Third ASTERICS-OBELICS Workshop
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Europe/Amsterdam
Postdoc Centre, Eddington
Postdoc Centre, Eddington
Postdoc Centre @ Eddington
105 Eddington Place
Cambridge
CB3 1AS
Tel: +44 (0)1223 336661
Description
Third ASTERICS-OBELICS Workshop : New paths in data analysis and open data provision in Astronomy and Astroparticle Physics
23-26 October 2018, Cambridge, UK.
23-26 October 2018, Cambridge, UK.
This workshop is organized in the framework of OBELICS (Observatory E-environments LINked by common ChallengeS) work package of H2020-ASTERICS. OBELICS activities aim at encouraging common developments and adoption of common solutions for data processing, archive, analysis and access among ESFRI and world class projects in Astronomy and Astroparticle Physics, such as CTA, SKA, KM3NeT, EUCLID, LSST, EGO-Virgo, E-ELT.
The ASTERICS – OBELICS workshops aim at building bridges between ESFRI projects, concerned scientific communities, e-infrastructures, industries and consortia. The 3rd ASTERICS – OBELICS Workshop will survey the development of new software analysis methods in Astronomy and Astroparticle Physics. Follow-up discussions about potential connections between the ESFRI projects and the implementation of EOSC will be part of the programme.
This third edition of the workshop is being organised by the French CNRS LAPP (Laboratoire d’Annecy de Physique de Particules) laboratory, the leading OBELICS institute and hosted locally by the University of Cambridge, UK one of the partner of H2020-ASTERICS project.
Participants
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08:45
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09:15
Registration 30m
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09:15
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09:30
Welcome Address 15mSpeaker: Prof. Paul Alexander (University of Cambridge)
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09:30
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10:00
WP-3 OBELICS overview 30mSpeakers: Dr Giovanni Lamanna (LAPP/IN2P3/CNRS), Mr Jayesh WAGH (Programme Coordinator-Obelics-Asterics)
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10:00
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11:05
Presentations from D-GEX
Data generation task
Conveners: Prof. Jose Luis Contreras (Universidad Complutense de Madrid), Dr Lucio Angelo Antonelli (INAF Osservatorio Astronomico di Roma)- 10:00
- 10:10
- 10:20
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10:30
HERA data management and Recipe minimal recomputation 15mSpeaker: Dr Kingsley Gale-Sides
- 10:45
- 10:55
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11:05
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11:30
Coffee break 25m
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11:30
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12:30
Presentations from D-INTConveners: Dr Tammo Jan Dijkema (Astron), Dr Thomas Vuillaume (LAPP)
- 11:30
- 11:45
- 11:55
- 12:05
- 12:20
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12:30
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14:00
Lunch break 1h 30m
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14:00
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15:30
Presentations from D-ANA
Data Analysis task
Conveners: Dr Bojan Nikolic (University of Cambridge), Dr Fabio Pasian (INAF - OATs)- 14:00
- 14:10
- 14:20
- 14:30
- 14:40
- 14:50
- 15:00
- 15:10
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15:30
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16:00
Coffee break 30m
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16:00
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17:00
Panel discussion: OBELICS review and conclusion 1h
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17:00
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18:00
Internal meeting - tasks leaders: Preparing OBELICS blue print
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18:30
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20:30
Welcome Reception 2h
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08:45
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09:15
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09:00
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09:30
Machine Learning applications in Gravitational Wave research 30mNoise of non-astrophysical origin contaminates science data taken by the gravitational-wave detectors. Characterization of instrumental and environmental noise has proven critical in identifying false positives in the first observing runs. In this context the application of different machine learning methods can help in achieving, for example, a fast classification of transient events to disentangle noise from gravitational signals helping a fast real time analysis. Moreover, these approaches could be used to disentangle Gravitational signals from noise. Deep Learning techniques could even be used to the aim of non linear noise cancellation to condition the data before any detection algorithm will be used and there are promising on-going studies on simulated data.Speaker: Dr Elena Cuoco
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09:30
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10:00
Time-domain Machine Learning - Opportunities and Challenges for the SKA 30mTo harness the discovery potential of data collected by the SKA, we require efficient and effective automated data processing methods. Machine learning tools have the potential to deliver this capability, as evidenced via their successful application to similar problems in the astronomy domain. This talk introduces the machine learning required for successful time-domain data processing (pulsar / transient discovery), and the infrastructure required to support it. Here the overriding aim is to increase awareness of what is required to facilitate the execution of automated learning methods, which we’ll need if we are to achieve the SKA's ambitious science goals.Speaker: Dr Robert Lyon (University of Manchester)
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10:00
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10:30
Applications of Machine Learning to Deblending in LSST 30mIt is estimated that at least 63% of sources observed in the 10 year LSST survey will have at least 2% of their flux blended with another object. Achieving many of the LSST science goals requires a working deblender to separate the flux from overlapping stars and galaxies by extracting morphological and spectral data for each source. The primary focus of this talk will be on scarlet, the python package I have been developing with Peter Melchior that will soon be implemented in the HSC and LSST software pipelines. Time permitted I will also mention more ambitious efforts by other members of the Dark Energy Science Collaboration (DESC) using neural networks to solve the blending problem.Speaker: Dr Fred Moolekamp
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10:30
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11:00
Coffee break 30m
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11:00
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11:30
Machine learning in the Cherenkov Telescope Array 30mThe sensitivity of ground-based gamma-ray telescopes based on the imaging atmospheric Cherenkov technique (IACTs) is driven by, among other factors, our ability to reconstruct the primary particles that originate the extended atmospheric showers that are imaged by the telescopes: this particle reconstruction enables us to classify gamma-ray events from the much more frequent background of cosmic-ray events. Supervised machine learning algorithms, like random forest or boosted decision trees, have been successfully applied to the task of event reconstruction by current generation IACTs, substantially improving their sensitivity. In this talk we will briefly review the state-of-the-art of machine-learning based event reconstruction for current-generation IACTs and will present an overview of the novel approaches, like deep learning, currently being explored for the Cherenkov Telescope Array, the next-generation gamma-ray observatory.Speaker: Dr Daniel Nieto
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11:30
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12:00
Machine learning in High Energy Physics 30mHigh Energy Physics probes the mysteries of the universe using some of the worlds largest experiments and datasets. To interpret and analyze this data in search of new physical phenomena, a wide array of domain specific algorithms have been developed. At the same time, recent advances in deep learning have seen great success in the realms of computer vision, natural language processing, and broadly in data science. By connecting the challenges in the HEP domain with those in deep learning, new and powerful approaches to analyzing HEP data are being developed. In this talk, I will discuss developments in the application of machine learning techniques to the analysis and interpretation of High Energy Physics data, with a focus on the Large Hadron Collider.Speaker: Dr Michael Kagan
- 12:00 → 12:30
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12:30
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14:00
Lunch break 1h 30m
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14:00
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14:30
Machine learning in solar physics 30mSpeaker: Dr Sebastian Hoch
- 14:30 → 15:00
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15:00
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15:30
A summary of the workshop "AI at CERN and SKA" 30mThe two-day workshop was held in the Alan Turing Institute in London (17-18.09.2018) where ATI staff members, AI researches from industry as well as CERN and SKA scientists discussed the application of Artificial Intelligence (AI) and Machine Learning (ML) for scientific discovery in High Energy Physics, astrophysics, cosmology and radio astronomy. An overview of the presentations will be given in this summary talk.Speaker: Dr Vlad Stolyarov
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15:30
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16:00
Coffee break 30m
- 16:00 → 17:30
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19:30
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21:30
Social Dinner 2h
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09:00
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09:30
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- 09:00 → 09:25
- 09:25 → 09:50
- 09:50 → 10:15
- 10:15 → 10:40
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10:40
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11:00
Coffee Break 20m
- 11:00 → 11:25
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11:25
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12:25
Interactive group discussion - Shaping the European Open Science Cloud service roadmap 1hDuring this session representatives from the European Open Science Cloud (EOSC) will introduce EOSC and provide an overview of existing services and then engage with the Asterics meeting participants in an interactive world cafe-style session on prospective future service provisioning, access models and concrete services that the RI would like to bring into EOSC. EOSC is an ambitious initiative aiming at the federation of existing and planned digital infrastructures for research. It seeks to remove barriers among disciplines and countries and make it easier for researchers to share and access the digital resources they need. Looking forward to the evolution of EOSC, it is essential to understand and prioritise what services are most needed and that should be added to the future EOSC service portfolio and importantly, what criteria are to be used for uptake in the service portfolio. The goal of this session is to take advantage of the collective knowledge of the audience to extract high-level needs for services and identify priorities for the coming years to develop a service roadmap. The outcome of the session will help us draw a more detailed picture of the European Open Science Cloud roadmap, and the services that ESFRI RIs, such as Asterics, would need and bring into it.Speakers: Dr Bjorn Backeberg, Dr J B Raymond Oonk, Matthew Viljoen (EGI Foundation), Dr Tiziana Ferrari (EGI Foundation)
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12:25
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13:55
Lunch Break 1h 30m
- 13:55 → 14:20
- 14:20 → 14:45
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14:45
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15:15
Coffee Break 30m
- 15:15 → 17:00