Exploiting the full potential of photometric quasar surveys: Optimal power spectra through blind mitigation of systematics

We present optimal measurements of the angular power spectrum of the XDQSOz catalogue of photometric quasars from the Sloan Digital Sky Survey. These measurements rely on a quadratic maximum likelihood estimator that simultaneously measures the auto- and cross-power spectra of four redshift samples, and provides minimum-variance, unbiased estimates even at the largest angular scales. Since photometric quasars are known to be strongly affected by systematics such as spatially-varying depth and stellar contamination, we introduce a new framework of extended mode projection to robustly mitigate the impact of systematics on the power spectrum measurements. This technique involves constructing template maps of potential systematics, decorrelating them on the sky, and projecting out modes which are significantly correlated with the data. Our method is able to simultaneously process several thousands of nonlinearly-correlated systematics, and mode projection is performed in a blind fashion. Using our final power spectrum measurements, we find a good agreement with theoretical predictions, and no evidence for further contamination by systematics. Extended mode projection not only obviates the need for aggressive sky and quality cuts, but also provides control over the level of systematics in the measurements, enabling the search for small signals of new physics while avoiding confirmation bias.Comment: 13 pages, 8 figures. v2: version accepted by MNRAS. v3: systematics templates publicly available on www.earlyuniverse.org/code, no change to pape

Internal Robustness: systematic search for systematic bias in SN Ia data

A great deal of effort is currently being devoted to understanding, estimating and removing systematic errors in cosmological data. In the particular case of type Ia supernovae, systematics are starting to dominate the error budget. Here we propose a Bayesian tool for carrying out a systematic search for systematic contamination. This serves as an extension to the standard goodness-of-fit tests and allows not only to cross-check raw or processed data for the presence of systematics but also to pin-point the data that are most likely contaminated. We successfully test our tool with mock catalogues and conclude that the Union2.1 data do not possess a significant amount of systematics. Finally, we show that if one includes in Union2.1 the supernovae that originally failed the quality cuts, our tool signals the presence of systematics at over 3.8-sigma confidence level.Comment: 14 pages, 15 figures; matches version accepted for publication in MNRA

Characterizing unknown systematics in large scale structure surveys

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.Comment: 24 pages, 6 figures; Expanded discussion of results, added figure 2; Version to be published in JCA

Systematic Bias in Cosmic Shear: Beyond the Fisher Matrix

We describe a method for computing the biases that systematic signals introduce in parameter estimation using a simple extension of the Fisher matrix formalism. This allows us to calculate the offset of the best fit parameters relative to the fiducial model, in addition to the usual statistical error ellipse. As an application, we study the impact that residual systematics in tomographic weak lensing measurements. In particular we explore three different types of shape measurement systematics: (i) additive systematic with no redshift evolution; (ii) additive systematic with redshift evolution; and (iii) multiplicative systematic. In each case, we consider a wide range of scale dependence and redshift evolution of the systematics signal. For a future DUNE-like full sky survey, we find that, for cases with mild redshift evolution, the variance of the additive systematic signal should be kept below 10^-7 to ensure biases on cosmological parameters that are sub-dominant to the statistical errors. For the multiplicative systematics, which depends on the lensing signal, we find the multiplicative calibration m0 needs to be controlled to an accuracy better than 10^-3. We find that the impact of systematics can be underestimated if their assumes redshift dependence is too simplistic. We provide simple scaling relations to extend these requirements to any survey geometry and discuss the impact of our results for current and future weak lensing surveys.Comment: Submitted to MNRAS. 11 pages, including 11 figures and 4 table

Systematics of 2+ states in semi-magic nuclei

We propose a simple systematics of low lying 2+ energy levels and electromagnetic transitions in semi-magic isotopic chains Z=28,50,82 and isotonic chains N=28,50,82,126. To this purpose we use a two-level pairing plus quadrupole Hamiltonian, within the spherical Quasiparticle Random Phase Approximation (QRPA). We derive a simple relation connecting the 2+ energy with the pairing gap and quadrupole-quadupole (QQ) interaction strength. It turns out that the systematics of energy levels and B(E2) values predicted by this simple model is fulfilled with a reasonable accuracy by all available experimental data. Both systematics suggest that not only active nucleons but also those filling closed shells play an important role

Probing the cosmic acceleration from combinations of different data sets

We examine in some detail the influence of the systematics in different data sets including type Ia supernova sample, baryon acoustic oscillation data and the cosmic microwave background information on the fitting results of the Chevallier-Polarski-Linder parametrization. We find that the systematics in the data sets does influence the fitting results and leads to different evolutional behavior of dark energy. To check the versatility of Chevallier-Polarski-Linder parametrization, we also perform the analysis on the Wetterich parametrization of dark energy. The results show that both the parametrization of dark energy and the systematics in data sets influence the evolutional behavior of dark energy.Comment: 15 pages, 5 figures and 1 table, major revision, delete bao a data, main results unchanged. jcap in press

Bayesian Methods for Exoplanet Science

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.Comment: Invited revie