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Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models which make use of CNNs are criticized as "black boxes" due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN) -- a fusion of CNN architecture with the framework of cosmological N-point correlation functions (NPCFs). We demonstrate that the output of this model can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we are able to open the "black box" by quantitatively ranking different orders of the interpretable convolution outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and Log-normal random fields and relating its outputs to the analytical NPCFs describing the two fields. Furthermore, we exhibit the model's ability to distinguish different dark energy scenarios ($w_0=-0.95$ and $-1.05$) using N-body simulated weak lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool with the potential to extract physical insights in a robust and explainable way from observational data., Comment: 19 pages, 8 figures, 5 tables; Comments are welcome!

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous works on the shear-only integrated 3PCF, we develop the theoretical framework for modelling 5 new statistics involving the galaxy field and its cross-correlations with cosmic shear. Using realistic galaxy and cosmic shear mocks from simulations, we determine the regime of validity of our models based on leading-order standard perturbation theory with an MCMC analysis that recovers unbiased constraints of the amplitude of fluctuations parameter $A_s$ and the linear and quadratic galaxy bias parameters $b_1$ and $b_2$. Using Fisher matrix forecasts for a DES-Y3-like survey, relative to baseline analyses with conventional 3$\times$2PCFs, we find that the addition of the shear-only integrated 3PCF can improve cosmological parameter constraints by $20-40\%$. The subsequent addition of the new statistics introduced in this paper can lead to further improvements of $10-20\%$, even when utilizing only conservatively large scales where the tree-level models are valid. Our results motivate future work on the galaxy and shear integrated 3PCFs, which offer a practical way to extend standard analyses based on 3$\times$2PCFs to systematically probe the non-Gaussian information content of cosmic density fields., Comment: 19 pages, 8 figures + appendix. Comments are welcome!

The integrated shear 3-point correlation function $\zeta_{\pm}$ measures the correlation between the local shear 2-point function $\xi_{\pm}$ and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, $\zeta_{\pm}$ can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using $\zeta_{\pm}$. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on $N$-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to $\xi_{\pm}$-only, adding $\zeta_{\pm}$ can lead to $\approx 10-25\%$ improvements on the constraints of parameters like $A_s$ (or $\sigma_8$) and $w_0$. We find no evidence in $\xi_{\pm} + \zeta_{\pm}$ constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where $\zeta_{\pm}$ is measured, and of the strategy to estimate the covariance matrix ($N$-body vs. lognormal). Our analysis solidifies the strong potential of the $\zeta_{\pm}$ statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data., Comment: 21 pages, 11 figures, 3 tables. Comments welcome

We present the integrated 3-point shear correlation function $i\zeta_{\pm}$ -- a higher-order statistic of the cosmic shear field -- which can be directly estimated in wide-area weak lensing surveys without measuring the full 3-point shear correlation function, making this a practical and complementary tool to 2-point statistics for weak lensing cosmology. We define it as the 1-point aperture mass statistic $M_{\mathrm{ap}}$ measured at different locations on the shear field correlated with the corresponding local 2-point shear correlation function $\xi_{\pm}$. Building upon existing work on the integrated bispectrum of the weak lensing convergence field, we present a theoretical framework for computing the integrated 3-point function in real space for any projected field within the flat-sky approximation and apply it to cosmic shear. Using analytical formulae for the non-linear matter power spectrum and bispectrum, we model $i\zeta_{\pm}$ and validate it on N-body simulations within the uncertainties expected from the sixth year cosmic shear data of the Dark Energy Survey. We also explore the Fisher information content of $i\zeta_{\pm}$ and perform a joint analysis with $\xi_{\pm}$ for two tomographic source redshift bins with realistic shape-noise to analyse its power in constraining cosmological parameters. We find that the joint analysis of $\xi_{\pm}$ and $i\zeta_{\pm}$ has the potential to considerably improve parameter constraints from $\xi_{\pm}$ alone, and can be particularly useful in improving the figure of merit of the dynamical dark energy equation of state parameters from cosmic shear data., Comment: Accepted for publication in MNRAS; v2 matches the accepted manuscript; 18 pages + appendix

Using the M31 PAndromeda Cepheid sample and the HST PHAT data we obtain the largest Cepheid sample in M31 with HST data in four bands. For our analysis we consider three samples: A very homogeneous sample of Cepheids based on the PAndromeda data, the mean magnitude corrected PAndromeda sample and a sample complementing the PAndromeda sample with Cepheids from literature. The latter results in the largest catalog with 522 fundamental mode (FM) Cepheids and 102 first overtone (FO) Cepheids with F160W and F110W data and 559 FM Cepheids and 111 FO Cepheids with F814W and F475W data. The obtained dispersion of the Period-Luminosity relations (PLRs) is very small (e.g. 0.138 mag in the F160W sample I PLR). We find no broken slope in the PLRs when analyzing our entire sample, but we do identify a subsample of Cepheids that causes the broken slope. However, this effect only shows when the number of this Cepheid type makes up a significant fraction of the total sample. We also analyze the sample selection effect on the Hubble constant., Comment: 32 pages, 19 figures, 9 tables, accepted for publication in ApJ, electronic data will be available on CDS

We present results from the Wendelstein Weak Lensing (WWL) pathfinder project, in which we have observed three intermediate redshift Planck clusters of galaxies with the new 30'$\times 30$' wide field imager at the 2m Fraunhofer Telescope at Wendelstein Observatory. We investigate the presence of biases in our shear catalogues and estimate their impact on our weak lensing mass estimates. The overall calibration uncertainty depends on the cluster redshift and is below 8.1-15 per cent for $z \approx 0.27-0.77$. It will decrease with improvements on the background sample selection and the multiplicative shear bias calibration. We present the first weak lensing mass estimates for PSZ1 G109.88+27.94 and PSZ1 G139.61+24.20, two SZ-selected cluster candidates. Based on Wendelstein colors and SDSS photometry, we find that the redshift of PSZ1 G109.88+27.94 has to be corrected to $z \approx 0.77$. We investigate the influence of line-of-sight structures on the weak lensing mass estimates and find upper limits for two groups in each of the fields of PSZ1 G109.88+27.94 and PSZ1 G186.98+38.66. We compare our results to SZ and dynamical mass estimates from the literature, and in the case of PSZ1 G186.98+38.66 to previous weak lensing mass estimates. We conclude that our pathfinder project demonstrates that weak lensing cluster masses can be accurately measured with the 2m Fraunhofer Telescope., Comment: 24 pages, 26 figures. Submitted to MNRAS

We present the largest Cepheid sample in M31 based on the complete Pan-STARRS1 survey of Andromeda (PAndromeda) in the $r_{\mathrm{P1}}$ , $i_{\mathrm{P1}}$ and $g_{\mathrm{P1}}$ bands. We find 2686 Cepheids with 1662 fundamental mode Cepheids, 307 first-overtone Cepheids, 278 type II Cepheids and 439 Cepheids with undetermined Cepheid type. Using the method developed by Kodric et al. (2013) we identify Cepheids by using a three dimensional parameter space of Fourier parameters of the Cepheid light curves combined with a color cut and other selection criteria. This is an unbiased approach to identify Cepheids and results in a homogeneous Cepheid sample. The Period-Luminosity relations obtained for our sample have smaller dispersions than in our previous work. We find a broken slope that we previously observed with HST data in Kodric et al. (2015), albeit with a lower significance., Comment: 79 pages, 39 figures, 8 tables, accepted for publication in AJ, K18b is submittted to ApJ, electronic data will be available on CDS

We reconstruct the two-dimensional (2D) matter distributions in 20 high-mass galaxy clusters selected from the CLASH survey by using the new approach of performing a joint weak lensing analysis of 2D shear and azimuthally averaged magnification measurements. This combination allows for a complete analysis of the field, effectively breaking the mass-sheet degeneracy. In a Bayesian framework, we simultaneously constrain the mass profile and morphology of each individual cluster assuming an elliptical Navarro-Frenk-White halo characterized by the mass, concentration, projected axis ratio, and position angle of the projected major axis.. We find that spherical mass estimates of the clusters from azimuthally averaged weak-lensing measurements in previous work are in excellent agreement with our results from a full 2D analysis. Combining all 20 clusters in our sample, we detect the elliptical shape of weak-lensing halos at the $5\sigma$ significance level within a scale of 2Mpc$/h$. The median projected axis ratio is $0.67\pm 0.07$ at a virial mass of $M_\mathrm{vir}=(15.2\pm 2.8) \times 10^{14} M_\odot$, which is in agreement with theoretical predictions of the standard collisionless cold dark matter model. We also study misalignment statistics of the brightest cluster galaxy, X-ray, thermal Sunyaev-Zel'dovich effect, and strong-lensing morphologies with respect to the weak-lensing signal. Among the three baryonic tracers studied here, we find that the X-ray morphology is best aligned with the weak-lensing mass distribution, with a median misalignment angle of $21\pm 7$ degrees. We also conduct a stacked quadrupole shear analysis assuming that the X-ray major axis is aligned with that of the projected mass distribution. This yields a consistent axis ratio of $0.67\pm 0.10$, suggesting again a tight alignment between the intracluster gas and dark matter., Comment: Minor changes to match the version published in ApJ. One of three new companion papers of the CLUMP-3D project (I-Non Chiu et al., arXiv:1804.00676; Mauro Sereno et al., arXiv:1804.00667)

The second most significant detection of the Planck Sunyaev Zel'dovich survey, PLCK~G287.0+32.9 ($z=0.385$) boasts two similarly bright radio relics and a radio halo. One radio relic is located $\sim 400$ kpc northwest of the X-ray peak and the other $\sim 2.8$ Mpc to the southeast. This large difference suggests that a complex merging scenario is required. A key missing puzzle for the merging scenario reconstruction is the underlying dark matter distribution in high resolution. We present a joint Subaru Telescope and {\it Hubble Space Telescope} weak-lensing analysis of the cluster. Our analysis shows that the mass distribution features four significant substructures. Of the substructures, a primary cluster of mass $M_{200\text{c}}=1.59^{+0.25}_{-0.22}\times 10^{15} \ h^{-1}_{70} \ \text{M}_{\odot}$ dominates the weak-lensing signal. This cluster is likely to be undergoing a merger with one (or more) subcluster whose mass is approximately a factor of 10 lower. One candidate is the subcluster of mass $M_{200\text{c}}=1.16^{+0.15}_{-0.13}\times 10^{14} \ h^{-1}_{70} \ \text{M}_{\odot}$ located $\sim 400$ kpc to the southeast. The location of this subcluster suggests that its interaction with the primary cluster could be the source of the NW radio relic. Another subcluster is detected $\sim 2$ Mpc to the SE of the X-ray peak with mass $M_{200\text{c}}=1.68^{+0.22}_{-0.20}\times 10^{14} \ h^{-1}_{70} \ \text{M}_{\odot}$. This SE subcluster is in the vicinity of the SE radio relic and may have created the SE radio relic during a past merger with the primary cluster. The fourth subcluster, $M_{200\text{c}}=1.87^{+0.24}_{-0.22}\times 10^{14} \ h^{-1}_{70} \ \text{M}_{\odot}$, is northwest of the X-ray peak and beyond the NW radio relic., Comment: 19 pages, 14 figures; Accepted to ApJ

Since galaxy clusters sit at the high-end of the mass function, the number of galaxy clusters both massive and concentrated enough to yield particularly large Einstein radii poses useful constraints on cosmological and structure formation models. To date, less than a handful of clusters are known to have Einstein radii exceeding $\sim40$" (for a source at $z_{s}\simeq2$, nominally). Here, we report an addition to that list of the Sunyaev-Zel'dovich (SZ) selected cluster, PLCK G287.0+32.9 ($z=0.38$), the second-highest SZ-mass ($M_{500}$) cluster from the Planck catalog. We present the first strong lensing analysis of the cluster, identifying 20 sets of multiply-imaged galaxies and candidates in new \emph{Hubble Space Telescope} data, including a long, $l\sim22$" giant arc, as well as a quadruply-imaged, apparently bright (magnified to J$_{F110W}=$25.3 AB), likely high-redshift dropout galaxy at $z_{phot}=6.90$ [6.13--8.43] (95\% C.I.). Our analysis reveals a very large critical area (1.55 arcmin$^{2}$, $z_{s}\simeq2$), corresponding to an effective Einstein radius of $\theta_{E}\sim42$". The model suggests the critical area will expand to 2.58 arcmin$^{2}$ ($\theta_{E}\sim54$") for sources at $z_{s}\sim10$. Our work adds to recent efforts to model very massive clusters towards the launch of the James Webb Space Telescope, in order to identify the most useful cosmic lenses for studying the early Universe. Spectroscopic redshifts for the multiply-imaged galaxies and additional HST data will be necessary for refining the lens model and verifying the nature of the $z\sim7$ dropout., Comment: Published in ApJL; 8 pages, 5 figures, 1 table; V2: accepted version

Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by choosing a suboptimal redshift histogram bin width and propose methods to resolve them. The selection of a too large bin width is shown to oversmooth small scale structure of the radial distribution of galaxies. This systematic error can significantly shift cosmological parameter constraints by up to $6 \, \sigma$ for the dark energy equation of state parameter $w$. Careful selection of bin width can reduce this systematic by a factor of up to 6 as compared with commonly used current binning approaches. We further discuss a generalised resampling method that can correct systematic and statistical errors in cosmological parameter constraints caused by uncertainties in the redshift distribution. This can be achieved without any prior assumptions about the shape of the distribution or the form of the redshift error. Our methodology allows photometric surveys to obtain unbiased cosmological parameter constraints using a minimum number of spectroscopic calibration data. For a DES-like galaxy clustering forecast we obtain unbiased results with respect to errors caused by suboptimal histogram bin width selection, using only 5k representative spectroscopic calibration objects per tomographic redshift bin., Comment: 14 pages, 1 table, 7 figures, updated to match version accepted in the MNRAS

We present measurements of the [NII]/Ha ratio as a probe of gas-phase oxygen abundance for a sample of 419 star-forming galaxies at z=0.6-2.7 from the KMOS3D near-IR multi-IFU survey. The mass-metallicity relation (MZR) is determined consistently with the same sample selection, metallicity tracer, and methodology over the wide redshift range probed by the survey. We find good agreement with long-slit surveys in the literature, except for the low-mass slope of the relation at z~2.3, where this sample is less biased than previous samples based on optical spectroscopic redshifts. In this regime we measure a steeper slope than some literature results. Excluding the AGN contribution from the MZR reduces sensitivity at the high mass end, but produces otherwise consistent results. There is no significant dependence of the [NII]/Ha ratio on SFR or environment at fixed redshift and stellar mass. The IFU data allow spatially resolved measurements of [NII]/Ha, from which we can infer abundance gradients for 180 galaxies, thus tripling the current sample in the literature. The observed gradients are on average flat, with only 15 gradients statistically offset from zero at >3sigma. We have modelled the effect of beam-smearing, assuming a smooth intrinsic radial gradient and known seeing, inclination and effective radius for each galaxy. Our seeing-limited observations can recover up to 70% of the intrinsic gradient for the largest, face-on disks, but only 30% for the smaller, more inclined galaxies. We do not find significant trends between observed or corrected gradients and any stellar population, dynamical or structural galaxy parameters, mostly in agreement with existing studies with much smaller sample sizes. In cosmological simulations, strong feedback is generally required to produce flat gradients at high redshift., Comment: submitted to ApJ

We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stacking round (or layer). To demonstrate the benefit of stacking, we apply the method to both unsupervised machine learning techniques based on self-organising maps (SOMs), and supervised machine learning methods based on decision trees. We explore a range of stacking architectures, such as the number of layers and the number of base learners per layer. Finally we explore the effectiveness of stacking even when using a successful algorithm such as AdaBoost. We observe a significant improvement of between 1.9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees). When applied to strong learning algorithms (such as AdaBoost) the ratio of improvement shrinks, but still remains positive and is between 0.4% and 2.5% for the explored metrics and comes at almost no additional computational cost., Comment: 13 pages, 3 tables, 7 figures version accepted by MNRAS, minor text updates. Results and conclusions unchanged

We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm' s arc elongation accuracy, completeness and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of $4 \pm 1$ arcs (with length $\ge 6"$ and length-to-width ratio $\ge 7$) per cluster for the X-ray selected CLASH sample, $4 \pm 1$ arcs per cluster for the MOKA simulated sample and $3 \pm 1$ arcs per cluster for the MUSIC simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift $z_s = 1.9$ with 33% of the detected arcs having $z_s > 3$. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g. the $c-M$ relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations., Comment: 54 pages, 23 figures, accepted for publication in the Astrophysical Journal

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data., Comment: 16 pages, 9 figures, updated to match MNRAS accepted version. Minor text changes, results unchanged

We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, that can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitudes less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular Neural Network code (ANNz). In our use case, this improvement reaches 50\% for high redshift objects ($z \geq 0.75$). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of four, when compared with less accurate PDFs obtained from commonly used methods. The cosmological analyses we examine and find improvement upon are the following: gravitational lensing cluster mass estimates, modelling of angular correlation functions, and modelling of cosmic shear correlation functions., Comment: 17 pages, 19 figures, updated to match version accepted in the MNRAS

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million 'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 'anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed 'anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80% when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample., Comment: 13 pages, 8 figures, 1 table, minor text updates to macth MNRAS accepted version

We present analyses of data augmentation for machine learning redshift estimation. Data augmentation makes a training sample more closely resemble a test sample, if the two base samples differ, in order to improve measured statistics of the test sample. We perform two sets of analyses by selecting 800k (1.7M) SDSS DR8 (DR10) galaxies with spectroscopic redshifts. We construct a base training set by imposing an artificial r band apparent magnitude cut to select only bright galaxies and then augment this base training set by using simulations and by applying the K-correct package to artificially place training set galaxies at a higher redshift. We obtain redshift estimates for the remaining faint galaxy sample, which are not used during training. We find that data augmentation reduces the error on the recovered redshifts by 40% in both sets of analyses, when compared to the difference in error between the ideal case and the non augmented case. The outlier fraction is also reduced by at least 10% and up to 80% using data augmentation. We finally quantify how the recovered redshifts degrade as one probes to deeper magnitudes past the artificial magnitude limit of the bright training sample. We find that at all apparent magnitudes explored, the use of data augmentation with tree based methods provide a estimate of the galaxy redshift with a negligible bias, although the error on the recovered values increases as we probe to deeper magnitudes. These results have applications for surveys which have a spectroscopic training set which forms a biased sample of all photometric galaxies, for example if the spectroscopic detection magnitude limit is shallower than the photometric limit., Comment: 13 pages, 4 figures, version accepted by MNRAS, added analysis, results and conclusions unchanged

Interaction of charges in CCDs with the already accumulated charge distribution causes both a flux dependence of the point-spread function (an increase of observed size with flux, also known as the brighter/fatter effect) and pixel-to-pixel correlations of the Poissonian noise in flat fields. We describe these effects in the Dark Energy Camera (DECam) with charge dependent shifts of effective pixel borders, i.e. the Antilogus et al. (2014) model, which we fit to measurements of flat-field Poissonian noise correlations. The latter fall off approximately as a power-law r^-2.5 with pixel separation r, are isotropic except for an asymmetry in the direct neighbors along rows and columns, are stable in time, and are weakly dependent on wavelength. They show variations from chip to chip at the 20% level that correlate with the silicon resistivity. The charge shifts predicted by the model cause biased shape measurements, primarily due to their effect on bright stars, at levels exceeding weak lensing science requirements. We measure the flux dependence of star images and show that the effect can be mitigated by applying the reverse charge shifts at the pixel level during image processing. Differences in stellar size, however, remain significant due to residuals at larger distance from the centroid., Comment: typo and formatting fixes, matches version published in JINST

We investigate whether the large scale structure environment of galaxy clusters imprints a selection bias on Sunyaev Zel'dovich (SZ) catalogs. Such a selection effect might be caused by line of sight (LoS) structures that add to the SZ signal or contain point sources that disturb the signal extraction in the SZ survey. We use the Planck PSZ1 union catalog (Planck Collab- oration et al. 2013a) in the SDSS region as our sample of SZ selected clusters. We calculate the angular two-point correlation function (2pcf) for physically correlated, foreground and background structure in the RedMaPPer SDSS DR8 catalog with respect to each cluster. We compare our results with an optically selected comparison cluster sample and with theoretical predictions. In contrast to the hypothesis of no environment-based selection, we find a mean 2pcf for background structures of -0.049 on scales of $\lesssim 40'$, significantly non-zero at $\sim 4 \sigma$, which means that Planck clusters are more likely to be detected in regions of low background density. We hypothesize this effect arises either from background estimation in the SZ survey or from radio sources in the background. We estimate the defect in SZ signal caused by this effect to be negligibly small, of the order of $\sim 10^{-4}$ of the signal of a typical Planck detection. Analogously, there are no implications on X-ray mass measurements. However, the environ- mental dependence has important consequences for weak lensing follow up of Planck galaxy clusters: we predict that projection effects account for half of the mass contained within a 15' radius of Planck galaxy clusters. We did not detect a background underdensity of CMASS LRGs, which also leaves a spatially varying redshift dependence of the Planck SZ selection function as a possible cause for our findings., Comment: 14 pages, 9 figures

We utilize the CLASH (Cluster Lensing And Supernova survey with Hubble) observations of 25 clusters to search for extreme emission-line galaxies (EELGs). The selections are carried out in two central bands: F105W (Y105) and F125W (J125), as the flux of the central bands could be enhanced by the presence of [O III] 4959, 5007 at redshift of about 0.93-1.14 and 1.57-1.79, respectively. The multi-band observations help to constrain the equivalent widths of emission lines. Thanks to cluster lensing, we are able to identify 52 candidates down to an intrinsic limiting magnitude of 28.5 and to a rest-frame [O III] 4959,5007 equivalent width of about 3737 angstrom. Our samples include a number of EELGs at lower luminosities that are missed in other surveys, and the extremely high equivalent width can be only found in such faint galaxies. These EELGs can mimic the dropout feature similar to that of high redshift galaxies and contaminate the color-color selection of high redshift galaxies when the S/N ratio is limited or the band coverage is incomplete. We predict that the fraction of EELGs in the future high redshift galaxy selections cannot be neglected., Comment: 17 pages, 9 figures, 2 tables, Accepted for publication in APJ

We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85 easily measured (or derived) photometric quantities (or `features') and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNN) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18% and decreases the catastrophic outlier rate by 32%. We further compare the redshift estimate using RDF with those from two different aNNs, and with photometric redshifts available from the SDSS. We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a machine learning redshift while reducing both the catastrophic outlier rate by up to 43%, and the redshift error by up to 25%. When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of catastrophic outliers by 57% from 2.32% to 0.99%., Comment: 10 pages, 4 figures, updated to match version accepted in MNRAS