Impact of gaps in the asteroseismic characterization of pulsating stars. I. On the efficiency of pre-whitening

It is known that the observed distribution of frequencies in CoRoT and Kepler {\delta} Scuti stars has no parallelism with any theoretical model. Pre-whitening is a widespread technique in the analysis of time series with gaps from pulsating stars located in the classical instability strip such as {\delta} Scuti stars. However, some studies have pointed out that this technique might introduce biases in the results of the frequency analysis. This work aims at studying the biases that can result from pre-whitening in asteroseismology. The results will depend on the intrinsic range and distribution of frequencies of the stars. The periodic nature of the gaps in CoRoT observations, just in the range of the pulsational frequency content of the {\delta} Scuti stars, is shown to be crucial to determine their oscillation frequencies, the first step to perform asteroseismolgy of these objects. Hence, here we focus on the impact of pre-whitening on the asteroseismic characterization of {\delta} Scuti stars. We select a sample of 15 {\delta} Scuti stars observed by the CoRoT satellite, for which ultra-high quality photometric data have been obtained by its seismic channel. In order to study the impact on the asteroseismic characterization of {\delta} Scuti stars we perform the pre-whitening procedure on three datasets: gapped data, linearly interpolated data, and ARMA interpolated data. The different results obtained show that at least in some cases pre-whitening is not an efficient procedure for the deconvolution of the spectral window. therefore, in order to reduce the effect of the spectral window to the minimum it is necessary to interpolate with an algorithm that is aimed to preserve the original frequency content, and not only to perform a pre-whitening of the data.Comment: 27 pages, 47 figures Tables and typos fixe

Self-consistent method to extract non-linearities from pulsating stars light curves I. Combination frequencies

Combination frequencies are not solutions of the perturbed stellar structure equations. In dense power spectra from a light curve of a given multi-periodic pulsating star, they can compromise the mode identification in an asteroseismic analysis, hence they must be treated as spurious frequencies and conveniently removed. In this paper, a method based on fitting the set of frequencies that best describe a general non-linear model, like the Volterra series, is presented. The method allows to extract these frequencies from the power spectrum, so helping to improve the frequency analysis enabling hidden frequencies to emerge from the initially considered as noise. Moreover, the method yields frequencies with uncertainties several orders of magnitude smaller than the Rayleigh dispersion, usually taken as the present error in a standard frequency analysis. Furthermore, it is compatible with the classical counting cycles method, the so-called O-C method, which is valid only for mono-periodic stars. The method opens the possibility to characterise the non-linear behaviour of a given pulsating star by studying in detail the complex generalised transfer functions.Comment: 10 pages, 4 figures. Submitted to MNRA

Comparative study of simulated and observed blended light curves for unambiguous stellar rotation period determinations

Gyrochronology postulates that the age of stars similar in mass to our Sun can be approximated based on their rotational period. With this in mind, determining accurate rotation periods using photometry data from missions such as Kepler, K2, and TESS is vital for accurate stellar age estimates. Blended light curves pose a particular problem: When conducting simple aperture photometry, neighboring targets can taint the resulting light curve. In most cases, this issue makes the data unusable for unambiguous determination of stellar rotation periods. In this poster, we outline our research project, which aims to provide a solution to the issue of blended light curves. The project consists of computing a grid of simulated blended light curves and comparing them to observed blended photometric data from Kepler, K2, and TESS. Simulations will be computed using Butterpy, a Python package that yields the light curve of a particular model of starspots evolving through the stellar surface. We expect to quantitatively match any simulation from the grid to any of the blended light curves in our sample. Success in the project results will significantly impact other fields of astronomy that also use photometric data by facilitating a new collection of previously unusable data

Properties of satellite galaxies in the SDSS photometric survey: luminosities, colours and projected number density profiles

We analyze photometric data in SDSS-DR7 to infer statistical properties of faint satellites associated to isolated bright galaxies (M_r<-20.5) in the redshift range 0.03<z<0.1. The mean projected radial profile shows an excess of companions in the photometric sample around the primaries, with approximately a power law shape that extends up to ~700kpc. Given this overdensity signal, a suitable background subtraction method is used to study the statistical properties of the population of bound satellites, down to magnitude M_r=-14.5, in the projected radial distance range 100 < r_p/kpc < 3 R_{vir}. We have also considered a color cut consistent with the observed colors of spectroscopic satellites in nearby galaxies so that distant redshifted galaxies do not dominate the statistics. We have tested the implementation of this procedure using a mock catalog. We find that the method is effective in reproducing the true projected radial satellite number density profile and luminosity distributions, providing confidence in the results derived from SDSS data. The spatial extent of satellites is larger for bright, red primaries. Also, we find a larger spatial distribution of blue satellites. For the different samples analyzed, we derive the average number of satellites and their luminosity distributions down to M_r=-14.5. The mean number of satellites depends very strongly on host luminosity. Bright primaries (M_r<-21.5) host on average ~6 satellites with M_r<-14.5, while primaries with -21.5<M_r<-20.5 have less than 1 satellite per host. We provide Schechter function fits to the luminosity distributions of satellite galaxies with faint-end slopes -1.3+/-0.2. This shows that satellites of bright primaries lack an excess population of faint objects, in agreement with the results in the Milky Way and nearby galaxies.Comment: 14 pages, 13 figures. Accepted for publication in Astronomical Journa

The PL diagram for δ\delta Sct stars: back in business as distance estimators

In this work, we focus on the period-luminosity relation (PLR) of $\delta$ Sct stars, in which mode excitation and selection mechanisms are still poorly constrained, and whose structure and oscillations are affected by rotation. We review the PLRs in the recent literature, and add a new inference from a large sample of $\delta$ Sct. We highlight the difficulty in identifying the fundamental mode and show that rotation-induced surface effects can impact the measured luminosities, explaining the broadening of the PLR. We derive a tight relation between the low-order large separation and the fundamental radial mode frequency (F0) that holds for rotating stars, thus paving the way towards mode identification. We show that the PLRs we obtain for different samples are compatible with each other and with the recent literature, and with most observed $\delta$ Sct stars when taking rotation effects into account. We also find that the highest-amplitude peak in the frequency spectrum corresponds to the fundamental mode in most $\delta$ Sct, thus shedding some light on their elusive mode selection mechanism.Comment: 10 pages, 5 figures, 1 table, IAU conference proceedin

Altered Breast Development in Young Girls from an Agricultural Environment

In several human populations, the age at which female breast development begins is reported to have declined over the last five decades. Much debate has occurred over whether this reported decline has actually occurred and what factors contribute to it. However, geographical patterns reflecting earlier developmental onset in some human populations suggest environmental factors influence this phenomenon. These factors include interactions between genetic makeup, nutrition, and possible cumulative exposure to estrogens, both endogenous as well as environmental beginning during in utero development. We examined the onset of breast development in a group of peripubertal girls from the Yaqui Valley of Sonora, Mexico. We observed that girls from valley towns, areas using modern agricultural practices, exhibited larger breast fields than those of girls living in the foothills who exhibited similar stature [e.g., weight, height, body mass index (BMI)], and genetic background. Further, girls from valley towns displayed a poorly defined relationship between breast size and mammary gland development, whereas girls from the Yaqui foothills, where traditional ranching occurs, show a robust positive relationship between breast size and mammary size. The differences noted were obtained by a medically based exam involving morphometric analysis and palpation of tissues, in contrast to visual staging alone. In fact, use of the Tanner scale, involving visual staging of breast development for puberty, detected no differences between the study populations. Mammary tissue, determined by palpation, was absent in 18.5% of the girls living in agricultural areas, although palpable breast adipose tissue was present. No relationship was seen between mammary diameter and weight or BMI in either population. These data suggest that future in-depth studies examining mammary tissue growth and fat deposition in breast tissue are required if we are to understand environmental influences on these phenomena