See Change: Classifying single observation transients from HST using SNCosmo

Caroline Sofiatti Nunes; Saul Perlmutter; Jakob Nordin; David Rubin; Chris Lidman; Susana E. Deustua; Andrew S. Fruchter; Greg Scott Aldering; Mark Brodwin; Carlos E. Cunha; Peter R. Eisenhardt; Anthony H. Gonzalez; Myungkook J. Jee; Hendrik Hildebrandt; Henk Hoekstra; Joana Santos; S. Adam Stanford; Dana R. Stern; Rene Fassbender; Johan Richard; Piero Rosati; Risa H. Wechsler; Adam Muzzin; Jon Willis; Hans Boehringer; Michael Gladders; Ariel Goobar; Rahman Amanullah; Isobel Hook; Dragan Huterer; Jiasheng Huang; Alex G. Kim; Marek Kowalski; Eric Linder; Reynald Pain; Clare Saunders; Nao Suzuki; Kyle H. Barbary; Eli S. Rykoff; Joshua Meyers; Anthony L. Spadafora; Brian Hayden; Gillian Wilson; Eduardo Rozo; Matt Hilton; Samantha Dixon; Mike Yen

See Change: Classifying single observation transients from HST using SNCosmo

Caroline Sofiatti Nunes, Saul Perlmutter, Jakob Nordin, David Rubin, Chris Lidman, Susana E. Deustua, Andrew S. Fruchter, Greg Scott Aldering, Mark Brodwin, Carlos E. Cunha, Peter R. Eisenhardt, Anthony H. Gonzalez, Myungkook J. Jee, Hendrik Hildebrandt, Henk Hoekstra, Joana Santos, S. Adam Stanford, Dana R. Stern, Rene Fassbender, Johan RichardPiero Rosati, Risa H. Wechsler, Adam Muzzin, Jon Willis, Hans Boehringer, Michael Gladders, Ariel Goobar, Rahman Amanullah, Isobel Hook, Dragan Huterer, Jiasheng Huang, Alex G. Kim, Marek Kowalski, Eric Linder, Reynald Pain, Clare Saunders, Nao Suzuki, Kyle H. Barbary, Eli S. Rykoff, Joshua Meyers, Anthony L. Spadafora, Brian Hayden, Gillian Wilson, Eduardo Rozo, Matt Hilton, Samantha Dixon, Mike Yen

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Abstract

The Supernova Cosmology Project (SCP) is executing "See Change", a large HST program to look for possible variation in dark energy using supernovae at z>1. As part of the survey, we often must make time-critical follow-up decisions based on multicolor detection at a single epoch. We demonstrate the use of the SNCosmo software package to obtain simulated fluxes in the HST filters for type Ia and core-collapse supernovae at various redshifts. These simulations allow us to compare photometric data from HST with the distribution of the simulated SNe through methods such as Random Forest, a learning method for classification, and Gaussian Kernel Estimation. The results help us make informed decisions about triggered follow up using HST and ground based observatories to provide time-critical information needed about transients. Examples of this technique applied in the context of See Change are shown.

Original language	English
Pages	139.10
Publication status	Published - 1 Jan 2016

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https://ui.adsabs.harvard.edu/abs/2016AAS...22713910S

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Sofiatti Nunes, C., Perlmutter, S., Nordin, J., Rubin, D., Lidman, C., Deustua, S. E., Fruchter, A. S., Aldering, G. S., Brodwin, M., Cunha, C. E., Eisenhardt, P. R., Gonzalez, A. H., Jee, M. J., Hildebrandt, H., Hoekstra, H., Santos, J., Stanford, S. A., Stern, D. R., Fassbender, R., ... Yen, M. (2016). See Change: Classifying single observation transients from HST using SNCosmo. 139.10. https://ui.adsabs.harvard.edu/abs/2016AAS...22713910S

@conference{d0d1ee768eda462ebead4acb482fff8d,

title = "See Change: Classifying single observation transients from HST using SNCosmo",

abstract = "The Supernova Cosmology Project (SCP) is executing {"}See Change{"}, a large HST program to look for possible variation in dark energy using supernovae at z>1. As part of the survey, we often must make time-critical follow-up decisions based on multicolor detection at a single epoch. We demonstrate the use of the SNCosmo software package to obtain simulated fluxes in the HST filters for type Ia and core-collapse supernovae at various redshifts. These simulations allow us to compare photometric data from HST with the distribution of the simulated SNe through methods such as Random Forest, a learning method for classification, and Gaussian Kernel Estimation. The results help us make informed decisions about triggered follow up using HST and ground based observatories to provide time-critical information needed about transients. Examples of this technique applied in the context of See Change are shown.",

author = "\{Sofiatti Nunes\}, Caroline and Saul Perlmutter and Jakob Nordin and David Rubin and Chris Lidman and Deustua, \{Susana E.\} and Fruchter, \{Andrew S.\} and Aldering, \{Greg Scott\} and Mark Brodwin and Cunha, \{Carlos E.\} and Eisenhardt, \{Peter R.\} and Gonzalez, \{Anthony H.\} and Jee, \{Myungkook J.\} and Hendrik Hildebrandt and Henk Hoekstra and Joana Santos and Stanford, \{S. Adam\} and Stern, \{Dana R.\} and Rene Fassbender and Johan Richard and Piero Rosati and Wechsler, \{Risa H.\} and Adam Muzzin and Jon Willis and Hans Boehringer and Michael Gladders and Ariel Goobar and Rahman Amanullah and Isobel Hook and Dragan Huterer and Jiasheng Huang and Kim, \{Alex G.\} and Marek Kowalski and Eric Linder and Reynald Pain and Clare Saunders and Nao Suzuki and Barbary, \{Kyle H.\} and Rykoff, \{Eli S.\} and Joshua Meyers and Spadafora, \{Anthony L.\} and Brian Hayden and Gillian Wilson and Eduardo Rozo and Matt Hilton and Samantha Dixon and Mike Yen",

year = "2016",

month = jan,

day = "1",

language = "English",

pages = "139.10",

}

Sofiatti Nunes, C, Perlmutter, S, Nordin, J, Rubin, D, Lidman, C, Deustua, SE, Fruchter, AS, Aldering, GS, Brodwin, M, Cunha, CE, Eisenhardt, PR, Gonzalez, AH, Jee, MJ, Hildebrandt, H, Hoekstra, H, Santos, J, Stanford, SA, Stern, DR, Fassbender, R, Richard, J, Rosati, P, Wechsler, RH, Muzzin, A, Willis, J, Boehringer, H, Gladders, M, Goobar, A, Amanullah, R, Hook, I, Huterer, D, Huang, J, Kim, AG, Kowalski, M, Linder, E, Pain, R, Saunders, C, Suzuki, N, Barbary, KH, Rykoff, ES, Meyers, J, Spadafora, AL, Hayden, B, Wilson, G, Rozo, E, Hilton, M, Dixon, S & Yen, M 2016, 'See Change: Classifying single observation transients from HST using SNCosmo', pp. 139.10. <https://ui.adsabs.harvard.edu/abs/2016AAS...22713910S>

TY - CONF

T1 - See Change: Classifying single observation transients from HST using SNCosmo

AU - Sofiatti Nunes, Caroline

AU - Perlmutter, Saul

AU - Nordin, Jakob

AU - Rubin, David

AU - Lidman, Chris

AU - Deustua, Susana E.

AU - Fruchter, Andrew S.

AU - Aldering, Greg Scott

AU - Brodwin, Mark

AU - Cunha, Carlos E.

AU - Eisenhardt, Peter R.

AU - Gonzalez, Anthony H.

AU - Jee, Myungkook J.

AU - Hildebrandt, Hendrik

AU - Hoekstra, Henk

AU - Santos, Joana

AU - Stanford, S. Adam

AU - Stern, Dana R.

AU - Fassbender, Rene

AU - Richard, Johan

AU - Rosati, Piero

AU - Wechsler, Risa H.

AU - Muzzin, Adam

AU - Willis, Jon

AU - Boehringer, Hans

AU - Gladders, Michael

AU - Goobar, Ariel

AU - Amanullah, Rahman

AU - Hook, Isobel

AU - Huterer, Dragan

AU - Huang, Jiasheng

AU - Kim, Alex G.

AU - Kowalski, Marek

AU - Linder, Eric

AU - Pain, Reynald

AU - Saunders, Clare

AU - Suzuki, Nao

AU - Barbary, Kyle H.

AU - Rykoff, Eli S.

AU - Meyers, Joshua

AU - Spadafora, Anthony L.

AU - Hayden, Brian

AU - Wilson, Gillian

AU - Rozo, Eduardo

AU - Hilton, Matt

AU - Dixon, Samantha

AU - Yen, Mike

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The Supernova Cosmology Project (SCP) is executing "See Change", a large HST program to look for possible variation in dark energy using supernovae at z>1. As part of the survey, we often must make time-critical follow-up decisions based on multicolor detection at a single epoch. We demonstrate the use of the SNCosmo software package to obtain simulated fluxes in the HST filters for type Ia and core-collapse supernovae at various redshifts. These simulations allow us to compare photometric data from HST with the distribution of the simulated SNe through methods such as Random Forest, a learning method for classification, and Gaussian Kernel Estimation. The results help us make informed decisions about triggered follow up using HST and ground based observatories to provide time-critical information needed about transients. Examples of this technique applied in the context of See Change are shown.

AB - The Supernova Cosmology Project (SCP) is executing "See Change", a large HST program to look for possible variation in dark energy using supernovae at z>1. As part of the survey, we often must make time-critical follow-up decisions based on multicolor detection at a single epoch. We demonstrate the use of the SNCosmo software package to obtain simulated fluxes in the HST filters for type Ia and core-collapse supernovae at various redshifts. These simulations allow us to compare photometric data from HST with the distribution of the simulated SNe through methods such as Random Forest, a learning method for classification, and Gaussian Kernel Estimation. The results help us make informed decisions about triggered follow up using HST and ground based observatories to provide time-critical information needed about transients. Examples of this technique applied in the context of See Change are shown.

M3 - Paper

SP - 139.10

ER -

See Change: Classifying single observation transients from HST using SNCosmo

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