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PynPoint: An Image Processing Package for Finding Exoplanets

Adam Amara, Sascha Quanz

arXiv:1207.6637v2astro-ph.IMastro-ph.EP

TL;DR

Direct exoplanet imaging requires processing methods that can separate faint planetary signals from bright stellar structure. The paper presents PynPoint, a PCA-based PSF-subtraction package, and finds improved detection and flux-measurement performance over LOCI, including strong gains at small inner working angles and a high-signal-to-noise detection of β Pic b.

  • Problem

    Directly imaged planets are much fainter than their host stars, while existing image-processing techniques have not yet been optimized for these data.

  • Method

    PynPoint uses principal component analysis to build an empirical basis from ADI imaging data and model and subtract the stellar PSF.

  • Results

    PynPoint outperforms LOCI in planet detection and flux measurement, achieving up to a factor of five better sensitivity at ∼0.29′′ and more stable flux estimates.

  • Takeaways & Limitations

    PynPoint can be applied to a wide range of high-contrast imaging datasets and clearly detects β Pic b with signal-to-noise close to ∼20.

  • Takeaways & Limitations

    The initial PSF fitting omits masking because blind masked searches are computationally intensive; masking is reserved for analyses at specific candidate locations.

Abstract

from arXiv · show

We present the scientific performance results of PynPoint, our Python-based software package that uses principle component analysis to detect and estimate the flux of exoplanets in two dimensional imaging data. Recent advances in adaptive optics and imaging technology at visible and infrared wavelengths have opened the door to direct detections of planetary companions to nearby stars, but image processing techniques have yet to be optimized. We show that the performance of our approach gives a marked improvement over what is presently possible using existing methods such as LOCI. To test our approach, we use real angular differential imaging (ADI) data taken with the adaptive optics assisted high resolution near-infrared camera NACO at the VLT. These data were taken during the commissioning of the apodising phase plate (APP) coronagraph. By inserting simulated planets into these data, we test the performance of our method as a function of planet brightness for different positions on the image. We find that in all cases PynPoint has a detection threshold that is superior to that given by our LOCI analysis when assessed in a common statistical framework. We obtain our best improvements for smaller inner working angles (IWA). For an IWA of ~ 0.29" we find that we achieve a detection sensitivity that is a factor of 5 better than LOCI. We also investigate our ability to correctly measure the flux of planets. Again, we find improvements over LOCI, with PynPoint giving more stable results. Finally, we apply our package to a non-APP dataset of the exoplanet beta Pictoris b and reveal the planet with high signal-to-noise. This confirms that PynPoint can potentially be applied with high fidelity to a wide range of high-contrast imaging datasets.

1 INTRODUCTION

Direct exoplanet imaging is difficult because planets are much fainter than their host stars and have small projected separations. PynPoint addresses this data-analysis challenge and reports improved performance over LOCI, especially at small inner working angles.

  • Direct imaging is challenging because planetary flux is substantially smaller than stellar flux and projected planet–star separations are small.
  • Adaptive optics, coronagraphs, and differential observing strategies reduce stellar PSF structure and speckles that can mimic or obscure planets.
  • PynPoint is a new data-analysis and PSF-subtraction package designed for ADI datasets and does not require a special observing strategy or instrument setup.
  • At separations of ∼0.29′′, PynPoint provides up to a factor of five greater sensitivity than existing methods.
  • The paper evaluates PynPoint through simulations, planet detection and flux measurements, comparisons with LOCI, and an application to β Pic b.

2 DESCRIPTION OF TEST DATA

The test data are real NACO/VLT ADI observations obtained during APP coronagraph commissioning. The authors create simulations by inserting artificial planets into reduced image cutouts at selected image positions.

  • The validation dataset comprises AO-assisted NACO images from the VLT obtained during commissioning of the APP coronagraph.
  • The observations targeted the young nearby debris-disk host star HD115892 in pupil-stabilised ADI mode using the NB4.05 filter.
  • Individual exposures used a detector integration time of 0.5 s, and the dataset had no detected faint companions around HD115892.
  • After background subtraction, bad-pixel correction, and image alignment, the authors created 59x59-pixel postage stamps centered on the star.
  • Fake planets were inserted into individual raw images, with each simulation containing one planet at one of three Figure 1 positions.

3 OVERVIEW

PynPoint processes high-contrast imaging data by modeling stellar PSFs with empirically derived PCA basis functions. Its workflow combines basis construction, PSF fitting, masking and weighting choices, and image-stack averaging, with trade-offs involving planet self-subtraction, fitting errors, and computational cost.

  • 3 OVERVIEW: PynPoint uses four main steps: constructing a basis set, fitting the stellar PSF, correcting for the PSF and star, and averaging the image stack.The package is designed for reduced stacks of postage-stamp images.
  • 3.1.1 Overview of Basis Functions: PCA provides the main improvement by empirically constructing a basis set from the data rather than relying on generic functions.Empirical basis functions can express the underlying function efficiently with fewer coefficients than non-efficient alternatives.
  • 3.1.1 Overview of Basis Functions: The stellar PSF is modeled as a linear combination of basis functions, with coefficients calculated from the image using orthogonal basis functions when convenient.The PSF represents the central star's image after telescope, instrument, and atmospheric effects.
  • 3.1.2 PCA Basis Sets: PCA components are calculated from the singular-value decomposition of an M×N array whose rows are vectorized images, with V containing the orthogonal PCA basis.N is the number of pixels per image and M is the number of images in the stack.
  • 3.1.2 PCA Basis Sets: Preprocessing includes subpixel sampling, central masking, and mean subtraction before PCA decomposition.Subpixel sampling can double the example image resolution from 59×59 to 118×118 pixels, while masking removes the saturated, information-poor core.
  • 3.1.3 Further Considerations: Using the same data to construct and analyze the PCA can incorporate and remove the planet, while independent training data reduce bias but may produce less efficient PCA functions.Large stacks also make the matrix operations memory intensive, creating a trade-off between stack size and recovered subpixel resolution.
  • 3.2 Fitting the PSF: Masked PSF fitting limits planet inclusion and reduces overcorrection and flux bias, but worsens the fit inside the mask and increases random error.Gaussian weighting near the planet position can improve the fit, although the optimal weighting depends on the instrument and telescope.

4 PLANET DETECTION

PynPoint was evaluated against LOCI using simulated planets inserted into real ADI data at three positions and varying contrasts. Its detection images and signal-to-noise analysis show stronger detectability, especially at the innermost 0.29′′ separation, while a masked reanalysis remains a planned candidate-refinement step.

  • Simulation setup: Planets with contrasts from 8.0 to 11.0 mag were simulated at positions P1, P2, and P3 and analyzed with both LOCI and PynPoint.P1 and P2 were at 0.52′′ and 0.29′′, respectively; P3 was at 0.52′′ in the APP’s bad hemisphere.
  • Detection-image construction: PynPoint detection images divide averaged residual images by the square root of the variance image to produce a more even noise distribution.This enables signal-to-noise analysis across the image rather than restricting measurements to rings around the star.
  • Visual comparison: In Figure 3, PynPoint showed a substantial visual detectability improvement over LOCI for P2 planets with contrasts of 8.0 and 10.5 mag.The figure places LOCI results in the top panels and PynPoint detection-image results in the lower panels.
  • Detection performance: 50%: PynPoint’s signal-to-noise boost at the two outer positions enabled detection of planets 0.5 to 1.0 magnitude fainter than with LOCI at fixed thresholds.The outer positions were P1 and P3.
  • Detection performance: 5×: PynPoint’s signal-to-noise boost at P2, separated by ∼0.29′′, exceeded LOCI, which detected no planets at that position across the tested brightnesses.PynPoint detected the planet at 5σ down to magnitude 8.6 and at 3σ down to magnitude 9.5.
  • Future improvements: A two-step strategy could use relaxed initial detection, such as peaks above 3σ, followed by masked reanalysis at candidate locations, but this optimization remains uninvestigated.Masking was omitted during the blind search because it is computationally intensive, though it was feasible for targeted flux analysis.

5 MEASURING THE PLANET FLUX

PynPoint provides more stable planet-flux recovery than LOCI, whose flux losses vary with parameters, separation, and companion brightness.

  • LOCI flux losses depend on its parameters, angular separation, and companion brightness, complicating accurate analytical correction.
  • PynPoint uses residual images and planet masking during PSF modeling to limit possible biases in flux recovery.
  • PynPoint produces reasonably stable flux offsets across the three tested cases, whereas LOCI shows a large performance range.
  • In the worst cases, LOCI underestimates planet flux by nearly a factor of 5, while PynPoint underestimates it by a factor of 2.

6 APPLIED EXAMPLE — THE EXOPLANET β PICTORIS B

PynPoint was applied to publicly available VLT/NACO L′ ADI data of β Pictoris b, with detection remaining stable across a threefold change in PCA coefficients.

  • The β Pictoris b dataset was obtained with VLT/NACO in the L′ filter using angular differential imaging on 2009 December 26.
  • The dataset contains 80 data cubes of 300 exposures each, with 0.2 s detector integration times and approximately 44° of field rotation.
  • PynPoint consistently detects β Pictoris b at signal-to-noise ratios of 15 to 20 when the PCA-coefficient parameter changes from 20 to 60.

7 SUMMARY AND OUTLOOK

PynPoint models and subtracts the stellar PSF with principal component analysis, outperforming LOCI in simulated ADI tests and detecting β Pictoris b in real data.

  • PynPoint uses principal component analysis to model and subtract the central star’s PSF, revealing faint planetary companions.
  • PynPoint achieves 50% larger detection signal-to-noise than LOCI at approximately 0.59″ and up to fivefold better performance near 0.29″.
  • PynPoint gives more stable, and hence more accurate, planet-flux measurements than LOCI.
  • PynPoint reveals β Pictoris b in publicly available non-APP L′ data with a signal-to-noise ratio close to 20.
  • The empirically constructed basis set makes PynPoint fairly generic across wavelengths, while requiring careful selection mainly of the number of basis coefficients.
  • Future work targets sparse basis sets, temporal averaging, spatial algorithms, and testing on shorter-wavelength and different-instrument datasets.
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