The Impact of Line Misidentification on Cosmological Constraints from Euclid and Other Spectroscopic Galaxy Surveys

Addison, Graeme E.; Bennett, C. L.; Jeong, Donghui; Komatsu, Eiichiro; Weiland, J. L.

doi:10.3847/1538-4357/ab22a0

Cited by 27 publications

(30 citation statements)

References 59 publications

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“…In our analysis, we have neglected observational systematics that are expected to arise in Euclid data, such as spectral line misidentification due to interlopers that can lead to catastrophic redshift errors (Pullen et al 2016;Leung et al 2017;Massara et al 2021). However, this effect mainly impacts the amplitude of multipoles (Addison et al 2019;Grasshorn Gebhardt et al 2019), so we anticipate it to be at least partially captured by the nuisance parameters in our model. We leave a more detailed investigation on the impact of survey systematics to a future work.…”

Section: Discussionmentioning

confidence: 88%

Euclid: Forecasts from redshift-space distortions and the Alcock–Paczynski test with cosmic voids

Hamaus

Aubert

Pisani

et al. 2022

A&A

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Euclid is poised to survey galaxies across a cosmological volume of unprecedented size, providing observations of more than a billion objects distributed over a third of the full sky. Approximately 20 million of these galaxies will have their spectroscopy available, allowing us to map the three-dimensional large-scale structure of the Universe in great detail. This paper investigates prospects for the detection of cosmic voids therein and the unique benefit they provide for cosmological studies. In particular, we study the imprints of dynamic (redshift-space) and geometric (Alcock–Paczynski) distortions of average void shapes and their constraining power on the growth of structure and cosmological distance ratios. To this end, we made use of the Flagship mock catalog, a state-of-the-art simulation of the data expected to be observed with Euclid. We arranged the data into four adjacent redshift bins, each of which contains about 11 000 voids and we estimated the stacked void-galaxy cross-correlation function in every bin. Fitting a linear-theory model to the data, we obtained constraints on f/b and DMH, where f is the linear growth rate of density fluctuations, b the galaxy bias, DM the comoving angular diameter distance, and H the Hubble rate. In addition, we marginalized over two nuisance parameters included in our model to account for unknown systematic effects in the analysis. With this approach, Euclid will be able to reach a relative precision of about 4% on measurements of f/b and 0.5% on DMH in each redshift bin. Better modeling or calibration of the nuisance parameters may further increase this precision to 1% and 0.4%, respectively. Our results show that the exploitation of cosmic voids in Euclid will provide competitive constraints on cosmology even as a stand-alone probe. For example, the equation-of-state parameter, w, for dark energy will be measured with a precision of about 10%, consistent with previous more approximate forecasts.

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Section: Discussionmentioning

confidence: 88%

Euclid: Forecasts from redshift-space distortions and the Alcock–Paczynski test with cosmic voids

Hamaus

Aubert

Pisani

et al. 2022

A&A

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“…However, the uncertainty associated with f c (s f c ) must be known to 1%. A more extensive discussion on the effects of line misidentification, for both Euclid and WFIRST, can be found in Addison et al (2019). With our analysis, we demonstrate that the goal of limiting the fraction of catastrophic failures ( f c ) below 20% can be achieved, even considering the poor photometric coverage characterizing the WISP survey.…”

Section: Analogies With the Future Euclid And Wfirst Surveysmentioning

confidence: 78%

Identification of Single Spectral Lines through Supervised Machine Learning in a Large HST Survey (WISP): A Pilot Study for Euclid and WFIRST

Baronchelli¹,

Scarlata²,

Rodighiero

et al. 2020

ApJS

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Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a sample of single emission lines extracted from the WFC3 IR Spectroscopic Parallel survey, one of the closest existing analogs to future slitless surveys. Our automatic software integrates a spectral energy distribution (SED)-fitting strategy with additional independent sources of information. We calibrated it and tested it on a "gold" sample of securely identified objects with multiple lines detected. The algorithm correctly classifies real emission lines with an accuracy of 82.6%, whereas the accuracy of the SEDfitting technique alone is low (∼50%) due to the limited amount of photometric data available (6 bands). While not specifically designed for the Euclid and WFIRST surveys, the algorithm represents an important precursor of similar algorithms to be used in these future missions.

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“…In our analysis we have neglected observational systematics that are expected to arise in Euclid data, such as spectral line misidentification due to interlopers that can lead to catastrophic redshift errors (Pullen et al 2016;Leung et al 2017;Massara et al 2020). However, this effect mainly impacts the amplitude of multipoles (Addison et al 2019;Grasshorn Gebhardt et al 2019), so we anticipate it to be at least partially captured by the nuisance parameters in our model. We leave a more detailed investigation on the impact of survey systematics to future work.…”

Section: Discussionmentioning

confidence: 99%

Euclid: Forecasts from redshift-space distortions and the Alcock-Paczynski test with cosmic voids

Hamaus,

Aubert,

Pisani

et al. 2021

Preprint

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Euclid will survey galaxies in a cosmological volume of unprecedented size, providing observations of more than a billion objects distributed over a third of the full sky. Approximately 20 million of these galaxies will have spectroscopy available, allowing us to map the three-dimensional large-scale structure of the Universe in great detail. This paper investigates prospects for the detection of cosmic voids therein, and the unique benefit they provide for cosmology. In particular, we study the imprints of dynamic (redshift-space) and geometric (Alcock-Paczynski) distortions of average void shapes and their constraining power on the growth of structure and cosmological distance ratios. To this end, we make use of the Flagship mock catalog, a state-of-the-art simulation of the data expected to be observed with Euclid. We arrange the data into four adjacent redshift bins, each of which contains about 11 000 voids, and estimate the stacked void-galaxy cross-correlation function in every bin. Fitting a linear-theory model to the data, we obtain constraints on f /b and D M H, where f is the linear growth rate of density fluctuations, b the galaxy bias, D M the comoving angular diameter distance, and H the Hubble rate. In addition, we marginalize over two nuisance parameters included in our model to account for unknown systematic effects in the analysis. With this approach Euclid will be able to reach a relative precision of about 4% on measurements of f /b and 0.5% on D M H in each redshift bin. Better modeling or calibration of the nuisance parameters may further increase this precision to 1% and 0.4%, respectively. Our results show that the exploitation of cosmic voids in Euclid will provide competitive constraints on cosmology even as a stand-alone probe. For example, the equation-of-state parameter w for dark energy will be measured with a precision of about 10%, consistent with earlier more approximate forecasts.

show abstract

The Impact of Line Misidentification on Cosmological Constraints from Euclid and Other Spectroscopic Galaxy Surveys

Cited by 27 publications

References 59 publications

Euclid: Forecasts from redshift-space distortions and the Alcock–Paczynski test with cosmic voids

Euclid: Forecasts from redshift-space distortions and the Alcock–Paczynski test with cosmic voids

Identification of Single Spectral Lines through Supervised Machine Learning in a Large HST Survey (WISP): A Pilot Study for Euclid and WFIRST

Euclid: Forecasts from redshift-space distortions and the Alcock-Paczynski test with cosmic voids

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