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Register a new userMode Angular Degree Identification in Subgiant Stars with Convolutional Neural Networks based on Power Spectrum
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Identifying the angular degrees $l$ of oscillation modes is essential for asteroseismology and depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial ($l$= 0) mode frequencies distributed linearly in frequency, while non-radial ($l$ >= 1) modes are p-g mixed modes that having a complex distribution in frequency, which increased the difficulty of identifying $l$. In this study, we trained a 1D convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved a 95 per cent accuracy on Kepler data.
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We present a measurement of the $B$-mode polarization power spectrum of the cosmic microwave background (CMB) using taken from July 2014 to December 2016 with the POLARBEAR experiment. The CMB power spectra are measured using observations at 150 GHz with an instantaneous array sensitivity of $mathrm{NET}_mathrm{array}=23, mu mathrm{K} sqrt{mathrm{s}}$ on a 670 square degree patch of sky centered at (RA, Dec)=($+0^mathrm{h}12^mathrm{m}0^mathrm{s},-59^circ18^prime$). A continuously rotating half-wave plate is used to modulate polarization and to suppress low-frequency noise. We achieve $32,mumathrm{K}$-$mathrm{arcmin}$ effective polarization map noise with a knee in sensitivity of $ell = 90$, where the inflationary gravitational wave signal is expected to peak. The measured $B$-mode power spectrum is consistent with a $Lambda$CDM lensing and single dust component foreground model over a range of multipoles $50 leq ell leq 600$. The data disfavor zero $C_ell^{BB}$ at $2.2sigma$ using this $ell$ range of POLARBEAR data alone. We cross-correlate our data with Planck high frequency maps and find the low-$ell$ $B$-mode power in the combined dataset to be consistent with thermal dust emission. We place an upper limit on the tensor-to-scalar ratio $r < 0.90$ at 95% confidence level after marginalizing over foregrounds.
Spectropolarimetric
Oscillation modes in fast-rotating stars can be split into several subclasses, each with their own properties. To date, seismology of these stars cannot rely on regular pattern analysis and scaling relations. However, recently there has been the promising discovery of large separations observed in spectra of fast-rotating $delta$ Scuti stars: they were attributed to the island-mode subclass, and linked to the stellar mean density through a scaling law. In this work, we investigate the relevance of this scaling relation by computing models of fast-rotating stars and their oscillation spectra. In order to sort the thousands of oscillation modes thus obtained, we train a convolutional neural network isolating the island modes with 96% accuracy. Arguing that the observed large separation is systematically smaller than the asymptotic one, we retrieve the observational $Delta u - overline{rho}$ scaling law. This relation will be used to drive forward modelling efforts, and is a first step towards mode identification and
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.
The existence of mixed modes in stars is a marker of stellar evolution. Their detection serves for a better determination of stellar age. The goal of this paper is to identify the dipole modes in an automatic manner without human intervention. I use the power spectra obtained by the Kepler mission for the application of the method. I compute asymptotic dipole mode frequencies as a function of coupling factor and dipole period spacing, and other parameters. For each star, I collapse the power in an echelle diagramme aligned onto the monopole and dipole mixed modes. The power at the null frequency is used as a figure of merit. Using a genetic algorithm, I then optimise the figure of merit by adjusting the location of the dipole frequencies in the power spectrum}. Using published frequencies, I compare the asymptotic dipole mode frequencies with published frequencies. I also used published frequencies for deriving coupling factor and dipole period spacing using a non-linear least squares fit. I use Monte-Carlo simulations of the non-linear least square fit for deriving error bars for each parameters. From the 44 subgiants studied, the automatic identification allows to retrieve within 3 $mu$Hz at least 80% of the modes for 32 stars, and within 6 $mu$Hz at least 90% of the modes for 37 stars. The optimised and fitted gravity-mode period spacing and coupling factor agree with previous measurements. Random errors for the mixed-mode parameters deduced from Monte-Carlo simulation are about 30-50 times smaller than previously determined errors, which are in fact systematic errors. The period spacing and coupling factors of mixed modes in subgiants are confirmed. The current automated procedure will need to be improved using a more accurate asymptotic model and/or proper statistical tests.
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