852 resultados para light curves
The SARS algorithm: detrending CoRoT light curves with Sysrem using simultaneous external parameters
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Surveys for exoplanetary transits are usually limited not by photon noise but rather by the amount of red noise in their data. In particular, although the CoRoT space-based survey data are being carefully scrutinized, significant new sources of systematic noises are still being discovered. Recently, a magnitude-dependant systematic effect was discovered in the CoRoT data by Mazeh et al. and a phenomenological correction was proposed. Here we tie the observed effect to a particular type of effect, and in the process generalize the popular Sysrem algorithm to include external parameters in a simultaneous solution with the unknown effects. We show that a post-processing scheme based on this algorithm performs well and indeed allows for the detection of new transit-like signals that were not previously detected.
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The population of space debris increased drastically during the last years. These objects have become a great threat for active satellites. Because the relative velocities between space debris and satellites are high, space debris objects may destroy active satellites through collisions. Furthermore, collisions involving massive objects produce large number of fragments leading to significant growth of the space debris population. The long term evolution of the debris population is essentially driven by so-called catastrophic collisions. An effective remediation measure in order to stabilize the population in Low Earth Orbit (LEO) is therefore the removal of large, massive space debris. To remove these objects, not only precise orbits, but also more detailed information about their attitude states will be required. One important property of an object targeted for removal is its spin period, spin axis orientation and their change over time. Rotating objects will produce periodic brightness variations with frequencies which are related to the spin periods. Such a brightness variation over time is called a light curve. Collecting, but also processing light curves is challenging due to several reasons. Light curves may be undersampled, low frequency components due to phase angle and atmospheric extinction changes may be present, and beat frequencies may occur when the rotation period is close to a multiple of the sampling period. Depending on the method which is used to extract the frequencies, also method-specific properties have to be taken into account. The astronomical Institute of the University of Bern (AIUB) light curve database will be introduced, which contains more than 1,300 light curves acquired over more than seven years. We will discuss properties and reliability of different time series analysis methods tested and currently used by AIUB for the light curve processing. Extracted frequencies and reconstructed phases for some interesting targets, e.g. GLONASS satellites, for which also SLR data were available for the period confirmation, will be presented. Finally we will present the reconstructed phase and its evolution over time of a High-Area-to-Mass-Ratio (HAMR) object, which AIUB observed for several years.
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The population of space debris increased drastically during the last years. Collisions involving massive objects may produce large number of fragments leading to significantly growth of the space debris population. An effective remediation measure in order to stabilize the population in LEO, is therefore the removal of large, massive space debris. To remove these objects, not only precise orbits, but also more detailed information about their attitude states will be required. One important property of an object targeted for removal is its spin period and spin axis orientation. If we observe a rotating object, the observer sees different surface areas of the object which leads to changes in the measured intensity. Rotating objects will produce periodic brightness vari ations with frequencies which are related to the spin periods. Photometric monitoring is the real tool for remote diagnostics of the satellite rotation around its center of mass. This information is also useful, for example, in case of contingency. Moreover, it is also important to take into account the orientation of non-spherical body (e.g. space debris) in the numerical integration of its motion when a close approach with the another spacecr aft is predicted. We introduce the two databases of light curves: the AIUB data base, which contains about a thousand light curves of LEO, MEO and high-altitude debris objects (including a few functional objects) obtained over more than seven years, and the data base of the Astronomical Observatory of Odessa University (Ukraine), which contains the results of more than 10 years of photometric monitoring of functioning satellites and large space debris objects in low Earth orbit. AIUB used its 1m ZIMLAT telescope for all light curves. For tracking low-orbit satellites, the Astronomical Observatory of Odessa used the KT-50 telescope, which has an alt-azimuth mount and allows tracking objects moving at a high angular velocity. The diameter of the KT-50 main mirror is 0.5 m, and the focal length is 3 m. The Odessa's Atlas of light curves includes almost 5,5 thousand light curves for ~500 correlated objects from a time period of 2005-2014. The processing of light curves and the determination of the rotation period in the inertial frame is challenging. Extracted frequencies and reconstructed phases for some interesting targets, e.g. GLONASS satellites, for which also SLR data were available for confirmation, will be presented. The rotation of the Envisat satellite after its sudden failure will be analyzed. The deceleration of its rotation rate within 3 years is studied together with the attempt to determine the orientation of the rotation axis.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The currently proposed space debris remediation measures include the active removal of large objects and “just in time” collision avoidance by deviating the objects using, e.g., ground-based lasers. Both techniques require precise knowledge of the attitude state and state changes of the target objects. In the former case, to devise methods to grapple the target by a tug spacecraft, in the latter, to precisely propagate the orbits of potential collision partners as disturbing forces like air drag and solar radiation pressure depend on the attitude of the objects. Non-resolving optical observations of the magnitude variations, so-called light curves, are a promising technique to determine rotation or tumbling rates and the orientations of the actual rotation axis of objects, as well as their temporal changes. The 1-meter telescope ZIMLAT of the Astronomical Institute of the University of Bern has been used to collect light curves of MEO and GEO objects for a considerable period of time. Recently, light curves of Low Earth Orbit (LEO) targets were acquired as well. We present different observation methods, including active tracking using a CCD subframe readout technique, and the use of a high-speed scientific CMOS camera. Technical challenges when tracking objects with poor orbit redictions, as well as different data reduction methods are addressed. Results from a survey of abandoned rocket upper stages in LEO, examples of abandoned payloads and observations of high area-to-mass ratio debris will be resented. Eventually, first results of the analysis of these light curves are provided.
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Creation of cold dark matter (CCDM) can macroscopically be described by a negative pressure, and, therefore, the mechanism is capable to accelerate the Universe, without the need of an additional dark energy component. In this framework, we discuss the evolution of perturbations by considering a Neo-Newtonian approach where, unlike in the standard Newtonian cosmology, the fluid pressure is taken into account even in the homogeneous and isotropic background equations (Lima, Zanchin, and Brandenberger, MNRAS 291, L1, 1997). The evolution of the density contrast is calculated in the linear approximation and compared to the one predicted by the Lambda CDM model. The difference between the CCDM and Lambda CDM predictions at the perturbative level is quantified by using three different statistical methods, namely: a simple chi(2)-analysis in the relevant space parameter, a Bayesian statistical inference, and, finally, a Kolmogorov-Smirnov test. We find that under certain circumstances, the CCDM scenario analyzed here predicts an overall dynamics (including Hubble flow and matter fluctuation field) which fully recovers that of the traditional cosmic concordance model. Our basic conclusion is that such a reduction of the dark sector provides a viable alternative description to the accelerating Lambda CDM cosmology.
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Aims. We report the discovery of CoRoT-8b, a dense small Saturn-class exoplanet that orbits a K1 dwarf in 6.2 days, and we derive its orbital parameters, mass, and radius. Methods. We analyzed two complementary data sets: the photometric transit curve of CoRoT-8b as measured by CoRoT and the radial velocity curve of CoRoT-8 as measured by the HARPS spectrometer**. Results. We find that CoRoT-8b is on a circular orbit with a semi-major axis of 0.063 +/- 0.001 AU. It has a radius of 0.57 +/- 0.02 R(J), a mass of 0.22 +/- 0.03 M(J), and therefore a mean density of 1.6 +/- 0.1 g cm(-3). Conclusions. With 67% of the size of Saturn and 72% of its mass, CoRoT-8b has a density comparable to that of Neptune (1.76 g cm(-3)). We estimate its content in heavy elements to be 47-63 M(circle plus), and the mass of its hydrogen-helium envelope to be 7-23 M(circle plus). At 0.063 AU, the thermal loss of hydrogen of CoRoT-8b should be no more than similar to 0.1% over an assumed integrated lifetime of 3 Ga.
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Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.
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Direct comparisons between photosynthetic O-2 evolution rate and electron transport rate (ETR) were made in situ over 24 h using the benthic macroalga Ulva lactuca (Chlorophyta), growing and measured at a depth of 1.8 m, where the midday irradiance rose to 400-600 mumol photons m(-2) s(-1). O-2 exchange was measured with a 5-chamber data-logging apparatus and ETR with a submersible pulse amplitude modulated (PAM) fluorometer (Diving-PAM). Steady-state quantum yield ((Fm'-Ft)/Fm') decreased from 0.7 during the morning to 0.45 at midday, followed by some recovery in the late afternoon. At low to medium irradiances (0-300 mumol photons m(-2) s(-1)), there was a significant correlation between O-2 evolution and ETR, but at higher irradiances, ETR continued to increase steadily, while O-2 evolution tended towards an asymptote. However at high irradiance levels (600-1200 mumol photons m-(2) s(-1)) ETR was significantly lowered. Two methods of measuring ETR, based on either diel ambient light levels and fluorescence yields or rapid light curves, gave similar results at low to moderate irradiance levels. Nutrient enrichment (increases in [NO3-], [NH4+] and [HPO42-] of 5- to 15-fold over ambient concentrations) resulted in an increase, within hours, in photosynthetic rates measured by both ETR and O-2 evolution techniques. At low irradiances, approximately 6.5 to 8.2 electrons passed through PS II during the evolution of one molecule of O-2, i.e., up to twice the theoretical minimum number of four. However, in nutrient-enriched treatments this ratio dropped to 5.1. The results indicate that PAM fluorescence can be used as a good indication of the photosynthetic rate only at low to medium irradiances.
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Wide-range spectral coverage of blazar-type active galactic nuclei is of paramount importance for understanding the particle acceleration mechanisms assumed to take place in their jets. The Major Atmospheric Gamma Imaging Cerenkov (MAGIC) telescope participated in three multiwavelength (MWL) campaigns, observing the blazar Markarian (Mkn) 421 during the nights of April 28 and 29, 2006, and June 14, 2006. Aims. We analyzed the corresponding MAGIC very-high energy observations during 9 nights from April 22 to 30, 2006 and on June 14, 2006. We inferred light curves with sub-day resolution and night-by-night energy spectra. Methods. MAGIC detects γ-rays by observing extended air showers in the atmosphere. The obtained air-shower images were analyzed using the standard MAGIC analysis chain. Results. A strong γ-ray signal was detected from Mkn 421 on all observation nights. The flux (E > 250 GeV) varied on night-by-night basis between (0.92±0.11) × 10-10 cm-2 s-1 (0.57 Crab units) and (3.21±0.15) × 10-10 cm-2 s-1 (2.0 Crab units) in April 2006. There is a clear indication for intra-night variability with a doubling time of 36± min on the night of April 29, 2006, establishing once more rapid flux variability for this object. For all individual nights γ-ray spectra could be inferred, with power-law indices ranging from 1.66 to 2.47. We did not find statistically significant correlations between the spectral index and the flux state for individual nights. During the June 2006 campaign, a flux substantially lower than the one measured by the Whipple 10-m telescope four days later was found. Using a log-parabolic power law fit we deduced for some data sets the location of the spectral peak in the very-high energy regime. Our results confirm the indications of rising peak energy with increasing flux, as expected in leptonic acceleration models.
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L’étoile Wolf-Rayet WR 46 est connue pour sa variabilité complexe sur des échelles de temps relativement courtes de quelques heures et sur des échelles de temps plus longues de plusieurs mois. Des décalages périodiques mais intermittents en vitesse radiale ont déjà été observés dans ses raies d’émission optiques. Plusieurs périodes photométriques ont aussi été mesurées dans le passé. Des pulsations non-radiales, une modulation liée à la rotation rapide, ou encore la présence d’un compagnon de faible masse dont la présence reste à confirmer ont été proposées pour expliquer le comportement de l’étoile sur des échelles de temps de quelques heures. Dans un effort pour dévoiler sa vraie nature, nous avons observé WR 46 avec le satellite FUSE sur plusieurs cycles de variabilité à court terme. Nous avons trouvé des variations sur une échelle de temps d’environ 7,5 heures dans le continu ultraviolet lointain, dans l’aile bleue de la composante d’absorption du profil P Cygni du doublet de O vi 1032, 1038, ainsi que dans la composante d’absorption du profil P Cygni de S vi 933, 944. Nous avons également récupéré des données archivées de cette étoile obtenues avec le satellite XMM-Newton. La courbe de lumière en rayons X montre des variations sur une échelle de temps similaire aux courbes de lumière du continu ultraviolet et ultraviolet lointain, et le spectre rayons X de WR 46 est très mou avec un pic d’émission à des énergies plus faibles que 1 keV. Nous discutons des différentes contraintes sur la nature de la variabilité de cette étoile que ces nouvelles observations aident à poser. Parmi les scénarios suggérés, nous concluons que celui des pulsations non-radiales est le plus probable, bien que nous soyons encore loin d’une compréhension détaillée de WR 46.
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Ce mémoire s’intéresse au système binaire massif CV Serpentis, composé d’une Wolf- Rayet riche en carbone et d’une étoile de la séquence principale, de type spectral O (WC8d + O8-9IV). D’abord, certains phénomènes affectant les étoiles massives sont mentionnés, de leur passage sur la séquence principale à leur mort (supernova). Au cours du premier cha- pitre, un rappel est fait concernant certaines bases de l’astrophysique stellaire observa- tionnelle (diagramme Hertzsprung-Russell, phases évolutives, etc...). Au chapitre suivant, un des aspects les plus importants de la vie des étoiles massives est abordé : la perte de masse sous forme de vents stellaires. Un historique de la découverte des vents ouvre le chapitre, suivi des fondements théoriques permettant d’expliquer ce phénomène. Ensuite, différents aspects propres aux vents stellaires sont présentés. Au troisième chapitre, un historique détaillé de CV Ser est présenté en guise d’introduc- tion à cet objet singulier. Ses principales caractéristiques connues y sont mentionnées. Finalement, le cœur de ce mémoire se retrouve au chapitre 4. Des courbes de lumière ultra précises du satellite MOST (2009 et 2010) montrent une variation apparente du taux de perte de masse de la WR de l’ordre de 62% sur une période orbitale de 29.701 jours. L’analyse des résidus permet de trouver une signature suggérant la présence de régions d’interaction en corotation (en anglais corotating interaction regions, ou CIR) dans le vent WR. Une nouvelle solution orbitale est présentée ainsi que les paramètres de la région de collision des vents et les types spectraux sont confirmés.
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Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.