Galaxy–Galaxy lensing in HSC: Validation tests and the impact of heterogeneous spectroscopic training sets

Speagle, Joshua S.; Leauthaud, Alexie; Huang, Song; Bradshaw, Christopher; Ardila, Felipe; Capak, P.; Eisenstein, Daniel J.; Masters, D. C.; Mandelbaum, Rachel; Simet, Melanie; Sifón, Cristobál

doi:10.1093/mnras/stz2968

Cited by 34 publications

(44 citation statements)

References 64 publications

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“…Similar efforts, mostly machine learning, have found strong lens candidates in deep imaging surveys (e.g., Jacobs et al 2019, Speagle et al 2019, and Huang et al 2020b. Figure 10 shows the GAMA equatorial lens candidates of this work and includes two candidates previously identified by DECaLS, SLACS (Bolton et al 2008a) in the GAMA equatorial regions that had a match in R.A./decl.…”

Section: Other Lens Searches and Future Effortsmentioning

confidence: 81%

“…Machine learning is gaining popularity as a method for identifying galaxy-galaxy lens candidates, e.g., in Subaru Hyper-Supreme Cam (Speagle et al 2019), DECAM (Huang et al 2020b), and Dark Energy Survey data (Jacobs et al 2019). Petrillo et al (2017Petrillo et al ( , 2019aPetrillo et al ( , 2019b introduced and developed a machine-learning technique to visually identify strong lens candidates by training the convolutional neural networks to recognize the characteristic arcs that appear next to a lensing elliptical galaxy using simulated images as their training set.…”

Section: Machine-learning Identification Of Lensesmentioning

confidence: 99%

“…With the Galaxy and Mass Assembly survey (GAMA) spectroscopy-selected sample (Holwerda et al 2015), a greater range in lens masses is now available. However, the drive for much larger samples has led to increased searches using machine learning to constrain ΛCDM in detail (Petrillo et al 2017(Petrillo et al , 2019a(Petrillo et al , 2019bJacobs et al 2019;Speagle et al 2019;Huang et al 2020aHuang et al , 2020bLi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Knabel

Steele

Holwerda

et al. 2020

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Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies-such as mixed spectroscopy of multiple galaxies along the line of sight, machine-learning algorithms, and citizen science-have been employed to identify these objects as new imaging surveys become available. We report on the comparison between spectroscopic, machine-learning, and citizenscience identification of galaxy-galaxy lens candidates from independently constructed lens catalogs in the common survey area of the equatorial fields of the Galaxy and Mass Assembly survey. In these, we have the opportunity to compare high completeness spectroscopic identifications against high-fidelity imaging from the Kilo Degree Survey used for both machine-learning and citizen-science lens searches. We find that the three methodsspectroscopy, machine learning, and citizen science-identify 47, 47, and 13 candidates, respectively, in the 180 square degrees surveyed. These identifications barely overlap, with only two identified by both citizen science and machine learning. We have traced this discrepancy to inherent differences in the selection functions of each of the three methods, either within their parent samples (i.e., citizen science focuses on low redshift) or inherent to the method (i.e., machine learning is limited by its training sample and prefers well-separated features, while spectroscopy requires sufficient flux from lensed features to lie within the fiber). These differences manifest as separate samples in estimated Einstein radius, lens stellar mass, and lens redshift. The combined sample implies a lens candidate sky density of ∼0.59 deg −2 and can inform the construction of a training set spanning a wider massredshift space. A combined approach and refinement of automated searches would result in a more complete sample of galaxy-galaxy lens candidates for future surveys.

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Section: Other Lens Searches and Future Effortsmentioning

confidence: 81%

Section: Machine-learning Identification Of Lensesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Knabel

Steele

Holwerda

et al. 2020

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“…We develop our method using lens samples derived from the Sloan Digital Sky Survey (SDSS)-III BOSS (Eisenstein et al 2011;Dawson et al 2012). BOSS is a spectroscopic survey with a complex sample selection function which is commonly used in cosmological analyses of galaxy clustering and GGL (Alam et al 2017;Sánchez et al 2017;Beutler et al 2017;Tröster et al 2020b;Speagle et al 2019;Heymans et al 2021). For more details about the nature of the galaxy selection process, see Alam et al (2015).…”

Section: Boss Dr12 Datamentioning

confidence: 99%

“…First, we produce forecasts for the GGL signals for a KiDS-1000+BOSS DR12 analysis as described in Joachimi et al (2021). Secondly, we produce similar forecasts for a GGL analysis of HSC Wide+BOSS DR12 similar to Speagle et al (2019), while using the source bins described in Hikage et al (2019). Lastly, we produce GGL forecasts for a potential Euclid-like+DESI-like analysis using the galaxy sample properties defined in the Euclid collaboration forecast choices (Blanchard et al 2019), The properties of all of the aforementioned galaxy samples are given in table 2 and their redshift distributions, ( ), are given in figure 8.…”

Section: Magnification Bias In Weak Lensing Measurementsmentioning

confidence: 99%

Magnification bias in galaxy surveys with complex sample selection functions

Wietersheim-Kramsta

Joachimi

Busch

et al. 2021

Monthly Notices of the Royal Astronomical Society

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Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes of large-scale structure if not accurately modelled. Standard approaches to modelling this magnification bias may not be applicable in practice as many galaxy samples have complex, often implicit, selection functions. We propose and test a procedure to quantify the magnification bias induced in clustering and galaxy-galaxy lensing (GGL) signals in galaxy samples subject to a selection function beyond a simple flux limit. The method employs realistic mock data to calibrate an effective luminosity function slope, αobs, from observed galaxy counts, which can then be used with the standard formalism. We demonstrate this method for two galaxy samples derived from the Baryon Oscillation Spectroscopic Survey (BOSS) in the redshift ranges 0.2 < z ≤ 0.5 and 0.5 < z ≤ 0.75, complemented by mock data built from the MICE2 simulation. We obtain αobs = 1.93 ± 0.05 and αobs = 2.62 ± 0.28 for the two BOSS samples. For BOSS-like lenses, we forecast a contribution of the magnification bias to the GGL signal between the multipole moments, ℓ, of 100 and 4600 with a cumulative signal-to-noise ratio between 0.1 and 1.1 for sources from the Kilo-Degree Survey (KiDS), between 0.4 and 2.0 for sources from the Hyper Suprime-Cam survey (HSC), and between 0.3 and 2.8 for ESA Euclid-like source samples. These contributions are significant enough to require explicit modelling in future analyses of these and similar surveys. Our code is publicly available within the MagBEt module (https://github.com/mwiet/MAGBET).

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Measuring photometric redshifts for high-redshift radio source surveys

Luken

Norris

Wang

et al. 2023

Publ. Astron. Soc. Aust.

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With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky MurchisonWidefield Array survey eXtended, Very Large Array Sky Survey and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs), and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry), and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈76% of the highest redshift sources — sources at redshift z > 2.51 — as being in either the highest bin (z > 2.51), or second highest (z = 2.25).

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Galaxy–Galaxy lensing in HSC: Validation tests and the impact of heterogeneous spectroscopic training sets

Cited by 34 publications

References 64 publications

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Magnification bias in galaxy surveys with complex sample selection functions

Measuring photometric redshifts for high-redshift radio source surveys

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