Applying Machine Learning for Gravitational-wave Burst Data Analysis
|
|
- Rosaline Dennis
- 5 years ago
- Views:
Transcription
1 Applying Machine Learning for Gravitational-wave Burst Data Analysis Junwei Cao LIGO Scientific Collaboration Research Group Research Institute of Information Technology Tsinghua University June 29,
2 Outline Introduction to Tsinghua Group LIGO Data & Computing Infrastructure Real-time / low-latency GW Burst Search» Applying machine learning for burst veto analysis Future Work Entering the era of GW astronomy jcao@tsinghua.edu.cn
3 Our Group The only LSC member group in mainland China, including 3 faculty members GW burst data analysis and computing infrastructure Also involved in KAGRA, AIGO and ASTROD With close collaboration with MIT, Caltech and UWA jcao@tsinghua.edu.cn
4 LIGO Data Data are comprised of:» Gravitational Wave channel (AS_Q)» Physical Environment Monitors» Internal Engineering Monitors Multiple data products beyond raw data» RDS_R_L1, including both GW and environmental channels» RDS_R_L3, including only AS_Q channel L-RDS_R_L gwf Site (H,L) Data Type Duration GPS Start Time
5 Challenges in AdvLIGO Era Larger Data Volume July 29, 2010 Cite from LIGO-G
6 The LSC Data Grid (LDG) Birmingham Cardiff AEI/Golm
7 Real-time Search Real-time: between online and offline mode for largescale data analysis Online Monitoring Data Streams On-site Real-time Search Data Streams+ Data Production On-site+ Off-site Offline Analysis Data Production Off-site
8 Motivation Prompt E/M follow-up by LIGO s external collaborators Detect astronomy events earlier than traditional observation methods Increase the confidence of the GW candidate event Obtain more information about GW candidate event and its source: more accurate sky position, distance, Rapid detector characterization jcao@tsinghua.edu.cn
9 An Example Implementation KleineWelle October LIGO Document ID: LIGO-G
10 Existing Veto Methods hveto: uses Poisson distribution to evaluate the coincidence significance for all auxiliary channels for a number of thresholds and time-windows UPV: finds time-coincident triggers between the GW channel and an auxiliary channel within a timewindow according to a series of self-defined metrics, used percentages Basically event-by-event methods jcao@tsinghua.edu.cn
11 Multivariate Veto Study the correlation between a GW channel trigger and adjacent triggers from auxiliary channels jcao@tsinghua.edu.cn
12 Veto Approach The veto process can be considered as a classification problem of instrument status:» The input of the classifier is the combination of properties of all coincident AUX/ENV triggers at the time t i, assuming there is a GC trigger at the time t i.» The output of the classifier is whether the instrument is fault or not. This is definitely a GW signal If yes, the GC trigger is a glitch; If not, the GC trigger is a GW signal We want to know if this is a signal or a glitch GC AUX AUXn ENV ENVm No signal and no glitch either! No instrument faults! perfect for training the classifier
13 13 1. Artificial neural networks 2. Random forests Some "Classic" classifiers from machine learning 3. Support Vector Machines (SVM)
14 14 Artificial Neural networks 1 coefficient for each input (linear combination) Function of the linear combination Example: Output > 0 = glitch predicted Input values = aux. channel vector
15 15 Random forests Based on decision trees. Predictions of multiple trees combined. Auxiliary channel vector Test on an auxiliary channel coordinate = auxiliary channel measurement Glitch / clean prediction
16 16 Support Vector Machines (SVM) Maps input vectors to a higher dimensional space and separate them with a (hyper) plane. Glitches Separation hyper-plane Clean samples
17 17 Prediction threshold between glitches and signals The prediction of a classifier is typically a number. Use of a threshold for separating glitches from signals. signals glitch Classifier prediction How to set the threshold? If too many glitches are predicted, we may not look often enough for gravitational waves. If too many clean predictions (signals) are made, then we analyze background noise too often.
18 True glitch rate 18 The receiver operating characteristic (ROC) The user of the classifier can choose the false glitch rate that he finds acceptable. The true glitch rate is given by the ROC, obtained by varying the glitch/signal threshold. "Every disturbance is a glitch" Glitch samples correctly predicted as glitches ROC Signals incorrectly predicted as glitches "Every disturbance is a signal (i.e. not a glitch)" False glitch rate
19 Observed performance for the gravitational wave channel veto at LIGO Performance of machine learning (ANN, RF, SVM): as good as the performance of the best designed algorithm but machine learning gives an automatic GW channel veto algorithm!
20
21 Beijing GW Workshop
22 Entering the era of GW Astronomy There could be GW signals at the same level of noises. More machine learning methods, e.g. deep learning, could be applied for actual GW signal discoveries. 输出层 隐层 输入层
A Multiple Signal Classification Method for Directional Gravitational-wave Burst Search
A Multiple Signal Classification Method for Directional Gravitational-wave Burst Search Junwei Cao LIGO Scientific Collaboration Research Group Tsinghua University, Beijing, China 3 rd Galileo - Xu Guangqi
More informationComputing Infrastructure for Gravitational Wave Data Analysis
Computing Infrastructure for Gravitational Wave Data Analysis Junwei Cao for the LIGO Scientific Collaboration Tsinghua University / MIT LIGO Laboratory The 3 rd China-U.S. Roundtable on Scientific Data
More informationSearches for Gravitational waves associated with Gamma-ray bursts
Searches for Gravitational waves associated with Gamma-ray bursts Raymond Frey University of Oregon for the LIGO Scientific Collaboration and the Virgo Collaboration 1 Current network of groundbased GW
More informationStrategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches
Strategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches E. Cuoco 1, A.Torres-Forné 2,J.A. Font 2,7, J.Powell 3, R.Lynch 4, D.Trifiró 5 2 Universitat
More informationSearch for Inspiral GW Signals Associated with Short GRBs using Artificial Neural Networks
Search for Inspiral GW Signals Associated with Short GRBs using Artificial Neural Networks Kyungmin Kim and Hyun Kyu Lee Hanyang Univ. In collaboration with Y.-M. Kim, C.-H. Lee (PNU), J.J. Oh, S.H. Oh,
More informationCurrent status of KAGRA data analysis, detector characterization. Kazuhiro Hayama (NAOJ)
Current status of KAGRA data analysis, detector characterization Kazuhiro Hayama (NAOJ) 1 KAGRA project 2 Start Tunnel Excavation Nozumi 350m completed New Atotsu 150m completed 3 Surface Building 4 Current
More informationDiscovery Through Situational Awareness
Discovery Through Situational Awareness BRETT AMIDAN JIM FOLLUM NICK BETZSOLD TIM YIN (UNIVERSITY OF WYOMING) SHIKHAR PANDEY (WASHINGTON STATE UNIVERSITY) Pacific Northwest National Laboratory February
More informationLIGO-Virgo Detector Characterization (DetChar)
LIGO-Virgo Detector Characterization (DetChar) 5th KAGRA Workshop Perugia, 14-15 February 2019 Nicolas Arnaud (narnaud@lal.in2p3.fr) Laboratoire de l Accélérateur Linéaire (CNRS/IN2P3 & Université Paris-Sud)
More informationSearch for Gravitational Wave Transients. Florent Robinet On behalf of the LSC and Virgo Collaborations
Search for Gravitational Wave Transients On behalf of the LSC and Virgo Collaborations 1 Gravitational Waves Gravitational waves = "ripples" in space time Weak field approximation : g = h h 1 Wave equation,
More informationLaser Interferometer Gravitational-Wave Observatory (LIGO)! A Brief Overview!
Laser Interferometer Gravitational-Wave Observatory (LIGO) A Brief Overview Sharon Brunett California Institute of Technology Pacific Research Platform Workshop October 15, 2015 Credit: AEI, CCT, LSU LIGO
More informationGravitational Wave Astronomy s Next Frontier in Computation
Gravitational Wave Astronomy s Next Frontier in Computation Chad Hanna - Penn State University Penn State Physics Astronomy & Astrophysics Outline 1. Motivation 2. Gravitational waves. 3. Birth of gravitational
More informationarxiv:gr-qc/ v1 4 Dec 2003
Testing the LIGO Inspiral Analysis with Hardware Injections arxiv:gr-qc/0312031 v1 4 Dec 2003 Duncan A. Brown 1 for the LIGO Scientific Collaboration 1 Department of Physics, University of Wisconsin Milwaukee,
More informationGRB-triggered searches for gravitational waves from compact binary inspirals in LIGO and Virgo data during S5/VSR1
GRB-triggered searches for gravitational waves from compact binary inspirals in LIGO and Virgo data during S5/VSR1 Nickolas Fotopoulos (UWM) for the LIGO Scientific Collaboration and the Virgo Collaboration
More informationSearching for Gravitational Waves from Binary Inspirals with LIGO
Searching for Gravitational Waves from Binary Inspirals with LIGO Duncan Brown University of Wisconsin-Milwaukee for the LIGO Scientific Collaboration Inspiral Working Group LIGO-G030671-00-Z S1 Binary
More informationData Mining Based Anomaly Detection In PMU Measurements And Event Detection
Data Mining Based Anomaly Detection In PMU Measurements And Event Detection P. Banerjee, S. Pandey, M. Zhou, A. Srivastava, Y. Wu Smart Grid Demonstration and Research Investigation Lab (SGDRIL) Energy
More informationOutline. 1. Basics of gravitational wave transient signal searches. 2. Reconstruction of signal properties
Gravitational Wave Transients state-of-the-arts: detection confidence and signal reconstruction G.A.Prodi, University of Trento and INFN, for the LIGO Scientific Collaboration and the Virgo Collaboration
More informationLIGO s continuing search for gravitational waves
LIGO s continuing search for gravitational waves Patrick Brady University of Wisconsin-Milwaukee LIGO Scientific Collaboration LIGO Interferometers LIGO is an interferometric detector» A laser is used
More informationGravitational-Wave Data Analysis
Gravitational-Wave Data Analysis Peter Shawhan Physics 798G April 12, 2007 Outline Gravitational-wave data General data analysis principles Specific data analysis methods Classification of signals Methods
More informationMIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October,
MIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October, 23 2013 The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run
More informationNEVER IGNORE A COINCIDENCE
ALEXANDER L. URBAN LEONARD E. PARKER CENTER FOR GRAVITATION, COSMOLOGY & ASTROPHYSICS NEVER IGNORE A COINCIDENCE ENHANCING LIGO SENSITIVITY TO COMPACT BINARY MERGERS WITH GRB COUNTERPARTS 23 RD MIDWEST
More informationSearching for Gravitational Waves from Coalescing Binary Systems
Searching for Gravitational Waves from Coalescing Binary Systems Stephen Fairhurst Cardiff University and LIGO Scientific Collaboration 1 Outline Motivation Searching for Coalescing Binaries Latest Results
More informationJames Clark For The LSC
A search for gravitational waves associated with the August 2006 timing glitch of the http://arxiv.org/abs/1011.1357 Vela pulsar James Clark For The LSC Introduction This talk: first search for GWs associated
More informationMultivariate statistical methods and data mining in particle physics
Multivariate statistical methods and data mining in particle physics RHUL Physics www.pp.rhul.ac.uk/~cowan Academic Training Lectures CERN 16 19 June, 2008 1 Outline Statement of the problem Some general
More informationApplication of machine learning in gravitational waves survey
Application of machine learning in gravitational waves survey Filip Morawski NCAC PAS, VIRGO GPU DAY 22.06.2018, Budapest, Hungary Gravitational waves Source: LIGO Caltech Something special happens when
More informationSearch for compact binary systems in LIGO data
Search for compact binary systems in LIGO data Thomas Cokelaer On behalf of the LIGO Scientific Collaboration Cardiff University, U.K. LIGO-G060630-00-Z Plan 1) Overview What kind of gravitational waves
More informationKM3NeT data analysis conventions
KM3NeT DATA 2014 001-dataAnalysisConvention ACreusot v1 December 15, 2014 KM3NeT data analysis conventions Robert Bormuth 1, Alexandre Creusot 2 1 Nikhef, 2 Laboratoire AstroParticules et Cosmologie, corresponding
More informationGravitational wave detection with Virgo and LIGO experiment - Case of the long bursts
Gravitational wave detection with Virgo and LIGO experiment - Case of the long bursts Samuel Franco Supervisor: Patrice Hello Laboratoire de l Accélérateur Linéaire (Orsay) 06/12/2013 Samuel Franco (LAL)
More informationLIGO IdM Update. Scott Koranda 11th FIM4R Workshop Montreal 18 Sep 2017
LIGO IdM Update Scott Koranda 11th FIM4R Workshop Montreal 18 Sep 2017 What's New? September 14, 2015 9:50:45 UTC Yes, yes. But what's new since the detection? LIGO/Virgo Collaboration News Third black
More informationMethods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data
Syracuse University SURFACE Physics College of Arts and Sciences 4-8-2010 Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data Duncan Brown Department of Physics,
More informationDetector Stability on short time scales
Detector Stability on short time scales 1 I am going to talk about... Tests used in an offline GRB analysis Cumulative Test Likelihood Test Tests for online burst filter 2 Tests used in an offline GRB
More informationHoldout and Cross-Validation Methods Overfitting Avoidance
Holdout and Cross-Validation Methods Overfitting Avoidance Decision Trees Reduce error pruning Cost-complexity pruning Neural Networks Early stopping Adjusting Regularizers via Cross-Validation Nearest
More informationGW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral Lazzaro Claudia for the LIGO Scientific Collaboration and the Virgo Collaboration 25 October 2017 GW170817 PhysRevLett.119.161101
More informationSearch for inspiralling neutron stars in LIGO S1 data
INSTITUTE OF PHYSICS PUBLISHING Class. Quantum Grav. 21 (2004) S691 S696 CLASSICAL AND QUANTUM GRAVITY PII: S0264-9381(04)68883-6 Search for inspiralling neutron stars in LIGO S1 data Gabriela González
More informationAdvanced statistical methods for data analysis Lecture 1
Advanced statistical methods for data analysis Lecture 1 RHUL Physics www.pp.rhul.ac.uk/~cowan Universität Mainz Klausurtagung des GK Eichtheorien exp. Tests... Bullay/Mosel 15 17 September, 2008 1 Outline
More informationAnomaly Detection for the CERN Large Hadron Collider injection magnets
Anomaly Detection for the CERN Large Hadron Collider injection magnets Armin Halilovic KU Leuven - Department of Computer Science In cooperation with CERN 2018-07-27 0 Outline 1 Context 2 Data 3 Preprocessing
More informationOn-Orbit Performance of the GLAST Burst Monitor
On-Orbit Performance of the GLAST Burst Monitor Curran D. Muhlberger (RA) Charles A. Meegan (PI) Cornell University University of Maryland August 8, 2008 What is GLAST? What is GBM? What is it Good for?
More informationSpatial Decision Tree: A Novel Approach to Land-Cover Classification
Spatial Decision Tree: A Novel Approach to Land-Cover Classification Zhe Jiang 1, Shashi Shekhar 1, Xun Zhou 1, Joseph Knight 2, Jennifer Corcoran 2 1 Department of Computer Science & Engineering 2 Department
More informationGravitational-Wave Data Analysis: Lecture 3
Gravitational-Wave Data Analysis: Lecture 3 Peter S. Shawhan Gravitational Wave Astronomy Summer School May 30, 2012 Outline for Today Gaining confidence in a signal candidate Consistency tests Data quality
More informationFirst upper limits from LIGO on gravitational wave bursts
INSTITUTE OF PHYSICS PUBLISHING Class. Quantum Grav. 21 (2004) S677 S684 CLASSICAL AND QUANTUM GRAVITY PII: S0264-9381(04)68358-4 First upper limits from LIGO on gravitational wave bursts Alan J Weinstein
More informationW vs. QCD Jet Tagging at the Large Hadron Collider
W vs. QCD Jet Tagging at the Large Hadron Collider Bryan Anenberg: anenberg@stanford.edu; CS229 December 13, 2013 Problem Statement High energy collisions of protons at the Large Hadron Collider (LHC)
More informationResults from LIGO Searches for Binary Inspiral Gravitational Waves
Results from LIGO Searches for Binary Inspiral Gravitational Waves Peter Shawhan (LIGO Laboratory / Caltech) For the LIGO Scientific Collaboration American Physical Society April Meeting May 4, 2004 Denver,
More informationArticle from. Predictive Analytics and Futurism. July 2016 Issue 13
Article from Predictive Analytics and Futurism July 2016 Issue 13 Regression and Classification: A Deeper Look By Jeff Heaton Classification and regression are the two most common forms of models fitted
More informationMachine Learning: Chenhao Tan University of Colorado Boulder LECTURE 9
Machine Learning: Chenhao Tan University of Colorado Boulder LECTURE 9 Slides adapted from Jordan Boyd-Graber Machine Learning: Chenhao Tan Boulder 1 of 39 Recap Supervised learning Previously: KNN, naïve
More informationThesis. Wei Tang. 1 Abstract 3. 3 Experiment Background The Large Hadron Collider The ATLAS Detector... 4
Thesis Wei Tang Contents 1 Abstract 3 2 Introduction 3 3 Experiment Background 4 3.1 The Large Hadron Collider........................... 4 3.2 The ATLAS Detector.............................. 4 4 Search
More informationLIGO Grid Applications Development Within GriPhyN
LIGO Grid Applications Development Within GriPhyN Albert Lazzarini LIGO Laboratory, Caltech lazz@ligo.caltech.edu GriPhyN All Hands Meeting 15-17 October 2003 ANL LIGO Applications Development Team (GriPhyN)
More informationDeep Learning for Gravitational Wave Analysis Results with LIGO Data
Link to these slides: http://tiny.cc/nips arxiv:1711.03121 Deep Learning for Gravitational Wave Analysis Results with LIGO Data Daniel George & E. A. Huerta NCSA Gravity Group - http://gravity.ncsa.illinois.edu/
More informationMachine Learning Applications in Astronomy
Machine Learning Applications in Astronomy Umaa Rebbapragada, Ph.D. Machine Learning and Instrument Autonomy Group Big Data Task Force November 1, 2017 Research described in this presentation was carried
More informationData Mining: Concepts and Techniques. (3 rd ed.) Chapter 8. Chapter 8. Classification: Basic Concepts
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 8 1 Chapter 8. Classification: Basic Concepts Classification: Basic Concepts Decision Tree Induction Bayes Classification Methods Rule-Based Classification
More informationData Characterization in Gravitational Waves
Data Characterization in Gravitational Waves Soma Mukherjee Max Planck Institut fuer Gravitationsphysik Golm, Germany. Talk at University of Texas, Brownsville. March 26, 2003 What data do we have? Consists
More informationM.-A. Bizouard, F. Cavalier. GWDAW 8 Milwaukee 19/12/03
Coincidence and coherent analyses for burst search using interferometers M.-A. Bizouard, F. Cavalier on behalf of the Virgo-Orsay group Network model Results of coincidence analysis: loose and tight coincidence
More informationMulti-Messenger Programs in ANTARES: Status and Prospects. Véronique Van Elewyck Laboratoire AstroParticule et Cosmologie (Paris)
Multi-Messenger Programs in ANTARES: Status and Prospects Laboratoire AstroParticule et Cosmologie (Paris) An overview Externally triggered neutrino searches: the example of GRBs Strategies for data acquisition
More informationMath 6330: Statistical Consulting Class 5
Math 6330: Statistical Consulting Class 5 Tony Cox tcoxdenver@aol.com University of Colorado at Denver Course web site: http://cox-associates.com/6330/ What is a predictive model? The probability that
More informationContext-based Reasoning in Ambient Intelligence - CoReAmI -
Context-based in Ambient Intelligence - CoReAmI - Hristijan Gjoreski Department of Intelligent Systems, Jožef Stefan Institute Supervisor: Prof. Dr. Matjaž Gams Co-supervisor: Dr. Mitja Luštrek Background
More informationStatus of LIGO. David Shoemaker LISA Symposium 13 July 2004 LIGO-G M
Status of LIGO David Shoemaker LISA Symposium 13 July 2004 Ground-based interferometric gravitational-wave detectors Search for GWs above lower frequency limit imposed by gravity gradients» Might go as
More informationHow the detection happened
For the first time, scientists have observed ripples in the fabric of spacetime called gravitational waves, arriving at the earth from a cataclysmic event in the distant universe. This confirms a major
More informationAn Aperçu about Gravitational Waves and Data Analysis
Ondes Gravitationnelles, Séminaire Poincaré XXII (2016) 81 86 Séminaire Poincaré An Aperçu about Gravitational Waves and Data Analysis Eric Chassande-Mottin APC AstroParticule et Cosmologie Université
More informationPreliminary results from gamma-ray observations with the CALorimeteric Electron Telescope (CALET)
Preliminary results from gamma-ray observations with the CALorimeteric Electron Telescope (CALET) Y.Asaoka for the CALET Collaboration RISE, Waseda University 2016/12/15 CTA-Japan Workshop The extreme
More informationPerformance Evaluation
Performance Evaluation David S. Rosenberg Bloomberg ML EDU October 26, 2017 David S. Rosenberg (Bloomberg ML EDU) October 26, 2017 1 / 36 Baseline Models David S. Rosenberg (Bloomberg ML EDU) October 26,
More informationSupporting Collaboration for the Gravitational Wave Astronomy Community with InCommon
Supporting Collaboration for the Gravitational Wave Astronomy Community with InCommon Scott Koranda for LIGO LIGO and University of Wisconsin-Milwaukee August 20, 2014 LIGO-XXXXXXX-v1 1 / 28 LIGO Science
More informationHeat Load Forecasting of District Heating System Based on Numerical Weather Prediction Model
2nd International Forum on Electrical Engineering and Automation (IFEEA 2) Heat Load Forecasting of District Heating System Based on Numerical Weather Prediction Model YANG Hongying, a, JIN Shuanglong,
More informationClass 4: Classification. Quaid Morris February 11 th, 2011 ML4Bio
Class 4: Classification Quaid Morris February 11 th, 211 ML4Bio Overview Basic concepts in classification: overfitting, cross-validation, evaluation. Linear Discriminant Analysis and Quadratic Discriminant
More informationVirtual Beach Building a GBM Model
Virtual Beach 3.0.6 Building a GBM Model Building, Evaluating and Validating Anytime Nowcast Models In this module you will learn how to: A. Build and evaluate an anytime GBM model B. Optimize a GBM model
More informationHow to measure a distance of one thousandth of the proton diameter? The detection of gravitational waves
How to measure a distance of one thousandth of the proton diameter? The detection of gravitational waves M. Tacca Laboratoire AstroParticule et Cosmologie (APC) - Paris Journée GPhys - 2016 July 6th General
More informationThe LSC glitch group: monitoring noise transients during the fifth LIGO science run
IOP PUBLISHING Class. Quantum Grav. 25 (2008) 184004 (10pp) CLASSICAL AND QUANTUM GRAVITY doi:10.1088/0264-9381/25/18/184004 The LSC glitch group: monitoring noise transients during the fifth LIGO science
More informationLIGO Present and Future. Barry Barish Directory of the LIGO Laboratory
LIGO Present and Future Barry Barish Directory of the LIGO Laboratory LIGO I Schedule and Plan LIGO I has been built by LIGO Lab (Caltech & MIT) 1996 Construction Underway (mostly civil) 1997 Facility
More informationCharacterization of Jet Charge at the LHC
Characterization of Jet Charge at the LHC Thomas Dylan Rueter, Krishna Soni Abstract The Large Hadron Collider (LHC) produces a staggering amount of data - about 30 petabytes annually. One of the largest
More informationMachine Learning for Gravitational Wave signals classification in LIGO and Virgo
Machine Learning for Gravitational Wave signals classification in LIGO and Virgo Elena Cuoco European Gravitational Observatory www.elenacuoco.com @elenacuoco 2 About me About me Working as Data Analyst
More informationDiagnostics. Gad Kimmel
Diagnostics Gad Kimmel Outline Introduction. Bootstrap method. Cross validation. ROC plot. Introduction Motivation Estimating properties of an estimator. Given data samples say the average. x 1, x 2,...,
More informationData Mining Prof. Pabitra Mitra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur
Data Mining Prof. Pabitra Mitra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture 21 K - Nearest Neighbor V In this lecture we discuss; how do we evaluate the
More informationFrom the Big Bang to Big Data. Ofer Lahav (UCL)
From the Big Bang to Big Data Ofer Lahav (UCL) 1 Outline What is Big Data? What does it mean to computer scientists vs physicists? The Alan Turing Institute Machine learning examples from Astronomy The
More informationData quality vetoes and their effect on searches for gravitational waves from compact binary systems
Data quality vetoes and their effect on searches for gravitational waves from compact binary systems Samantha Usman August 12, 2014 Abstract Data quality flags have been designed to prevent problems caused
More informationLinear Classifiers: Expressiveness
Linear Classifiers: Expressiveness Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1 Lecture outline Linear classifiers: Introduction What functions do linear classifiers express?
More informationData Analysis Pipeline: The Search for Gravitational Waves in Real life
Data Analysis Pipeline: The Search for Gravitational Waves in Real life Romain Gouaty LAPP - Université de Savoie - CNRS/IN2P3 On behalf of the LIGO Scientific Collaboration and the Virgo Collaboration
More informationApplication and Challenges of Artificial Intelligence in Exploration
Application and Challenges of Artificial Intelligence in Exploration XPLOR 2017 John McGaughey 2017 Mira Geoscience Ltd. Artificial Intelligence Artificial Intelligence is colossally hyped these days,
More informationParticle Identification at LHCb. IML Workshop. April 10, 2018
Particle Identification at LHCb Miriam Lucio, on behalf of the LHCb Collaboration IML Workshop April 10, 2018 M. Lucio Particle Identification at LHCb April 10, 2018 1 Outline 1 Introduction 2 Neutral
More informationAnomaly Detection. Jing Gao. SUNY Buffalo
Anomaly Detection Jing Gao SUNY Buffalo 1 Anomaly Detection Anomalies the set of objects are considerably dissimilar from the remainder of the data occur relatively infrequently when they do occur, their
More informationRoberto Perdisci^+, Guofei Gu^, Wenke Lee^ presented by Roberto Perdisci. ^Georgia Institute of Technology, Atlanta, GA, USA
U s i n g a n E n s e m b l e o f O n e - C l a s s S V M C l a s s i f i e r s t o H a r d e n P a y l o a d - B a s e d A n o m a l y D e t e c t i o n S y s t e m s Roberto Perdisci^+, Guofei Gu^, Wenke
More informationSVOM Science Ground Segment
SVOM Science Ground Segment Maohai Huang on behalf of SVOM GS team 2016-4-11 SVOM Workshop, Les Houches, M. Huang 1 SVOM Observing Programs Core Programs (GRB) General Programs ToO Programs Nominal ToO
More informationEnsemble Methods. NLP ML Web! Fall 2013! Andrew Rosenberg! TA/Grader: David Guy Brizan
Ensemble Methods NLP ML Web! Fall 2013! Andrew Rosenberg! TA/Grader: David Guy Brizan How do you make a decision? What do you want for lunch today?! What did you have last night?! What are your favorite
More informationPython in Gravitational Waves communities
Python in Gravitational Waves communities Elena Cuoco (elena.cuoco@ego-gw.it) European Gravitational Observatory on behalf of Virgo Collaboration VIR-0346A-16 Ph.D in Physics, working at EGO Physicist
More informationMidterm: CS 6375 Spring 2015 Solutions
Midterm: CS 6375 Spring 2015 Solutions The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run out of room for an
More informationDreem Challenge report (team Bussanati)
Wavelet course, MVA 04-05 Simon Bussy, simon.bussy@gmail.com Antoine Recanati, arecanat@ens-cachan.fr Dreem Challenge report (team Bussanati) Description and specifics of the challenge We worked on the
More informationSearch for the gravity wave signature of GRB030329/SN2003dh
Laser Interferometer Gravitational-Wave Observatory (LIGO) Search for the gravity wave signature of GRB030329/SN2003dh ABSTRACT Optimal Integration length Well detectable Sine- Gaussian simulation One
More informationFinding Black Holes with Lasers
Finding Black Holes with Lasers Andreas Freise Royal Institute of Great Brtitain 18.02.2013 [Image shows guide laser at Allgäu Public Observatory in Ottobeuren, Germany. Credit: Martin Kornmesser] LIGO-G1300827
More informationClick Prediction and Preference Ranking of RSS Feeds
Click Prediction and Preference Ranking of RSS Feeds 1 Introduction December 11, 2009 Steven Wu RSS (Really Simple Syndication) is a family of data formats used to publish frequently updated works. RSS
More informationBroad-band CW searches in LIGO and GEO S2 and S3 data. B. Allen, Y. Itoh, M.A. Papa, X. Siemens. AEI and UWM
Broad-band CW searches in LIGO and GEO S2 and S3 data B. Allen, Y. Itoh, M.A. Papa, X. Siemens AEI and UWM GWDAW-8 December 18, 2003 LIGO Scientific Collaboration, UW - Milwaukee 1LIGO-G030651-00-Z What
More informationFault prediction of power system distribution equipment based on support vector machine
Fault prediction of power system distribution equipment based on support vector machine Zhenqi Wang a, Hongyi Zhang b School of Control and Computer Engineering, North China Electric Power University,
More informationReading, UK 1 2 Abstract
, pp.45-54 http://dx.doi.org/10.14257/ijseia.2013.7.5.05 A Case Study on the Application of Computational Intelligence to Identifying Relationships between Land use Characteristics and Damages caused by
More informationGeneralization to a zero-data task: an empirical study
Generalization to a zero-data task: an empirical study Université de Montréal 20/03/2007 Introduction Introduction and motivation What is a zero-data task? task for which no training data are available
More informationSearch for gravitational wave bursts with the first science data from LIGO
Search for gravitational wave bursts with the first science data from LIGO Alan Weinstein, Caltech For the LSC Burst ULWG LIGO PAC14 May 6, 2003 G030267-00-Z Outline: Bursts: what are we looking for? Burst
More informationUnsupervised Learning Methods
Structural Health Monitoring Using Statistical Pattern Recognition Unsupervised Learning Methods Keith Worden and Graeme Manson Presented by Keith Worden The Structural Health Monitoring Process 1. Operational
More informationStatus and Prospects for LIGO
Status and Prospects for LIGO Crab Pulsar St Thomas, Virgin Islands Barry C. Barish Caltech 17-March-06 LIGO Livingston, Louisiana 4 km 17-March-06 Confronting Gravity - St Thomas 2 LIGO Hanford Washington
More informationFinal Examination CS 540-2: Introduction to Artificial Intelligence
Final Examination CS 540-2: Introduction to Artificial Intelligence May 7, 2017 LAST NAME: SOLUTIONS FIRST NAME: Problem Score Max Score 1 14 2 10 3 6 4 10 5 11 6 9 7 8 9 10 8 12 12 8 Total 100 1 of 11
More informationSupport Vector Machines
Support Vector Machines Stephan Dreiseitl University of Applied Sciences Upper Austria at Hagenberg Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support Overview Motivation
More informationLIGO-G Test of scalar-tensor gravity theory from observations of gravitational wave bursts with advanced detector network.
LIGO-G1139 Test of scalar-tensor gravity theory from observations of gravitational wave bursts with advanced detector network Kazuhiro Hayama National Astronomical Observatory of Japan Max Planck Institute
More informationNumerical Learning Algorithms
Numerical Learning Algorithms Example SVM for Separable Examples.......................... Example SVM for Nonseparable Examples....................... 4 Example Gaussian Kernel SVM...............................
More informationIntroduction to Machine Learning Midterm Exam
10-701 Introduction to Machine Learning Midterm Exam Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes, but
More informationLearning Classification with Auxiliary Probabilistic Information Quang Nguyen Hamed Valizadegan Milos Hauskrecht
Learning Classification with Auxiliary Probabilistic Information Quang Nguyen Hamed Valizadegan Milos Hauskrecht Computer Science Department University of Pittsburgh Outline Introduction Learning with
More informationDECAM SEARCHES FOR OPTICAL SIGNATURES OF GW Soares-Santos et al arxiv:
DECAM SEARCHES FOR OPTICAL SIGNATURES OF GW150914 Soares-Santos et al. 2016 arxiv:1602.04198 Marcelle Soares-Santos The DES Collaboration Fermilab Moriond Cosmology Conference March 20, 2016 INTRODUCTION
More informationActive Detection via Adaptive Submodularity
Active Detection via Adaptive Submodularity Yuxin Chen, Hiroaki Shioi, Cesar Antonio Fuentes Montesinos! Lian Pin Koh, Serge Wich and Andreas Krause! ICML Beijing June 23, 2014! Motivating Example: Biodiversity
More information