Optimizing photometric selection criteria for Type Ia supernovae E. Gjergo1,2, J. Duggan3, J. Cunningham3, S. Kuhlmann2, R. Biswas2, E. Kovacs2

1.Illinois Institute of Technology, 2.Argonne National Laboratory, 3.Loyola University Chicago

The rate of expansion of the late Universe has been increasing. This revolutionary discovery was awarded with the 2011 Physics Nobel prize. If General Relativity is an accurate description of the cosmic scales, today 73% of the Universe content should possess negative pressure, this “substance” has been named dark energy.

For details on derivations, mathematical tools, or theory, please visit/contact: Homepage: http://www.hep.anl.gov/ac.eda.gjergo email: egjergo@iit.edu

Algorithms for the selection of Type Ia supernovae

Introduction: The expansion of the Universe is accelerating

Poster creation date: April 09, 2012

Theory for Cosmology parameters

Research objective • Optimize the selection criteria for Type Ia Supernovae • Constrain parameters for CPL model (w0 and wa)

On the right: (Top) An exaggerated plot of a(t),-see next section- the scale factor of

the Universe relative to today. 9.5 billion years after the big bang, dark

energy becomes predominant. (Bottom) Percentage change of the

components in the Universe over time.

The Dark Energy Survey (DES): international collaboration designed to gather substantial data to study Dark Energy A CCD camera will collect photometric data from a suitable portion of the sky

One of the 4 probes DES uses is Type Ia supernovae

Why Type Ia Supernovae?

Supernovae (SNe): explosions of stars. Briefly become as bright as the host galaxy. Type Ia SNe are almost “standard candles”: their peak absolute brightness is standardizable (on the right). The flux of light coming from the SNe allows to determine their “distance”

DES collects photometric data, but pure samples of Type Ia SNe are obtained only with spectroscopy. Photometry will give a contaminated sample. Summary

SN 1994D in outskirts of galaxy NGC 4526

Credit: High-Z Supernova Search Team, HST, NASA

We present the results of a study of selection criteria to photometrically identify Type Ia supernovae in a simulated mixed sample of Type Ia supernovae and core collapse supernovae. The simulated sample is a mockup of the upcoming Dark Energy Survey. Fits to the fpMLCS and fpSALT Type Ia supernova models are compared and used to help separate the Type Ia supernovae from the core collapse sample. The Dark Energy Task Force Figure of Merit (modified to include core collapse supernovae systematics) is used to discriminate among the various selection criteria. This study of varying selection cuts for Type Ia supernova candidates is the first to evaluate core collapse contamination using the Figure of Merit. Different factors that contribute to the Figure of Merit are detailed.

•  Fit probability for Multicolor Light Curve Shapes (fpMLCS) (Jha et al. 2007) •  Fit probability for Spectral Adaptive Light-curve Templates (fpSALT) (Guy et al. 2007) As DES has not started collecting data yet, our study is based on simulations with SNANA (Kessler et al. 2009).

Expansion  rate,  from  General  Rela3vity  (Friedman  equa3ons)  

  Where a(t) is the scale factor: scales the “distance” of two objects over time [d(t)], relative to today’s distance [d0] - i.e. d(t)=a(t) d0. Today a(0) = 1.   Each Ω represents a density ratio for a component of the Universe, and is determined empirically Ω m – non-relativistic matter (0.27) Ω r – photons and relativistic neutrinos (10-4) Ω k – curvature of the Universe (0 for flat Universe) Ω Λ – dark energy (0.73 today, cosmological constant if w=-1)

  H0 is Hubble’s constant, 72 km s-1 Mpc-1, measured empirically

  What is w for the dark energy component? Is it time dependent or not?   (Redshift z in cosmology is a function of time)   We use the CPL parametrization (by Chevallier, Polarski and Linder) to model a time dependence of w:

€

w(z) = w0 + waz

1+ z, where

z =λobservedλemitted

−1 =

w =pρ

=pressure density

redshift

€

˙ a a⎛

⎝ ⎜ ⎞

⎠ ⎟

2

= H02 Ωma−3 +Ωra

−4 +Ωka−2 +ΩDEa−3 1+w( )[ ]

How to observe these quantities?  The next generation survey:

On top: SNe Types are classified according to

their chemical composition. In red we find

Type Ia SNe.

Measurable data (also extrapolated from theory)

© 2003 American Institute of Physics S-0031-9228-0304-030-4

The distance modulus µ

• dL is what is known from theory • M is the absolute magnitude • m is the apparent magnitude (observed) €

µ = 5log10(dL ) − 5 = m −MThe redshift z

• See on the left, theory section

On the right: Cerro Tololo Inter-

American Observatory in Chile, where the

DES camera is mounted, and will

operate starting from late 2012

Below: Figure of Merit as advocated in the Dark Energy Task Force (DETF, Albrecht et al. 2006). Constrains the values of wa and w0, from the CPL model.

DETF Figure of Merit

 Number of Supernovae  Distance Modulus error  Core Collapse contamination

What affects the FoM:

We aim to find the best algorithm and signal to noise ratio cuts to select the purest sample of SNIa from the observed supernovae (once data becomes available – results with simulations below)

fpMLCS fpSALT

SNR

-10-

5-5

SNR

-3-3

-0

Above: what is the probability that each supernova is Type Ia? Histogram for simulations of Type Ia (white) and core collapse (green).

fpMLCS fpSALT

SNR

-10-

5-5

SNR

-3-3

-0

Above: (red) Efficiency – how many Type Ia were selected with the algorithm? (black) Purity – how many SNe selected to be Ia were really Ia? i.e. Contamination

SNR

-10-

5-5

SNR

-3-3

-0

Above: (left) Figure of Merit as a function of number of supernovae. Simulated sample of Ia only on both plots. The red plot has only DES SNIa as input, the black plot includes data known from previous experiments. (right) Average error on distance modulus is bigger in core collapse than for each simulated sample. Left: distance modulus vs. redshift, for Type Ia and core collapse. Type Ia trace very well the theoretical relationship between the two quantities. Below: Summary of results.

Below: The most important factors that can change the DETF Figure of Merit are demonstrated with an example. (SNR-3-3-0, fpMLCS >0.1) [DOF: degrees of freedom]

Bernstein et al. (2011)