HD 46375: seismic and spectropolarimetric analysis of a young Sun hosting a Saturn-like planet

HD 46375 is known to host a Saturn-like exoplanet orbiting at 0.04 AU from its host star. Stellar light reflected by the planet was tentatively identified in the 34-day CoRoT run acquired in October-November 2008. We constrain the properties of the magnetic field of HD 46375 based on spectropolarimetric observations with the NARVAL spectrograph at the Pic du Midi observatory. In addition, we use a high-resolution NARVAL flux spectrum to contrain the atmospheric parameters. With these constraints, we perform an asteroseismic analysis and modelling of HD 46375 using the frequencies extracted from the CoRoT light curve. We used Zeeman Doppler imaging to reconstruct the magnetic map of the stellar surface. In the spectroscopic analysis we fitted isolated lines using 1D LTE atmosphere models. This analysis was used to constrain the effective temperature, surface gravity, and chemical composition of the star. To extract information about the p-mode oscillations, we used a technique based on the envelope autocorrelation function (EACF). From the Zeeman Doppler imaging observations, we observe a magnetic field of ~5 gauss. From the spectral analysis, HD 46375 is inferred to be an unevolved K0 type star with high metallicity [Fe/H]=+0.39. Owing to the relative faintness of the star (m_hip=8.05), the signal-to-noise ratio is too low to identify individual modes. However, we measure the p-mode excess power and large separation Delta nu_0=153.0 +/- 0.7 muHz. We are able do constrain the fundamental parameters of the star thanks to spectrometric and seismic analyses. We conclude that HD 46375 is similar to a young version of Alpha-CenB. This work is of special interest because of its combination of exoplanetary science and asteroseismology, which are the subjects of the current Kepler mission and the proposed PLATO mission.Comment: Accepted in Astronomy & Astrophysics. 8 pages, 9 figure

Spectroscopic monitoring of the Herbig Ae star HD 104237. II. Non-radial pulsations, mode analysis and fundamental stellar parameters

Herbig Ae/Be stars are intermediate-mass pre-main sequence (PMS) stars showing signs of intense activity and strong stellar winds, whose origin is not yet understood in the frame of current theoretical models of stellar evolution for young stars. The evolutionary tracks of the earlier Herbig Ae stars cross a recently discovered PMS instability strip. Many of these stars exhibit pulsations of delta Scuti type. HD 104237 is a well-known pulsating Herbig Ae star. In this article, we reinvestigated an extensive high-resolution quasi-continuous spectroscopic data set in order to search for very faint indications of non-radial pulsations in the line profile. To do this, we worked on dynamical spectra of equivalent photospheric (LSD) profiles of HD 104237. A 2D Fourier analysis (F2D) was performed of the entire profile and the temporal variation of the central depth of the line was studied with the time-series analysis tools Period04 and SigSpec. We present a mode identification corresponding to the detected dominant frequency. We perform a new accurate determination of the fundamental stellar parameters in view of a forthcoming asteroseismic modeling. Following the previous studies on this star, our analysis of the dynamical spectrum of recentered LSD profiles corresponding to the 22nd -25th of April 1999 nights spectra has confirmed the presence of multiple oscillation modes of low-degree l in HD 104237 and led to the first direct detection of a non-radial pulsation mode in this star: the dominant mode F1 was identified by the Fourier 2D method having a degree l value comprised between 1 and 2, the symmetry of the pattern variation indicating an azimuthal order of +1 or -1. The detailed study of the fundamental stellar parameters has provided a Teff, log g and iron abundance of 8550 +/- 150K, 3.9 +/- 0.3 and -4.38 +/- 0.19 (i.e. [Fe/H]=+0.16 +/- 0.19), respectively

Testing the chemical tagging technique with open clusters

Context. Stars are born together from giant molecular clouds and, if we assume that the priors were chemically homogeneous and well-mixed, we expect them to share the same chemical composition. Most of the stellar aggregates are disrupted while orbiting the Galaxy and most of the dynamic information is lost, thus the only possibility of reconstructing the stellar formation history is to analyze the chemical abundances that we observe today. Aims. The chemical tagging technique aims to recover disrupted stellar clusters based merely on their chemical composition. We evaluate the viability of this technique to recover co-natal stars that are no longer gravitationally bound. Methods. Open clusters are co-natal aggregates that have managed to survive together. We compiled stellar spectra from 31 old and intermediate-age open clusters, homogeneously derived atmospheric parameters, and 17 abundance species, and applied machine learning algorithms to group the stars based on their chemical composition. This approach allows us to evaluate the viability and efficiency of the chemical tagging technique. Results. We found that stars at different evolutionary stages have distinct chemical patterns that may be due to NLTE effects, atomic diffusion, mixing, and biases. When separating stars into dwarfs and giants, we observed that a few open clusters show distinct chemical signatures while the majority show a high degree of overlap. This limits the recovery of co-natal aggregates by applying the chemical tagging technique. Nevertheless, there is room for improvement if more elements are included and models are improved.Comment: accepted for publication in Astronomy and Astrophysics. Corrected typo

A mathematical theory of resolution limits for super-resolution of positive sources

The superresolving capacity for number and location recoveries in the super-resolution of positive sources is analyzed in this work. Specifically, we introduce the computational resolution limit for respectively the number detection and location recovery in the one-dimensional super-resolution problem and quantitatively characterize their dependency on the cutoff frequency, signal-to-noise ratio, and the sparsity of the sources. As a direct consequence, we show that targeting at the sparest positive solution in the super-resolution already provides the optimal resolution order. These results are generalized to multi-dimensional spaces. Our estimates indicate that there exist phase transitions in the corresponding reconstructions, which are confirmed by numerical experiments. Our theory fills in an important puzzle towards fully understanding the super-resolution of positive sources

Study on a miniaturized satellite payload for atmospheric temperature measurements

The atmospheric temperature reflects the thermal balance of the atmosphere and is a valuable indicator of climate change. It has been widely recognized that the atmospheric gravity wave activity has a profound effect on the large-scale circulation, thermal and constituent structures in the mesosphere and lower thermosphere (MLT). Temperature distribution in this region is an essential component to identify and quantify gravity waves. Observation from remote sensing instruments on satellite platforms is an effective way to measure the temperature in the MLT region. A miniaturized satellite payload is developed to measure the atmospheric temperature in the MLT region via observing the O2A-band emission. Following a Boltzmann distribution, the relative intensities of the emission lines can be used to derive the temperature profile. Based on the spatial heterodyne spectroscopy, this instrument is capable of resolving individual emission lines in the O2A-band for the spatial and spectral information simultaneously. The monolithic and compact feature of this spectrometer makes it suitable for operating on satellite platforms. In this work, the characterization of the instrument is investigated for the purpose of simultaneously measuring multiple emission lines of the O2A-band. The instrument is explored through a series of experimental methods, providing characteristics of the instrument and evaluation of its performance. In spatial and spectral domain, Level- 0 and Level-1 data processors are developed to convert the raw data to the calibrated spectral radiance for further temperature and gravity wave characterization. Within this framework, the performance of the utilized detector is evaluated along with its radiation tolerance in space environment. In the processor, the detector artifacts are corrected based on the measurements in laboratory or in space. The radiometric response of the instrument is characterized on a pixel-by-pixel basis using a blackbody. An interferogram distortion correction algorithm is developed to correct for the spatial and phase distortion induced by the detector optics. Further, localized phase distortion correction is implemented to correct for the remaining phase error. Unwanted ghost emission lines are removed based on two dimensional Fourier transform. In the spectral domain, the processing steps mainly consist of wavelength calibration and instrument spectral response correction, including filter response correction and modulation efficiency correction. As an in-orbit verification, the AtmoSHINE instrument was successfully deployed in space on 22th of December, 2018. In the first test phase, the functionality and the performance of the instrument in space were verified. The detector dark current measurement in orbit is consistent with the ground-based results. Based on the the calibration procedures and the developed data processing algorithms, the O2A-band emission lines can be successfully resolved. A cross-verification of the AtmoSHINE limb radiance profile with other satellite payload measurements indicates that the radiometric performance of the instrument is within the expectation. The retrieved temperature parameters are studied with respect to different number of samples and different objective functions in the optimization. This work verifies the ability of the instrument to derive the atmospheric temperature in the MLT region and its potential application in gravity wave detections

Sparse Modeling of Grouped Line Spectra

This licentiate thesis focuses on clustered parametric models for estimation of line spectra, when the spectral content of a signal source is assumed to exhibit some form of grouping. Different from previous parametric approaches, which generally require explicit knowledge of the model orders, this thesis exploits sparse modeling, where the orders are implicitly chosen. For line spectra, the non-linear parametric model is approximated by a linear system, containing an overcomplete basis of candidate frequencies, called a dictionary, and a large set of linear response variables that selects and weights the components in the dictionary. Frequency estimates are obtained by solving a convex optimization program, where the sum of squared residuals is minimized. To discourage overfitting and to infer certain structure in the solution, different convex penalty functions are introduced into the optimization. The cost trade-off between fit and penalty is set by some user parameters, as to approximate the true number of spectral lines in the signal, which implies that the response variable will be sparse, i.e., have few non-zero elements. Thus, instead of explicit model orders, the orders are implicitly set by this trade-off. For grouped variables, the dictionary is customized, and appropriate convex penalties selected, so that the solution becomes group sparse, i.e., has few groups with non-zero variables. In an array of sensors, the specific time-delays and attenuations will depend on the source and sensor positions. By modeling this, one may estimate the location of a source. In this thesis, a novel joint location and grouped frequency estimator is proposed, which exploits sparse modeling for both spectral and spatial estimates, showing robustness against sources with overlapping frequency content. For audio signals, this thesis uses two different features for clustering. Pitch is a perceptual property of sound that may be described by the harmonic model, i.e., by a group of spectral lines at integer multiples of a fundamental frequency, which we estimate by exploiting a novel adaptive total variation penalty. The other feature, chroma, is a concept in musical theory, collecting pitches at powers of 2 from each other into groups. Using a chroma dictionary, together with appropriate group sparse penalties, we propose an automatic transcription of the chroma content of a signal

A novel NMF-based DWI CAD framework for prostate cancer.

In this thesis, a computer aided diagnostic (CAD) framework for detecting prostate cancer in DWI data is proposed. The proposed CAD method consists of two frameworks that use nonnegative matrix factorization (NMF) to learn meaningful features from sets of high-dimensional data. The first technique, is a three dimensional (3D) level-set DWI prostate segmentation algorithm guided by a novel probabilistic speed function. This speed function is driven by the features learned by NMF from 3D appearance, shape, and spatial data. The second technique, is a probabilistic classifier that seeks to label a prostate segmented from DWI data as either alignat, contain cancer, or benign, containing no cancer. This approach uses a NMF-based feature fusion to create a feature space where data classes are clustered. In addition, using DWI data acquired at a wide range of b-values (i.e. magnetic field strengths) is investigated. Experimental analysis indicates that for both of these frameworks, using NMF producing more accurate segmentation and classification results, respectively, and that combining the information from DWI data at several b-values can assist in detecting prostate cancer