HAFFET: Hybrid Analytic Flux FittEr for Transients

The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences...
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Autor*in:	Sheng Yang [verfasserIn] Jesper Sollerman [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Astronomy software Astronomy data modeling Supernovae Open source software

Übergeordnetes Werk:	In: The Astrophysical Journal Supplement Series - IOP Publishing, 2022, 269(2023), 2, p 40
Übergeordnetes Werk:	volume:269 ; year:2023 ; number:2, p 40

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4365/acfcb4

Katalog-ID:	DOAJ094552762

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520			\|a The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ).
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spelling	10.3847/1538-4365/acfcb4 doi (DE-627)DOAJ094552762 (DE-599)DOAJ2b43bd6ea4ed4f34990f4adc59643808 DE-627 ger DE-627 rakwb eng QB460-466 Sheng Yang verfasserin aut HAFFET: Hybrid Analytic Flux FittEr for Transients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ). Astronomy software Astronomy data modeling Supernovae Open source software Astrophysics Jesper Sollerman verfasserin aut In The Astrophysical Journal Supplement Series IOP Publishing, 2022 269(2023), 2, p 40 (DE-627)312200196 (DE-600)2006860-8 15384365 nnns volume:269 year:2023 number:2, p 40 https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/article/2b43bd6ea4ed4f34990f4adc59643808 kostenfrei https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/toc/0067-0049 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 269 2023 2, p 40
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allfieldsGer	10.3847/1538-4365/acfcb4 doi (DE-627)DOAJ094552762 (DE-599)DOAJ2b43bd6ea4ed4f34990f4adc59643808 DE-627 ger DE-627 rakwb eng QB460-466 Sheng Yang verfasserin aut HAFFET: Hybrid Analytic Flux FittEr for Transients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ). Astronomy software Astronomy data modeling Supernovae Open source software Astrophysics Jesper Sollerman verfasserin aut In The Astrophysical Journal Supplement Series IOP Publishing, 2022 269(2023), 2, p 40 (DE-627)312200196 (DE-600)2006860-8 15384365 nnns volume:269 year:2023 number:2, p 40 https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/article/2b43bd6ea4ed4f34990f4adc59643808 kostenfrei https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/toc/0067-0049 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 269 2023 2, p 40
allfieldsSound	10.3847/1538-4365/acfcb4 doi (DE-627)DOAJ094552762 (DE-599)DOAJ2b43bd6ea4ed4f34990f4adc59643808 DE-627 ger DE-627 rakwb eng QB460-466 Sheng Yang verfasserin aut HAFFET: Hybrid Analytic Flux FittEr for Transients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ). Astronomy software Astronomy data modeling Supernovae Open source software Astrophysics Jesper Sollerman verfasserin aut In The Astrophysical Journal Supplement Series IOP Publishing, 2022 269(2023), 2, p 40 (DE-627)312200196 (DE-600)2006860-8 15384365 nnns volume:269 year:2023 number:2, p 40 https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/article/2b43bd6ea4ed4f34990f4adc59643808 kostenfrei https://doi.org/10.3847/1538-4365/acfcb4 kostenfrei https://doaj.org/toc/0067-0049 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 269 2023 2, p 40
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source	In The Astrophysical Journal Supplement Series 269(2023), 2, p 40 volume:269 year:2023 number:2, p 40
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callnumber-first	Q - Science
author	Sheng Yang
spellingShingle	Sheng Yang misc QB460-466 misc Astronomy software misc Astronomy data modeling misc Supernovae misc Open source software misc Astrophysics HAFFET: Hybrid Analytic Flux FittEr for Transients
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title	HAFFET: Hybrid Analytic Flux FittEr for Transients
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title_sort	haffet: hybrid analytic flux fitter for transients
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title_auth	HAFFET: Hybrid Analytic Flux FittEr for Transients
abstract	The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ).
abstractGer	The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ).
abstract_unstemmed	The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ).
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title_short	HAFFET: Hybrid Analytic Flux FittEr for Transients
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