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Register a new userLRP2020: Machine Learning Advantages in Canadian Astrophysics
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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.
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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.
Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Omega_m$, $sigma_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.
[Highly abridged, from executive summary] As much as NewSpace presents opportunities, there are significant challenges that must be overcome, requiring engagement with policy makers to influence domestic and international space governance. Failure to do so could result in a range of long-lasting negative outcomes for science and space stewardship. How will the Canadian astronomical community engage with NewSpace? What are the implications for NewSpace on the astro-environment, including Earth orbits, lunar and cis-lunar orbits, and surfaces of celestial bodies? This white paper analyzes the rapid changes in space use and what those changes could mean for Canadian astronomers. Our recommendations are as follows: Greater cooperation between the astronomical and the Space Situational Awareness communities is needed. Build closer ties between the astronomical community and Global Affairs Canada (GAC). Establish a committee for evaluating the astro-environmental impacts of human space use, including on and around the Moon and other bodies. CASCA and the Tri-Council should coordinate to identify programs that would enable Canadian astronomers to participate in pay-for-use services at appropriate funding levels. CASCA should continue to foster a relationship with CSA, but also build close ties to the private space industry. Canadian-led deep space missions are within Canadas capabilities, and should be pursued.
We survey the present landscape in submillimetre astronomy for Canada and describe a plan for continued engagement in observational facilities to ~2020. Building on Canadas decadal Long Range Plan process, we emphasize that continued involvement in a large, single-dish facility is crucial given Canadas substantial investment in ALMA and numerous PI-led submillimetre experiments. In particular, we recommend: i) an extension of Canadian participation in the JCMT until at least the unique JCMT Legacy Survey program is able to realize the full scientific potential provided by the world-leading SCUBA-2 instrument; and ii) involvement of the entire Canadian community in CCAT, with a large enough share in the partnership for Canadian astronomers to participate at all levels of the facility. We further recommend continued participation in ALMA development, involvement in many focused PI-led submillimetre experiments, and partnership in SPICA.
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