Impact of angular differential imaging on circumstellar disk images

Authors: J. Milli, D. Mouillet, A. M. Lagrange, A. Boccaletti, D. Mawet, G. Chauvin, M. Bonnefoy

arXiv: 1207.5909v1 - DOI (astro-ph.EP)

Abstract: Direct imaging of circumstellar disks requires high-contrast and high-resolution techniques. The angular differential imaging (ADI) technique is one of them, initially developed for point-like sources but now increasingly applied to extended objects. This new field of application raises many questions because the disk images reduced with ADI depend strongly on the amplitude of field rotation and the ADI data reduction strategy. Both of them directly affect the disk observable properties. Our aim is to characterize the applicability and biases of some ADI data reduction strategies for different disk morphologies. A particular emphasis is placed on parameters mostly used for disks: their surface brightness, their width for a ring, and local features such as gaps or asymmetries. We first present a general method for predicting and quantifying those biases. In a second step we illustrate them for some widely used ADI algorithms applied to typical debris disk morphologies: inclined rings with various inner/outer slopes and width. Last, our aim is also to propose improvements of classical ADI to limit the biases on extended objects. Simulated disks seen under various observing conditions were used to reduce ADI data and quantify the resulting biases. These conclusions complements previous results from NaCo L' real-disk images of HR4796A. ADI induces flux losses on disks. This makes this technique appropriate only for low- to medium-inclination disks. A theoretical criterion is derived to predict the amount of flux loss for a given disk morphology, and quantitative estimates of the biases are given in some specific configurations. These biases alter the disk observable properties, such as the slopes of their surface brightness or their radial/azimuthal extent. Additionally, this work demonstrates that ADI can very easily create artificial features without involving astrophysical processes.

Submitted to arXiv on 25 Jul. 2012

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.