cosmological biases from supernova photometric …maria vincenzi (2nd year phd), mark sullivan, bob...

Maria Vincenzi (2nd year PhD), Mark Sullivan, Bob Nichol, Claudia Gutierrez, Chris Frohmaier, Mat Smith, Charlotte Angus,

Anais Moller, Rob Firth

Supernova Ia Cosmology Analysis MeetingChicago, 4 October 2019

Cosmological biases from supernova photometric classification

Core collapse contamination


BEAMS, BBC (Kuntz et al. 2007, Kessler et al. 2016)

Photometric classifiers P(Ia) for each SN ➔


BEAMS, BBC (Kuntz et al. 2007, Kessler et al. 2016)

Simulations of SNe Ia + CC

➔ ➔

Photometric classifiers P(Ia) for each SN ➔

Jones et al. 2017,2018

DataSimulated SNe Ia

Simulated CC SNe

Hubble residuals


Jones et al. 2017,2018

DataSimulated SNe Ia

Simulated CC SNe

Hubble residuals

Under-predicting contamination


Templates How do we simulate CC SNe?

(SNe II, SNe IIb, SNe IIn, SNe Ib, SNe Ic/Ic-BL)

➔ Intrinsic properties(Luminosity function)

➔ Templates

➔ Rates

➔ Dust extinction



➔ Templates➔ 65 new Templates

for CC SNe

(Vincenzi et al. 2019)


➔ Rates

➔ Dust extinction

Core Collapse templates 65 new Templates65 well observed CC SNe

Modjaz et al. 2014, Bianco et al. 2014, Hicken et al. 2017, Taddia et al. 2013…

Wavelength

Templates

Spectroscopy

Arbi

trary

TimeFlu

x (e

rg s

-1 c

m-2

A-1

) Photometry


Mangling

Credit: Rob Firth

Flux Calibration

Wavelength

Spectroscopy

Arbi

trary

TimeFlu

x (e

rg s

-1 c

m-2

A-1

) Photometry


UV extension

Wavelength

UV flux?

Spectro-photometry

Flux

(erg

s-1

cm

-2 A

-1)


Templates

2D Gaussian Processes

UV broad-band photometry

Wavelength

Time

Flux

Wavelength

Spectro-photometry

Flux

(erg

s-1

cm

-2 A

-1)

Wavelength

Core Collapse templates

1800 - 9000

Å

65 new Templates65 well observed CC SNe

Templates

Time series of Spectro-photometry

Flux

(erg

s-1

cm

-2 A

-1)


Previous core collapse models Vincenzi et al. 2019

Templates

➔ Diversity, flexibility➔ One “average” spectral evolution template

Previous core collapse models

➔ UV extension for simulations at high redshift➔ Poor UV extension

Vincenzi et al. 2019Templates


➔ NO dust corrections ➔ Dust corrected: any dust model can be applied

Previous core collapse models Vincenzi et al. 2019

Templates

➔ UV extension for simulations at high redshift➔ Poor UV extension




➔ Templates➔ 65 new Templates

for CC SNe



➔ Rates

➔ Dust extinction



➔ 65 new Templatesfor CC SNe

➔ Revised from Li et al. 2011



➔ Templates

➔ Rates

➔ Dust extinction

-20 -18 -16 -14 -12 -10Intrinsic brightness R band

Luminosity function

1st APPLICATION: simulating PanSTARRs

3rd APPLICATION: testing photometric

classifiers

2nd APPLICATION: Rates


Vincenzi et al. 2019

Hubble residuals

Is it a problem of Luminosity

function?

DataSimulated SNe IaSimulated CC SNe



classifiers



classifiers


Simulation Test (1) Simulation Test (2)

Hubble residuals



Luminosity Functions Luminosity Functions x Rate


Jones et al. 2017

(PanSTARRs)




….adjusted Luminosity functions

Simulationsof CC SNe PTF

Surveyefficiency

PTF observed~20 Ibc + ~50 II SNe

+

↕



classifiers



Frohmaier, Angus, Vincenzi, Sullivan (in prep.)


Surveyefficiency


+

↕



classifiers



Volumetric Rates of Ibc and II SNe !!!




classifiers


Volumetric Rate of SNe Ibc and SNe II


0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)

Literature RatesLiterature Rates

0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)


0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)Literature RatesLiterature Rates


Surveyefficiency


+

↕




classifiers


Volumetric Rate of SNe Ibc and SNe II


0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)


0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)


0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)Literature RatesLiterature Rates

0.0 0.1 0.2 0.3 0.4 0.5

Redshift

10�5

10�4

10�3

Rat

e(S

ESN

yr�

1M

pc�

3)

Literature Rates

Frohmaier et al. CCSNe

Frohmaier et al. SESNe

CCSNe Normalized SFH (Li 2008)

Predicted SESN rate (Shivvers et al. 2017)

CCSNe Normalized SFH (Li 2008)

Predicted SESN rate (Shivvers et al. 2017)Ibc rateIbc & II rate


Surveyefficiency


+

↕



classifiers



3rd APPLICATION: testing photometric classifiers

Simulate SNe Ia & CC (“DES-like” survey)(1)

Gaussian LFs & new templates

(3)Dust +

new templates

(2)Skewed LFs & new templates

Baseline:Adjusted LFs & old templates


(1)Gaussian LFs & new templates

(3)Dust +

new templates



Simulate SNe Ia & CC (“DES-like” survey)

…



(3)Dust +

new templates



Independently trained Photometric classifier


…


w and Ωm ??

SALT2 cuts, Selection bias correction,

BEAMS

Independently trained Photometric classifier


(3)Dust +

new templates




…


Moller et al. 2019

w and Ωm ??


BEAMS


(3)Dust +

new templates




…


(3)Dust +

new templates


% misclassified

as SN Ia

(3)Dust +

new templates


% misclassified

as SN Ia

AV (Host extinction)

AV=0.22

NOT de-reddened template

(3)Dust +

new templates


% misclassified

as SN Ia


AV=0.22


De-reddened template

AV=0

(3)Dust +

new templates


% misclassified

as SN Ia


AV=0.22



(3)Dust +

new templates


% misclassified

as SN Ia


AV=0.22



Bright host bias ↔ highly extinct bias?

(3)Dust +

new templates


Simulate MANY mixes of SNe Ia & CC:

Moller et al. 2019


(3)Dust +

new templates


Baseline:Adjusted LFs & old approach

w and Ωm ??


BEAMS

…



(3)Dust +

new templates



Simulate more scenarios:- Luminosity Functions, Relative Rates evolution with z ! - Different cadences? (test for LSST) - No spec-z, only photo-z?

Comparison with data- Dark Energy Survey 5YR sample. Data vs Sim discrepancy?

Future work

Compare different classifiers:- SuperNNova (Moller et al. 2019) - PSNID (Sako et al. 2010) - SNmachine (Lochner et al. 2016) - SNIRF (Kovacs, in prep.)

CC properties with ZTF (unbiased and complete survey!)- Better Luminosity Functions? Color/Extinction distribution?

Back-up Slides

Templates - Luminosity functionsVi

ncen

zi e

t al.

2019

GA

USS

IAN

Vinc

enzi

et a

l. 20

19SK

EWED

Templates - Luminosity functions

Bright host bias ↔ highly extinct bias?

LFs vs My Templates


(3)Dust +

new templates


Baseline:Adjusted LFs & old approach


cosmological biases from supernova photometric …maria vincenzi (2nd year phd), mark sullivan, bob...

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