Novel methods of supernova classification and type probability estimation

Future photometric surveys will provide vastly more supernovae than have presently been observed, the majority of which will not be spectroscopically typed. Key to extracting information from these future datasets will be the efficient use of light-curves. In the first part of this thesis we introduce two methods for distinguishing type Ia supernovae from their contaminating counterparts, kernel density estimation and boosting. In the second half of this thesis we shift focus from classification to the related problem of type probability estimation, and ask how best to use type probabilities

Towards the Future of Supernova Cosmology

For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the data, but we show that the resulting non-Ia contamination can lead to biases in the estimation of cosmological parameters. A different method, which allows the use of the full dataset and results in unbiased cosmological parameter estimation, is Bayesian Estimation Applied to Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which includes the uncertainty in the types in the evaluation of the posterior. Here we outline the theory of BEAMS and demonstrate its effectiveness using both simulated datasets and SDSS-II data. We also show that it is possible to use BEAMS if the data are correlated, by introducing a numerical marginalisation over the types of the objects. This is largely a pedagogical introduction to BEAMS with references to the main BEAMS papers.Comment: Replaced under married name Lochner (formally Knights). 3 pages, 2 figures. To appear in the Proceedings of 13th Marcel Grossmann Meeting (MG13), Stockholm, Sweden, 1-7 July 201

Extending BEAMS to incorporate correlated systematic uncertainties

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2^N terms, where N is the number of supernova candidates. This `exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, \tau_i, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2^N problem into an N^2 or N^3 one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.Comment: Resubmitted under married name Lochner (formally Knights). Version 3: major changes, including a large scale analysis with thousands of MCMC chains. Matches version published in JCAP. 23 pages, 8 figure

Fast K-Means with Accurate Bounds

We propose a novel accelerated exact $k$-means algorithm, which outperforms the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3$\times$ faster. We also propose a general improvement of existing state-of-the-art accelerated exact $k$-means algorithms through better estimates of the distance bounds used to reduce the number of distance calculations, obtaining speedups in 36 of 44 experiments, of up to 1.8$\times$. We have conducted experiments with our own implementations of existing methods to ensure homogeneous evaluation of performance, and we show that our implementations perform as well or better than existing available implementations. Finally, we propose simplified variants of standard approaches and show that they are faster than their fully-fledged counterparts in 59 of 62 experiments

Photometric Supernova Cosmology with BEAMS and SDSS-II

Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either biased due to contamination or have significantly larger contours in the cosmological parameters due to small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (\Omega_m,\Omega_\Lambda) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are \Omega_m(BEAMS)=0.194\pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.Comment: 25 pages, 15 figures, submitted to Ap

Epidermal growth factor can signal via β-catenin to control proliferation of mesenchymal stem cells independently of canonical Wnt signalling

Bone marrow mesenchymal stem/stromal cells (MSCs) maintain bone homeostasis and repair through the ability to expand in response to mitotic stimuli and differentiate into skeletal lineages. Signalling mechanisms that enable precise control of MSC function remain unclear. Here we report that by initially examining differences in signalling pathway expression profiles of individual MSC clones, we identified a previously unrecognised signalling mechanism regulated by epidermal growth factor (EGF) in primary human MSCs. We demonstrate that EGF is able to activate -catenin, a key component of the canonical Wnt signalling pathway. EGF is able to induce nuclear translocation of - catenin in human MSCs but does not drive expression of Wnt target genes or T cell factor (TCF) activity in MSC reporter cell lines. Using an efficient Design of Experiments (DoE) statistical analysis, with different combinations and concentrations of EGF and Wnt ligands, we were able to confirm that EGF does not influence the Wnt/-catenin pathway in MSCs. We show that the effects of EGF on MSCs are temporally regulated to initiate early “classical” EGF signalling mechanisms (e.g via mitogen activated protein kinase) with delayed activation of -catenin. By RNA-sequencing, we identified gene sets that were exclusively regulated by the EGF/-catenin pathway, which were distinct from classical EGF-regulated genes. However, subsets of classical EGF gene targets were significantly influenced by EGF/-catenin activation. These signalling pathways cooperate to enable EGF-mediated proliferation of MSCs by alleviating the suppression of cell cycle pathways induced by classical EGF signallin

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.Comment: accepted by PAS

miR-132 suppresses transcription of ribosomal proteins to promote protective Th1 immunity

Determining the mechanisms that distinguish protective immunity from pathological chronic inflammation remains a fundamental challenge. miR-132 has been shown to play largely immunoregulatory roles in immunity, however its role in CD4+ T cell function is poorly understood. Here, we show that CD4+ 38 T cells express high levels of miR-132 and that T cell activation leads to miR-132 upregulation. The transcriptomic hallmark of splenic CD4+ 40 T cells lacking the miR 132/212 cluster during chronic infection is an increase in mRNAs levels of ribosomal protein (RP) genes. BTAF1, a co-factor of B-TFIID and novel miR132/212-3p target, and p300 contribute towards miR-132/212-mediated regulation of RP transcription. Following infection with Leishmania donovani miR-132-/- CD4+ T cells display enhanced expression of IL-10 and decreased IFNg. This is associated with reduced hepatosplenomegaly and enhanced pathogen load. The enhanced IL-10 expression in miR-132-/- Th1 cells is recapitulated in vitro following treatment with phenylephrine, a drug reported to promote ribosome synthesis. Our results uncover that miR-132/212-mediated regulation of RP expression is critical for optimal CD4+ 50 T cell activation and protective immunity against pathogen

Integrated miRNA/cytokine/chemokine profiling reveals severity-associated step changes and principal correlates of fatality in COVID-19

Inflammatory cytokines and chemokines (CC) drive COVID-19 pathology. Yet, patients with similar circulating CC levels present with different disease severity. Here, we determined 171 microRNAomes from 58 hospitalized COVID-19 patients (Cohort 1) and levels of 25 cytokines and chemokines (CC) in the same samples. Combining microRNA (miRNA) and CC measurements allowed for discrimination of severe cases with greater accuracy than using miRNA or CC levels alone. Severity group-specific associations between miRNAs and COVID-19-associated CC (e.g., IL6, CCL20) or clinical hallmarks of COVID-19 (e.g., neutrophilia, hypoalbuminemia) separated patients with similar CC levels but different disease severity. Analysis of an independent cohort of 108 patients from a different center (Cohort 2) demonstrated feasibility of CC/miRNA profiling in leftover hospital blood samples with similar severe disease CC and miRNA profiles, and revealed CCL20, IL6, IL10, and miR-451a as key correlates of fatal COVID-19. These findings highlight that systemic miRNA/CC networks underpin severe COVID-19