A Study of Hadronic Backgrounds to Isolated Hard Photon Production with L3

I describe two methods for studying hadronic backgrounds to prompt photon production with L3, and compare the observed background rates with Monte Carlo predictions. I find that the Monte Carlo models JETSET and HERWIG underestimate the production of isolated neutral hadrons in hadronic Z decays at LEP. By extrapolating results obtained with L3, I estimate that the rate of prompt-photon + jet background to a H -> gamma gamma search at the LHC will be larger than Monte Carlo predictions by a factor of 1.5-2.5.Comment: 4 page

Batch-Incremental Learning for Mining Data Streams

The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, a model may only use a constant amount of memory, and must be ready for prediction at any point in time. We attempt to overcome these restrictions by presenting a data stream classification algorithm where the data is split into a stream of disjoint batches. Single batches of data can be processed one after the other by any standard non-incremental learning algorithm. Our approach uses ensembles of decision trees. These tree ensembles are iteratively merged into a single interpretable model of constant maximal size. Using benchmark datasets the algorithm is evaluated for accuracy against state-of-the-art algorithms that make use of the entire dataset

Model independent inference of the expansion history and implications for the growth of structure

We model the expansion history of the Universe as a Gaussian Process and find constraints on the dark energy density and its low-redshift evolution using distances inferred from the Luminous Red Galaxy (LRG) and Lyman-alpha (Ly$\alpha$) datasets of the Baryon Oscillation Spectroscopic Survey, supernova data from the Joint Light-curve Analysis (JLA) sample, Cosmic Microwave Background (CMB) data from the Planck satellite, and local measurement of the Hubble parameter from the Hubble Space Telescope ($\mathsf H0$). Our analysis shows that the CMB, LRG, Ly$\alpha$, and JLA data are consistent with each other and with a $\Lambda$CDM cosmology, but the ${\mathsf H0}$ data is inconsistent at moderate significance. Including the presence of dark radiation does not alleviate the ${\mathsf H0}$ tension in our analysis. While some of these results have been noted previously, the strength here lies in that we do not assume a particular cosmological model. We calculate the growth of the gravitational potential in General Relativity corresponding to these general expansion histories and show that they are well-approximated by $\Omega_{\rm m}^{0.55}$ given the current precision. We assess the prospects for upcoming surveys to measure deviations from $\Lambda$CDM using this model-independent approach.Comment: 13 pages, 7 figures, code available at: https://github.com/dkirkby/gphis

Implications of a transition in the dark energy equation of state for the H0H_0 and σ8\sigma_8 tensions

We explore the implications of a rapid appearance of dark energy between the redshifts ($z$) of one and two on the expansion rate and growth of perturbations. Using both Gaussian process regression and a parameteric model, we show that this is the preferred solution to the current set of low-redshift ($z<3$) distance measurements if $H_0=73~\rm km\,s^{-1}\,Mpc^{-1}$ to within 1\% and the high-redshift expansion history is unchanged from the $\Lambda$CDM inference by the Planck satellite. Dark energy was effectively non-existent around $z=2$, but its density is close to the $\Lambda$CDM model value today, with an equation of state greater than $-1$ at $z<0.5$. If sources of clustering other than matter are negligible, we show that this expansion history leads to slower growth of perturbations at $z<1$, compared to $\Lambda$CDM, that is measurable by upcoming surveys and can alleviate the $\sigma_8$ tension between the Planck CMB temperature and low-redshift probes of the large-scale structure.Comment: 24 pages, 16 figure

ON JUDGEMENT: PSYCHOLOGICAL GENESIS, INTENTIONALITY AND GRAMMAR

This thesis explores conceptions of judgement which have been central to various philosophical and scientific traditions. Beginning with Hume, I situate his conception of judgement within his overarching constructivist program, his science of man. Defending Hume from criticism regarding the naturalistic credentials of this program, I argue that Hume’s science of man, along with the conception of judgement which is integral to it, is appropriately understood as a forerunner to contemporary cognitive science. Despite this, I contend that Hume’s conception of judgement prompts a problem regarding the intentionality of judgement – a problem which he does not adequately address. In the second part of my thesis I show how the intentionality problem which Hume grapples with is also crucial, constituting a point of departure, for Kant’s transcendental undertaking. Following Kant’s reasoning, I illustrate how an original concern with this intentionality issue leads Kant to a distinct conception of judgement, according to which concepts only exist in the context of a judgement. Having arrived at Kant’s conception of a judgement, the remainder of the thesis is devoted to the issue of judgement forms. Kant’s postulation of these forms is closely related to his conception of judgement, and I seek to establish both how these forms ought to be understood and how they might be derived. In relation to this latter issue, I suggest that there may a role for contemporary work in Generative Grammar. Specifically, I suggest that it may be viable to understand the forms of judgement as grammatical in nature, thereby securing an interdisciplinary connection between a philosophy of judgement and the empirical investigation of grammar

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

In Stage-IV imaging surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. The cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a framework based on the Fisher formalism to analyze effects of overlapping sources (blending) on the estimation of cosmic shear. For the Rubin Observatory Legacy Survey of Space and Time (LSST), we present the expected loss in statistical sensitivity for the ten-year survey due to blending. We find that for approximately 62% of galaxies that are likely to be detected in full-depth LSST images, at least 1% of the flux in their pixels is from overlapping sources. We also find that the statistical correlations between measures of overlapping galaxies and, to a much lesser extent the higher shot noise level due to their presence, decrease the effective number density of galaxies, $N_{eff}$, by $\sim$18%. We calculate an upper limit on $N_{eff}$ of 39.4 galaxies per arcmin$^2$ in $r$ band. We study the impact of varying stellar density on $N_{eff}$ and illustrate the diminishing returns of extending the survey into lower Galactic latitudes. We extend the Fisher formalism to predict the increase in pixel-noise bias due to blending for maximum-likelihood (ML) shape estimators. We find that noise bias is sensitive to the particular shape estimator and measure of ensemble-average shape that is used, and properties of the galaxy that include redshift-dependent quantities such as size and luminosity.Comment: Accepted for publication in JCAP. 45 pages, 19 figure