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SNITCH: seeking a simple, informative star formation history inference tool

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SNITCH : seeking a simple, informative star formation history inference tool. / Smethurst, R. J.; Merrifield, M.; Lintott, C. J.; Masters, K. L.; Simmons, B. D.; Fraser-McKelvie, A.; Peterken, T.; Boquien, M.; Riffel, R. A.; Drory, N.

In: Monthly Notices of the Royal Astronomical Society, Vol. 484, No. 3, 11.04.2019, p. 3590-3603.

Research output: Contribution to journalArticlepeer-review

Harvard

Smethurst, RJ, Merrifield, M, Lintott, CJ, Masters, KL, Simmons, BD, Fraser-McKelvie, A, Peterken, T, Boquien, M, Riffel, RA & Drory, N 2019, 'SNITCH: seeking a simple, informative star formation history inference tool', Monthly Notices of the Royal Astronomical Society, vol. 484, no. 3, pp. 3590-3603. https://doi.org/10.1093/mnras/stz239

APA

Smethurst, R. J., Merrifield, M., Lintott, C. J., Masters, K. L., Simmons, B. D., Fraser-McKelvie, A., Peterken, T., Boquien, M., Riffel, R. A., & Drory, N. (2019). SNITCH: seeking a simple, informative star formation history inference tool. Monthly Notices of the Royal Astronomical Society, 484(3), 3590-3603. https://doi.org/10.1093/mnras/stz239

Vancouver

Smethurst RJ, Merrifield M, Lintott CJ, Masters KL, Simmons BD, Fraser-McKelvie A et al. SNITCH: seeking a simple, informative star formation history inference tool. Monthly Notices of the Royal Astronomical Society. 2019 Apr 11;484(3):3590-3603. https://doi.org/10.1093/mnras/stz239

Author

Smethurst, R. J. ; Merrifield, M. ; Lintott, C. J. ; Masters, K. L. ; Simmons, B. D. ; Fraser-McKelvie, A. ; Peterken, T. ; Boquien, M. ; Riffel, R. A. ; Drory, N. / SNITCH : seeking a simple, informative star formation history inference tool. In: Monthly Notices of the Royal Astronomical Society. 2019 ; Vol. 484, No. 3. pp. 3590-3603.

Bibtex

@article{cb1210aa15544a21af9a2c77f3386ecc,
title = "SNITCH: seeking a simple, informative star formation history inference tool",
abstract = "Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in Python, developed to quickly (~2 minutes) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionisation. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy et al. (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customisable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user-defined analytic SFH model for specific science cases. Herein we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCH is sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra. ",
keywords = "astro-ph.GA",
author = "Smethurst, {R. J.} and M. Merrifield and Lintott, {C. J.} and Masters, {K. L.} and Simmons, {B. D.} and A. Fraser-McKelvie and T. Peterken and M. Boquien and Riffel, {R. A.} and N. Drory",
note = "Accepted 2019 January 21. Received 2019 January 17; in original form 2018 October 12",
year = "2019",
month = apr,
day = "11",
doi = "10.1093/mnras/stz239",
language = "English",
volume = "484",
pages = "3590--3603",
journal = "MNRAS",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "3",

}

RIS

TY - JOUR

T1 - SNITCH

T2 - seeking a simple, informative star formation history inference tool

AU - Smethurst, R. J.

AU - Merrifield, M.

AU - Lintott, C. J.

AU - Masters, K. L.

AU - Simmons, B. D.

AU - Fraser-McKelvie, A.

AU - Peterken, T.

AU - Boquien, M.

AU - Riffel, R. A.

AU - Drory, N.

N1 - Accepted 2019 January 21. Received 2019 January 17; in original form 2018 October 12

PY - 2019/4/11

Y1 - 2019/4/11

N2 - Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in Python, developed to quickly (~2 minutes) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionisation. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy et al. (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customisable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user-defined analytic SFH model for specific science cases. Herein we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCH is sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra.

AB - Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in Python, developed to quickly (~2 minutes) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionisation. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy et al. (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customisable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user-defined analytic SFH model for specific science cases. Herein we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCH is sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra.

KW - astro-ph.GA

U2 - 10.1093/mnras/stz239

DO - 10.1093/mnras/stz239

M3 - Article

VL - 484

SP - 3590

EP - 3603

JO - MNRAS

JF - MNRAS

SN - 0035-8711

IS - 3

ER -

ID: 12969487