Galaxy Light Profile Convolutional Neural Networks (GaLNets). I. Fast and Accurate Structural Parameters for Billion-galaxy Samples

Li, R.; Napolitano, N. R.; Roy, N.; Tortora, C.; Sonnenfeld, Alessandro; Qiu, Chunyin; S., Liu,

doi:10.3847/1538-4357/ac5ea0

Cited by 16 publications

(8 citation statements)

References 79 publications

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“…This rapidly growing sample of galaxies with quality imaging has sparkled renovated interests in the development of codes for the measurement of galaxy structural parameters and galaxy morphological classification that are accurate, time efficient, and require very few input from the user. These codes involve the use of non-parametric fitting (CAS Conselice, 2003, GINI Lotz et al, 2004, MORFOMETRYKA Ferrari et al, 2015), parametric fitting (GIM2D Simard et al, 2002, BUDDA de Souza et al, 2004, GASP2D Méndez-Abreu et al, 2008, PYMORPH Vikram et al, 2010, GALAPAGOS Häußler et al, 2011Häußler et al, 2013, IMFIT Erwin, 2015, GALIGHT Ding et al, 2021, galapagos-2 Häußler et al, 2022 or machine learning methodologies ( DEEPLEGATO Tuccillo et al, 2018, GAMORNET Ghosh et al, 2020, GALNETS Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

MORPHOFIT: An automated galaxy structural parameters fitting package

Tortorelli

Mercurio²

2023

Front. Astron. Space Sci.

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In today’s modern wide-field galaxy surveys, there is the necessity for parametric surface brightness decomposition codes characterised by accuracy, small degree of user intervention, and high degree of parallelisation. We try to address this necessity by introducing MORPHOFIT, a highly parallelisable python package for the estimate of galaxy structural parameters. The package makes use of wide-spread and reliable codes, namely, SEXTRACTOR and GALFIT. It has been optimised and tested in both low-density and crowded environments, where blending and diffuse light makes the structural parameters estimate particularly challenging. MORPHOFIT allows the user to fit multiple surface brightness components to each individual galaxy, among those currently implemented in the code. Using simulated images of single Sérsic and bulge plus disk galaxy light profiles with different bulge-to-total luminosity (B/T) ratios, we show that MORPHOFIT is able to recover the input structural parameters of the simulated galaxies with good accuracy. We also compare its estimates against existing literature studies, finding consistency within the errors. We use the package in ? to measure the structural parameters of cluster galaxies in order to study the wavelength dependence of the Kormendy relation of early-type galaxies. The package is available on github1 and on the Pypi server2.

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Section: Introductionmentioning

confidence: 99%

MORPHOFIT: An automated galaxy structural parameters fitting package

Tortorelli

Mercurio²

2023

Front. Astron. Space Sci.

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“…Top row shows the results as a function of magnitude while the bottom row is as a function of radius. Figure adapted from Li et al (2021). …”

Section: Deep Learning For Inferring Physical Properties Of Galaxiesmentioning

confidence: 99%

“…More recently, Li et al (2021) attempted a similar approach applied to ground-based observations. The training is still done on simulations but with realistic backgrounds.…”

Section: Deep Learning For Inferring Physical Properties Of Galaxiesmentioning

confidence: 99%

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Huertas-Company

Lanusse²

2023

Publ. Astron. Soc. Aust.

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The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.

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“…As this leads to no improvement, we considered networks accepting eight frames instead of four, and added the PSF images directly as input images. To this end, we subsampled the PSF with a linear interpolation to 64 × 64 pixels, matching the size of the images, and passed them independently of the images through a mirrored branch of convolutional layers, combining the intermediate outputs directly before the FC layers as suggested by Maresca et al (2021) and Li et al (2022). Again, no improvement is seen with this option, possibly because such networks, with their relatively small kernel sizes, perform well in pattern recognition but perhaps not as well in analyzing very similar and completely smooth images like a PSF.…”

Section: Variations Of the Input Datamentioning

confidence: 99%

Holismokes

Schuldt

Cañameras

Shu

et al. 2023

A&A

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Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the Rubin Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and time-consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a singular isothermal ellipsoid (SIE) profile (lens center x and y, ellipticity ex and ey, Einstein radius θE) and the external shear (γext, 1, γext, 2) from ground-based imaging data. In contrast to our previous CNN, this ResNet further predicts the 1σ uncertainty for each parameter. To train our network, we use our improved pipeline to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low ground-based image resolution is the limiting factor in predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the network-predicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a first-order estimate of time delays on real lens systems and requires further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxy-scale lenses expected in the near future.

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Galaxy Light Profile Convolutional Neural Networks (GaLNets). I. Fast and Accurate Structural Parameters for Billion-galaxy Samples

Cited by 16 publications

References 79 publications

MORPHOFIT: An automated galaxy structural parameters fitting package

MORPHOFIT: An automated galaxy structural parameters fitting package

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Holismokes

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