(2 Day Workshop)
Thursday, 7 January | 11:00 18:00 (ET)
Friday, 8 January | 11:00 18:00 (ET)
As the astronomical community moves into an era of big data, the paradigm of data processing is changing. We are transitioning from local end-to-end data processing (from taking or simulating observations to publishing the data) to retrieving pre-processed large datasets through database queries. The growing importance of such transactions are evident with current projects such as Tess, Gaia, SDSS, ZTF, HST, and illustris and will become a necessity to fully utilize the next generation of astronomical surveys, telescopes, and simulations. Interaction with these databases and visualization of these complex datasets will be essential skills. This workshop will introduce participants to selecting information from an online database in an efficient and reproducible way and effectively visualizing the results.The Astronomy Data Carpentry Workshop at the 237th AAS consists of short tutorials alternating with hands-on practical exercises focused on building complex SQL queries using Astroquery, working with the retrieved data as Pandas data frames, storing the data locally for future use, and communicating the results with clear and compelling figures using Matplotlib. The workshop will be run by Carpentries Instructor and lesson developer Allen Downey and Carpentries Instructor Azalee Bostroem as well as a team of helpers.This course is aimed at astronomers at all stages of their education and careers. Participants are expected to have knowledge equivalent to the Software Carpentry Python Curriculum: the ability to write a function in Python, familiarity with Python built-in types such as lists and dictionaries, and the ability to navigate directories using the command line.Registration is for both days. Participants will need personal workstations, internet connectivity, zoom, and be able to install software in advance of the workshop. A group list will be compiled approximately one month prior to the workshop to distribute software requirements and provide collaborative troubleshooting. Workshop participants are also encouraged to participate in the Hack Day to apply their workshop skills. More information on the Data Carpentry project can be found at https://datacarpentry.org.
Time
1/7/2021 11:00 AM - 1/8/2021 6:00 PM
11:00 AM
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(2 day workshop)
Thursday, 7 January | 11:00 18:00 (ET)
Friday, 8 January | 11:00 18:00 (ET)
Computing is now an integral part of every aspect of astronomy and astrophysics, but most scientists are never taught how to build, use, validate, and share software. As a result, many spend hours or days doing things badly that could be done well in just a few minutes. The goal of the Software Carpentry Workshop is to change that by teaching best practices. The tools presented at the two day workshop will enable astronomers to spend less time wrestling with software and more time doing useful research. Furthermore, good quality, well tested code will make their science results easier to reproduce, distribute, and update. The Software Carpentry Workshop will consist of short tutorials alternating with hands-on practical exercises and will cover the core software skills needed construct, use, verify, and share software in astronomy. The first day’s tutorials will consist of shell automation of tasks, basic python programming, and an introduction to code review. The second day’s sessions will shift to focus on advanced python and version control with git/GitHub. The workshop will be run by a set of two certified instructors and a team of helpers.The workshop is aimed at astronomers at all stages of their education and careers who wish to learn computational tools to increase the reproducibility and efficiency of their work. Participants should have some knowledge of programming (not necessarily Python) and have some familiarity with the shell command line (i.e. navigating directories on the shell command line). Specific knowledge of Python and git are not required. Registration is for both days. Participants will need personal workstations, internet connectivity, zoom, and be able to install software in advance of the workshop. A group list will be compiled approximately one month prior to the workshop to distribute software requirements and for collaborative installation troubleshooting. Workshop participants are also encouraged to participate in the Hack Day to apply their workshop skills. More information on the Software Carpentry project can be found at http://software-carpentry.org.
Time
1/7/2021 11:00 AM - 1/8/2021 1:00 PM
11:00 AM
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(2 day workshop)
Thursday, 7 January | 11:00 13:00
(ET)
Friday, 8 January | 11:00 13:00 (ET)
How do you present data in your research articles? Do you include a few plots and tables? Or do you enhance your narrative through the use of animations and interactive figures? Make flip books out of figure sets? Supply the underlying data behind your tables and figures so other authors can reproduce your work?The AAS Journals (ApJ, ApJL, ApJS, AJ, RNAAS, and PSJ) support all of these options and more - and 20% of our published content already contains at least one of these types of data products. If your research involves data and you want to learn how to better integrate it into your articles and present it in a way that will increase the readability, usefulness, and citations of your work, register now for the AAS journals' newest workshop!In the first day of this workshop, the AAS data editors will discuss all of the ways ambitious authors can boost their future manuscripts. In addition to discussions of various available data products, this will also include tutorials on working with the latest versions of AASTeX and using the Overleaf collaborative environment, and there will be plenty of time for questions and answers. The second day is left open so that participants can drop in when they please with their own projects and receive one-on-one instruction and advice from the data editors.
Time
1/7/2021 11:00 AM - 1/8/2021 1:00 PM
11:00 AM
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Now is a perfect time to bring the opportunity to do remote imaging and data analysis to students around the globe. Several programs have been developed over recent years to do just that. In this workshop, participants will learn how the Astronomy Research Seminar guides college and high school instructors and students through the process of selecting a system to study, whether it’s a double star system for astrometric measurement or an exoplanet transiting system for photometry, collecting data using remote telescopes, analyzing that data, and most importantly writing up a rigorous scientific paper for publication. Both the Boyce Research Initiative and Education Foundation (BRIEF) and the Institute for Student Astronomical Research (InStAR) have successful been helping students publish their research for the past 5 years, with dozens of papers published in the Journal of Double Star Observations (JDSO), conference proceedings, and the newly established ATOM (Astronomy Theory, Observations, and Methods) Journal. These two organizations are education partners in the Las Cumbres Observatory’s (LCO) Global Sky Partners program, with access to their network of 0.4-meter telescopes located in the northern and southern hemispheres around the globe. Students and teachers learn to use the same LCO online observing portal that professional astronomers use, and are integrated into the Community of Practice, working with experts in the field.
11:00 AM
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(2 day workshop)
Thursday, 7 January | 11:00 18:00
(ET)
Friday, 8 January | 11:00 18:00 (ET)
Chandra/CIAO workshops are aimed at helping users, especially graduate students, post-doctoral fellows and early-career researcher to work with Chandra data and the Chandra Interactive Analysis of Observations (CIAO) software. Several workshops have been previously organized at the Chandra X-Ray Center (see http://cxc.harvard.edu/ciao/workshop/ for more details) and this is the third time a CIAO workshop is organized in connection with the AAS. The workshop will feature talks on introductory and advanced X-ray data analysis, statistics, and topics in Chandra calibration. The workshop will also include hands-on sessions where students can practice X-ray data analysis following a workbook of CIAO exercises or perform their own analysis with members of the CIAO team ready to assist. Participants are required to have their own laptop with CIAO installed (we will help with the installation if needed).
Time
1/7/2021 11:00 AM - 1/8/2021 6:00 PM
11:00 AM
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(beginners)
Thursday, 7 January | 11:00 14:00 (ET)
This workshop will cover the use of Python tools for astronomical data analysis and visualization, with the focus primarily on tools in the Astropy library and its affiliated packages. The goal is to introduce participants to the variety of tools which are available inside the Astropy library and to provide time for participants to explore the science analysis capabilities which the scientific Python ecosystem and community provide. The format will include short presentations followed by instructor-guided tutorials where participants will use the tools and be able to ask questions in the company of expert users and developers. The first session will introduce the core Astropy package and will cover units, quantities, and constants; coordinates; FITS, ASCII, and Astropy tables; and images and their visualization. The second session will be organized around specialized topics such as CCD image reduction (ccdproc), photometry (photutils), and spectroscopy (specutils and related packages).
11:00 AM
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Using R, LaTeX, and Jupyter Notebooks to Create Reproducible Research in Astronomy
This workshop will cover how to use the R statistical software environment for computation and graphics (http://www.r-project.org), and related software, to model and analyze astronomical data. R is open source software, freely available for Linux, Windows and Mac operating systems, and is unmatched in resources and functionality for statistical analysis by any other software. In addition to the base system, there are currently approximately 20,000 contributed packages freely available for specialized applications. This course will show how to use R to do statistical computations, access data sources, execute statistical functions from packages, write your own specialized functions, and create packages for your own use or for general distribution via the Comprehensive R Archive Network (CRAN). Some other specific topics in R include: 1) installing the software 2) choosing an interface, 3) choosing and creating different types of objects, 4) accessing FITS formatted files and other types of files, 5) manipulating data and constructing tables, 6) summarizing data, 7) creating statistical models, 8) processing images, 9) using astronomy specific packages, 10) installing packages, 11) creating graphics, plots and figures. In addition, the use of the knitr package to integrate R statistical computations and graphics with LaTeX to create fully reproducible astronomical research will be covered in detail. This approach completely documents the analysis and avoids the potential for error involved in cutting and pasting computations, tables and figures into documents. Instead, a document is created that intersperses narrative text, LaTeX code, and R code chunks that, via compilation, produces a final publication-ready document (or presentation) that includes all narrative, computations, tables, figures, and references. If additional and/or corrected data becomes available, the final document can be easily updated and recreated simply by recompiling. Finally, the workshop will cover how to use interactive Jupyter notebooks with an R kernel, how to call Python from R, call R from Python, and R contributed packages of particular interest to astronomers. There will be four two-hour sessions (8 hours total). The first hour of each session will cover how to apply the software and the second hour will be used by the attendees to work on exercises and examples with the help of the instructor. To facilitate learning to use the software and avoid installation issues, a CoCalc project will be available (http://cocalc.com). Attendees will be given a link to connect to the workshop project. Each attendee will have access to 3 cores, 8 GB of memory, 1 GB of disk storage and all the software tools, data, examples, Jupyter notebooks, and assignments. CoCalc has all required software (R, contributed packages, LaTeX, knitr, Jupyter, astronomical example data, etc.) installed and ready to use along with internet access. CoCalc will also make it possible for the instructor and attendees to interactively work together to complete the example assignments. No software needs to be installed by the attendees. All that is needed is a browser such as Firefox, Chrome, etc. to access the CoCalc website. The instructor has been a statistician for more than 30 years with nearly 100 published peer-reviewed scientific journal papers and has taught numerous courses in statistical modeling and analysis using R, LaTeX, and Jupyter, among others.
11:00 AM
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Obtaining your PhD is likely the most difficult academic challenge you will ever face, yet few people are truly prepared for how to best navigate their journey and come out prepared for the variety of jobs available to them. This interactive workshop helps identify the most critical elements needed for a successful PhD regardless of your particular field of study, where success is defined as “obtaining a PhD and finding a job”. We will discuss the timeline of a PhD and the critical strategies for success, including how to make the most of your supervisory team and how to tackle writing. We will present research on the career tracks of recent PhD recipients to give participants a realistic view of the post-PhD job market. We will discuss transferable skills that are critical for success on different career tracks and help participants identify paths to developing these skills in graduate school. This workshop is appropriate for students at all levels, in particular students at the beginning of their PhD.Session 1: Dr. Kim-Vy Tran will lead the first half of the workshop which will focus on success during the PhD program itself. This portion will include a discussion on mapping a timeline, strategies for success, how to make the most of your supervisory team, how to tackle writing, and identifying warning signs. Slides for basic framework of the session are linked here.Session 2: Dr. Ivelina Momcheva will lead the second part of the discussion which will focus on developing skills important for the post-PhD job market. This portion will include a discussion of different career tracks based on the work presented in a recent Decadal White Paper, identifying skills important for different career tracks and discussing strategies to develop these skills. :: About the Workshop Presenters ::Dr. Ivelina Momcheva is currently a scientist at the Space Telescope Science Institute where she is the Mission Scientist of the Data Science Mission Office. She received her PhD from University of Arizona and has held postdoctoral appointments at Carnegie Observatories and Yale University. She is a member of the AAS Demographics Committee and a coordinator for the STScI summer internship program. She is passionate about mentoring and career development. Dr. Kim-Vy Tran is currently on the faculty in the School of Physics at the University of New South Wales and also was professor at Texas A&M University and the University of Zürich. Kim-Vy has supervised 40+ students in research and currently shepherds 70+ graduate students as a Post-Graduate Coordinator for Progressions. Her work as an educator includes traditional classroom lectures as well as an expanding range of professional development workshops for scientists, in particular PhD students and early career researchers. She is passionate about promoting equity, diversity, and inclusion at all levels and helping people achieve their full potential.:: Session Justification :: Students at the start of their PhD rarely have a strategy for being successful in graduate school while acquiring the marketable skills that would help them in post-PhD employment. Variations in mentoring, advising and networking leads to an uneven playing field for many students. This workshop aims to help the participants navigate the years of the PhD program successfully by providing practical and actionable advice that students can adapt to their own careers. We will provide factual information on the career prospects based on data from recent PhD recipients which will help dispel myths and fears. Furthermore, frank discussion of the skills valued in different career tracks will allow students to plan for their post-PhD success rather than face the job market filled with anxiety.
12:00 PM
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Hands-on Machine Learning for Astronomers: Artificial Intelligence for Big-Data Astronomy (2 day workshop)
Thursday, 7 January | 12:00 15:00 (ET)
Friday, 8 January | 12:00 15:00 (ET)
Next-generation astronomical facilities will produce datasets whose volume and rates will challenge traditional reduction, analysis, and visualization techniques. In order to start addressing these nascent problems, a number of astronomers are exploring the use of machine learning (ML) algorithms, obtaining very promising results in the context of large, incomplete, and heterogeneous datasets. In view of these developments, it has become apparent that ML-based data analytics will play a central role in next-generation surveys.
We are hosting a 2-day hands-on workshop that brings together ML experts and astronomers to discuss, teach, and evaluate different ML techniques that are being applied in astronomy big-data analytics. We will explore the capabilities of ML algorithms for the classification and analysis of astronomical sources in large datasets. This includes also deriving statistical correlation between multiple parameters, data reduction tasks, and dimensionality-reduction of high-dimensional data for their study and visualization.
The workshop is split in two days. On the first day, a short introductory presentation will illuminate the basic methods of ML algorithms and how they can be applied to astronomical problems. Subsequently, through hands-on tutorials of several simple use cases showing different ML techniques, the participants will get a basic understanding of ML algorithms to mine diverse datasets including IRSA, Spitzer, SDSS, and DES. These tutorials include data analysis as well as visualization use cases. The second day of the workshop will be devoted to a Q&A session with ML experts in astronomy. The participants are encouraged to bring their specific problems and datasets to which they want to apply ML techniques and discuss them with the experts. More details and additions on the tutorials of the previous day are also covered if requested.
This workshop is targeting astronomers in any research field and at any career-stage, who would like to apply ML methods to their current or future work. The participants should have a basic background in Python.
Time
1/7/2021 12:00 PM - 1/8/2021 3:00 PM
12:00 PM
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Online Data Investigations for Your Classroom from Vera C. Rubin Observatory
Are you trying to find an innovative way to bring real astronomical data into your classroom? Come join your colleagues for an engaging workshop to get hands-on experience with educational investigations being developed by Vera C. Rubin Observatory’s Education and Public Outreach Team. Using the upcoming Legacy Survey of Space and Time (LSST) data, Rubin Observatory is developing a series of online astronomy investigations that allow students to engage in a rich and interactive experience with topics central to the teaching of introductory astronomy. In this workshop you will explore investigations that range from Solar System to stars and galaxies topics, and learn about additional resources to support your teaching and assessment of these investigations. We will also provide details on how you can be an early adopter of bringing these exciting investigations into your classroom through our pilot-testing opportunities.
1:00 PM
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