full_name":"Cepeda Humerez, Sarah A","last_name":"Cepeda Humerez","first_name":"Sarah A"}],"publisher":"Institute of Science and Technology Austria","page":"135","file_date_updated":"2020-07-14T12:47:31Z","publication_identifier":{"issn":["2663-337X"]},"supervisor":[{"full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2019-05-23T00:00:00Z","type":"dissertation","file":[{"access_level":"closed","relation":"source_file","creator":"scepeda","file_id":"6480","file_size":23937464,"checksum":"75f9184c1346e10a5de5f9cc7338309a","date_created":"2019-05-23T11:18:16Z","file_name":"Thesis_Cepeda.zip","content_type":"application/zip","date_updated":"2020-07-14T12:47:31Z"},{"date_updated":"2020-07-14T12:47:31Z","file_name":"CepedaThesis.pdf","content_type":"application/pdf","date_created":"2019-05-23T11:18:13Z","file_size":16646985,"checksum":"afdc0633ddbd71d5b13550d7fb4f4454","file_id":"6481","creator":"scepeda","relation":"main_file","access_level":"open_access"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","related_material":{"record":[{"relation":"dissertation_contains","id":"6900","status":"public"},{"status":"public","relation":"dissertation_contains","id":"281"},{"status":"public","relation":"dissertation_contains","id":"2016"},{"relation":"dissertation_contains","id":"1576","status":"public"}]},"oa_version":"Published Version","month":"05","has_accepted_license":"1","language":[{"iso":"eng"}],"keyword":["Information estimation","Time-series","data analysis"]},{"external_id":{"arxiv":["1809.05105"]},"date_updated":"2022-08-22T07:41:07Z","year":"2018","citation":{"ista":"Bugnet LA, García RA, Davies GR, Mathur S, Corsaro E, Hall OJ, Rendle BM. 2018. FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. Astronomy &#38; Astrophysics. 620, A38.","mla":"Bugnet, Lisa Annabelle, et al. “FliPer: A Global Measure of Power Density to Estimate Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.” <i>Astronomy &#38; Astrophysics</i>, vol. 620, A38, EDP Sciences, 2018, doi:<a href=\"https://doi.org/10.1051/0004-6361/201833106\">10.1051/0004-6361/201833106</a>.","short":"L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, E. Corsaro, O.J. Hall, B.M. Rendle, Astronomy &#38; Astrophysics 620 (2018).","ieee":"L. A. Bugnet <i>et al.</i>, “FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants,” <i>Astronomy &#38; Astrophysics</i>, vol. 620. EDP Sciences, 2018.","chicago":"Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, E. Corsaro, O. J. Hall, and B. M. Rendle. “FliPer: A Global Measure of Power Density to Estimate Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.” <i>Astronomy &#38; Astrophysics</i>. EDP Sciences, 2018. <a href=\"https://doi.org/10.1051/0004-6361/201833106\">https://doi.org/10.1051/0004-6361/201833106</a>.","apa":"Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Corsaro, E., Hall, O. J., &#38; Rendle, B. M. (2018). FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. <i>Astronomy &#38; Astrophysics</i>. EDP Sciences. <a href=\"https://doi.org/10.1051/0004-6361/201833106\">https://doi.org/10.1051/0004-6361/201833106</a>","ama":"Bugnet LA, García RA, Davies GR, et al. FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. <i>Astronomy &#38; Astrophysics</i>. 2018;620. doi:<a href=\"https://doi.org/10.1051/0004-6361/201833106\">10.1051/0004-6361/201833106</a>"},"abstract":[{"lang":"eng","text":"Asteroseismology provides global stellar parameters such as masses, radii, or surface gravities using mean global seismic parameters and effective temperature for thousands of low-mass stars (0.8 M⊙ < M < 3 M⊙). This methodology has been successfully applied to stars in which acoustic modes excited by turbulent convection are measured. Other methods such as the Flicker technique can also be used to determine stellar surface gravities, but only works for log g above 2.5 dex. In this work, we present a new metric called FliPer (Flicker in spectral power density, in opposition to the standard Flicker measurement which is computed in the time domain); it is able to extend the range for which reliable surface gravities can be obtained (0.1 < log g < 4.6 dex) without performing any seismic analysis for stars brighter than Kp < 14. FliPer takes into account the average variability of a star measured in the power density spectrum in a given range of frequencies. However, FliPer values calculated on several ranges of frequency are required to better characterize a star. Using a large set of asteroseismic targets it is possible to calibrate the behavior of surface gravity with FliPer through machine learning. This calibration made with a random forest regressor covers a wide range of surface gravities from main-sequence stars to subgiants and red giants, with very small uncertainties from 0.04 to 0.1 dex. FliPer values can be inserted in automatic global seismic pipelines to either give an estimation of the stellar surface gravity or to assess the quality of the seismic results by detecting any outliers in the obtained νmax values. FliPer also constrains the surface gravities of main-sequence dwarfs using only long-cadence data for which the Nyquist frequency is too low to measure the acoustic-mode properties."}],"doi":"10.1051/0004-6361/201833106","arxiv":1,"day":"01","extern":"1","volume":620,"acknowledgement":"We thank the anonymous referee for the very useful comments. We would also like to thank M. Benbakoura for his help in analyzing the light curves of several binary systems included in our set of stars. L.B. and R.A.G. acknowledge the support from PLATO and GOLF CNES grants. S.M. acknowledges support from the National Aeronautics and Space Administration under Grant NNX15AF13G, the National Science Foundation grant AST-1411685, and the Ramon y Cajal fellowship no. RYC-2015-17697. E.C. is funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 664931. O.J.H and B.M.R. acknowledge the support of the UK Science and Technology Facilities Council (STFC). Funding for the Stellar Astrophysics Centre is provided by the Danish National Research Foundation (Grant DNRF106). This research has made use of NASA’s Astrophysics Data System. Data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.","author":[{"full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","last_name":"Bugnet","first_name":"Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"last_name":"García","first_name":"R. A.","full_name":"García, R. A."},{"first_name":"G. R.","last_name":"Davies","full_name":"Davies, G. R."},{"last_name":"Mathur","first_name":"S.","full_name":"Mathur, S."},{"last_name":"Corsaro","first_name":"E.","full_name":"Corsaro, E."},{"full_name":"Hall, O. J.","last_name":"Hall","first_name":"O. J."},{"full_name":"Rendle, B. M.","last_name":"Rendle","first_name":"B. M."}],"_id":"11618","scopus_import":"1","title":"FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants","intvolume":"       620","publication_status":"published","article_processing_charge":"No","date_created":"2022-07-18T14:37:39Z","quality_controlled":"1","article_type":"original","publisher":"EDP Sciences","date_published":"2018-12-01T00:00:00Z","type":"journal_article","oa":1,"publication_identifier":{"issn":["0004-6361"],"eissn":["1432-0746"]},"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://arxiv.org/abs/1809.05105","open_access":"1"}],"publication":"Astronomy & Astrophysics","month":"12","article_number":"A38","oa_version":"Preprint","language":[{"iso":"eng"}],"keyword":["Space and Planetary Science","Astronomy and Astrophysics","asteroseismology / methods","data analysis / stars","oscillations"]},{"publication":"arXiv","_id":"11631","author":[{"last_name":"Bugnet","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"full_name":"García, R. A.","last_name":"García","first_name":"R. A."},{"full_name":"Davies, G. R.","last_name":"Davies","first_name":"G. R."},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"full_name":"Hall, O. J.","last_name":"Hall","first_name":"O. J."},{"full_name":"Rendle, B. M.","last_name":"Rendle","first_name":"B. M."}],"oa_version":"Preprint","publication_status":"submitted","date_created":"2022-07-21T07:05:23Z","article_processing_charge":"No","month":"11","title":"FliPer: Classifying TESS pulsating stars","article_number":"1811.12140","language":[{"iso":"eng"}],"keyword":["asteroseismology - methods","data analysis - stars","oscillations"],"date_updated":"2022-08-22T08:41:55Z","year":"2018","citation":{"ama":"Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying TESS pulsating stars. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1811.12140\">10.48550/arXiv.1811.12140</a>","apa":"Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Hall, O. J., &#38; Rendle, B. M. (n.d.). FliPer: Classifying TESS pulsating stars. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1811.12140\">https://doi.org/10.48550/arXiv.1811.12140</a>","chicago":"Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M. Rendle. “FliPer: Classifying TESS Pulsating Stars.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1811.12140\">https://doi.org/10.48550/arXiv.1811.12140</a>.","ieee":"L. A. Bugnet, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M. Rendle, “FliPer: Classifying TESS pulsating stars,” <i>arXiv</i>. .","short":"L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, O.J. Hall, B.M. Rendle, ArXiv (n.d.).","mla":"Bugnet, Lisa Annabelle, et al. “FliPer: Classifying TESS Pulsating Stars.” <i>ArXiv</i>, 1811.12140, doi:<a href=\"https://doi.org/10.48550/arXiv.1811.12140\">10.48550/arXiv.1811.12140</a>.","ista":"Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying TESS pulsating stars. arXiv, 1811.12140."},"date_published":"2018-11-29T00:00:00Z","type":"preprint","external_id":{"arxiv":["1811.12140"]},"doi":"10.48550/arXiv.1811.12140","arxiv":1,"day":"29","abstract":[{"text":"The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few thousand stars observed by the CoRoT, Kepler and K2 missions. However, most of the TESS targets have not yet been properly classified and characterized. In order to improve the analysis of the TESS data, it is crucial to determine the type of stellar pulsations in a timely manner. We propose an automatic method to classify stars attending to their pulsation properties, in particular, to identify solar-like pulsators among all TESS targets. It relies on the use of the global amount of power contained in the power spectrum (already known as the FliPer method) as a key parameter, along with\r\nthe effective temperature, to feed into a machine learning classifier. Our study, based on TESS simulated datasets, shows that we are able to classify pulsators with a 98% accuracy.","lang":"eng"}],"oa":1,"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.1811.12140","open_access":"1"}],"extern":"1","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"author":[{"id":"d9edb345-f866-11ec-9b37-d119b5234501","orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","first_name":"Lisa Annabelle","last_name":"Bugnet"},{"full_name":"Garcia, R. A.","last_name":"Garcia","first_name":"R. A."},{"last_name":"Davies","first_name":"G. R.","full_name":"Davies, G. R."},{"full_name":"Mathur, S.","last_name":"Mathur","first_name":"S."},{"full_name":"Corsaro, E.","last_name":"Corsaro","first_name":"E."}],"_id":"11633","publication":"arXiv","title":"FliPer: Checking the reliability of global seismic parameters from automatic pipelines","month":"11","article_number":"1711.02890","oa_version":"Preprint","publication_status":"submitted","date_created":"2022-07-21T07:13:13Z","article_processing_charge":"No","language":[{"iso":"eng"}],"keyword":["asteroseismology - methods","data analysis - stars","oscillations"],"date_published":"2017-11-08T00:00:00Z","external_id":{"arxiv":["1711.02890"]},"type":"preprint","date_updated":"2022-08-22T08:45:42Z","year":"2017","citation":{"mla":"Bugnet, Lisa Annabelle, et al. “FliPer: Checking the Reliability of Global Seismic Parameters from Automatic Pipelines.” <i>ArXiv</i>, 1711.02890, doi:<a href=\"https://doi.org/10.48550/arXiv.1711.02890\">10.48550/arXiv.1711.02890</a>.","short":"L.A. Bugnet, R.A. Garcia, G.R. Davies, S. Mathur, E. Corsaro, ArXiv (n.d.).","ista":"Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the reliability of global seismic parameters from automatic pipelines. arXiv, 1711.02890.","apa":"Bugnet, L. A., Garcia, R. A., Davies, G. R., Mathur, S., &#38; Corsaro, E. (n.d.). FliPer: Checking the reliability of global seismic parameters from automatic pipelines. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1711.02890\">https://doi.org/10.48550/arXiv.1711.02890</a>","ama":"Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the reliability of global seismic parameters from automatic pipelines. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1711.02890\">10.48550/arXiv.1711.02890</a>","ieee":"L. A. Bugnet, R. A. Garcia, G. R. Davies, S. Mathur, and E. Corsaro, “FliPer: Checking the reliability of global seismic parameters from automatic pipelines,” <i>arXiv</i>. .","chicago":"Bugnet, Lisa Annabelle, R. A. Garcia, G. R. Davies, S. Mathur, and E. Corsaro. “FliPer: Checking the Reliability of Global Seismic Parameters from Automatic Pipelines.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1711.02890\">https://doi.org/10.48550/arXiv.1711.02890</a>."},"abstract":[{"lang":"eng","text":"Our understanding of stars through asteroseismic data analysis is limited by our ability to take advantage of the huge amount of observed stars provided by space missions such as CoRoT, Kepler , K2, and soon TESS and PLATO. Global seismic pipelines provide global stellar parameters such as mass and radius using the mean seismic parameters, as well as the effective temperature. These pipelines are commonly used automatically on thousands of stars observed by K2 for 3 months (and soon TESS for at least ∼ 1 month). However, pipelines are not immune from misidentifying noise peaks and stellar oscillations. Therefore, new validation techniques are required to assess the quality of these results. We present a new metric called FliPer (Flicker in Power), which takes into account the average variability at all measured time scales. The proper calibration of FliPer enables us to obtain good estimations of global stellar parameters such as surface gravity that are robust against the influence of noise peaks and hence are an excellent way to find faults in asteroseismic pipelines."}],"oa":1,"doi":"10.48550/arXiv.1711.02890","arxiv":1,"day":"08","extern":"1","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1711.02890","open_access":"1"}]},{"date_published":"2013-08-01T00:00:00Z","type":"journal_article","publication_identifier":{"eissn":["1862-278X"],"issn":["0013-5585"]},"oa":1,"file":[{"checksum":"cdfc5339b530a25d6079f7223f0b1f16","file_size":149825,"date_created":"2021-12-01T14:38:08Z","content_type":"application/pdf","file_name":"Schloegl_Abstract-BMT2013.pdf","date_updated":"2021-12-01T14:38:08Z","access_level":"open_access","success":1,"relation":"main_file","creator":"schloegl","file_id":"10397"}],"status":"public","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","publication":"Biomedical Engineering / Biomedizinische Technik","has_accepted_license":"1","oa_version":"Submitted Version","month":"08","article_number":"000010151520134181","language":[{"iso":"eng"}],"keyword":["biomedical engineering","data analysis","free software"],"conference":{"start_date":"2013-09-19","name":"BMT: Biomedizinische Technik ","location":"Graz, Austria","end_date":"2013-09-21"},"date_updated":"2021-12-02T12:51:12Z","citation":{"ista":"Schlögl A, Jonas PM, Schmidt-Hieber C, Guzman SJ. 2013. Stimfit: A fast visualization and analysis environment for cellular neurophysiology. Biomedical Engineering / Biomedizinische Technik. 58(SI-1-Track-G), 000010151520134181.","short":"A. Schlögl, P.M. Jonas, C. Schmidt-Hieber, S.J. Guzman, Biomedical Engineering / Biomedizinische Technik 58 (2013).","mla":"Schlögl, Alois, et al. “Stimfit: A Fast Visualization and Analysis Environment for Cellular Neurophysiology.” <i>Biomedical Engineering / Biomedizinische Technik</i>, vol. 58, no. SI-1-Track-G, 000010151520134181, De Gruyter, 2013, doi:<a href=\"https://doi.org/10.1515/bmt-2013-4181\">10.1515/bmt-2013-4181</a>.","ieee":"A. Schlögl, P. M. Jonas, C. Schmidt-Hieber, and S. J. Guzman, “Stimfit: A fast visualization and analysis environment for cellular neurophysiology,” <i>Biomedical Engineering / Biomedizinische Technik</i>, vol. 58, no. SI-1-Track-G. De Gruyter, 2013.","chicago":"Schlögl, Alois, Peter M Jonas, C. Schmidt-Hieber, and S. J. Guzman. “Stimfit: A Fast Visualization and Analysis Environment for Cellular Neurophysiology.” <i>Biomedical Engineering / Biomedizinische Technik</i>. De Gruyter, 2013. <a href=\"https://doi.org/10.1515/bmt-2013-4181\">https://doi.org/10.1515/bmt-2013-4181</a>.","ama":"Schlögl A, Jonas PM, Schmidt-Hieber C, Guzman SJ. Stimfit: A fast visualization and analysis environment for cellular neurophysiology. <i>Biomedical Engineering / Biomedizinische Technik</i>. 2013;58(SI-1-Track-G). doi:<a href=\"https://doi.org/10.1515/bmt-2013-4181\">10.1515/bmt-2013-4181</a>","apa":"Schlögl, A., Jonas, P. M., Schmidt-Hieber, C., &#38; Guzman, S. J. (2013). Stimfit: A fast visualization and analysis environment for cellular neurophysiology. <i>Biomedical Engineering / Biomedizinische Technik</i>. Graz, Austria: De Gruyter. <a href=\"https://doi.org/10.1515/bmt-2013-4181\">https://doi.org/10.1515/bmt-2013-4181</a>"},"year":"2013","external_id":{"pmid":["24042795"]},"doi":"10.1515/bmt-2013-4181","day":"01","abstract":[{"text":"Stimfit is a free cross-platform software package for viewing and analyzing electrophysiological data. It supports most standard file types for cellular neurophysiology and other biomedical formats. Its analysis algorithms have been used and validated in several experimental laboratories. Its embedded Python scripting interface makes Stimfit highly extensible and customizable.","lang":"eng"}],"volume":58,"ddc":["005","610"],"_id":"10396","pmid":1,"author":[{"last_name":"Schlögl","first_name":"Alois","full_name":"Schlögl, Alois","orcid":"0000-0002-5621-8100","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87"},{"id":"353C1B58-F248-11E8-B48F-1D18A9856A87","first_name":"Peter M","last_name":"Jonas","orcid":"0000-0001-5001-4804","full_name":"Jonas, Peter M"},{"full_name":"Schmidt-Hieber, C.","first_name":"C.","last_name":"Schmidt-Hieber"},{"full_name":"Guzman, S. J.","last_name":"Guzman","first_name":"S. J."}],"issue":"SI-1-Track-G","publication_status":"published","date_created":"2021-12-01T14:35:35Z","department":[{"_id":"PeJo"}],"article_processing_charge":"No","title":"Stimfit: A fast visualization and analysis environment for cellular neurophysiology","intvolume":"        58","quality_controlled":"1","file_date_updated":"2021-12-01T14:38:08Z","publisher":"De Gruyter","article_type":"original"}]