Update on the Systematics in the ALMA Proposal Review Process After Cycle 8

Carpenter, John M.; Corvillon, Andrea; Meyer, Jennifer Donovan; Plunkett, Adele; Kurowski, Robert; Chalevin, Alex; Macías, Enrique

doi:10.1088/1538-3873/ac5b89

Cited by 8 publications

(12 citation statements)

References 10 publications

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“…A multiscale CLEAN approach extends the work of Högbom (1974) allowing point sources to be Gaussian distributed instead of delta functions (Cornwell 2008). ALMA has been a game changer in high resolution aperture synthesis imaging (ALMA Partnership et al 2015), and a development roadmap (Carpenter et al 2022) has been approved with the goal of meeting its high standards.…”

Section: Deep Learning and Almamentioning

confidence: 99%

3D detection and characterization of ALMA sources through deep learning

Veneri

Tychoniec²,

Guglielmetti³

et al. 2022

Monthly Notices of the Royal Astronomical Society

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We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a Convolutional Autoencoder for source detection within the spatial domain of the integrated data cubes, a Recurrent Neural Network (RNN) for denoising and peak detection within the frequency domain, and four Residual Neural Networks (ResNets) for source characterization. The combination of spatial and frequency information improves completeness while decreasing spurious signal detection. To train and test the pipeline, we developed a simulation algorithm able to generate realistic ALMA observations, i.e. both sky model and dirty cubes. The algorithm simulates always a central source surrounded by fainter ones scattered within the cube. Some sources were spatially superimposed in order to test the pipeline deblending capabilities. The detection performances of the pipeline were compared to those of other methods and significant improvements in performances were achieved. Source morphologies are detected with subpixel accuracies obtaining mean residual errors of 10−3 pixel (0.1 mas) and 10−1 mJy/beam on positions and flux estimations, respectively. Projection angles and flux densities are also recovered within $10\%$ of the true values for $80\%$ and $73\%$ of all sources in the test set, respectively. While our pipeline is fine-tuned for ALMA data, the technique is applicable to other interferometric observatories, as SKA, LOFAR, VLBI, and VLTI.

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Section: Deep Learning and Almamentioning

confidence: 99%

3D detection and characterization of ALMA sources through deep learning

Veneri

Tychoniec²,

Guglielmetti³

et al. 2022

Monthly Notices of the Royal Astronomical Society

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“…In Cycle 8, ALMA used distributed peer review to assess proposals that requested less than 25 h on the ALMA 12-m Array or less than 150 h on the 7-m Array in "standalone" mode. The remaining proposals were peer reviewed using the traditional panel system as described in Carpenter (2020). The decision to use distributed peer review for only relatively smaller proposals was based on input from advisory committees and surveys of the participants (both reviewers and PIs) in the Cycle 7 Supplemental Call (Carpenter et al 2020).…”

Section: The Cycle 8 Distributed Peer Review Processmentioning

confidence: 99%

“…The remaining proposals were peer reviewed using the traditional panel system as described in Carpenter (2020). The decision to use distributed peer review for only relatively smaller proposals was based on input from advisory committees and surveys of the participants (both reviewers and PIs) in the Cycle 7 Supplemental Call (Carpenter et al 2020). This section presents an overview of the overall review process as implemented in Cycle 8, including highlighting the significant changes introduced since the Cycle 7 Supplemental Call.…”

Section: The Cycle 8 Distributed Peer Review Processmentioning

confidence: 99%

“…Many of the survey questions were adopted from a similar ESO survey in their pilot review using distributed peer review (Patat et al 2019). ALMA PIs were also asked similar questions in the surveys conducted after the Cycle 7 Main Call which used panel reviews, and the Cycle 7 Supplemental Call with distributed peer review (see Carpenter et al 2020). The response rate for the Cycle 7 surveys were 23% and 70% for the Main and Supplemental calls, respectively.…”

Section: Scientific Category/keyword Pairsmentioning

confidence: 99%

“…3 Finally, ALMA used the distributed peer review process to review 249 proposals in the Cycle 7 Supplemental Call as a pilot program to gauge whether the process would be suitable for a Main Call. Carpenter et al (2020) presented an extensive analysis of the Supplemental Call and found that PIs gave similar marks to the overall clarity, accuracy, and helpfulness of the reviewer comments as in panel reviews. The majority of reviewers and PIs from the Supplemental Call supported using distributed peer review for smaller proposals.…”

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Analysis of the ALMA Cycle 8 Distributed Peer Review Process

Meyer

Corvillón

Carpenter

et al. 2022

Bulletin of the AAS

Self Cite

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In response to the challenges presented by high reviewer workloads in traditional panel reviews and increasing numbers of submitted proposals, ALMA implemented distributed peer review to assess the majority of proposals submitted to the Cycle 8 Main Call. In this paper, we present an analysis of this review process. Over 1000 reviewers participated in the process to review 1497 proposals, making it the largest implementation of distributed peer review to date in astronomy, and marking the first time this process has been used to award the majority of observing time at an observatory. We describe the process to assign proposals to reviewers, analyze the nearly 15,000 ranks and comments submitted by reviewers to identify any trends and systematics, and gather feedback on the process from reviewers and Principal Investigators (PIs) through surveys. Approximately 90% of the proposal assignments were aligned with the expertise of the reviewer, as measured both by the expertise keywords provided by the reviewers and the reviewers' self-assessment of their expertise on their assigned proposals. PIs rated 73% of the individual review comments as helpful, and even though the reviewers had a broad range of experience levels, PIs rated the quality of the comments received from students and senior researchers similarly. The primary concerns raised by PIs were the quality of some reviewer comments and high dispersions in the ranks. The ranks and comments are correlated with various demographics to identify the main areas in which the review process can be improved in future cycles.

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The ALMA Telescope

Bout

Dickman

Plunkett

2023

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ALMA, the Atacama Large Millimeter/submillimeter Array, situated high in the Chilean desert, is the largest ground-based telescope on Earth. This is an insiders' account of how this complex mega-project came to fruition from authors with intimate knowledge of its past and present. The separate roots of ALMA in the United States, Europe, and Japan are traced to their merger into an international partnership involving more than 20 countries. The book relates the search for a suitable telescope site, challenges encountered in organization, funding, and construction, and lessons learned along the way. It closes with a review of the most significant results from ALMA, now one of the most productive telescopes in the world. Written for a broad spectrum of readers, including astronomers, engineers, project managers, science historians, government officials, and the general public, the eBook edition is available to download as an Open Access publication on Cambridge Core.

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Update on the Systematics in the ALMA Proposal Review Process After Cycle 8

Cited by 8 publications

References 10 publications

3D detection and characterization of ALMA sources through deep learning

3D detection and characterization of ALMA sources through deep learning

Analysis of the ALMA Cycle 8 Distributed Peer Review Process

The ALMA Telescope

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