Science Week - 2019

19 Aug 2019, 07:00 → 23 Aug 2019, 18:00 Canada/Pacific

Auditorium (TRIUMF)

SCIENCE WEEK – 2019

Registration Was closed on Thursday for Catering counts

 

TRIUMF's Science Week 2019 will be held from August 19-23.

This year's program features a summer school for IAMI, a workshop to discuss the first science of the ARIEL era with CANREB, a symposium celebrating 20 years of ISAC science, the TRIUMF Users’ Group (TUG) Annual General Meeting, and a data science and quantum computing workshop. Science Week is a wonderful opportunity for the whole TRIUMF community to come together, exchange ideas, and learn about the multidisciplinary program of the lab.

 

Please join us at TRIUMF for a week of activities highlighting our exciting science – past, present, and future!

Should you wish to attend these events the cost to attend for regular participants is $40 for all events, but an a la carte registration is available at a cost of $10 per meeting.

Graduate and Undergraduate Students may attend these same meetings at a cost of $20 for all meetings or an a la carte option of $10 per meeting is also available.

Please go to:

http://mis.triumf.ca/events/event.jsf?confcode=SW2019

Registration for the BBQ on Wednesday, August 21 after the ISAC20 Symposium event is incuded in registration, we require you to sign up for catering numbers.

Monday, 19 August

IAMI Summer School: Welcome and IAMI introduction

Convener: Dr Cornelia Hoehr (TRIUMF)

IAMI Summer School: Radiochemsitry 101 - Introduction to Nuclear Medicine

Convener: Prof. Caterina Ramogida

IAMI Summer School: From target to tracer

Convener: Dr Valery Radchenko (TRIUMF)

IAMI Summer School: Radiotherapy - different treatments

Convener: Dr Cornelia Hoehr (TRIUMF)

10:30 Coffee Break

IAMI Summer School: Cyclotron production of Ga-68 and Zr-89 using pressed targets

Convener: Prof. Brigitte Guerin

IAMI Summer School: Targeted radionuclide therapy for cancer and infections

Convener: Prof. Kate Dadachova

12:30 Lunch

IAMI Summer School: Hands on Lab Session and Outreach Workshop (Students only)

Conveners: Prof. Caterina Ramogida, Dr Valery Radchenko (TRIUMF)

IAMI Summer School: Outreach computing session - bring your own laptop

Convener: Prof. Christian Diget

15:30 Coffee Break

IAMI Summer School: Hands on Lab Session and Outreach Workshop (Students only)

Conveners: Prof. Caterina Ramogida, Dr Valery Radchenko (TRIUMF)

IAMI Summer School: Outreach computing session - bring your own laptop

Convener: Prof. Christian Diget

Tuesday, 20 August

10:20 Coffee Break

12:30 Lunch

15:40 Coffee Break

Wednesday, 21 August

ISAC20 Symposium: Session 1: co-chair Gordon Ball/Rick Baartman

Convener: Gordon Ball (TRIUMF)

1 Welcome

Speaker: Dr Jonathan Bagger (TRIUMF)

2 Introduction

Speaker: Dr Gordon Ball (TRIUMF)

group photo ISAC20_Group_photo.jpg

Slides ISAC20_introduction_ball.pdf

Video ISAC20_TRIUMF_2019_August_Video_produced_by_Mindy_Hapke.mp4

3 ISAC RIB Production Systems and target development

Speaker: Dr Pierre Bricault

Slides ISAC20-RIB-DevPB21082019-0658.pdf

4 ISAC Source Development

Speaker: Dr Jens Lassen (TRIUMF)

5 ISAC Linear Accelerators

Speaker: Robert Laxdal (TRIUMF)

Slides isac-20-accelerator-laxdal-14.ppt

6 Application of ISAC facility for production of medical radionuclides: past, present and future perspectives

Speaker: Dr Valery Radchenko (TRIUMF)

Slides Radchenko_TRIUMF_Science_week_ISAC.pptx

10:05 Coffee Break

ISAC20 Symposium: Session 2: co-chair Kyle Leach/Alan Shotter

Convener: Gordon Ball (TRIUMF)

7 Angular correlation measurements at ISAC: using the atomic nucleus to search for BSM physics for 20 yrs

Speaker: Prof. Dan Melconian

Slides melconian-ISAC20.pdf

8 Superallowed Fermi Beta Decay: Two decades of fundamental research at ISAC

Speaker: Prof. Gwen Grinyer (University of Regina)

Slides Grinyer_ISAC20_2019.pdf

9 Early Days of beta-NMR at TRIUMF

Speaker: Prof. Rob Kiefl (TRIUMF and UBC)

Slides ISAC20Rob1.pdf

10 ISAC’s continuing mission to explore the stars

Speaker: Prof. Alison Laird (York University)

Slides ISAC20_AML_stars.pdf

11 Twenty+ years of ab initio nuclear theory

Speaker: Dr Petr Navratil (TRIUMF)

Slides Navratil_ISAC20_2019.pptx

12:30 Lunch

ISAC20 Symposium: Session 3: co-chair Corina Andreoiu/Andrew MacFarlane

Convener: Gordon Ball

12 Unmasking nuclear halo features with two decades of ISOL beams at TRIUMF

Speaker: Prof. Rituparna Kanungo (Saint Mary's University)

Slides Ritu_ISAC20_2019.pdf

13 Nuclear shapes, coexistence and collective behaviors studied at ISAC

Speaker: Dr Adam Garnsworthy (TRIUMF)

Slides Garns-ISAC-20-Celebration.pdf

14 Indirect studies of astrophysics around the Coulomb barrier

Speaker: Dr Barry Davids (TRIUMF)

Slides ISAC20_davids.pdf

15 From structural distortions to weak magnetism - exploring the capabilities of beta-NMR

Speaker: Dr Zaher Salman

Slides Salman_ISAC20.pdf

15:15 Coffee Break

ISAC20 Symposium: Session 4: co-chair Greg Hackman/Dennis Muecher

Convener: Gordon Ball (TRIUMF)

16 Pushing the limits: Towards the neutron drip line with ISAC

Speaker: Dr Iris Dillmann (TRIUMF)

Slides ISAC20_Dillmann.pptx

17 The Emergence of nuclear-shell-model studies at ISAC

Speaker: Dr Anna Kwiatkowski (TRIUMF)

Slides kwiatkowski_shellModel.pdf

18 Francium: Uranium and hydrogen baked into one

Speaker: Prof. Gerald Gwinner (University of Manitoba)

Slides gwinner_2019_ISAC_20_talk.pdf

19 Advances in beta-NMR @ ISAC

Speaker: Dr Sarah Dunsiger (TRIUMF)

Slides Dunsiger_ISAC20.pdf

20 Symposium Summary

Speaker: Prof. Alan Shotter

Slides TRIUMF_ISOL.pdf

Video Astronomers_Find_Cannonball_Pulsar_Speeding_Through_Space_(1).mp4

BBQ (time to be confirmed)

Convener: Prof. Reiner Kruecken (TRIUMF)

Thursday, 22 August

TUG AGM: Introduction and Welcome

Conveners: Caterina Ramogida, Prof. Gwen Grinyer (University of Regina)

21 Welcome

Speaker: Dr Jonathan Bagger (TRIUMF)

22 Introduction

Speaker: Dr Anna Kwiatkowski (TRIUMF)

Slides 190820_scienceWk-tugAGM_intro.pdf

23 Physical Sciences Division Report

Speaker: Prof. Jens Dilling (TRIUMF)

Slides User_group_science_week_PSD_JD_2019.pptx

24 Life Sciences Division Report

Speaker: Dr Cornelia Hoehr (TRIUMF)

25 Accelerator Division Report

Speaker: Dr Oliver Kester (TRIUMF)

Slides TUG_AGM_WS2019_ACC_division_OK_final.pptx

26 Update to TRIUMF Governance

Speaker: Dr Jonathan Bagger (TRIUMF)

27 Update to TRIUMF Org Chart

Speaker: Anne Louise Aboud (TRIUMF)

10:30 Coffee Break

TUG AGM

Conveners: Caterina Ramogida, Prof. Gwen Grinyer (University of Regina)

28 TRIUMF Users Office

Speaker: Dr Marcello Pavan (TRIUMF)

Slides UsersOffice-TUG_AGM_2019.pdf

29 Beam time planning for 2020

Speaker: Prof. Reiner Kruecken (TRIUMF)

Slides BeamSchedule_5YP_TUGAGM_2019.pdf

30 Q&A with TRIUMF Management

12:30 Lunch

TUG AGM

Conveners: Caterina Ramogida, Prof. Gwen Grinyer (University of Regina)

31 New electron gun for the TITAN-EBIT (Student Prize Talk)

Speaker: Kilian Dietrich (TRIUMF)

32 TUG Poster Slam!

15:15 Coffee Break

TUG AGM

Conveners: Caterina Ramogida, Prof. Gwen Grinyer (University of Regina)

33 Ab initio predictions of light nuclei

Speaker: Dr Anna McCoy

34 Range Verification in Proton Therapy using a Tumor Marker and Gamma Ray Spectroscopy

Speaker: Prof. Dennis Muecher

35 Current-Induced Non-Magnetic State in Mott Insulator Ca2RuO4

Speaker: Prof. Graeme Luke

36 ALPHA, the Trapped Antihydrogen Experiment: Status and Prospects

Speaker: Dr Andrea Capra

Slides ALPHA_Status_and_Prospects_v2.pdf

37 TUG News and Information

Speaker: Prof. Gwen Grinyer (University of Regina)

Slides Grinyer_TUG_2019.pdf

38 Student Prize Announcements

Friday, 23 August

Data Science and Quantum Computing Workshop: Hands-on Session: Machine Learning

Convener: Dr Wojciech Fedorko (TRIUMF)

slides participants_handles.txt

39 Hands on Machine Learning session

We will develop a deep learning solution for event classification in a partice physics experiment

Speaker: Dr Wojciech Fedorko (TRIUMF)

Slides

• https://github.com/wfedorko/Science_Week_ML_tutorial
• nutshell_slides.pdf
• nutshell_slides.pptx

10:30 Coffee Break

Data Science and Quantum Computing Workshop: Hands-on Session: Quantum Computing

Convener: Dr Olivia Di Matteo (TRIUMF)

slides participants_handles.txt

40 Hands-on Session: Developing Quantum Applications in Q#

In this workshop, participants will learn to use the Quantum Development Kit and the Q# programming language to develop quantum applications and test them using simulators. The workshop consists of a short lecture and a series of hands-on exercises, covering a wide variety of tasks and concepts. No prior software installation needed to participate, all materials are available online.

Speaker: Dr Christopher Granade (Microsoft)

12:30 Lunch

Data Science and Quantum Computing Workshop

Conveners: Dr Olivia Di Matteo (TRIUMF), Dr Wojciech Fedorko (TRIUMF)

slides participants_handles.txt

41 Deep Learning Applications to Medical Imaging

Medical imaging has revolutionized medicine. Now medical imaging itself is witnessing a deep learning revolution. Clinically-relevant medical image interpretation tasks (e.g. image segmentation and image classification) have been re-formulated under a deep learning framework with impressive results. These early successes have been attributed to three factors: data, learning algorithms, and fast computation. I will present examples of deep learning applications to medical imaging from our research group highlighting the versatility, opportunities, and challenges in this area (website: www.MedicalImageAnalysis.com).

Speaker: Dr Ghassan Hamarneh

Slides 201908_Hamarneh_TRIUMF-for-PDF.pdf

42 Quantum Variational Autoencoders and their applications

Generative models are among the most promising approaches toward understanding unlabelled data. They have a wide range of applications in structured prediction, molecular & material design, image analysis, speech synthesis, and computer vision. They pair with supervised learning models to help perform ML tasks when labelling data is expensive or labels are only available in a different domain. Quantum Boltzmann machine is a powerful generative model that can naturally be implemented on a quantum annealing device. However, the development of quantum-classical hybrid (QCH) algorithms is critical to deploy state-of-the-art computational models on current commercially available devices. A Quantum Variational Autoencoder (QVAE) is one such hybrid algorithm that consists of a latent generative process, formalized as a quantum or classical Boltzmann machine (QBM or BM). A quantum annealing processor is used for sampling from the Boltzmann prior distribution. The classical autoencoding structure is realized by a deep neural network, which allows inference to and generating samples from, the latent space. We have successfully employed D-Wave quantum annealers as Boltzmann samplers to train end-to-end QVAE. The hybrid structure of QVAE allows us to deploy current quantum annealing devices in a QCH generative model with latent variables that achieves competitive performance on datasets such as MNIST.

Speaker: Dr Hossein Sadeghi (D-Wave)

43 Enhancing quantum simulators with neural networks

The recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware, with promising progress towards scalable simulation of quantum matter and materials. In order to enhance the capabilities of this class of quantum devices, some of the more arduous experimental tasks can be off-loaded to classical algorithms running on conventional computers. In this talk, I will present recent efforts in deploying machine learning algorithms on data generated by quantum simulators, and show how neural networks can be trained to detect quantum phase transitions and reconstruct experimental wavefunctions.

Speaker: Dr Giacomo Torlai (Flatiron Institute)

44 Quantum Algorithms for Solving Dynamic Programming Problems

We introduce quantum algorithms for solving finite-horizon and infinite-horizon dynamic programming problems. We visit the query complexity lower bounds for classical randomized algorithms for the same tasks and consequently demonstrate a polynomial separation between the query complexity of our quantum algorithms and best-case query complexity of classical randomized algorithms. Up to polylogarithmic factors, our quantum algorithms provide quadratic advantage in terms of the numbers of states and actions in the dynamic programming problem. Nevertheless, the speed-up achieved is at the expense of appearance of other polynomial factors in the scaling of the algorithm which contribute to the precision of the solution. Our framework pertains to discrete and combinatorial optimization problems solved classically using dynamic programming techniques. As an example, we show how quantum computers can solve the travelling salesperson problem quadratically faster than the Bellman–Held–Karp algorithm does.

Speaker: Dr Pooya Ronagh (1QBit, IQC, UW)

Coffee Break

Data Science and Quantum Computing Workshop

Conveners: Dr Olivia Di Matteo (TRIUMF), Dr Wojciech Fedorko (TRIUMF)

slides participants_handles.txt

45 Projects at TRIUMF

A foreword on Machine Learning projects started at TRIUMF

Speaker: Dr Wojciech Fedorko (TRIUMF)

Slides TRIUMF_DSQC_Intro.pptx

46 Variational AutoEncoders (VAEs) for water Cherenkov detectors

A Variational AutoEncoder (VAE) is a generative method used to approximate the probability distribution of processes in very high dimensional spaces. We apply VAEs for generative modelling of Water Cherenkov detectors which are used to perform precision measurements on neutrinos. In this talk, I will discuss the steps and challenges in applying VAEs to simulated neutrino events in the proposed Intermediate Water Cherenkov Detector (IWCD). Initial results from the project show promise in the application of VAEs for synthetic data generation and unsupervised learning from labelled and unlabelled datasets.

Speaker: Mr Abhishek Kajal (TRIUMF/University of Manitoba)

47 Extrapolating Nuclear Many-Body Calculations with Constrained Gaussian Processes

The properties of nuclei can be computed from first principles starting from realistic interactions between nucleons. Using suitable basis functions, the many-body wavefunction is found by diagonalizing a Hamiltonian matrix (i.e. solving the Schrödinger equation). Due to limited computational resources only a finite basis size can be used. This is frequently insufficient for complete convergence. The "true value" of a calculated quantity (e.g. ground state energy) is predicted by extrapolating to infinite basis size. The functional form of such an extrapolation is unknown but by the variational principle the signs of the derivatives are known. In this work we use knowledge of monotonicity and convexity to constrain a Gaussian Process (GP) model and predict ground state energies. A GP is a machine learning tool which generates a distribution of viable functions which satisfy known data points. It is easy to compute and automatically produces uncertainties on predictions. However, applying derivative constraints is not simple and requires iteratively tightening the constraints and improving the GP model via Sequential Monte Carlo. This novel method shows promise in providing more meaningful confidence intervals on theoretical predictions than existing methods, allowing a more useful comparison to experiment.

Speaker: Mr Peter Gysbers

48 Expressive Structured Representations in Deep Generative Models

Deep generative models, such as variational autoencoders and generative adversarial networks, are among the most exciting recent developments in machine learning. Variational autoencoders, in particular, have seen a tremendous rise in popularity due to their principled variational framework and powerful neural approximations to previously infeasible inference tasks, including marginal and posterior inference with arbitrary distributions. Their applications comprise a broad range of topics in computer science, such as image and video synthesis, temporal forecasting, and feature learning. In this talk, we first revisit the basic principles of variational autoencoders with a focus on the underlying implicit modeling assumptions. Using these insights, we identify their representational limitations and reinterpret them from the viewpoint of probabilistic graphical models. Finally, we discuss a structure learning approach that overcomes these limitations through an explicit and dynamic encoding of latent dependencies, leading to an efficient and more expressive variant of traditional variational learning. The benefits of this approach will be illustrated with applications in computer vision and computer graphics. _________________________ Short Bio Andreas Lehrmann works at the intersection of machine learning, quantitative finance, and computer vision. His research focuses on the development of expressive neural architectures for structured data and approximate methods for the associated inference tasks. He is also interested in deep generative models exploiting contextual information in non-stationary time-series. Fields of application in finance and vision include volatility and hedging of derivatives, natural language processing, conditional video synthesis, and scene understanding. Before assuming his current role as a machine learning research team lead with Borealis AI, Andreas was a postdoctoral research scientist at Facebook Reality Labs and Disney Research (United States). Prior to that, he was a Microsoft Research Ph.D. scholar at ETH Zurich (Switzerland) and the Max-Planck-Institute for Intelligent Systems (Germany).

Speaker: Dr Andreas Lehrmann

49 Solving Technological Challenges with the Fujitsu Digital Annealer

The Fujitsu Digital Annealer is a new technology that is used to solve large-scale combinatorial optimization problems instantly. The Digital Annealer uses a digital circuit design and can solve problems which are intractable for classical computers. In this workshop, we introduce how the Digital Annealer works for solving combinatorial optimization problems with use cases drawn from scientific fields including health care, pharmaceuticals, and physics. Included is an introduction to expressing technical problems in a quadratic unconstrained binary optimization (QUBO) format for solution using the Digital Annealer.

Speakers: Mr Jeffrey English (Fujitsu), Mr Tadayoshi Ozaki (Fujitsu)

Slides Digital_Annealer_20190823.pdf (Protected)