arXiv subjects

P. T. P. Viana

Publications and source records attributed to P. T. P. Viana.

At least 19 recordsLinked to original sources

arXiv2026

A census of Dark Energy Survey Year 3 redMaPPer galaxy clusters in a high exposure eROSITA All Sky Survey region

We present the cross-match of galaxy clusters detected by redMaPPer using the first three years of Dark Energy Survey (DESY3RM) operations, and the first release of the eROSITA-All Sky Survey (eRASS1) taken by eROSITA on board the Spectrum Roentgen Gamma telescope. We define a sky region ($60^{\circ}<{\rm R.A.}<90^{\circ}$ and $-58^{\circ}<{\rm Dec}<-48^{\circ}$) whereby the mean eRASS1 exposure time of clusters falling within this footprint is $\sim$2.5 times longer than the mean exposure time of the full eRASS1--DESY3RM overlap. Limiting to a redshift range $0.2 < z < 0.65$ and redMaPPer estimated richnesses to $>$50, we obtain a sample of 57 clusters. We match these clusters to the cluster catalogue constructed for the eRASS1, finding 45 clusters with a match and 12 with no match in the catalogue. X-ray properties of the 12 unmatched clusters are estimated via a forced photometry analysis. We investigate the form of the luminosity-richness relation, finding the relation using the full 57 cluster sample is statistically consistent with using only the 45 matched clusters, and will require the deeper data of future eRASS releases (or dedicated X-ray follow-up) to explore further potential differences. Using external data from XMM, ACT and SPT, we find detections of 5 of the 12 unmatched clusters, indicating the presence of virialised gas. Finally, we found the 12 unmatched clusters occupy a higher redshift range and lower richness range (compared to the matched clusters), and find no evidence for an underlying physical reason for the lack of X-ray emission.

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arXiv2026

The impact of interpolation in high-resolution spectroscopy -- The overlooked role of interpolation in radial velocity extraction

We explore the impact of spectral interpolation in radial velocity (RV) time-series extracted through template-based methods. We build synthetic datasets with Gaussian profiles to evaluate flux residuals and line asymmetry that are a result from changing the sampling location of the lines. We generate synthetic spectra as a sum of Gaussian functions whose parameters were determined through an observed spectrum. The s-BART pipeline was applied to them, allowing to evaluate any biases in RV extraction introduced by its internal assumptions in line shape. Lastly, we apply the s-BART pipeline to ESPRESSO observations of four stars: two that use high-cadence observations over a single night, and two that have observations spread over multiple nights. When extracting RVs from stellar spectra, we change the interpolation algorithm, used in the process of constructing the stellar template and, afterwards, during RV extraction, comparing them with RVs extracted with a widely-used cubic-spline interpolation. We find that synthetic datasets reveal systematic biases with the largest peak-to-peak amplitudes reaching $\sim$ 20 m/s in low SNR cases, with the amplitude decreasing as the SNR of the spectra increases. In the extreme case of noise-free data, we still recover a systematic bias, albeit at the mm/s level, significantly smaller than the RV precision of state-of-the-art instruments. With real observations we find that those from high-cadence observations with small BERV variation are impacted by the choice of the interpolation algorithm. This impact is smaller in higher-SNR cases, where the peak-to-peak amplitude reaches $\sim$ 1 m/s. In the comparatively lower-SNR case we find peak-to-peak residuals as large as $\sim$ 25 m/s . In cases where the observations are spread over a larger BERV window, we find an upper limit of 20 cm/s of RV scatter for this systematic signal.

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arXiv2026

Exploring the surface of HD 189733 via Doppler shadow analysis of planetary transits

Transmission spectroscopy has greatly advanced the study of exoplanet atmospheres, but stellar surface heterogeneities can contaminate transit spectra. Characterising how stellar spectra vary across the stellar disc is therefore essential to disentangle stellar and planetary contributions. Transit observations can probe the local stellar spectra along the planet's transit chord. We study centre-to-limb variations of line profiles across the surface of HD 189733 using the ESPRESSO spectrograph. Building on previous work, we assess the feasibility of applying the Doppler shadow technique with ESPRESSO and compare the results with solar observations and numerical simulations. We analyse spectra obtained during two transits of HD 189733 b. Each spectrum was cross-correlated with two masks of selected Fe I lines, producing four sets of cross-correlation functions (CCFs). Using a Doppler shadow methodology, we retrieved local stellar profiles along the transit chord. These were compared with previous studies, with disc-resolved solar spectra from IAG ATLAS and with transit simulations generated using SOAPv4 and synthetic spectra from Turbospectrum based on MARCS stellar atmosphere models under LTE and NLTE conditions. For three Fe I CCF sets we detect a statistically significant increase in line depth from stellar limb to centre, consistent with Turbospectrum predictions, although solar data show a weaker gradient. For one CCF set we also find that line widths decrease from limb to centre, consistent with solar observations but not reproduced by the simulations. These results demonstrate the capability of ESPRESSO to measure centre-to-limb variations of spectral line profiles on other stars. While the local CCF profiles of HD 189733 agree with solar data, discrepancies in line widths suggest that additional physical processes are required to reproduce the observed profiles.

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arXiv2026

ASTRA: A Python Package for Cross-Instrument Stellar and Telluric Template Construction

ASTRA is a Python package that provides a modular, instrument-independent interface for working with high-resolution stellar spectra. Designed to support data from multiple spectrographs, including ESPRESSO (Pepe et al., 2021), HARPS (Mayor et al., 2003; Pepe et al., 2002), MAROON-X (Seifahrt et al., 2022), and CARMENES (Quirrenbach et al., 2014). ASTRA offers a unified abstraction over their data formats, enabling consistent access to fluxes, wavelengths, uncertainties, and metadata across instruments. Furthermore, it applies the necessary wavelength and flux calibrations that are needed, as described by the official pipelines of each instrument.

astro-ph.IM↗

arXiv2025

A systematic bias in template-based RV extraction algorithms

In this paper we identify and explore a previously unidentified, multi meter-per-second, systematic correlation between time and RVs inferred through TM and LBL methods. We evaluate the influence of the data-driven stellar template in the RV bias and hypothesize on the possible sources of this effect. We first use the s-BART pipeline to extract RVs from three different datasets gathered over four nights of ESPRESSO and HARPS observations. Then, we demonstrate that the effect can be recovered on a larger sample of 19 targets, totaling 4124 ESPRESSO observations spread throughout 38 nights. We also showcase the presence of the bias in RVs extracted with the SERVAL and ARVE pipelines. Lastly, we explore the construction of the stellar template through the 5 years of ESPRESSO observations of HD10700, totalling more than 2000 observations. We find that a systematic quasi-linear bias affects the RV extraction with slopes that vary from -0.3 m/s-1/h-1 to -52 m/s-1/h-1 in our sample. This trend is not observed in CCF RVs and appears when all observations of a given star are collected within a short time-period (timescales of hours). We show that this systematic contamination exists in the RV time-series of two different template-matching pipelines, one line-by-line pipeline, and that it is agnostic to the spectrograph. We also find that this effect is connected with the construction of the stellar template, as we are able to mitigate it through a careful selection of the observations used to construct it. Our results suggest that a contamination of micro-telluric features, coupled other sources of correlated noise, could be the driving factor of this effect. We also show that this effect does not impact the usual usage of template-matching for the detection and characterization of exoplanets. Other short-timescale science cases can however be severely affected.

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arXiv2024

The XMM Cluster Survey: Automating the estimation of hydrostatic mass for large samples of galaxy clusters I -- Methodology, Validation, & Application to the SDSSRM-XCS sample

We describe features of the X-ray: Generate and Analyse (XGA) open-source software package that have been developed to facilitate automated hydrostatic mass ($M_{\rm hydro}$) measurements from XMM X-ray observations of clusters of galaxies. This includes describing how XGA measures global, and radial, X-ray properties of galaxy clusters. We then demonstrate the reliability of XGA by comparing simple X-ray properties, namely the X-ray temperature and gas mass, with published values presented by the XMM Cluster Survey (XCS), the Ultimate XMM eXtragaLactic survey project (XXL), and the Local Cluster Substructure Survey (LoCuSS). XGA measured values for temperature are, on average, within 1% of the values reported in the literature for each sample. XGA gas masses for XXL clusters are shown to be ${\sim}$10% lower than previous measurements (though the difference is only significant at the $\sim$1.8$σ$ level), LoCuSS $R_{2500}$ and $R_{500}$ gas mass re-measurements are 3% and 7% lower respectively (representing a 1.5$σ$ and 3.5$σ$ difference). Like-for-like comparisons of hydrostatic mass are made to LoCuSS results, which show that our measurements are $10{\pm}3%$ ($19{\pm}7%$) higher for $R_{2500}$ ($R_{500}$). The comparison between $R_{500}$ masses shows significant scatter. Finally, we present new $M_{\rm hydro}$ measurements for 104 clusters from the SDSS DR8 redMaPPer XCS sample (SDSSRM-XCS). Our SDSSRM-XCS hydrostatic mass measurements are in good agreement with multiple literature estimates, and represent one of the largest samples of consistently measured hydrostatic masses. We have demonstrated that XGA is a powerful tool for X-ray analysis of clusters; it will render complex-to-measure X-ray properties accessible to non-specialists.

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arXiv2023

Dark Energy Survey Year 3 Results: Mis-centering calibration and X-ray-richness scaling relations in redMaPPer clusters

We use Dark Energy Survey Year 3 (DES Y3) clusters with archival X-ray data from XMM-Newton and Chandra to assess the centering performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. In terms of centering, we find that 10-20% of redMaPPer clusters are miscentered with no significant difference in bins of low versus high richness ($20<λ<40$ and $λ>40$) or redshift ($0.2<z<0.4$ and $0.4 <z < 0.65$). We also investigate the richness bias induced by miscentering. The dominant reasons for miscentering include masked or missing data and the presence of other bright galaxies in the cluster; for half of the miscentered clusters the correct central was one of the other possible centrals identified by redMaPPer, while for $\sim 40$% of miscentered clusters the correct central is not a redMaPPer member with most of these cases due to masking. In addition, we fit the scaling relations between X-ray temperature and richness and between X-ray luminosity and richness. We find a T$_X$-$λ$ scatter of $0.21 \pm 0.01$. While the scatter in T$_X$-$λ$ is consistent in bins of redshift, we do find modestly different slopes with high-redshift clusters displaying a somewhat shallower relation. Splitting based on richness, we find a marginally larger scatter for our lowest richness bin, $20 < λ< 40$. The X-ray properties of detected, serendipitous clusters are generally consistent with those for targeted clusters, but the depth of the X-ray data for undetected clusters is insufficient to judge whether they are X-ray underluminous in all but one case.

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arXiv2023

The XMM Cluster Survey: Exploring scaling relations and completeness of the Dark Energy Survey Year 3 redMaPPer cluster catalogue

We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of three years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg$^2$, bounded by the area of 4 contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray selected sample is fully matched with entries in the redMaPPer catalogue, above $λ>$20 and within 0.1$< z <$0.9. Conversely, only 38\% of the redMaPPer catalogue is matched to an X-ray extended source. Next, using 120 optically clusters and 184 X-ray selected clusters, we investigate the form of the X-ray luminosity-temperature ($L_{X}-T_{X}$), luminosity-richness ($L_{X}-λ$) and temperature-richness ($T_{X}-λ$) scaling relations. We find that the fitted forms of the $L_{X}-T_{X}$ relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e., $L_{X}-λ$ and $T_{X}-λ$) between the optical and X-ray selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray selected samples compared to optically selected samples.

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arXiv2022

Modelling stellar activity with Gaussian process regression networks

Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of simultaneous time-series, combining the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian Processes. Using HARPS-N solar spectroscopic observations encompassing three years, we demonstrate that this framework is capable of jointly modelling RV data and traditional stellar activity indicators. Although we consider only the simplest GPRN configuration, we are able to describe the behaviour of solar RV data at least as accurately as previously published methods. We confirm the correlation between the RV and stellar activity time series reaches a maximum at separations of a few days, and find evidence of non-stationary behaviour in the time series, associated with an approaching solar activity minimum.

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arXiv2022

The XMM Cluster Survey analysis of the SDSS DR8 redMaPPer Catalogue: Implications for scatter, selection bias, and isotropy in cluster scaling relations

In this paper we present the X-ray analysis of SDSS DR8 redMaPPer (SDSSRM) clusters using data products from the $XMM$ Cluster Survey (XCS). In total, 1189 SDSSRM clusters fall within the $XMM$-Newton footprint. This has yielded 456 confirmed detections accompanied by X-ray luminosity ($L_{X}$) measurements. Of the detected clusters, 382 have an associated X-ray temperature measurement ($T_{X}$). This represents one of the largest samples of coherently derived cluster $T_{X}$ values to date. Our analysis of the X-ray observable to richness ($λ$) scaling relations has demonstrated that scatter in the $T_{X}-λ$ relation is roughly a third of that in the $L_{X}-λ$ relation, and that the $L_{X}-λ$ scatter is intrinsic, i.e. will not be significantly reduced with larger sample sizes. Our analysis of the scaling relation between $L_{X}$ and $T_{X}$ has shown that the fits are sensitive to the selection method of the sample, i.e. whether the sample is made up of clusters detected "serendipitously" compared to those deliberately targeted by $XMM$. These differences are also seen in the $L_{X}-λ$ relation and, to a lesser extent, in the $T_{X}-λ$ relation. Exclusion of the emission from the cluster core does not make a significant impact to the findings. A combination of selection biases is a likely, but as yet unproven, reason for these differences. Finally, we have also used our data to probe recent claims of anisotropy in the $L_{X}-T_{X}$ relation across the sky. We find no evidence of anistropy, but stress that this may be masked in our analysis by the incomplete declination coverage of the SDSS DR8 sample.

astro-ph.CO↗

arXiv2022

Velocity Dispersions of Clusters in the Dark Energy Survey Y3 redMaPPer Catalog

We measure the velocity dispersions of clusters of galaxies selected by the redMaPPer algorithm in the first three years of data from the Dark Energy Survey (DES), allowing us to probe cluster selection and richness estimation, $λ$, in light of cluster dynamics. Our sample consists of 126 clusters with sufficient spectroscopy for individual velocity dispersion estimates. We examine the correlations between cluster velocity dispersion, richness, X-ray temperature and luminosity as well as central galaxy velocity offsets. The velocity dispersion-richness relation exhibits a bimodal distribution. The majority of clusters follow scaling relations between velocity dispersion, richness, and X-ray properties similar to those found for previous samples; however, there is a significant population of clusters with velocity dispersions which are high for their richness. These clusters account for roughly 22\% of the $λ< 70$ systems in our sample, but more than half (55\%) of $λ< 70$ clusters at $z>0.5$. A couple of these systems are hot and X-ray bright as expected for massive clusters with richnesses that appear to have been underestimated, but most appear to have high velocity dispersions for their X-ray properties likely due to line-of-sight structure. These results suggest that projection effects contribute significantly to redMaPPer selection, particularly at higher redshifts and lower richnesses. The redMaPPer determined richnesses for the velocity dispersion outliers are consistent with their X-ray properties, but several are X-ray undetected and deeper data is needed to understand their nature.

astro-ph.CO↗

arXiv2022

A novel framework for semi-Bayesian radial velocities through template matching

The detection and characterization of an increasing variety of exoplanets has been in part possible thanks to the continuous development of high-resolution, stable spectrographs, and using the Doppler radial-velocity (RV) method. The Cross Correlation Function (CCF) method is one of the traditional approaches for RV extraction. More recently, template matching was introduced as an advantageous alternative for M-dwarf stars. In this paper, we describe a new implementation of template matching within a semi-Bayesian framework, providing a more statistically principled characterization of the RV measurements. In this context, a common RV shift is used to describe the difference between each spectral order of a given stellar spectrum and a template built from the available observations. Posterior probability distributions are obtained for the relative RV associated with each spectrum, after marginalizing with respect to the continuum. This methodology was named S-BART: Semi-Bayesian Approach for RVs with Template-matching, and it can be applied to HARPS and ESPRESSO. The application of our method to HARPS archival observations of Barnard's star allowed us to validate our implementation against HARPS-TERRA and SERVAL. Then, we applied it to 33 ESPRESSO targets, evaluating its performance and comparing it with the CCF method. We found a decrease in the median RV scatter of \sim 10\% and \sim 4\% for M- and K-type stars, respectively. S-BART yields more precise RV estimates than the CCF method, particularly in the case of M-type stars where a median uncertainty of \sim 15 cm/s is achieved over 309 observations. Further, we estimated the nightly zero point (NZP) of ESPRESSO, finding a weighted NZP scatter below \sim 0.7 m/s. As this includes stellar variability, photon noise, and potential planetary signals, it should be taken as an upper limit of the RV precision attainable with ESPRESSO data.

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arXiv2021

The XMM Cluster Survey: An independent demonstration of the fidelity of the eFEDS galaxy cluster data products and implications for future studies

We present the first comparison between properties of clusters of galaxies detected by the eROSITA Final Equatorial-Depth Survey (eFEDS) and the XMM Cluster Survey (XCS). We have compared, in an ensemble fashion, properties from the eFEDS X-ray cluster catalogue with those from the Ultimate XMM eXtragaLactic (XXL) survey project (XXL-100-GC). We find the distributions of redshift and X-ray temperature ($T_{\rm X}$) to be broadly similar between the two surveys, with a larger proportion of clusters above 4 keV in the XXL-100-GC sample. We find 62 eFEDS cluster candidates with XMM data (eFEDS-XMM sample); 10 do not have good enough XMM data to confirm or deny, 11 are classed as sample contaminants, and 4 have their X-ray flux contaminated by another source. The majority of eFEDS-XMM sources have a longer exposure in XMM than eFEDS, and the majority of eFEDS positions are within 100 kpc of XCS positions. Our eFEDS-XCS sample of 37 clusters is used to calculate minimum sample contamination fractions of ${\sim}$18% and ${\sim}$9% in the eFEDS X-ray and optically confirmed samples respectively, in general agreement with eFEDS findings. We compare 29 X-ray luminosities ($L_{\rm X}$) measured by eFEDS and XCS, which are in excellent agreement. Eight clusters have a $T_{\rm X}$ measured by XMM and eROSITA, and we find that XMM temperatures are 25$\pm$9% larger than their eROSITA counterparts. Finally, we construct $L_{\rm X}$ - $T_{\rm X}$ scaling relations based on eFEDS and XCS measurements, which are in tension; the tension is decreased when we measure a third scaling relation with calibrated XCS temperatures.

astro-ph.CO↗

arXiv2020

The XMM Cluster Survey: new evidence for the 3.5 keV feature in clusters is inconsistent with a dark matter origin

There have been several reports of a detection of an unexplained excess of X-ray emission at $\simeq$ 3.5 keV in astrophysical systems. One interpretation of this excess is the decay of sterile neutrino dark matter. The most influential study to date analysed 73 clusters observed by the XMM-Newton satellite. We explore evidence for a $\simeq$ 3.5 keV excess in the XMM-PN spectra of 117 redMaPPer galaxy clusters ($0.1 < z < 0.6$). In our analysis of individual spectra, we identify three systems with an excess of flux at $\simeq$ 3.5 keV. In one case (XCS J0003.3+0204) this excess may result from a discrete emission line. None of these systems are the most dark matter dominated in our sample. We group the remaining 114 clusters into four temperature ($T_{\rm X}$) bins to search for an increase in $\simeq$ 3.5 keV flux excess with $T_{\rm X}$ - a reliable tracer of halo mass. However, we do not find evidence of a significant excess in flux at $\simeq$ 3.5 keV in any $T_{\rm X}$ bins. To maximise sensitivity to a potentially weak dark matter decay feature at $\simeq$ 3.5 keV, we jointly fit 114 clusters. Again, no significant excess is found at $\simeq$ 3.5 keV. We estimate the upper limit of an undetected emission line at $\simeq$ 3.5 keV to be $2.41 \times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$, corresponding to a mixing angle of $\sin^2(2θ)=4.4 \times 10^{-11}$, lower than previous estimates from cluster studies. We conclude that a flux excess at $\simeq$ 3.5 keV is not a ubiquitous feature in clusters and therefore unlikely to originate from sterile neutrino dark matter decay.

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arXiv2019

Decoding the radial velocity variations of HD41248 with ESPRESSO

Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD41248 is an interesting planet-host candidate, with RV observations plagued by activity-induced signals. We report on ESPRESSO observations of HD41248 and analyse them together with previous observations from HARPS with the goal of evaluating the presence of orbiting planets. Using different noise models within a general Bayesian framework designed for planet detection in RV data, we test the significance of the various signals present in the HD41248 dataset. We use Gaussian processes as well as a first-order moving average component to try to correct for activity-induced signals. At the same time, we analyse photometry from the TESS mission, searching for transits and rotational modulation in the light curve. The number of significantly detected Keplerian signals depends on the noise model employed, which can range from 0 with the Gaussian process model to 3 with a white noise model. We find that the Gaussian process alone can explain the RV data while allowing for the stellar rotation period and active region evolution timescale to be constrained. The rotation period estimated from the RVs agrees with the value determined from the TESS light curve. Based on the data that is currently available, we conclude that the RV variations of HD41248 can be explained by stellar activity (using the Gaussian process model) in line with the evidence from activity indicators and the TESS photometry.

astro-ph.EP↗

arXiv2019

Stellar mass as a galaxy cluster mass proxy: application to the Dark Energy Survey redMaPPer clusters

We introduce a galaxy cluster mass observable, $μ_\star$, based on the stellar masses of cluster members, and we present results for the Dark Energy Survey (DES) Year 1 observations. Stellar masses are computed using a Bayesian Model Averaging method, and are validated for DES data using simulations and COSMOS data. We show that $μ_\star$ works as a promising mass proxy by comparing our predictions to X-ray measurements. We measure the X-ray temperature-$μ_\star$ relation for a total of 150 clusters matched between the wide-field DES Year 1 redMaPPer catalogue, and Chandra and XMM archival observations, spanning the redshift range $0.1<z<0.7$. For a scaling relation which is linear in logarithmic space, we find a slope of $α= 0.488\pm0.043$ and a scatter in the X-ray temperature at fixed $μ_\star$ of $σ_{{\rm ln} T_X|μ_\star}=0.266^{+0.019}_{-0.020}$ for the joint sample. By using the halo mass scaling relations of the X-ray temperature from the Weighing the Giants program, we further derive the $μ_\star$-conditioned scatter in mass, finding $σ_{{\rm ln} M|μ_\star}=0.26^{+ 0.15}_{- 0.10}$. These results are competitive with well-established cluster mass proxies used for cosmological analyses, showing that $μ_\star$ can be used as a reliable and physically motivated mass proxy to derive cosmological constraints.

astro-ph.CO↗

arXiv2019

Galaxies in X-ray Selected Clusters and Groups in Dark Energy Survey Data II: Hierarchical Bayesian Modeling of the Red-Sequence Galaxy Luminosity Function

Using $\sim 100$ X-ray selected clusters in the Dark Energy Survey Science Verification data, we constrain the luminosity function (LF) of cluster red sequence galaxies as a function of redshift. This is the first homogeneous optical/X-ray sample large enough to constrain the evolution of the luminosity function simultaneously in redshift ($0.1<z<1.05$) and cluster mass ($13.5 \le \rm{log_{10}}(M_{200crit}) \sim< 15.0$). We pay particular attention to completeness issues and the detection limit of the galaxy sample. We then apply a hierarchical Bayesian model to fit the cluster galaxy LFs via a Schecter function, including its characteristic break ($m^*$) to a faint end power-law slope ($α$). Our method enables us to avoid known issues in similar analyses based on stacking or binning the clusters. We find weak and statistically insignificant ($\sim 1.9 σ$) evolution in the faint end slope $α$ versus redshift. We also find no dependence in $α$ or $m^*$ with the X-ray inferred cluster masses. However, the amplitude of the LF as a function of cluster mass is constrained to $\sim 20\%$ precision. As a by-product of our algorithm, we utilize the correlation between the LF and cluster mass to provide an improved estimate of the individual cluster masses as well as the scatter in true mass given the X-ray inferred masses. This technique can be applied to a larger sample of X-ray or optically selected clusters from the Dark Energy Survey, significantly improving the sensitivity of the analysis.

astro-ph.CO↗

arXiv2019

Dark Energy Survey Year 1 Results: Calibration of Cluster Mis-centering in the redMaPPer Catalogs

The center determination of a galaxy cluster from an optical cluster finding algorithm can be offset from theoretical prescriptions or $N$-body definitions of its host halo center. These offsets impact the recovered cluster statistics, affecting both richness measurements and the weak lensing shear profile around the clusters. This paper models the centering performance of the \RM~cluster finding algorithm using archival X-ray observations of \RM-selected clusters. Assuming the X-ray emission peaks as the fiducial halo centers, and through analyzing their offsets to the \RM~centers, we find that $\sim 75\pm 8 \%$ of the \RM~clusters are well centered and the mis-centered offset follows a Gamma distribution in normalized, projected distance. These mis-centering offsets cause a systematic underestimation of cluster richness relative to the well-centered clusters, for which we propose a descriptive model. Our results enable the DES Y1 cluster cosmology analysis by characterizing the necessary corrections to both the weak lensing and richness abundance functions of the DES Y1 redMaPPer cluster catalog.

astro-ph.CO↗