About Astro2020: The 2020-2030 Astronomy and Astrophysics Decadal Survey (Astro2020) is a partnership between the National Academies and the astronomical community to survey the field of astronomy and astrophysics and provide priorities for the most important scientific and technical activities of the decade 2020-2030. It serves as a guide for scientists, policy makers, and agencies alike invested in the astronomical sciences. The survey is driven by input from the scientific community and led by a survey committee.

More details on Astro2020 and the white paper submission form can be found at:

http://sites.nationalacademies.org/SSB/CurrentProjects/SSB_185159

The deadline for submission is January 18th, 2019

Information on past decadal astronomy surveys at:

http://sites.nationalacademies.org/BPA/BPA_049491

Astrominer TF members Guiseppe Longo, Erzsébet Merényi, Peter Tino are organizing a focus issue on Machine Intelligence in Astronomy and Astrophysics for the Publications of the Astronomical Society of the Pacific (PASP) journal. Below you will find the call for papers, more information can be found at the focus issue website

Call For Papers
Astronomical observations already produce vast amounts of data through a new generation of telescopes (Atacama Large Millimeter Array (ALMA), Jansky VLA) and through large surveys (,e.g., SDSS, ZTF, PanSTARRS, VLT Survey Telescope – VST, and many others)) that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope (LSST) and the Square Kilometer Array (SKA) are planned to become operational in this decade and the next, and will increase the data volume by many orders-of-magnitude. The increased spatial and spectral resolution affords a powerful magnifying lens on the physical processes that underlie the data, but at the same time it generates unprecedented complexity hard to exploit for knowledge extraction. It is therefore imperative to develop machine intelligence suitable for processing the amount and variety of astronomical data that will be collected, and capable of answering scientific questions based on the data.

Astronomical data exhibit the usual challenges associated with “big data” such as immense volumes, high dimensionality, missing or highly distorted observations. In addition, astronomical data can exhibit large continuous observational gaps, and low signal-to-noise ratio. A crucial distinguishing factor of astronomical data sets is that, unlike, e.g., in medical or social domains, there are strict laws of physics behind the data production and often those can be assimilated into machine learning mechanisms to improve over general off-the-shelf state-of-the-art methods. An additional peculiarity of astronomical data is that these large and heterogeneous data sets need to be simultaneously queued, merged and mined by many independent groups of researchers, thus posing problems not common in many other application domains.

Significant progress in the face of these challenges can be achieved only via a true symbiosis of diverse disciplines, such as machine learning, probabilistic modelling, astronomy and astrophysics, statistics, distributed computing and natural computation. The importance of the task resulted in the emergence of dedicated research groups around the world, as well as focused professional bodies and dedicated conferences. This Focus Issue is intended to showcase the progress of recent years in enabling scientific discoveries using machine learning.

Special Issue Topics
We are seeking papers that present novel machine intelligence techniques suitable for processing, analyzing or modelling astronomical data in an automated manner; or novel ways or methodologies for applying machine intelligence to specific problems in astronomical data analysis (including the infusion of external knowledge in the processing). The papers should demonstrate a novel use of ML methods as applied on real astronomical data, producing some new results. Straightforward application of off-the-shelf machine intelligence techniques to astronomical data is not in the remit of this Focus Issue. Authors will be expected to demonstrate effectiveness of methods on real astronomical problems; to present rigorous performance assessment (e.g., accuracy evaluation) for machine learning algorithms, and to discuss the results in terms of the benefits for answering the scientific question at hand.

Topics that may be addressed include, but are not limited to:

• novel machine intelligence methods for astronomical data analysis
• novel methodologies in applying / synthetizing machine intelligence techniques for specific problems formulated on astronomical data
• novel applications of machine learning based methods to the resolution of complex astronomical problems
• probabilistic modelling and inference on astronomical data
• fusion of prior astrophysical / astronomical knowledge with data driven approaches
• scalable model based methods for automated learning on astronomical data
• assimilation of data into models and using models to guide data-driven learning
• representation and visualization of complex data spaces

Anticipated Timetable

• Articles due: June 1, 2018
• 1st review round completed: July 31, 2018
• 2nd review round completed: September 30, 2018
• Author notification: October 15, 2018
• Final submission of accepted articles due: October 31, 2018
• Expected publication date: December, 2018

Page charges: standard charges will apply, please see details at the journal web site.
Journal URL: http://iopscience.iop.org/journal/1538-3873

Guest editors

Please direct questions and inquiries to the co-ordinating editor, E. Merényi.

• Giuseppe Longo, Department of Physics, University Federico II in Napoli (I), Naples, Italy, Email: longouniversita@gmail.com
• Erzsébet Merényi (co-ordinating editor), Rice University, Department of Statistics, and Department of Electrical and Computer Engineering, Houston, Texas, U.S.A., Email: erzsebet@rice.edu
• Somak Raychaudhury, The Inter-University Centre for Astronomy and Astrophysics, IUCAA, S P Pune University Campus, India, Email: somak@iucaa.in
• Maria Teresa Ruiz, The University of Chile (Universidad de Chile), Santiago, Chile, Email: mtruiz@das.uchile.cl
• Peter Tino, The University of Birmingham School of Computer Science, Birmingham, UK, Email: P.Tino@cs.bham.ac.uk

Astrominer TF is hosting a special session at IEEE CIDM 2017 Symposium, Honolulu, Hawaii, Nov 27- Dic 1, 2017. Papers due July 2

Official Call for papers can be found here

Astrominer TF is hosting a special session at IEEE CIDM 2016 Symposium, Athens, Greece, Dec 6-9. Papers due Aug 15.

More details of the special session and the official Call for papers can be found here

The IAU Symposium 325 on Astroinformatics, Sorrento, October 20th, 2016 counted with the participation of TF members: Massimo Breccia (LOC), George Djorgovski (conference co-chair), Pablo Estevez (SOC), Francisco Forster, Matthew Graham, Pablo Huijse, Giussepe Longo (conference co-chair), Erzsebet Merenyi (SOC), Fionn Murtagh (SOC), Kai Polsterer, Pavlos Protopapas, Alex Szalay (SOC) and Ricardo Vilalta (SOC), who gave invited talks and served on panels. Presentation slides can be found at http://dame.dsf.unina.it/astroinformatics2016/media.html

Astrominer TF members Matthew Graham and Umaa Rebbapragada worked on the organization of the “Hot Wiring the Transient Universe 5” meeting held at the Villanova University Conference Center, Philadelphia, USA (October 10-14, 2016). Presentations in pdf format can be found at http://hotwired5.villanova.edu/archive

Our TF member Pablo Estevez was one of the three invited plenary speakers, on the topic of SOM’s role in mining complex astronomical data, at the bi-annual international conference “11th Workshop on Self-Organizing Maps”, Rice University, Houston, Jan 6-8, 2016. http://WSOM2016.rice.edu

Astrominer TF members Matthew Graham and Kai Polsterer, participated and gave talks at the 6th International conference on Astroinformatics held at Dubrovnik, Croatia during the 5-9th of October, 2015. The conference program can be found at: http://iszd.hr/AstroInfo2015/program.php.

The astrominer TF website has moved to the organizations section at ASAIP. The original website can be found at http://astrominertf.joomla.com/

Cheers!

Pablo Huijse

Astrominer TF members Matthew Graham, Brian Bue, Umaa Rebbapragada and Francisco Forster, participated and presented at the “Hot wiring the Transient Universe 4 Workshop” held at Santa Barbara, CA during the 12-15th of May, 2015. Their presentations can be found at: http://lcogt.net/hotwired-iv-science-topics/. Matthew Graham was also part of the organizing comittee of the workshop.

The astrominer TF welcomes its new members:

• Prof. Tamás Budavári, Dept. of Applied Mathematics, John Hopkins University
• Dr. Francisco Forster, Center for Mathematical Modelling, Universidad de Chile and Millenium Institute of Astrophysics
• Prof. Chris Mattmann, Department of Computer Science, University of Southern California
• Dr. Umaa Rebbapragada, JPL MLS, Caltech
• Prof. Alex Szalay, Department of Physics and Astronomy, John Hopkins University

Several of our TF members attended and gave talks at the NOAO workshop on Tools for Astronomical Big Data, during March 9-11 in Tucson AZ. The meeting was attended by more than 120 astronomers, computer scientists, data analysts, and other similar collaborative disciplines. Talks by our members (in alphabetical order):

• Brian Bue, “Leveraging Annotated Archival Data with Domain Adaptation to Improve Data Triage in Optical Astronomy” (contributed talk)
• Francisco Forster, “The High Cadence Transient Survey – HiTS” (invited talk at the DECam Community Science Workshop)
• Matthew Graham, “Characterizing the variable sky with CRTS” (contributed talk)
• Pablo Huijse, “Mining periodic variable stars using Information Theory and GPGPU” (poster presentation)
• Erzsébet Merényi, “Knowledge discovery from the Hyperspectral sky” (invited talk).

All presentations, along with the conference program are available for download at the workshop’s website. (You could also link the “available for download at the workshop’d web site” to the download URL.)

The center for data-driven discovery (CD(cube)) at Caltech is a multidisciplinary effort focusing on the methodologies for handling and analysis of large and complex data sets, facilitating the data-to-discovery process. TF member George Djorgovski is the director of the CD(cube). TF member Matthew Graham is a research scientist at the CD(cube). The center has been operating since fall 2014. Check their website and read George’s interview about the center here: http://www.caltech.edu/news/new-center-supports-data-driven-research-44589