Third ASTERICS-OBELICS Workshop

23 Oct 2018, 07:00 → 26 Oct 2018, 13:00 Europe/Amsterdam

Postdoc Centre, Eddington

Third ASTERICS-OBELICS Workshop : New paths in data analysis and open data provision in Astronomy and Astroparticle Physics
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.

Tuesday, 23 October

1 Registration

2 Welcome Address

Speaker: Prof. Paul Alexander (University of Cambridge)

3 WP-3 OBELICS overview

Speakers: Dr Giovanni Lamanna (LAPP/IN2P3/CNRS), Mr Jayesh WAGH (Programme Coordinator-Obelics-Asterics)

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)

4 Work on DL3 and Gammpy

Speaker: Prof. Jose Luis Contreras (Universidad Complutense de Madrid)

Slides Asterics-UCM-IFAE-Cambridge-v1.pdf

5 Update on benchmarking of data formats for VHE

Speaker: Prof. Jose Luis Contreras (Universidad Complutense de Madrid)

Slides Asterics-CTA-LowLevel-v1.pdf

6 Data Format Generator

Speakers: Dr Pierre Aubert (LAPP, CNRS), Dr Thomas Vuillaume (LAPP)

Slides data_format_generator.pdf

7 HERA data management and Recipe minimal recomputation

Speaker: Dr Kingsley Gale-Sides

8 Casacore data storage

Speaker: Dr Tammo Jan Dijkema (Astron)

Slides d-gex-ms.pptx

9 Real-Time Data Streaming Architecture Survey

Speaker: Dr Lucio Angelo Antonelli (INAF Osservatorio Astronomico di Roma)

Slides Antonelli_DataStream.pdf

11:05 Coffee break

Presentations from D-INT

Conveners: Dr Tammo Jan Dijkema (Astron), Dr Thomas Vuillaume (LAPP)

10 Data and software preservation through containerisation

Speaker: Mr Tamas Gal (Friedrich-Alexander-University Erlangen)

Slides Data_and_Software_Preservation_through_Containerisation.pdf

11 INAF Data integration Services for the Cherenkov Telescope Array

Speaker: Dr Alessandro Costa (INAF)

Slides INAF@ASTERICS_D-INT.pdf

12 Polynomial data compression for large-scale physics experiments

Speaker: Dr Thomas Vuillaume (LAPP)

Slides data_compression.pdf

13 First experience with Common Workflow Language for LOFAR and Apertif data reduction

Speaker: Dr Tammo Jan Dijkema (Astron)

Slides d-int-apertif-cwl.pdf

14 Update on STOA workflow management system and its applications

Speaker: Dr Peter Hague (University of Cambridge)

Slides Stoa_new.pptx

12:30 Lunch break

Presentations from D-ANA

Data Analysis task

Conveners: Dr Bojan Nikolic (University of Cambridge), Dr Fabio Pasian (INAF - OATs)

15 hipeCTA, high-performance algorithms for CTA

Speaker: Dr Florian Gate (LAPP, CNRS)

Slides hipeCTA_3RD_ASTERICS.pdf

16 INAF Data Analysis Services for the Cherenkov Telescope Array

Speaker: Dr Alessandro Costa

Slides INAF@ASTERICS_D-ANA.pdf

17 Recent advances in LOFAR calibration and imaging

Speaker: Dr Tammo Jan Dijkema (Astron)

Slides d-ana-lofar-imaging.pdf

18 GammaLearner: a Deep Learning framework for CTA

Speaker: Mr Mikael Jacquemont (LAPP, CNRS)

Slides GammaLearn_Obelics_oct_18.pdf

19 Efficient remote processing using CASA and Jupyter

Speaker: Dr Aard Keimpema

Slides Cambridge2018-Keimpema.pdf

20 DNN classification of signals and glitches in time-domain gravitational-wave data

Speaker: Dr Michal Bejger

Slides bejger_obelics_cambridge231018.pdf

21 Accelerating Science by Repurposing Machine Learning Software

Speakers: Dr Bojan Nikolic (University of Cambridge), Dr Vladislav STOLYAROV

Slides 2018-PyTorch-AstericsWorkshop_UCam.pdf 2018-PyTorch-AstericsWorkshop_UCam.pdf

22 Bayesian source finding for interferometers with BaSC

Speaker: Dr Peter Hague (University of Cambridge)

Slides Basc_new.pptx

15:30 Coffee break

23 Panel discussion: OBELICS review and conclusion

Internal meeting - tasks leaders: Preparing OBELICS blue print

24 Welcome Reception

Wednesday, 24 October

25 Machine Learning applications in Gravitational Wave research

Noise 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

Slides ECuoco_3rd.pdf

26 Time-domain Machine Learning - Opportunities and Challenges for the SKA

To 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)

Slides MLForSKA_Lyon_2018_Asterics.pdf

27 Applications of Machine Learning to Deblending in LSST

It 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

Slides scarlet.pdf

10:30 Coffee break

28 Machine learning in the Cherenkov Telescope Array

The 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

Slides DNieto_Asterics18.pdf

29 Machine learning in High Energy Physics

High 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

Slides Kagan_Asterics-Obelics_2018_new.pdf

30 Machine learning in KM3NeT

Speaker: Ms Chiara De Sio (University of Salerno)

Slides KM3NeT-MachineLearning_Cdesio_ASTERICS.pdf

12:30 Lunch break

31 Machine learning in solar physics

Speaker: Dr Sebastian Hoch

32 Bayesian statistics

Speaker: Dr Will Handley

Slides talk.pdf

33 A summary of the workshop "AI at CERN and SKA"

The 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

Slides V.Stolyarov_-_A_summary_of_the_workshop__AI_at_CERN_and_SKA_.pdf

15:30 Coffee break

Uniformisation in machine learning: Panel Discussion

Convener: Dr Thomas Vuillaume (LAPP)

slides machine_learning_survey.pdf

34 Social Dinner

Thursday, 25 October

35 DATA LAKE - WLCG computing model evolution

Speaker: Dr Ian Bird (CERN)

Slides ComputingModel-Evolution-OBELICS-251018.pptx

36 EOSC overview and paths

Speaker: Dr Brian Matthews

Slides EOSC-AstericsWorkshop-Oct18-new.pptx

37 ASTERICS-OBELICS software and services contribution to EOSC needs

Speaker: Dr Giovanni Lamanna (LAPP/IN2P3/CNRS)

Slides 3rdWorkshop_OBELICS_GLamanna_25Oct2018_V0.pdf

38 EOSC-hub: project status and plans, presentation of service catalogue

Speakers: Matthew Viljoen (EGI Foundation), Dr Tiziana Ferrari (EGI Foundation)

Slides 2018-10-25_ASTERICS-OBELICS.pptx

39 Coffee Break

40 Rules of participation (state of play) for service providers: EOSCpilot outcome and EOSC-hub experience

Speaker: Matthew Viljoen (EGI Foundation)

Slides 20181025-asterics_rop_and_onboarding_.pptx

41 Interactive group discussion - Shaping the European Open Science Cloud service roadmap

During 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)

42 Lunch Break

43 H2020-HNSCiCloud project update and future directions

Speaker: Dr Tony Wildish

Slides HNSCiCloud_project_update_and_future_directions_-_Tony_Wildish.pptx

44 H2020 AENEAS results and objectives

Speaker: Dr Rob van der Meer (ASTRON)

Slides H2020_AENEAS-at-OBELICS-Workshop-20181025.pptx

45 Coffee Break

46 Panel Discussion

Slides OneSlide_GLamanna_25Oct2018_V0.pptx