---
_id: '11608'
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.'
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 .'
article_number: A125
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: S. N.
full_name: Breton, S. N.
last_name: Breton
- first_name: A. R. G.
full_name: Santos, A. R. G.
last_name: Santos
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: R. A.
full_name: García, R. A.
last_name: García
- first_name: P. L.
full_name: Pallé, P. L.
last_name: Pallé
citation:
ama: 'Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. ROOSTER:
A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy
& Astrophysics. 2021;647. doi:10.1051/0004-6361/202039947'
apa: 'Breton, S. N., Santos, A. R. G., Bugnet, L. A., Mathur, S., García, R. A.,
& Pallé, P. L. (2021). ROOSTER: A machine-learning analysis tool for Kepler
stellar rotation periods. Astronomy & Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202039947'
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.” Astronomy & Astrophysics. EDP Sciences,
2021. https://doi.org/10.1051/0004-6361/202039947.'
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,” Astronomy & Astrophysics, vol. 647. EDP Sciences, 2021.'
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
& Astrophysics. 647, A125.'
mla: 'Breton, S. N., et al. “ROOSTER: A Machine-Learning Analysis Tool for Kepler
Stellar Rotation Periods.” Astronomy & Astrophysics, vol. 647, A125,
EDP Sciences, 2021, doi:10.1051/0004-6361/202039947.'
short: S.N. Breton, A.R.G. Santos, L.A. Bugnet, S. Mathur, R.A. García, P.L. Pallé,
Astronomy & Astrophysics 647 (2021).
date_created: 2022-07-18T12:21:32Z
date_published: 2021-03-19T00:00:00Z
date_updated: 2022-08-22T08:47:47Z
day: '19'
doi: 10.1051/0004-6361/202039947
extern: '1'
external_id:
arxiv:
- '2101.10152'
intvolume: ' 647'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- 'methods: data analysis / stars: solar-type / stars: activity / stars: rotation
/ starspots'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2101.10152
month: '03'
oa: 1
oa_version: Preprint
publication: Astronomy & Astrophysics
publication_identifier:
eissn:
- 1432-0746
issn:
- 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 647
year: '2021'
...
---
_id: '11615'
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.
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.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Marc
full_name: Hon, Marc
last_name: Hon
- first_name: Dennis
full_name: Stello, Dennis
last_name: Stello
- first_name: Rafael A
full_name: García, Rafael A
last_name: García
- first_name: Savita
full_name: Mathur, Savita
last_name: Mathur
- first_name: Sanjib
full_name: Sharma, Sanjib
last_name: Sharma
- first_name: Isabel L
full_name: Colman, Isabel L
last_name: Colman
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
citation:
ama: Hon M, Stello D, García RA, et al. A search for red giant solar-like oscillations
in all Kepler data. Monthly Notices of the Royal Astronomical Society.
2019;485(4):5616-5630. doi:10.1093/mnras/stz622
apa: Hon, M., Stello, D., García, R. A., Mathur, S., Sharma, S., Colman, I. L.,
& Bugnet, L. A. (2019). A search for red giant solar-like oscillations in
all Kepler data. Monthly Notices of the Royal Astronomical Society. Oxford
University Press. https://doi.org/10.1093/mnras/stz622
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.” Monthly Notices of the Royal Astronomical
Society. Oxford University Press, 2019. https://doi.org/10.1093/mnras/stz622.
ieee: M. Hon et al., “A search for red giant solar-like oscillations in all
Kepler data,” Monthly Notices of the Royal Astronomical Society, vol. 485,
no. 4. Oxford University Press, pp. 5616–5630, 2019.
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.
mla: Hon, Marc, et al. “A Search for Red Giant Solar-like Oscillations in All Kepler
Data.” Monthly Notices of the Royal Astronomical Society, vol. 485, no.
4, Oxford University Press, 2019, pp. 5616–30, doi:10.1093/mnras/stz622.
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.
date_created: 2022-07-18T14:26:03Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2022-08-22T07:35:19Z
day: '01'
doi: 10.1093/mnras/stz622
extern: '1'
external_id:
arxiv:
- '1903.00115'
intvolume: ' 485'
issue: '4'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- asteroseismology
- 'methods: data analysis'
- 'techniques: image processing'
- 'stars: oscillations'
- 'stars: statistics'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1903.00115
month: '06'
oa: 1
oa_version: Preprint
page: 5616-5630
publication: Monthly Notices of the Royal Astronomical Society
publication_identifier:
eissn:
- 1365-2966
issn:
- 0035-8711
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: A search for red giant solar-like oscillations in all Kepler data
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 485
year: '2019'
...
---
_id: '11623'
abstract:
- lang: eng
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.
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. -"
article_number: '21'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: A. R. G.
full_name: Santos, A. R. G.
last_name: Santos
- first_name: R. A.
full_name: García, R. A.
last_name: García
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: J. L.
full_name: van Saders, J. L.
last_name: van Saders
- first_name: T. S.
full_name: Metcalfe, T. S.
last_name: Metcalfe
- first_name: G. V. A.
full_name: Simonian, G. V. A.
last_name: Simonian
- first_name: M. H.
full_name: Pinsonneault, M. H.
last_name: Pinsonneault
citation:
ama: Santos ARG, García RA, Mathur S, et al. Surface rotation and photometric activity
for Kepler targets. I. M and K main-sequence stars. The Astrophysical Journal
Supplement Series. 2019;244(1). doi:10.3847/1538-4365/ab3b56
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. The Astrophysical
Journal Supplement Series. IOP Publishing. https://doi.org/10.3847/1538-4365/ab3b56
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.” The Astrophysical Journal Supplement Series. IOP Publishing, 2019.
https://doi.org/10.3847/1538-4365/ab3b56.
ieee: A. R. G. Santos et al., “Surface rotation and photometric activity
for Kepler targets. I. M and K main-sequence stars,” The Astrophysical Journal
Supplement Series, vol. 244, no. 1. IOP Publishing, 2019.
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.” The Astrophysical Journal Supplement
Series, vol. 244, no. 1, 21, IOP Publishing, 2019, doi:10.3847/1538-4365/ab3b56.
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).
date_created: 2022-07-19T09:21:58Z
date_published: 2019-09-19T00:00:00Z
date_updated: 2022-08-22T08:10:38Z
day: '19'
doi: 10.3847/1538-4365/ab3b56
extern: '1'
external_id:
arxiv:
- '1908.05222'
intvolume: ' 244'
issue: '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
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1908.05222
month: '09'
oa: 1
oa_version: Preprint
publication: The Astrophysical Journal Supplement Series
publication_identifier:
issn:
- 0067-0049
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence
stars
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 244
year: '2019'
...
---
_id: '11630'
abstract:
- lang: eng
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.'
article_number: '1906.09611'
article_processing_charge: No
arxiv: 1
author:
- first_name: A. Le
full_name: Saux, A. Le
last_name: Saux
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: S. N.
full_name: Breton, S. N.
last_name: Breton
- first_name: R. A.
full_name: Garcia, R. A.
last_name: Garcia
citation:
ama: Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification
of K2 pulsating stars using machine learning techniques. arXiv. doi:10.48550/arXiv.1906.09611
apa: Saux, A. L., Bugnet, L. A., Mathur, S., Breton, S. N., & Garcia, R. A.
(n.d.). Automatic classification of K2 pulsating stars using machine learning
techniques. arXiv. https://doi.org/10.48550/arXiv.1906.09611
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.” ArXiv, n.d. https://doi.org/10.48550/arXiv.1906.09611.
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,” arXiv.
.
ista: Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification
of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.
mla: Saux, A. Le, et al. “Automatic Classification of K2 Pulsating Stars Using Machine
Learning Techniques.” ArXiv, 1906.09611, doi:10.48550/arXiv.1906.09611.
short: A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.).
date_created: 2022-07-21T06:57:10Z
date_published: 2019-06-23T00:00:00Z
date_updated: 2022-08-22T08:20:29Z
day: '23'
doi: 10.48550/arXiv.1906.09611
extern: '1'
external_id:
arxiv:
- '1906.09611'
keyword:
- asteroseismology - methods
- data analysis - thecniques
- machine learning - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1906.09611
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Automatic classification of K2 pulsating stars using machine learning techniques
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...
---
_id: '6473'
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. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Sarah A
full_name: Cepeda Humerez, Sarah A
id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87
last_name: Cepeda Humerez
citation:
ama: Cepeda Humerez SA. Estimating information flow in single cells. 2019. doi:10.15479/AT:ISTA:6473
apa: Cepeda Humerez, S. A. (2019). Estimating information flow in single cells.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:6473
chicago: Cepeda Humerez, Sarah A. “Estimating Information Flow in Single Cells.”
Institute of Science and Technology Austria, 2019. https://doi.org/10.15479/AT:ISTA:6473.
ieee: S. A. Cepeda Humerez, “Estimating information flow in single cells,” Institute
of Science and Technology Austria, 2019.
ista: Cepeda Humerez SA. 2019. Estimating information flow in single cells. Institute
of Science and Technology Austria.
mla: Cepeda Humerez, Sarah A. Estimating Information Flow in Single Cells.
Institute of Science and Technology Austria, 2019, doi:10.15479/AT:ISTA:6473.
short: S.A. Cepeda Humerez, Estimating Information Flow in Single Cells, Institute
of Science and Technology Austria, 2019.
date_created: 2019-05-21T00:11:23Z
date_published: 2019-05-23T00:00:00Z
date_updated: 2025-05-28T11:57:00Z
day: '23'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:6473
file:
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creator: scepeda
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has_accepted_license: '1'
keyword:
- Information estimation
- Time-series
- data analysis
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '135'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '6900'
relation: dissertation_contains
status: public
- id: '281'
relation: dissertation_contains
status: public
- id: '2016'
relation: dissertation_contains
status: public
- id: '1576'
relation: dissertation_contains
status: public
status: public
supervisor:
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
title: Estimating information flow in single cells
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '11618'
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.
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.
article_number: A38
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: R. A.
full_name: García, R. A.
last_name: García
- first_name: G. R.
full_name: Davies, G. R.
last_name: Davies
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: E.
full_name: Corsaro, E.
last_name: Corsaro
- first_name: O. J.
full_name: Hall, O. J.
last_name: Hall
- first_name: B. M.
full_name: Rendle, B. M.
last_name: Rendle
citation:
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. Astronomy & Astrophysics. 2018;620. doi:10.1051/0004-6361/201833106'
apa: 'Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Corsaro, E., Hall,
O. J., & Rendle, B. M. (2018). FliPer: A global measure of power density to
estimate surface gravities of main-sequence solar-like stars and red giants. Astronomy
& Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/201833106'
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.” Astronomy
& Astrophysics. EDP Sciences, 2018. https://doi.org/10.1051/0004-6361/201833106.'
ieee: 'L. A. Bugnet et al., “FliPer: A global measure of power density to
estimate surface gravities of main-sequence solar-like stars and red giants,”
Astronomy & Astrophysics, vol. 620. EDP Sciences, 2018.'
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 & 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.”
Astronomy & Astrophysics, vol. 620, A38, EDP Sciences, 2018, doi:10.1051/0004-6361/201833106.'
short: L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, E. Corsaro, O.J. Hall,
B.M. Rendle, Astronomy & Astrophysics 620 (2018).
date_created: 2022-07-18T14:37:39Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2022-08-22T07:41:07Z
day: '01'
doi: 10.1051/0004-6361/201833106
extern: '1'
external_id:
arxiv:
- '1809.05105'
intvolume: ' 620'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- asteroseismology / methods
- data analysis / stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1809.05105
month: '12'
oa: 1
oa_version: Preprint
publication: Astronomy & Astrophysics
publication_identifier:
eissn:
- 1432-0746
issn:
- 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'FliPer: A global measure of power density to estimate surface gravities of
main-sequence solar-like stars and red giants'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 620
year: '2018'
...
---
_id: '11631'
abstract:
- lang: eng
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."
article_number: '1811.12140'
article_processing_charge: No
arxiv: 1
author:
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: R. A.
full_name: García, R. A.
last_name: García
- first_name: G. R.
full_name: Davies, G. R.
last_name: Davies
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: O. J.
full_name: Hall, O. J.
last_name: Hall
- first_name: B. M.
full_name: Rendle, B. M.
last_name: Rendle
citation:
ama: 'Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying
TESS pulsating stars. arXiv. doi:10.48550/arXiv.1811.12140'
apa: 'Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Hall, O. J., &
Rendle, B. M. (n.d.). FliPer: Classifying TESS pulsating stars. arXiv.
https://doi.org/10.48550/arXiv.1811.12140'
chicago: 'Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, O. J. Hall,
and B. M. Rendle. “FliPer: Classifying TESS Pulsating Stars.” ArXiv, n.d.
https://doi.org/10.48550/arXiv.1811.12140.'
ieee: 'L. A. Bugnet, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M.
Rendle, “FliPer: Classifying TESS pulsating stars,” arXiv. .'
ista: 'Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying
TESS pulsating stars. arXiv, 1811.12140.'
mla: 'Bugnet, Lisa Annabelle, et al. “FliPer: Classifying TESS Pulsating Stars.”
ArXiv, 1811.12140, doi:10.48550/arXiv.1811.12140.'
short: L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, O.J. Hall, B.M. Rendle,
ArXiv (n.d.).
date_created: 2022-07-21T07:05:23Z
date_published: 2018-11-29T00:00:00Z
date_updated: 2022-08-22T08:41:55Z
day: '29'
doi: 10.48550/arXiv.1811.12140
extern: '1'
external_id:
arxiv:
- '1811.12140'
keyword:
- asteroseismology - methods
- data analysis - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1811.12140'
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'FliPer: Classifying TESS pulsating stars'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '11633'
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.
article_number: '1711.02890'
article_processing_charge: No
arxiv: 1
author:
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: R. A.
full_name: Garcia, R. A.
last_name: Garcia
- first_name: G. R.
full_name: Davies, G. R.
last_name: Davies
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: E.
full_name: Corsaro, E.
last_name: Corsaro
citation:
ama: 'Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the
reliability of global seismic parameters from automatic pipelines. arXiv.
doi:10.48550/arXiv.1711.02890'
apa: 'Bugnet, L. A., Garcia, R. A., Davies, G. R., Mathur, S., & Corsaro, E.
(n.d.). FliPer: Checking the reliability of global seismic parameters from automatic
pipelines. arXiv. https://doi.org/10.48550/arXiv.1711.02890'
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.” ArXiv, n.d. https://doi.org/10.48550/arXiv.1711.02890.'
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,”
arXiv. .'
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.'
mla: 'Bugnet, Lisa Annabelle, et al. “FliPer: Checking the Reliability of Global
Seismic Parameters from Automatic Pipelines.” ArXiv, 1711.02890, doi:10.48550/arXiv.1711.02890.'
short: L.A. Bugnet, R.A. Garcia, G.R. Davies, S. Mathur, E. Corsaro, ArXiv (n.d.).
date_created: 2022-07-21T07:13:13Z
date_published: 2017-11-08T00:00:00Z
date_updated: 2022-08-22T08:45:42Z
day: '08'
doi: 10.48550/arXiv.1711.02890
extern: '1'
external_id:
arxiv:
- '1711.02890'
keyword:
- asteroseismology - methods
- data analysis - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1711.02890
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'FliPer: Checking the reliability of global seismic parameters from automatic
pipelines'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '10396'
abstract:
- lang: eng
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.
article_number: '000010151520134181'
article_processing_charge: No
article_type: original
author:
- first_name: Alois
full_name: Schlögl, Alois
id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
last_name: Schlögl
orcid: 0000-0002-5621-8100
- first_name: Peter M
full_name: Jonas, Peter M
id: 353C1B58-F248-11E8-B48F-1D18A9856A87
last_name: Jonas
orcid: 0000-0001-5001-4804
- first_name: C.
full_name: Schmidt-Hieber, C.
last_name: Schmidt-Hieber
- first_name: S. J.
full_name: Guzman, S. J.
last_name: Guzman
citation:
ama: 'Schlögl A, Jonas PM, Schmidt-Hieber C, Guzman SJ. Stimfit: A fast visualization
and analysis environment for cellular neurophysiology. Biomedical Engineering
/ Biomedizinische Technik. 2013;58(SI-1-Track-G). doi:10.1515/bmt-2013-4181'
apa: 'Schlögl, A., Jonas, P. M., Schmidt-Hieber, C., & Guzman, S. J. (2013).
Stimfit: A fast visualization and analysis environment for cellular neurophysiology.
Biomedical Engineering / Biomedizinische Technik. Graz, Austria: De Gruyter.
https://doi.org/10.1515/bmt-2013-4181'
chicago: 'Schlögl, Alois, Peter M Jonas, C. Schmidt-Hieber, and S. J. Guzman. “Stimfit:
A Fast Visualization and Analysis Environment for Cellular Neurophysiology.” Biomedical
Engineering / Biomedizinische Technik. De Gruyter, 2013. https://doi.org/10.1515/bmt-2013-4181.'
ieee: 'A. Schlögl, P. M. Jonas, C. Schmidt-Hieber, and S. J. Guzman, “Stimfit: A
fast visualization and analysis environment for cellular neurophysiology,” Biomedical
Engineering / Biomedizinische Technik, vol. 58, no. SI-1-Track-G. De Gruyter,
2013.'
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.'
mla: 'Schlögl, Alois, et al. “Stimfit: A Fast Visualization and Analysis Environment
for Cellular Neurophysiology.” Biomedical Engineering / Biomedizinische Technik,
vol. 58, no. SI-1-Track-G, 000010151520134181, De Gruyter, 2013, doi:10.1515/bmt-2013-4181.'
short: A. Schlögl, P.M. Jonas, C. Schmidt-Hieber, S.J. Guzman, Biomedical Engineering
/ Biomedizinische Technik 58 (2013).
conference:
end_date: 2013-09-21
location: Graz, Austria
name: 'BMT: Biomedizinische Technik '
start_date: 2013-09-19
date_created: 2021-12-01T14:35:35Z
date_published: 2013-08-01T00:00:00Z
date_updated: 2021-12-02T12:51:12Z
day: '01'
ddc:
- '005'
- '610'
department:
- _id: PeJo
doi: 10.1515/bmt-2013-4181
external_id:
pmid:
- '24042795'
file:
- access_level: open_access
checksum: cdfc5339b530a25d6079f7223f0b1f16
content_type: application/pdf
creator: schloegl
date_created: 2021-12-01T14:38:08Z
date_updated: 2021-12-01T14:38:08Z
file_id: '10397'
file_name: Schloegl_Abstract-BMT2013.pdf
file_size: 149825
relation: main_file
success: 1
file_date_updated: 2021-12-01T14:38:08Z
has_accepted_license: '1'
intvolume: ' 58'
issue: SI-1-Track-G
keyword:
- biomedical engineering
- data analysis
- free software
language:
- iso: eng
month: '08'
oa: 1
oa_version: Submitted Version
pmid: 1
publication: Biomedical Engineering / Biomedizinische Technik
publication_identifier:
eissn:
- 1862-278X
issn:
- 0013-5585
publication_status: published
publisher: De Gruyter
quality_controlled: '1'
status: public
title: 'Stimfit: A fast visualization and analysis environment for cellular neurophysiology'
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 58
year: '2013'
...