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An investigation of spectral line stacking techniques and application to the detection of HC11N

Abstract

As the inventory of interstellar molecules continues to grow, the gulf between small species, whose individual rotational lines can be observed with radio telescopes, and large ones, such as polycyclic aromatic hydrocarbons best studied in bulk via infrared and optical observations, is slowly being bridged. Understanding the connection between these two molecular reservoirs is critical to understanding the interstellar carbon cycle, but will require pushing the boundaries of how far we can probe molecular complexity while still retaining observational specificity. Towards this end, we present a method for detecting and characterizing new molecular species in single-dish observations towards sources with sparse line spectra. We have applied this method to data from the ongoing GOTHAM (GBT Observations of TMC-1: Hunting Aromatic Molecules) Green Bank Telescope large programme, discovering six new interstellar species. Here we highlight the detection of HC11N, the largest cyanopolyyne in the interstellar medium.

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Fig. 1: Schematic diagram of our method for molecular detection and characterization.
Fig. 2: Schematic showing two spatial distribution regimes into which the emission from TMC-1 may fall and the approximations we use in our analysis.
Fig. 3: Individual line detections of HC9N in the GOTHAM data.
Fig. 4: HC9N spectra in TMC-1.
Fig. 5: Fractional modification to rotational constants plotted versus normalized matched filter response.
Fig. 6: Individual line observations of HC11N in the GOTHAM data.
Fig. 7: HC11N spectra in TMC-1.
Fig. 8: A comparison of cyanopolyyne column densities in TMC-1 from observations and a chemical model.

Data availability

The datasets analysed during the current study are available in the Green Bank Telescope archive (https://archive.nrao.edu/archive/advquery.jsp; PI: B.A.M.). A user manual for their reduction and analysis is also available (https://greenbankobservatory.org/science/gbt-observers/visitor-facilities-policies/data-reduction-gbt-using-idl/). The complete, reduced survey data in the X band are available as supplementary information in ref. 8. The individual portions of the reduced spectra used in the analysis of the individual species presented here are available in the Harvard Dataverse Archive42.

Code availability

All the codes used in the MCMC fitting and stacking analysis presented in this paper are open source and publicly available at https://github.com/ryanaloomis/TMC1_mcmc_fitting. The open source code for our spectral simulator can be found at https://github.com/ryanaloomis/spectral_simulator.

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Acknowledgements

A.M.B. acknowledges support from the Smithsonian Institution as a Submillimeter Array (SMA) Fellow. M.C.M. and K.L.K.L. acknowledge support from NSF grant number AST-1615847 and NASA grant number 80NSSC18K0396. Support for B.A.M. during the initial portions of this work was provided by NASA through Hubble Fellowship grant number HST-HF2-51396 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract number NAS5-26555. C.N.S. thanks the Alexander von Humboldt Stiftung/Foundation for their support, as well as V. Wakelam for use of the NAUTILUS v1.1 code. C.X. is a Grote Reber Fellow, and support for this work was provided by the NSF through the Grote Reber Fellowship Program administered by Associated Universities, Inc./National Radio Astronomy Observatory and the Virginia Space Grant Consortium. E.H. thanks the National Science Foundation for support through grant number AST 1906489. S.B.C. and M.A.C. were supported by the NASA Astrobiology Institute through the Goddard Center for Astrobiology. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Green Bank Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.

Author information

Authors and Affiliations

Authors

Contributions

R.A.L. wrote the manuscript and developed the MCMC and spectral stacking analysis code described here. M.C.M. and K.L.K.L. performed the laboratory experiments and theoretical calculations for several of the catalogues used in this analysis, and helped revise the manuscript. A.M.B and B.A.M. performed the astronomical observations and subsequent data reduction. E.H. determined and/or estimated rate coefficients and designed many of the original chemical simulations. A.M.B. and C.N.S. contributed or undertook the astronomical modelling and simulations. E.R.W., M.A.C., S.B.C., S.K. and B.A.M. contributed to the design of the GOTHAM survey, and helped revise the manuscript. C.X. modified and contributed the chemical networks of the related species and helped revise the manuscript. C.X. and A.J.R. performed the astronomical observations.

Corresponding authors

Correspondence to Ryan A. Loomis or Brett A. McGuire.

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The authors declare no competing interests.

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Peer review information Nature Astronomy thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Parameter covariances and marginalized posterior distributions for the HC9N MCMC fit.

16th, 50th, and 84th confidence intervals (corresponding to ± 1 sigma for a Gaussian posterior distribution) are shown as vertical lines.

Extended Data Fig. 2 HC9N best-fit parameters from MCMC analysis.

The quoted uncertainties represent the 16th and 84th percentile (1σ for a Gaussian distribution) uncertainties. †Column density values are highly covariant with the derived source sizes. The marginalized uncertainties on the column densities are therefore dominated by the largely unconstrained nature of the source sizes, and not by the signal-to-noise of the observations. ‡Uncertainties derived by adding the uncertainties of the individual components in quadrature.

Extended Data Fig. 3 Parameter covariances and marginalized posterior distributions for the HC13N MCMC fit.

The 97.8th confidence interval (corresponding to 2σ for a Gaussian posterior distribution) is shown as a vertical line.

Extended Data Fig. 4 Parameter covariances and marginalized posterior distributions for the HC11N MCMC fit.

16th, 50th, and 84th confidence intervals (corresponding to ± 1σ for a Gaussian posterior distribution) are shown as vertical lines.

Extended Data Fig. 5 HC11N best-fit parameters from MCMC analysis.

The quoted uncertainties represent the 16th and 84th percentile (1σ for a Gaussian distribution) uncertainties. Values in the table are also available in the files provided at ref. 42. †Column density values are highly covariant with the derived source sizes. The marginalized uncertainties on the column densities are therefore dominated by the largely unconstrained nature of the source sizes, and not by the signal-to-noise of the observations. ‡Uncertainties derived by adding the uncertainties of the individual components in quadrature.

Supplementary information

Supplementary Information

Supplementary Figs. 1–25, Tables 1–7 and discussion.

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Loomis, R.A., Burkhardt, A.M., Shingledecker, C.N. et al. An investigation of spectral line stacking techniques and application to the detection of HC11N. Nat Astron 5, 188–196 (2021). https://doi.org/10.1038/s41550-020-01261-4

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