اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFliPer: Checking the reliability of global seismic parameters from automatic pipelines
56
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
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 around 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.
قيم البحث
اقرأ أيضاً
Calibration issues associated to scrambled collateral smear affecting the Kepler short-cadence data were discovered in the Data Release 24 and were found to be present in all the previous data releases since launch. In consequence, a new Data Release
25 was reprocessed to correct for these problems. We perform here a preliminary study to evaluate the impact on the extracted global seismic and background parameters between data releases. We analyze the sample of seismic solar analogs observed by Kepler in short cadence between Q5 and Q17. We start with this set of stars as it constitutes the best sample to put the Sun into context along its evolution, and any significant differences on the seismic and background parameters need to be investigated before any further studies of this sample can take place. We use the A2Z pipeline to derive both global seismic parameters and background parameters from the Data Release 25 and previous data releases and report on the measured differences.
Seismology of stars that exhibit solar-like oscillations develops a growing interest with the wealth of observational results obtained with the CoRoT and Kepler space-borne missions. In this framework, relations between asteroseismic quantities and s
tellar parameters provide a unique opportunity to derive model-independent determinations of stellar parameters (e.g., masses and radii) for a large sample of stars. I review those scaling relations with particular emphasis on the underlying physical processes governing those relations, as well as their uncertainties.
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 o
bserved by the CoRoT, $textit{Kepler}$ 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 the 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.
Asteroseismology provides global stellar parameters such as masses, radii or surface gravities using the mean global seismic parameters as well as the effective temperature for thousands of low-mass stars $(0.8 M_odot <M<3 M_odot)$. This methodology
has been successfully applied to stars in which acoustic modes excited by turbulent convection are measured. Other techniques such as the Flicker 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 (the acronym stands for Flicker in spectral power density, in opposition to the standard Flicker measurement which is computed in the time domain that 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 $textit{Kp}$ $<$ 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 $ u_{max}$ values. FliPer also constrain 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.
Asteroseismology is well-established in astronomy as the gold standard for determining precise and accurate fundamental stellar properties like masses, radii, and ages. Several tools have been developed for asteroseismic analyses but many of them are
closed-source and therefore not accessible to the general astronomy community. Here we present $texttt{pySYD}$, a Python package for detecting solar-like oscillations and measuring global asteroseismic parameters. $texttt{pySYD}$ was adapted from the IDL-based $texttt{SYD}$ pipeline, which was extensively used to measure asteroseismic parameters for $textit{Kepler}$ stars. $texttt{pySYD}$ was developed using the same well-tested methodology and comes with several new improvements to provide accessible and reproducible results. Well-documented, open-source asteroseismology software that has been benchmarked against closed-source tools are critical to ensure the reproducibility of legacy results from the $textit{Kepler}$ mission. Moreover, $texttt{pySYD}$ will also be a promising tool for the broader astronomy community to analyze current and forthcoming data from the NASA TESS mission.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
