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(Abridged from Executive Summary) This white paper focuses on the interdisciplinary fields of astrostatistics and astroinformatics, in which modern statistical and computational methods are applied to and developed for astronomical data. Astrostatistics and astroinformatics have grown dramatically in the past ten years, with international organizations, societies, conferences, workshops, and summer schools becoming the norm. Canadas formal role in astrostatistics and astroinformatics has been relatively limited, but there is a great opportunity and necessity for growth in this area. We conducted a survey of astronomers in Canada to gain information on the training mechanisms through which we learn statistical methods and to identify areas for improvement. In general, the results of our survey indicate that while astronomers see statistical methods as critically important for their research, they lack focused training in this area and wish they had received more formal training during all stages of education and professional development. These findings inform our recommendations for the LRP2020 on how to increase interdisciplinary connections between astronomy and statistics at the institutional, national, and international levels over the next ten years. We recommend specific, actionable ways to increase these connections, and discuss how interdisciplinary work can benefit not only research but also astronomys role in training Highly Qualified Personnel (HQP) in Canada.
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The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010. With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.
Currently, postdoctoral fellow (PDF) researchers in Canada face challenges due to the precarious nature of their employment and their overall low compensation and benefits coverage. This report presents three themes, written as statements of need, to support an inclusive and thriving PDF community. These themes are the need for better terms of employment and conditions, the need for access to grants by non-permanent research staff, and the need for a sustainable PDF hiring model that considers the outcomes for the PDFs. We make six recommendations: R1. PDFs should be hired and compensated as skilled experts in their areas, not as trainees. R2. Standard PDF hiring practices should be revised to be more inclusive of different life circumstances. - R2.1 Allow PDFs the option of part-time employment. - R2.2 Remove years-since-PhD time limits from PDF jobs. - R2.3 Financially support PDF hires for relocation and visa expenses. R3. CASCA should form a committee to advocate for and provide support to astronomy PDFs in Canada. R4. CASCA should encourage universities to create offices dedicated to their PDFs. R5. PDFs and other PhD-holding term researchers with a host institution should be able to compete for and win grants to self-fund their own research. R6. Astronomy in Canada should hire general-purpose continuing support scientist positions instead of term PDFs to fill project or mission-specific requirements. In short, we ask for prioritization of people over production of papers.
In the past two years, the environment within which astronomers conduct their data analysis and management has rapidly changed. Working Groups associated with international societies and Big Data projects have emerged to support and stimulate the new fields of astroinformatics and astrostatistics. Sponsoring societies include the Intenational Statistical Institute, International Astronomical Union, American Astronomical Society, and Large Synoptic Survey Telescope project. They enthusiastically support cross-disciplinary activities where the advanced capabilities of computer science, statistics and related fields of applied mathematics are applied to advance research on planets, stars, galaxies and the Universe. The ADASS community is encouraged to join these organizations and to explore and engage in their public communication Web site, the Astrostatistics and Astroinformatics Portal (http://asaip.psu.edu).
This Astro2020 State of the Profession Consideration White Paper highlights the growth of astrostatistics and astroinformatics in astronomy, identifies key issues hampering the maturation of these new subfields, and makes recommendations for structural improvements at different levels that, if acted upon, will make significant positive impacts across astronomy.
The history and current status of the cross-disciplinary fields of astrostatistics and astroinformatics are reviewed. Astronomers need a wide range of statistical methods for both data reduction and science analysis. With the proliferation of high-throughput telescopes, efficient large scale computational methods are also becoming essential. However, astronomers receive only weak training in these fields during their formal education. Interest in the fields is rapidly growing with conferences organized by scholarly societies, textbooks and tutorial workshops, and research studies pushing the frontiers of methodology. R, the premier language of statistical computing, can provide an important software environment for the incorporation of advanced statistical and computational methodology into the astronomical community.
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