MORPHOFIT: An automated galaxy structural parameters fitting package

Tortorelli, Luca; Mercurio, A.

doi:10.3389/fspas.2023.989443

Cited by 6 publications

(11 citation statements)

References 63 publications

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“…The AS1063 and M1149 spectroscopically confirmed cluster members we use in our analysis are the same as those selected in Tortorelli et al 2018 using VLT/MUSE IFU spectra. They are 95 for AS1063, having redshifts in the range 0.335 ≤ z ≤ 0.361 (Karman et al 2015), and 68 with redshifts 0.52 ≤ z ≤ 0.57 (Grillo et al 2016) for M1149.…”

Section: Datasetmentioning

confidence: 99%

“…They are 119 with redshifts 0.38 ≤ z ≤ 0.41. The description of the member selection is detailed in Annunziatella et al 2017 andCaminha et al 2017. In Tortorelli et al 2018, we defined and compared four different galaxy samples according to (a) Sérsic indices: early-type ('ETG'), (b) visual inspection: 'ellipticals', (c) colours: 'red', (d) spectral properties: 'passive'. We showed that the KR built with the 'ETG' sample is fully consistent with the ones obtained with the 'elliptical' and 'passive' samples.…”

Section: Datasetmentioning

confidence: 99%

“…Saracco et al 2010;Andreon et al 2016;Fagioli et al 2016). In Tortorelli et al 2018, we investigated the effect of the sample selection and we found that a selection purely based on colours may bias the KR parameters estimate, while galaxies selected through Sérsic indices (ETGs), visual inspection (ellipticals) and spectra (passives) constitute a more coeval population.…”

Section: Introductionmentioning

confidence: 99%

“…In this letter, we use data from the Hubble Space Telescope (HST) Frontier Fields (FF) survey (Lotz et al 2017) to study the KR of ETGs as a function of wavelength in three clusters at intermediate redshift, namely Abell S1063, at z = 0.348, MACS J0416.1-2403, at z = 0.396, and MACS J1149.5+2223, at z = 0.542. We build the KR for ETGs selected via Sérsic indices only, following the conclusions in Tortorelli et al 2018 about consistently selecting ETGs at different redshifts.…”

Section: Introductionmentioning

confidence: 99%

“…The structural parameters are measured using a new python package we develop called morphofit (Tortorelli & Mercurio 2023). We use the data analysis pipeline first introduced in Tortorelli et al 2018 and refined in Tortorelli & Mercurio 2023 that involves galaxies' surface brightness profile fit in images of increasing sizes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403, and MACS J1149.5+2223

Tortorelli¹,

Mercurio²,

Granata³

et al. 2023

Preprint

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403, and MACS J1149.5+2223

Tortorelli¹,

Mercurio²,

Granata³

et al. 2023

Preprint

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Searching for strong galaxy-scale lenses in galaxy clusters with deep networks

Angora

Rosati

Meneghetti³

et al. 2023

A&A

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Strong galaxy-scale lenses in galaxy clusters provide a unique tool with which to investigate the inner mass distribution of these clusters and the subhalo density profiles in the low-mass regime, which can be compared with predictions from ΛCDM cosmological simulations. We search for galaxy–galaxy strong-lensing systems in the Hubble Space Telescope (HST) multi-band imaging of galaxy cluster cores by exploring the classification capabilities of deep learning techniques. Convolutional neural networks (CNNs) are trained utilising highly realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members (CLMs). To this aim, we take advantage of extensive spectroscopic information available in 16 clusters and accurate knowledge of the deflection fields in half of these from high-precision strong-lensing models. Using observationally based distributions, we sample the magnitudes (down to F814W = 29 AB), redshifts, and sizes of the background galaxy population. By placing these sources within the secondary caustics associated with the cluster galaxies, we build a sample of approximately 3000 strong galaxy–galaxy lenses, which preserve the full complexity of real multi-colour data and produce a wide diversity of strong-lensing configurations. We study two deep learning networks, processing a large sample of image cutouts, in three bands, acquired by HST Advanced Camera for Survey (ACS), and we quantify their classification performance using several standard metrics. We find that both networks achieve a very good trade-off between purity and completeness (85%–95%), as well as a good stability, with fluctuations within 2%–4%. We characterise the limited number of false negatives (FNs) and false positives (FPs) in terms of the physical properties of the background sources (magnitudes, colours, redshifts, and effective radii) and CLMs (Einstein radii and morphology). We also demonstrate the high degree of generalisation of the neural networks by applying our method to HST observations of 12 clusters with previously known galaxy-scale lensing systems.

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Fitting pseudo-Sérsic (Spergel) light profiles to galaxies in interferometric data: The excellence of the uυ-plane

Tan,

Daddi,

de Souza Magalhães

et al. 2024

A&A

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Modern (sub)millimeter interferometers, such as ALMA and NOEMA, offer high angular resolution and unprecedented sensitivity. This provides the possibility to characterize the morphology of the gas and dust in distant galaxies. To assess the capabilities of the current software in recovering morphologies and surface brightness profiles in interferometric observations, we tested the performance of the Spergel model for fitting in the $uv$-plane, which has been recently implemented in the IRAM software GILDAS ( uv$\_$fit ). Spergel profiles provide an alternative to the \ profile, with the advantage of having an analytical Fourier transform, making them ideal for modeling visibilities in the $uv$-plane. We provide an approximate conversion between the Spergel index and the \ index, which depends on the ratio of the galaxy size to the angular resolution of the data. We show through extensive simulations that Spergel modeling in the $uv$-plane is a more reliable method for parameter estimation than modeling in the image plane, as it returns parameters that are less affected by systematic biases and results in a higher effective signal-to-noise ratio. The better performance in the $uv$-plane is likely driven by the difficulty of accounting for a correlated signal in interferometric images. Even in the $uv$-plane, the integrated source flux needs to be at least 50 times larger than the noise per beam to enable a reasonably good measurement of a Spergel index. We characterized the performance of Spergel model fitting in detail by showing that parameter biases are generally low (< 10<!PCT!>) and that uncertainties returned by uv$\_$fit are reliable within a factor of two. Finally, we showcase the power of Spergel fitting by re-examining two claims of extended halos around galaxies from the literature, showing that galaxies and halos can be successfully fitted simultaneously with a single Spergel model.

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MORPHOFIT: An automated galaxy structural parameters fitting package

Cited by 6 publications

References 63 publications

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403, and MACS J1149.5+2223

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403, and MACS J1149.5+2223

Searching for strong galaxy-scale lenses in galaxy clusters with deep networks

Fitting pseudo-Sérsic (Spergel) light profiles to galaxies in interferometric data: The excellence of the uυ-plane

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