suk yee yong - swin · narrow disk-wind model invoke wind component to explain emission and...

Suk Yee Yong

Supervisor: Professor Rachel Webster

Co-supervisor: Anthea King

Collaborators: Kathleen Labrie (Gemini);

Matthew O’Dowd (CUNY); Nick Bate (Cambridge)

Infer Structure of Quasar with Machine

Learning

ASA 2018

Overview

1 Quasar Population

2 MethodologySamplesApproach

3 ResultsMachine LearningStatistical TestsImplicationsProposed Disk-wind Model

4 Summary

Quasar Population

Broad absorption line quasars(BALQs)

Show BAL feature

∼ 20% of all quasars

Possible explanations:

I Evolutionary: BALQ →non-BALQ

I Orientation in a narrowwind context

Image credit: http://www.sdss3.

org/dr10/algorithms/qso_catalog.

php

Suk Yee Yong Infer Quasar Structure with ML [arXiv: 1806.07090] 1/11

http://www.sdss3.org/dr10/algorithms/qso_catalog.php



https://arxiv.org/abs/1806.07090

Narrow Disk-wind Model

Invoke wind component toexplain emission andabsorption features of quasarspectra

BALs are viewed throughthe narrow wind

Expect differences inemission line propertiesdepending on theorientation:

I Equatorial narrow wind:broad line width, lessblueshifted, dominated byrotational motion

I Polar narrow wind: narrowline width, more blueshifted



Testing the Orientation Paradigm

VSBALQs non-BALQs

Are there any differences in their properties?



Methodology

Samples

Data: 2773 quasars from Sloan Digital Sky Survey III(SDSS-III) Data Release 12 Quasar (DR12Q; Paris et al.2017) catalogue

C iv BALs: 313 quasars using traditional BAL definition ofWeymann et al. (1991)

Emission lines: C iv and Mg iv



Methodology

Approach

Supervised machine learning classification

I Decision tree, random forest, logistic regression, support vectormachine (SVM)

I Conduct grid and randomised search of parameter space

I Extract the feature importance and weighting

Additionally for comparison, conduct statistical tests:

I Anderson-Darling (A–D), Kolmogorov-Smirnov (K–S)

I Null hypothesis being samples from the same distribution

I p-value< 5% as significant, suggesting the two samples are notdrawn from the same distribution

Input features: Continuum and emission line properties



Methodology

List of Investigated Features

Property Feature Description

Continuum imag Absolute magnitude in i-band at z = 2z.pca PCA redshiftalphanu Spectral index

Emission fw(civ) FWHM of C ivciv ratioskew Asymmetry of C ivw(civ) EW of C ivfw(mgii) FWHM of Mg iimgii ratioskew Asymmetry of Mg iiw(mgii) EW of Mg iicivmgii diffv Velocity offsets of C iv and Mg iicivmgii ratiofwhm FWHM ratio of C iv and Mg iicivmgii ratioew EW ratio of C iv and Mg ii



Results

Machine Learning

alphanu

civ_ratio

skew

civmgii_diffv

civmgii_ratio

fwhm

civmgii_ratio

ewz.p

cafw

(civ)

fw(m

gii)

mgii_ratio

skeww(civ)

w(mgii)

imag

Feature

0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Imp

ort

an

ce

decision tree (grid)decision tree (rand)random forest (grid)random forest (rand)

Decision tree and random forest

alphanu

civ_ratio

skew

fw(m

gii)

civmgii_ratio

ew

mgii_ratio

skeww(civ)

fw(civ)

civmgii_diffv z.p

caim

ag

w(mgii)

civmgii_ratio

fwhm

Feature

0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Weig

ht

logistic regression (grid)logistic regression (rand)svm (grid)svm (rand)

Logistic regression and SVM



Results

Machine Learning: Comparison to predictions

Algorithm Decision Tree Random Forest Logistic Regression SVM

Class non-BALQ BALQ non-BALQ BALQ non-BALQ BALQ non-BALQ BALQ

Gri

dSea

rch

non-BALQ 73.40 48.57 79.59 41.43 66.60 31.43 70.52 34.29

BALQ 26.60 51.43 20.41 58.57 33.40 68.57 29.48 65.71

Ran

dom

ised

Sea

rch non-BALQ 74.64 45.71 79.38 41.43 66.60 31.43 70.52 34.29

BALQ 25.36 54.29 20.62 58.57 33.40 68.57 29.48 65.71



Results

Statistical Tests

3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5

alphanu

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fre

qu

en

cy

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve P

rob

ab

ilit

y

Non-BAL

BAL

EDF

Non-BAL BAL

balciv

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0.5

1.0

1.5

alp

han

u

A–D α=0.04%; K–S p-value=3.05× 10−7%

0.0 0.5 1.0 1.5 2.0 2.5 3.0

civmgii_ratiofwhm

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fre

qu

en

cy

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve P

rob

ab

ilit

y

Non-BAL

BAL

EDF

Non-BAL BAL

balciv

0.0

0.5

1.0

1.5

2.0

2.5

3.0

civm

gii

_rati

ofw

hm

A–D α=0.06%; K–S p-value=2.06%

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

civmgii_ratioew

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Fre

qu

en

cy

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve P

rob

ab

ilit

y

Non-BAL

BAL

EDF

Non-BAL BAL

balciv

0

2

4

6

8

10

12

14

civm

gii

_rati

oew

A–D α=0.04%; K–S p-value=0.09%

0.0 0.5 1.0 1.5 2.0 2.5 3.0

civ_ratioskew

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fre

qu

en

cy

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve P

rob

ab

ilit

y

Non-BAL

BAL

EDF

Non-BAL BAL

balciv

0

1

2

3

4

5

6

civ_

rati

osk

ew

A–D α=0.08%; K–S p-value=0.21%Suk Yee Yong Infer Quasar Structure with ML [arXiv: 1806.07090] 9/11


Results

Implications

Difficult to distinguish BALQs from non-BALQs

The two populations generally exhibit similar continuum andemission line properties



So can we use this information to infer the structure of quasars?



Results

Proposed Disk-wind Model



Results


Black holeDisk Torus

Ionisation cone



Results


Wide wind

Dense radial streams



Results




Summary

Aim: Test orientation based on narrow disk-windexplanation for BAL phenomenon

Method: Using statistical tests and supervised machinelearning in an attempt to separate BALQs and non-BALQsbased on their continuum and emission line properties

Result: The two populations show similar properties

Proposed disk-wind model: Wide wind opening angle withmultiple radial streams of dense clumps

arXiv:1806.07090


suk yee yong - swin · narrow disk-wind model invoke wind component to explain emission and...

Documents