TY - JOUR
T1 - Applying a temporal systematics model to vector Apodizing Phase Plate coronagraphic data: TRAP4vAPP
AU - Liu, Pengyu
AU - Bohn, Alexander J.
AU - Doelman, David S.
AU - Sutlieff, Ben J.
AU - Samland, Matthias
AU - Kenworthy, Matthew A.
AU - Snik, Frans
AU - Birkby, Jayne L.
AU - Biller, Beth A.
AU - Males, Jared R.
AU - Morzinski, Katie M.
AU - Close, Laird M.
AU - Otten, Gilles P.P.L.
N1 - Funding Information:
We thank the referee for providing constructive comments that helped to improve the clarity of this paper. B.J.S. is fully supported by the Netherlands Research School for Astronomy (NOVA). J.L.B. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No 805445. This paper includes data gathered with the 6.5 metre Magellan Telescopes located at Las Campanas Observatory, Chile, and the Large Binocular Telescope located in Arizona. The LBT is an international collaboration among institutions in the United States, Italy and Germany. LBT Corporation partners are: The University of Arizona on behalf of the Arizona university system; Istituto Nazionale di Astrofísica, Italy; LBT Beteiligungsgesellschaft Germany, representing the Max-Planck Society, the Astrophysical Institute Potsdam, and Heidelberg University; The Ohio State University, and The Research Corporation, on behalf of The University of Notre Dame, University of Minnesota, and University of Virginia. We thank all LBTI team members for their efforts that enabled this work. We gratefully acknowledge the use of Native land for our observations. LBT observations were conducted on the stolen land of the Ndee/Nnee, Chiricahua, Mescalero and San Carlos Apache tribes. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013, 2018), SciPy (Virtanen et al. 2020), Scikit-learn (Pedregosa et al. 2011), NumPy (Harris et al. 2020) and Matplotlib, a Python library for publication quality graphics (Hunter 2007).
Publisher Copyright:
© The Authors 2023.
PY - 2023/6/12
Y1 - 2023/6/12
N2 - Context. The vector Apodizing Phase Plate (vAPP) is a pupil plane coronagraph that suppresses starlight by forming a dark hole in its point spread function (PSF). The unconventional and non-axisymmetrical PSF arising from the phase modification applied by this coronagraph presents a special challenge to post-processing techniques. Aims. We aim to implement a recently developed post-processing algorithm, temporal reference analysis of planets (TRAP) on vAPP coronagraphic data. The property of TRAP that uses non-local training pixels, combined with the unconventional PSF of vAPP, allows for more flexibility than previous spatial algorithms in selecting reference pixels to model systematic noise. Methods. Datasets from two types of vAPPs are analysed: a double grating-vAPP (dgvAPP360) that produces a single symmetric PSF and a grating-vAPP (gvAPP180) that produces two D-shaped PSFs. We explore how to choose reference pixels to build temporal systematic noise models in TRAP for them. We then compare the performance of TRAP with previously implemented algorithms that produced the best signal-to-noise ratio (S/N) in companion detections in these datasets. Results. We find that the systematic noise between the two D-shaped PSFs is not as temporally associated as expected. Conversely, there is still a significant number of systematic noise sources that are shared by the dark hole and the bright side in the same PSF. We should choose reference pixels from the same PSF when reducing the dgvAPP360 dataset or the gvAPP180 dataset with TRAP. In these datasets, TRAP achieves results consistent with previous best detections, with an improved S/N for the gvAPP180 dataset.
AB - Context. The vector Apodizing Phase Plate (vAPP) is a pupil plane coronagraph that suppresses starlight by forming a dark hole in its point spread function (PSF). The unconventional and non-axisymmetrical PSF arising from the phase modification applied by this coronagraph presents a special challenge to post-processing techniques. Aims. We aim to implement a recently developed post-processing algorithm, temporal reference analysis of planets (TRAP) on vAPP coronagraphic data. The property of TRAP that uses non-local training pixels, combined with the unconventional PSF of vAPP, allows for more flexibility than previous spatial algorithms in selecting reference pixels to model systematic noise. Methods. Datasets from two types of vAPPs are analysed: a double grating-vAPP (dgvAPP360) that produces a single symmetric PSF and a grating-vAPP (gvAPP180) that produces two D-shaped PSFs. We explore how to choose reference pixels to build temporal systematic noise models in TRAP for them. We then compare the performance of TRAP with previously implemented algorithms that produced the best signal-to-noise ratio (S/N) in companion detections in these datasets. Results. We find that the systematic noise between the two D-shaped PSFs is not as temporally associated as expected. Conversely, there is still a significant number of systematic noise sources that are shared by the dark hole and the bright side in the same PSF. We should choose reference pixels from the same PSF when reducing the dgvAPP360 dataset or the gvAPP180 dataset with TRAP. In these datasets, TRAP achieves results consistent with previous best detections, with an improved S/N for the gvAPP180 dataset.
KW - Instrumentation: high angular resolution
KW - Methods: data analysis
KW - Planets and satellites: detection
KW - Techniques: high angular resolution
KW - Techniques: image processing
UR - http://www.scopus.com/inward/record.url?scp=85163220567&partnerID=8YFLogxK
U2 - 10.1051/0004-6361/202245333
DO - 10.1051/0004-6361/202245333
M3 - Article
AN - SCOPUS:85163220567
SN - 0004-6361
VL - 674
SP - 1
EP - 15
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A115
ER -