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Data Science for Physics and Astronomy Lightning Talks Data Science for Physics & Astronomy Classification of Transients Catarina Alves Data science for Physics and Astronomy Lightning talks Data science for Physics & Astronomy Classification of transients Catarina Alves PhD @ Supervisors: ● Prof Hiranya Peiris ● Dr Jason McEwen [email protected] Supervised Learning Classification Unbalanced data Non representative data catarina-alves Catarina-Alves Unsupervised Learning Real time classification Anomaly detection Large datasets 2 Data science for Physics & Astronomy Adrian Bevan Particle physics experimentalist with a keen interest in DS & AI Parameter estimation: ● 2 decades of likelihood fitting experience spanning NA48, BaBar and ATLAS experiments; currently using ML on the ATLAS and MoEDAL experiments @ LHC. ● Latest relevant work: https://royalsocietypublishing.org/doi/10.1098/rsta.2019.0392 Machine learning: ● Broad interest in algorithms: domains of validity to understand what to use when and why to optimise the use of algorithms for a given problem domain. Have used SVMs, BDTs, MLPs, CNNs, Deep Networks, KNN for analysis work. ● Working on understanding why algorithms make a given inference or decision based on input feature space (explainability and interpretability). ● Want to explore unsupervised learning and bayesian networks when an appropriate problem presents itself. Have taught or currently teach data science and machine learning undergrad and grad courses. [email protected] 3 Data science for Physics & Astronomy David Berman 4 Data science for Physics & Astronomy Richard Bielby 5 Data science for Nachiketa Chakraborty Physics & Astronomy 6 Data science for Physics & Astronomy ElenaElena CuocoCuoco 7 Data science for Physics & Astronomy Guy Davies [email protected] @CitationWarrior Expertise: Astrophysics - Sun, Stars, Planets, & Galaxies Instrumentation - Resonant Scattering Spectrometers Data - Statistics, Bayesian, Generative models Problem: Over/under fitting and/or systematics Got my eye on: Massively Hierarchical Bayesian Models Gaussian Processes for everything Tensorflow Probability Want to know more about: Everything Hierarchical Neural Nets 8 Data science for Physics & Astronomy Joe Davies What do I hope to gain from today: - Knowledge of how to understand ‘black box’ algorithms - Understand the kind of systematic uncertainties that can arise from using these type of models - Any useful techniques for writing more efficient code ann: artificial neural network dt: decision tree lr: logistic regression rf: random forest 9 svm: support vector machine Data science for Physics & Astronomy Damien Di Mijolla 10 Data science for Physics & Astronomy Caterina Doglioni Caterina My expertise is: Dark matter, measurements and searches with Doglioni hadronic jets, real-time analysis in ATLAS A real-time problem I’m grappling with: Synergies between particle, astroparticle and nuclear physics in terms of common tools and [email protected] @CatDogLund Senior Lecturer physics questions My research: I’ve got my eyes on: I am a researcher at Lund University and a member of the Ways to discover weird jets from dark sector ATLAS Collaboration at the LHC. I search for new physics particle cascades, axion-like particles phenomena that can be produced in proton-proton collisions, motivated by the presence of dark matter in I want to know more about: our universe. I work on the DARKJETS ERC project Reproducible and understandable machine together with a post-doctoral researcher and students, learning algorithms, real-time analysis in looking for the particles that mediate the interaction astrophysics between known particles and Dark Matter particles. I am a PI in the INSIGHTS ITN on statistics and machine learning, and I am a member/WG convenor of the HEP Software Foundation. 11 Data science for Physics & Astronomy Conor My expertise is: Fitzpatrick Particle Physicist Real-time analysis with high LHCb Experiment @CERN throughput data UKRI Future Leaders A problem I’m grappling with: Fellow, Uni. Manchester How to optimise expenditure on [email protected] hardware between buffer/processing @fitzparticle in an evolving and correlated trigger & DAQ system My research: Matter-antimatter asymmetries at LHCb I’ve got my eyes on: Software Trigger and Real-Time Analysis GPGPU for trigger & DAQ Optimisation problems for HEP experiments Price/performance trends in Beginning involvement in the SKA processing radiotelescope I want to know more about: Training useful ML algorithms on finite and highly imbalanced data Transients and how to find them Anomaly detection for data quality monitoring 12 Data science for Physics & Astronomy Sotiria Fotopoulou Vice Chancellor Fellow Astrophysics: - Black-hole galaxy coevolution - Large multiwavelength surveys Machine-learning: - Classification - Outlier detection - Unsupervised learning Random HDBSCAN Forest Application, Application, Logan & Fotopoulou Fotopoulou & Paltani 2019 2018 arXiv:1911.05107 arXiv:1808.04977v1 13 Data science for Physics & Astronomy 14 Data science for Physics & Astronomy Sarah Gibson My PhD… ● Studied Gamma-Ray Bursts with NASA’s Swift satellite ● Used Monte Carlo methods to optimise models to data Currently… ● Research Data Scientist/Software Engineer at the Turing ● Help promote reproducible and shareable research with Project Binder: https://mybinder.org ● Also launched The Turing Way: A Handbook for Data Science: https://the-turing-way.netlify.com/ Email… ● [email protected] 15 Data science for Physics & Astronomy Imogen Gingell 16 Data science for Physics & Astronomy 17 Data science for Peter Hatfield - Postdoc, Oxford Physics Physics & Astronomy Peter [email protected] Hatfield Physics interests: - Large galaxy surveys - Laboratory astrophysics Machine-learning projects: - Machine learning for photometric redshifts - ICF Experiment Design - Classification of particle tracks in radiation detectors (project with the Institute for Research in Schools) 18 Data science for Physics & Astronomy [email protected] Jonathan Holdship jonathan-holdship Who I am jonholdship.github.io An STFC/DiRAC funded researcher at NHS Guy’s and St Thomas Trust My current work I am using classification algorithms to predict hospital attendance and adverse patient outcomes My Astrophysics research My usual research is focused on modelling and observing chemistry in star forming regions Machine Learning My immediate goal is to train a neural network to emulate my numerical chemical model so that it can be used in radiative-hydrodynamic codes with little computation cost 19 Data science for Physics & Astronomy Omar WIMP Deep Learning to find Dark Matter Jahangir Who am I: How does it work? I am a 3rd year PhD We cool ~10T of Liquid Xenon to Student at UCL, and am -170C, and place it ~1 mile in an part of the Centre of unused Gold Mine. We then wait for a Doctoral Training in Weakly Interacting Massive Particle Data Intensive Science. (WIMP) to recoil with the Xe. My Research: What ML do I use? My research involves Use a variety of Neural Networks to using Deep Learning to analyze data to find the unique WIMP find Dark Matter. My signal within all our background data. experiment is based at the LZ Experiment in Currently doing an Internship at South Dakota, US. Babylon Health, working on Causal Discovery for Medical Diagnosis and Disease prediction Supervisors: Omar Jahangir Dr Chamkaur Ghag - HEP [email protected] Dr Ingo Waldmann - Astrophysics Data Science Workshop - ATI Dr Tim Scanlon - HEP 02/12/201920 Data science for Physics & Astronomy 21 Data science for Physics & Astronomy Benjamin Joachimi Key data science interests: ● Principled Bayesian inference ● Physical interpretability of machine learning ● Robustness of machine learning ~20 model The challenge: parameters highly nonlinear highly ~100PB raw data 3.5x1011 sources 22 Data science for Physics & Astronomy Andreas Korn Particle Physicist at the Large Hadron Collider ● Interest in Particle Tracking ● Track vertexing to identify decay signatures ● Interest in new Neural Net Methodologies for situations with variable inputs (RNN, Graph Nets etc) ● Interest in multidisciplinary projects (Dark Matter) 23 Data science for Physics & Astronomy Ofer Lahav Cosmology with AI ● Perren Professor of Astronomy and Co-Director of the CDT in Data Intensive Science (UCL) ● Research in Observational Cosmology: Dark Matter and Dark Energy ● Galaxy surveys: DES, DESI, Euclid, LSST ● AI/ML applications: ; object classification, photomeric redshifts. map reconstruction, gravitational dynamics, multi-messenger; ● AI/ML topics: interpretability of deep learning, augmentation ● Recent and current PhD students in DIS-topics: Antonella Palmese, Davide Gualdi, Michael McLeod, John Soo, Lucy Clerkin, Niall Jeffrey, Krishna Naidoo, Ben Henghes, Constantina Nicolaou, Sunil Mucesh Minimum Spanning Tree MiSTree DeepMass applied to DES (Naidoo et al. 1907.00989) 24 (Jeffrey et al.1908.005543) Data science for Physics & Astronomy Sam Sam Lawrence Lawrence PhD Student Institute of Cosmology & Gravitation, Portsmouth [email protected] Interests: ● Relativistic effects in large scale structure ● Computational cosmology (second order Boltzmann codes) ● Neural networks ● Blob detection 25 Data science for Physics & Astronomy Michaela MichaelaGLawrence Lawrence MichaelaLawrence MichaelaLawrenceONS M.G.Lawrence at sussex.ac.uk I work on: I also work on: ● Testing alternative theories of gravity ● Creating synthetic versions of the annual using gravitational waves business survey using SMOTE, GANs and ● Challenging the cosmological constant
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