Maximising the scientific return from cosmic non-gaussianity. Christian Byrnes (University of Bielefeld)

Size: px
Start display at page:

Download "Maximising the scientific return from cosmic non-gaussianity. Christian Byrnes (University of Bielefeld)"

Transcription

1 Maximising the scientific return from cosmic non-gaussianity Christian Byrnes (University of Bielefeld)

2 Specific motivations There is a lot we don't know about inflation Perturbations may be generated during inflation: Multifield inflation, particle production during inflation Or after inflation using seed perturbations from inflation Curvaton scenario late decaying scalar field whose energy density grows with time Talk: Fonseca, Lerner, Seto Modulated (p)reheating Talk: McAllister, Shellard, Riotto This wonderful session! Huston, Mulryne, D. Battefeld Talk: Zavala More! Non-canonical kinetic term, non standard gravity, include vector perturbations, gauge issues... Talk: Fasiello, Rodriguez Garcia, Noller, Ribeiro, Dimopoulos, Urakawa, Koh Need many observables to discriminate between scenarios E.g. non-gaussianity: Scale-dependence of the polyspectra Prepare for Planck, predictions should come first! Plus lots more talks and relevant posters a hot topic

3 The bispectrum Simplest definition, motivated but not exact local model Can picture the bispectrum as a triangle, with wavenumbers k denoting the side lengths Often reduced to an amplitude times scale-independent shape function Focus on quasi-local shape Other shapes: Equilateral, folded, orthogonal Overview non-gaussianity talk: Shellard

4 Some scale dependence is expected! For any fixed triangle shape CB, Nurmi, Tasinato and Wands, '09 Analogous to the power spectrum, f NL (local) should have a mild scale dependence Also true for other bispectral shapes, e.g. equilateral Varying sound speed in DBI (equilateral form of non-gaussianity): Chen '05 Reflects evolution/dynamics during inflation (e.g. it ends) Breaks degeneracy between early universe models As well as the trispectrum Can distinguish between different non-gaussian scenarios, not just between Gaussian and non-gaussian models The amplitude of f NL can be tuned in most non-gaussian models, so a precise measurement of f NL wont do this Avoid posterior detections (hard to quantify the significance)

5 Observational prospects Planck could reach a tight constraint Predicted to reach for CMBPol (COrE) has double this sensitivity CMB: Sefusatti, Ligouri, Yadav, Jackson, Pajer; '09 Galaxy clusters should later provide tighter constraints First LSS simulations: Shandera, Dalal & Huterer '10 LSS: Becker, Huterer, Kadota '10 Error bar is inversely proportional to the fiducial value of f NL It is possible that Planck will provide the first detection of non- Gaussianity, and simultaneously detect its scale dependence! We have a separable ansatz for the bispectrum CB, Gerstenlauer, Nurmi, Tasinato & Wands; '10

6 Interacting curvaton scenario: Intro Strength of self interaction (at horizon exit, *) In the limit of s=0 recover scale invariance - because the quadratic curvaton perturbation has a linear equation of motion Energy density of the curvaton is subdominant during inflation, but it grows relative to that of radiation (from the decayed inflaton) while it oscillates about the (quadratic) minimum of its potential Energy density of curvaton at time of decay CB, Enqvist, Nurmi, Takahashi; '11

7 Scale dependence can be very large Small s regime: CB, Enqvist, Takahashi '10 Any s (self-interaction) regime: CB, Enqvist, Nurmi, Takahashi '11 Axionic curvaton potential: Huang '10 See: Riotto & Sloth '10 for a step-function like f NL Typically the scale dependence grows with the interaction strength, but there are large spikes even for s<1. However spikes tend to correspond to small values of the non-linearity parameters. No scale dependence for s=0, large s regime shown soon.

8 f NL and its scale dependence The black lines show, the regions outside of these lines are detectable with Planck at 1-sigma and CMBPol/CORE at 2-sigma. Real chance of a detection.

9 This complex model can be ruled out In spite of the many free parameters (compared to the quadratic model), observation of f NL and g NL with scale dependence can rule out the model, most regions of the plots cannot be realised for any parameter values

10 Large self-interaction limit Consider s>>1, i.e. potential is dominated by the selfinteraction term during inflation It will eventually oscillate in a quadratic minimum before decaying n=4: n=6: non-linearity parameters are small n=8: So scale dependence is an order of magnitude larger than the spectral index which makes this topic very interesting We could probe the self interactions of a field which is always subdominant

11 Single-source models Models where any single field generates the perturbations Not assumed to be the inflaton, could be the curvaton Scale dependence arises from the non-linearity of the field evolution just after horizon exit Only exception is a free test field (quadratic potential) has a linear equation of motion The assumption that f NL is scale independent is only valid in the simplest toy models! Neither the spectral index, nor its running, probe higher derivatives of the isocurvature's field potential Easy to apply our formulas, please do! See:

12 Mixed inflaton-curvaton scenario The inflaton phi has Gaussian perturbations, the curvaton field sigma (quadratic potential) is non- Gaussian phi and sigma have different spectral indices assume a small field model of inflation New consistency relation Trispectrum where

13 Conclusions Almost every non-gaussian models has a scale dependence Should include this scale-dependence (it could be significant) Powerful observable Unique probe of early universe models Probes self-interactions Probes whether a model is single or multi-source Easy to calculate using our formalism Together with the trispectrum it can break the degeneracy between the plethora of non-gaussian models CB, Nurmi, Tasinato & Wands; [astro-ph.co] CB, Gerstenlauer, Nurmi, Tasinato & Wands; [astro-ph.co] CB, Enqvist, Takahashi; [astro-ph.co] CB, Enqvist, Nurmi, Takahashi; [astro-ph.co]

14 Simple extension of local f NL The multivariate local model phi is the Gaussian inflaton field, uncorrelated sigma generates non-gaussianity quite general - applies to mixed inflaton and curvaton/modulated reheating scenarios, provided is a constant Bispectrum has the usual local shape not changed So a scale dependence of f NL is simple and natural Trispectrum

15 Two-component hybrid inflation If we choose initial conditions to maximise f NL then N is the number of e-foldings from horizon crossing till the end of inflation; Scales which exit earlier are more non-gaussian First to calculate scale dependence of local model: Byrnes, Choi & Hall '08 ii)

16 Loop corrections? With extreme parameter values, the bispectrum can be large through a loop correction The bispectrum diverges in the IR Applying a sharp IR cut-off L Boubekeur & Lyth; '05 Suyama & Takahashi; '08 Preheating: Chambers and Rajantie '08 delta N application: Byrnes et al '10 Review: Seery '10 If we take L~1/H - then on CMB scales Kumar, Leblond & Rajaraman; '09 Could be distinguishable from power law scale dependence

Axion/Saxion Cosmology Revisited

Axion/Saxion Cosmology Revisited Axion/Saxion Cosmology Revisited Masahiro Yamaguchi (Tohoku University) Based on Nakamura, Okumura, MY, PRD77 ( 08) and Work in Progress 1. Introduction Fine Tuning Problems of Particle Physics Smallness

More information

Probing Dark Energy with Baryon Acoustic Oscillations from Future Large Galaxy Redshift Surveys

Probing Dark Energy with Baryon Acoustic Oscillations from Future Large Galaxy Redshift Surveys Probing Dark Energy with Baryon Acoustic Oscillations from Future Large Galaxy Redshift Surveys Hee-Jong Seo (Steward Observatory) Daniel J. Eisenstein (Steward Observatory) Martin White, Edwin Sirko,

More information

World of Particles Big Bang Thomas Gajdosik. Big Bang (model)

World of Particles Big Bang Thomas Gajdosik. Big Bang (model) Big Bang (model) What can be seen / measured? basically only light (and a few particles: e ±, p, p, ν x ) in different wave lengths: microwave to γ-rays in different intensities (measured in magnitudes)

More information

Modified Gravity and the CMB

Modified Gravity and the CMB Modified Gravity and the CMB Philippe Brax, IphT Saclay, France arxiv:1109.5862 PhB, A.C. Davis Work in progress PhB, ACD, B. Li Minneapolis October 2011 PLANCK will give us very precise information on

More information

Christfried Webers. Canberra February June 2015

Christfried Webers. Canberra February June 2015 c Statistical Group and College of Engineering and Computer Science Canberra February June (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 829 c Part VIII Linear Classification 2 Logistic

More information

Probability and Random Variables. Generation of random variables (r.v.)

Probability and Random Variables. Generation of random variables (r.v.) Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly

More information

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

More information

Transcript 22 - Universe

Transcript 22 - Universe Transcript 22 - Universe A few introductory words of explanation about this transcript: This transcript includes the words sent to the narrator for inclusion in the latest version of the associated video.

More information

Part IV. Conclusions

Part IV. Conclusions Part IV Conclusions 189 Chapter 9 Conclusions and Future Work CFD studies of premixed laminar and turbulent combustion dynamics have been conducted. These studies were aimed at explaining physical phenomena

More information

The accurate calibration of all detectors is crucial for the subsequent data

The accurate calibration of all detectors is crucial for the subsequent data Chapter 4 Calibration The accurate calibration of all detectors is crucial for the subsequent data analysis. The stability of the gain and offset for energy and time calibration of all detectors involved

More information

Linear Models for Classification

Linear Models for Classification Linear Models for Classification Sumeet Agarwal, EEL709 (Most figures from Bishop, PRML) Approaches to classification Discriminant function: Directly assigns each data point x to a particular class Ci

More information

Searching for Cosmic Strings in New Observational Windows

Searching for Cosmic Strings in New Observational Windows 1 / 62 in Searching for s in New Observational Windows Robert Brandenberger McGill University, Montreal, Canada; and Institute for Theoretical Studies, ETH Zuerich, Switzerland KITPC, Sept. 25 2015 2 /

More information

Data Provided: A formula sheet and table of physical constants is attached to this paper. DARK MATTER AND THE UNIVERSE

Data Provided: A formula sheet and table of physical constants is attached to this paper. DARK MATTER AND THE UNIVERSE Data Provided: A formula sheet and table of physical constants is attached to this paper. DEPARTMENT OF PHYSICS AND ASTRONOMY Autumn Semester (2014-2015) DARK MATTER AND THE UNIVERSE 2 HOURS Answer question

More information

Gravitomagnetism and complex orbit dynamics of spinning compact objects around a massive black hole

Gravitomagnetism and complex orbit dynamics of spinning compact objects around a massive black hole Gravitomagnetism and complex orbit dynamics of spinning compact objects around a massive black hole Kinwah Wu Mullard Space Science Laboratory University College London United Kingdom kw@mssl.ucl.ac.uk

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

More information

Chapter 9 Summary and outlook

Chapter 9 Summary and outlook Chapter 9 Summary and outlook This thesis aimed to address two problems of plasma astrophysics: how are cosmic plasmas isotropized (A 1), and why does the equipartition of the magnetic field energy density

More information

Magnetic Fields. I. Magnetic Field and Magnetic Field Lines

Magnetic Fields. I. Magnetic Field and Magnetic Field Lines Magnetic Fields I. Magnetic Field and Magnetic Field Lines A. The concept of the magnetic field can be developed in a manner similar to the way we developed the electric field. The magnitude of the magnetic

More information

Exploring dark energy models with linear perturbations: Fluid vs scalar field. Masaaki Morita (Okinawa Natl. College Tech., Japan)

Exploring dark energy models with linear perturbations: Fluid vs scalar field. Masaaki Morita (Okinawa Natl. College Tech., Japan) Exploring dark energy models with linear perturbations: Fluid vs scalar field Masaaki Morita (Okinawa Natl. College Tech., Japan) September 11, 008 Seminar at IAP, 008 1 Beautiful ocean view from my laboratory

More information

AP Physics 1 and 2 Lab Investigations

AP Physics 1 and 2 Lab Investigations AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks

More information

RANDOM VIBRATION AN OVERVIEW by Barry Controls, Hopkinton, MA

RANDOM VIBRATION AN OVERVIEW by Barry Controls, Hopkinton, MA RANDOM VIBRATION AN OVERVIEW by Barry Controls, Hopkinton, MA ABSTRACT Random vibration is becoming increasingly recognized as the most realistic method of simulating the dynamic environment of military

More information

Biggar High School Mathematics Department. National 5 Learning Intentions & Success Criteria: Assessing My Progress

Biggar High School Mathematics Department. National 5 Learning Intentions & Success Criteria: Assessing My Progress Biggar High School Mathematics Department National 5 Learning Intentions & Success Criteria: Assessing My Progress Expressions & Formulae Topic Learning Intention Success Criteria I understand this Approximation

More information

Localization of scalar fields on Branes with an Asymmetric geometries in the bulk

Localization of scalar fields on Branes with an Asymmetric geometries in the bulk Localization of scalar fields on Branes with an Asymmetric geometries in the bulk Vladimir A. Andrianov in collaboration with Alexandr A. Andrianov V.A.Fock Department of Theoretical Physics Sankt-Petersburg

More information

arxiv:hep-ph/9902288v1 9 Feb 1999

arxiv:hep-ph/9902288v1 9 Feb 1999 A Quantum Field Theory Warm Inflation Model VAND-TH-98-01 Arjun Berera Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37235, USA arxiv:hep-ph/9902288v1 9 Feb 1999 Abstract A

More information

Roots of Equations (Chapters 5 and 6)

Roots of Equations (Chapters 5 and 6) Roots of Equations (Chapters 5 and 6) Problem: given f() = 0, find. In general, f() can be any function. For some forms of f(), analytical solutions are available. However, for other functions, we have

More information

Gravity Testing and Interpreting Cosmological Measurement

Gravity Testing and Interpreting Cosmological Measurement Cosmological Scale Tests of Gravity Edmund Bertschinger MIT Department of Physics and Kavli Institute for Astrophysics and Space Research January 2011 References Caldwell & Kamionkowski 0903.0866 Silvestri

More information

4F7 Adaptive Filters (and Spectrum Estimation) Least Mean Square (LMS) Algorithm Sumeetpal Singh Engineering Department Email : sss40@eng.cam.ac.

4F7 Adaptive Filters (and Spectrum Estimation) Least Mean Square (LMS) Algorithm Sumeetpal Singh Engineering Department Email : sss40@eng.cam.ac. 4F7 Adaptive Filters (and Spectrum Estimation) Least Mean Square (LMS) Algorithm Sumeetpal Singh Engineering Department Email : sss40@eng.cam.ac.uk 1 1 Outline The LMS algorithm Overview of LMS issues

More information

Self Calibration of Cluster Counts: Observable-Mass Distribution. Wayne Hu Kona, March 2005

Self Calibration of Cluster Counts: Observable-Mass Distribution. Wayne Hu Kona, March 2005 Self Calibration of Cluster Counts: Observable-Mass Distribution Wayne Hu Kona, March 2005 Self Calibration of Cluster Counts: Observable-Mass Distribution Wayne Hu Kona, August 2004 Scattered Forecasts

More information

Axion Cosmology. Axion cold dark matter, axions from string theory, and all that. Andreas Ringwald (DESY)

Axion Cosmology. Axion cold dark matter, axions from string theory, and all that. Andreas Ringwald (DESY) Axion Cosmology. Axion cold dark matter, axions from string theory, and all that Andreas Ringwald (DESY) Implications of the early LHC for cosmology, DESY, Hamburg, 18-20 April 2012 Cold Dark Matter Candidates

More information

1 Teaching notes on GMM 1.

1 Teaching notes on GMM 1. Bent E. Sørensen January 23, 2007 1 Teaching notes on GMM 1. Generalized Method of Moment (GMM) estimation is one of two developments in econometrics in the 80ies that revolutionized empirical work in

More information

A Simple Pseudo Random Number algorithm

A Simple Pseudo Random Number algorithm Lecture 7 Generating random numbers is a useful technique in many numerical applications in Physics. This is because many phenomena in physics are random, and algorithms that use random numbers have applications

More information

Infrared Spectroscopy: Theory

Infrared Spectroscopy: Theory u Chapter 15 Infrared Spectroscopy: Theory An important tool of the organic chemist is Infrared Spectroscopy, or IR. IR spectra are acquired on a special instrument, called an IR spectrometer. IR is used

More information

Least-Squares Intersection of Lines

Least-Squares Intersection of Lines Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

More information

Group Theory and Chemistry

Group Theory and Chemistry Group Theory and Chemistry Outline: Raman and infra-red spectroscopy Symmetry operations Point Groups and Schoenflies symbols Function space and matrix representation Reducible and irreducible representation

More information

99.37, 99.38, 99.38, 99.39, 99.39, 99.39, 99.39, 99.40, 99.41, 99.42 cm

99.37, 99.38, 99.38, 99.39, 99.39, 99.39, 99.39, 99.40, 99.41, 99.42 cm Error Analysis and the Gaussian Distribution In experimental science theory lives or dies based on the results of experimental evidence and thus the analysis of this evidence is a critical part of the

More information

TASI 2012 Lectures on Inflation

TASI 2012 Lectures on Inflation TASI 2012 Lectures on Inflation Leonardo Senatore Stanford Institute for Theoretical Physics Department of Physics, Stanford University, Stanford, CA 94306 Kavli Institute for Particle Astrophysics and

More information

Dark Energy, Modified Gravity and The Accelerating Universe

Dark Energy, Modified Gravity and The Accelerating Universe Dark Energy, Modified Gravity and The Accelerating Universe Dragan Huterer Kavli Institute for Cosmological Physics University of Chicago Makeup of universe today Dark Matter (suspected since 1930s established

More information

EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines

EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines EECS 556 Image Processing W 09 Interpolation Interpolation techniques B splines What is image processing? Image processing is the application of 2D signal processing methods to images Image representation

More information

Searching for Cosmic Strings in New Obervational Windows

Searching for Cosmic Strings in New Obervational Windows 1 / 42 Searching for s in New Obervational Windows Robert Brandenberger McGill University Sept. 28, 2010 2 / 42 Outline 1 2 3 4 s in 5 3 / 42 Plan 1 2 3 4 s in 5 s T. Kibble, J. Phys. A 9, 1387 (1976);

More information

USB 3.0 CDR Model White Paper Revision 0.5

USB 3.0 CDR Model White Paper Revision 0.5 USB 3.0 CDR Model White Paper Revision 0.5 January 15, 2009 INTELLECTUAL PROPERTY DISCLAIMER THIS WHITE PAPER IS PROVIDED TO YOU AS IS WITH NO WARRANTIES WHATSOEVER, INCLUDING ANY WARRANTY OF MERCHANTABILITY,

More information

Elasticity Theory Basics

Elasticity Theory Basics G22.3033-002: Topics in Computer Graphics: Lecture #7 Geometric Modeling New York University Elasticity Theory Basics Lecture #7: 20 October 2003 Lecturer: Denis Zorin Scribe: Adrian Secord, Yotam Gingold

More information

Physical Self-Calibration of X-ray and SZ Surveys

Physical Self-Calibration of X-ray and SZ Surveys Physical Self-Calibration of X-ray and SZ Surveys Greg L. Bryan, Zoltan Haiman (Columbia University) and Joshua D. Younger (CfA) 1. Cluster Surveys and Self-Calibration Clusters of galaxies form at the

More information

How to compute Random acceleration, velocity, and displacement values from a breakpoint table.

How to compute Random acceleration, velocity, and displacement values from a breakpoint table. How to compute Random acceleration, velocity, and displacement values from a breakpoint table. A random spectrum is defined as a set of frequency and amplitude breakpoints, like these: 0.050 Acceleration

More information

Copyright 2011 Casa Software Ltd. www.casaxps.com

Copyright 2011 Casa Software Ltd. www.casaxps.com Table of Contents Variable Forces and Differential Equations... 2 Differential Equations... 3 Second Order Linear Differential Equations with Constant Coefficients... 6 Reduction of Differential Equations

More information

CHAOS LIMITATION OR EVEN END OF SUPPLY CHAIN MANAGEMENT

CHAOS LIMITATION OR EVEN END OF SUPPLY CHAIN MANAGEMENT CHAOS LIMITATION OR EVEN END OF SUPPLY CHAIN MANAGEMENT Michael Grabinski 1 Abstract Proven in the early 196s, weather forecast is not possible for an arbitrarily long period of time for principle reasons.

More information

Prelab Exercises: Hooke's Law and the Behavior of Springs

Prelab Exercises: Hooke's Law and the Behavior of Springs 59 Prelab Exercises: Hooke's Law and the Behavior of Springs Study the description of the experiment that follows and answer the following questions.. (3 marks) Explain why a mass suspended vertically

More information

Essential Mathematics for Computer Graphics fast

Essential Mathematics for Computer Graphics fast John Vince Essential Mathematics for Computer Graphics fast Springer Contents 1. MATHEMATICS 1 Is mathematics difficult? 3 Who should read this book? 4 Aims and objectives of this book 4 Assumptions made

More information

Nonlinear Iterative Partial Least Squares Method

Nonlinear Iterative Partial Least Squares Method Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., Richard-Plouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for

More information

Heating & Cooling in Molecular Clouds

Heating & Cooling in Molecular Clouds Lecture 8: Cloud Stability Heating & Cooling in Molecular Clouds Balance of heating and cooling processes helps to set the temperature in the gas. This then sets the minimum internal pressure in a core

More information

arxiv:1111.4354v2 [physics.acc-ph] 27 Oct 2014

arxiv:1111.4354v2 [physics.acc-ph] 27 Oct 2014 Theory of Electromagnetic Fields Andrzej Wolski University of Liverpool, and the Cockcroft Institute, UK arxiv:1111.4354v2 [physics.acc-ph] 27 Oct 2014 Abstract We discuss the theory of electromagnetic

More information

Chapter 10: Linear Kinematics of Human Movement

Chapter 10: Linear Kinematics of Human Movement Chapter 10: Linear Kinematics of Human Movement Basic Biomechanics, 4 th edition Susan J. Hall Presentation Created by TK Koesterer, Ph.D., ATC Humboldt State University Objectives Discuss the interrelationship

More information

INFRARED SPECTROSCOPY (IR)

INFRARED SPECTROSCOPY (IR) INFRARED SPECTROSCOPY (IR) Theory and Interpretation of IR spectra ASSIGNED READINGS Introduction to technique 25 (p. 833-834 in lab textbook) Uses of the Infrared Spectrum (p. 847-853) Look over pages

More information

Accuracy of the coherent potential approximation for a onedimensional array with a Gaussian distribution of fluctuations in the on-site potential

Accuracy of the coherent potential approximation for a onedimensional array with a Gaussian distribution of fluctuations in the on-site potential Accuracy of the coherent potential approximation for a onedimensional array with a Gaussian distribution of fluctuations in the on-site potential I. Avgin Department of Electrical and Electronics Engineering,

More information

Lab 4: Magnetic Force on Electrons

Lab 4: Magnetic Force on Electrons Lab 4: Magnetic Force on Electrons Introduction: Forces on particles are not limited to gravity and electricity. Magnetic forces also exist. This magnetic force is known as the Lorentz force and it is

More information

Cosmological and Solar System Tests of. f (R) Cosmic Acceleration

Cosmological and Solar System Tests of. f (R) Cosmic Acceleration Cosmological and Solar System Tests of f (R) Cosmic Acceleration Wayne Hu Origins Institute, May 2007 Why Study f(r)? Cosmic acceleration, like the cosmological constant, can either be viewed as arising

More information

Pretest Ch 20: Origins of the Universe

Pretest Ch 20: Origins of the Universe Name: _Answer key Pretest: _2_/ 58 Posttest: _58_/ 58 Pretest Ch 20: Origins of the Universe Vocab/Matching: Match the definition on the left with the term on the right by placing the letter of the term

More information

Module 13 : Measurements on Fiber Optic Systems

Module 13 : Measurements on Fiber Optic Systems Module 13 : Measurements on Fiber Optic Systems Lecture : Measurements on Fiber Optic Systems Objectives In this lecture you will learn the following Measurements on Fiber Optic Systems Attenuation (Loss)

More information

Understanding domain wall network evolution

Understanding domain wall network evolution Physics Letters B 610 (2005) 1 8 www.elsevier.com/locate/physletb Understanding domain wall network evolution P.P. Avelino a,b, J.C.R.E. Oliveira a,b, C.J.A.P. Martins a,c a Centro de Física do Porto,

More information

Big Bang Cosmology. Big Bang vs. Steady State

Big Bang Cosmology. Big Bang vs. Steady State Big Bang vs. Steady State Big Bang Cosmology Perfect cosmological principle: universe is unchanging in space and time => Steady-State universe - Bondi, Hoyle, Gold. True? No! Hubble s Law => expansion

More information

SUSY Breaking and Axino Cosmology

SUSY Breaking and Axino Cosmology SUSY Breaking and Axino Cosmology Masahiro Yamaguchi Tohoku University Nov. 10, 2010 ExDiP2010@KEK, Japan 1. Introduction Fine Tuning Problems of Particle Physics Smallness of electroweak scale Smallness

More information

Gaussian Processes in Machine Learning

Gaussian Processes in Machine Learning Gaussian Processes in Machine Learning Carl Edward Rasmussen Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany carl@tuebingen.mpg.de WWW home page: http://www.tuebingen.mpg.de/ carl

More information

Interactive simulation of an ash cloud of the volcano Grímsvötn

Interactive simulation of an ash cloud of the volcano Grímsvötn Interactive simulation of an ash cloud of the volcano Grímsvötn 1 MATHEMATICAL BACKGROUND Simulating flows in the atmosphere, being part of CFD, is on of the research areas considered in the working group

More information

Active Vibration Isolation of an Unbalanced Machine Spindle

Active Vibration Isolation of an Unbalanced Machine Spindle UCRL-CONF-206108 Active Vibration Isolation of an Unbalanced Machine Spindle D. J. Hopkins, P. Geraghty August 18, 2004 American Society of Precision Engineering Annual Conference Orlando, FL, United States

More information

Design of FIR Filters

Design of FIR Filters Design of FIR Filters Elena Punskaya www-sigproc.eng.cam.ac.uk/~op205 Some material adapted from courses by Prof. Simon Godsill, Dr. Arnaud Doucet, Dr. Malcolm Macleod and Prof. Peter Rayner 68 FIR as

More information

Astro 102 Test 5 Review Spring 2016. See Old Test 4 #16-23, Test 5 #1-3, Old Final #1-14

Astro 102 Test 5 Review Spring 2016. See Old Test 4 #16-23, Test 5 #1-3, Old Final #1-14 Astro 102 Test 5 Review Spring 2016 See Old Test 4 #16-23, Test 5 #1-3, Old Final #1-14 Sec 14.5 Expanding Universe Know: Doppler shift, redshift, Hubble s Law, cosmic distance ladder, standard candles,

More information

Dynamic Process Modeling. Process Dynamics and Control

Dynamic Process Modeling. Process Dynamics and Control Dynamic Process Modeling Process Dynamics and Control 1 Description of process dynamics Classes of models What do we need for control? Modeling for control Mechanical Systems Modeling Electrical circuits

More information

How To Understand General Relativity

How To Understand General Relativity Chapter S3 Spacetime and Gravity What are the major ideas of special relativity? Spacetime Special relativity showed that space and time are not absolute Instead they are inextricably linked in a four-dimensional

More information

Lecture L22-2D Rigid Body Dynamics: Work and Energy

Lecture L22-2D Rigid Body Dynamics: Work and Energy J. Peraire, S. Widnall 6.07 Dynamics Fall 008 Version.0 Lecture L - D Rigid Body Dynamics: Work and Energy In this lecture, we will revisit the principle of work and energy introduced in lecture L-3 for

More information

Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary

Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:

More information

Neutrino properties from Cosmology

Neutrino properties from Cosmology Neutrino properties from Cosmology Anže Slosar, BNL Neutrino16, July 16 1 / 30 plan for the talk Pedagogical introduction to the role neutrinos play in Cosmology aimed at a non-cosmo community Neutrinos

More information

Fundamentals of Plasma Physics Waves in plasmas

Fundamentals of Plasma Physics Waves in plasmas Fundamentals of Plasma Physics Waves in plasmas APPLAuSE Instituto Superior Técnico Instituto de Plasmas e Fusão Nuclear Vasco Guerra 1 Waves in plasmas What can we study with the complete description

More information

Nonlinear evolution of unstable fluid interface

Nonlinear evolution of unstable fluid interface Nonlinear evolution of unstable fluid interface S.I. Abarzhi Department of Applied Mathematics and Statistics State University of New-York at Stony Brook LIGHT FLUID ACCELERATES HEAVY FLUID misalignment

More information

Non Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization

Non Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization Non Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization Jean- Damien Villiers ESSEC Business School Master of Sciences in Management Grande Ecole September 2013 1 Non Linear

More information

Chapter 6. Work and Energy

Chapter 6. Work and Energy Chapter 6 Work and Energy The concept of forces acting on a mass (one object) is intimately related to the concept of ENERGY production or storage. A mass accelerated to a non-zero speed carries energy

More information

Origins of the Cosmos Summer 2016. Pre-course assessment

Origins of the Cosmos Summer 2016. Pre-course assessment Origins of the Cosmos Summer 2016 Pre-course assessment In order to grant two graduate credits for the workshop, we do require you to spend some hours before arriving at Penn State. We encourage all of

More information

Rotation: Moment of Inertia and Torque

Rotation: Moment of Inertia and Torque Rotation: Moment of Inertia and Torque Every time we push a door open or tighten a bolt using a wrench, we apply a force that results in a rotational motion about a fixed axis. Through experience we learn

More information

Classification Problems

Classification Problems Classification Read Chapter 4 in the text by Bishop, except omit Sections 4.1.6, 4.1.7, 4.2.4, 4.3.3, 4.3.5, 4.3.6, 4.4, and 4.5. Also, review sections 1.5.1, 1.5.2, 1.5.3, and 1.5.4. Classification Problems

More information

CS 688 Pattern Recognition Lecture 4. Linear Models for Classification

CS 688 Pattern Recognition Lecture 4. Linear Models for Classification CS 688 Pattern Recognition Lecture 4 Linear Models for Classification Probabilistic generative models Probabilistic discriminative models 1 Generative Approach ( x ) p C k p( C k ) Ck p ( ) ( x Ck ) p(

More information

NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS

NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS TEST DESIGN AND FRAMEWORK September 2014 Authorized for Distribution by the New York State Education Department This test design and framework document

More information

AP PHYSICS C Mechanics - SUMMER ASSIGNMENT FOR 2016-2017

AP PHYSICS C Mechanics - SUMMER ASSIGNMENT FOR 2016-2017 AP PHYSICS C Mechanics - SUMMER ASSIGNMENT FOR 2016-2017 Dear Student: The AP physics course you have signed up for is designed to prepare you for a superior performance on the AP test. To complete material

More information

ANALYTICAL METHODS FOR ENGINEERS

ANALYTICAL METHODS FOR ENGINEERS UNIT 1: Unit code: QCF Level: 4 Credit value: 15 ANALYTICAL METHODS FOR ENGINEERS A/601/1401 OUTCOME - TRIGONOMETRIC METHODS TUTORIAL 1 SINUSOIDAL FUNCTION Be able to analyse and model engineering situations

More information

SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY

SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY 3 th World Conference on Earthquake Engineering Vancouver, B.C., Canada August -6, 24 Paper No. 296 SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY ASHOK KUMAR SUMMARY One of the important

More information

State of Stress at Point

State of Stress at Point State of Stress at Point Einstein Notation The basic idea of Einstein notation is that a covector and a vector can form a scalar: This is typically written as an explicit sum: According to this convention,

More information

Galaxy Survey data analysis using SDSS-III as an example

Galaxy Survey data analysis using SDSS-III as an example Galaxy Survey data analysis using SDSS-III as an example Will Percival (University of Portsmouth) showing work by the BOSS galaxy clustering working group" Cosmology from Spectroscopic Galaxy Surveys"

More information

Statistical Machine Learning

Statistical Machine Learning Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

More information

Chapter 8 - Power Density Spectrum

Chapter 8 - Power Density Spectrum EE385 Class Notes 8/8/03 John Stensby Chapter 8 - Power Density Spectrum Let X(t) be a WSS random process. X(t) has an average power, given in watts, of E[X(t) ], a constant. his total average power is

More information

d di Flux (B) Current (H)

d di Flux (B) Current (H) Comparison of Inductance Calculation Techniques Tony Morcos Magnequench Technology Center Research Triangle Park, North Carolina 1 VCM Baseline: Geometry Axially-magnetized MQ3-F 42 NdFeB disk Br = 131kG

More information

ASEN 3112 - Structures. MDOF Dynamic Systems. ASEN 3112 Lecture 1 Slide 1

ASEN 3112 - Structures. MDOF Dynamic Systems. ASEN 3112 Lecture 1 Slide 1 19 MDOF Dynamic Systems ASEN 3112 Lecture 1 Slide 1 A Two-DOF Mass-Spring-Dashpot Dynamic System Consider the lumped-parameter, mass-spring-dashpot dynamic system shown in the Figure. It has two point

More information

YOU CAN COUNT ON NUMBER LINES

YOU CAN COUNT ON NUMBER LINES Key Idea 2 Number and Numeration: Students use number sense and numeration to develop an understanding of multiple uses of numbers in the real world, the use of numbers to communicate mathematically, and

More information

Bandwidth Selection for Nonparametric Distribution Estimation

Bandwidth Selection for Nonparametric Distribution Estimation Bandwidth Selection for Nonparametric Distribution Estimation Bruce E. Hansen University of Wisconsin www.ssc.wisc.edu/~bhansen May 2004 Abstract The mean-square efficiency of cumulative distribution function

More information

v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors.

v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors. 3. Cross product Definition 3.1. Let v and w be two vectors in R 3. The cross product of v and w, denoted v w, is the vector defined as follows: the length of v w is the area of the parallelogram with

More information

The Role of Electric Polarization in Nonlinear optics

The Role of Electric Polarization in Nonlinear optics The Role of Electric Polarization in Nonlinear optics Sumith Doluweera Department of Physics University of Cincinnati Cincinnati, Ohio 45221 Abstract Nonlinear optics became a very active field of research

More information

New Observational Windows to Probe Fundamental Physics

New Observational Windows to Probe Fundamental Physics 1 / 46 String String New Observational Windows to Probe Fundamental Physics Searching for String Signals Robert Brandenberger McGill University May 23, 2012 2 / 46 Outline 1 String String 2 String 3 String

More information

Supporting Information

Supporting Information S1 Supporting Information GFT NMR, a New Approach to Rapidly Obtain Precise High Dimensional NMR Spectral Information Seho Kim and Thomas Szyperski * Department of Chemistry, University at Buffalo, The

More information

Taking the Mystery out of the Infamous Formula, "SNR = 6.02N + 1.76dB," and Why You Should Care. by Walt Kester

Taking the Mystery out of the Infamous Formula, SNR = 6.02N + 1.76dB, and Why You Should Care. by Walt Kester ITRODUCTIO Taking the Mystery out of the Infamous Formula, "SR = 6.0 + 1.76dB," and Why You Should Care by Walt Kester MT-001 TUTORIAL You don't have to deal with ADCs or DACs for long before running across

More information

Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY

Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to

More information

Organic Chemistry Tenth Edition

Organic Chemistry Tenth Edition Organic Chemistry Tenth Edition T. W. Graham Solomons Craig B. Fryhle Welcome to CHM 22 Organic Chemisty II Chapters 2 (IR), 9, 3-20. Chapter 2 and Chapter 9 Spectroscopy (interaction of molecule with

More information

1 The Brownian bridge construction

1 The Brownian bridge construction The Brownian bridge construction The Brownian bridge construction is a way to build a Brownian motion path by successively adding finer scale detail. This construction leads to a relatively easy proof

More information

This unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.

This unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions. Algebra I Overview View unit yearlong overview here Many of the concepts presented in Algebra I are progressions of concepts that were introduced in grades 6 through 8. The content presented in this course

More information

Problem of the Month Through the Grapevine

Problem of the Month Through the Grapevine The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards: Make sense of problems

More information