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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2019)

In the late 1990s, precise distance measurements with Type Ia supernovae (SNe Ia) were used to show that the expansion of the universe is accelerating. One possibility is that this accelerated expansion is due to an additional form of energy referred to as “dark energy” which contributes roughly 70% of the total energy in the present day universe. The properties of dark energy are not currently well-constrained, and a wide range of different cosmological probes are currently being designed to explore the fundamental nature of the accelerated expansion of the universe. SNe Ia have remained one of the strongest cosmological probes, and upcoming experiments such as the Large Synoptic Survey Telescope (LSST) are expected to discover over 100,000 SNe Ia that can be used for cosmology. The uncertainties on cosmological parameters derived from these large samples of SNe Ia will be entirely dominated by the systematic uncertainties of distance measurements to SNe Ia. In this dissertation, we discuss several different methods of improving the systematic uncertainties in distance measurements to SNe Ia.

This dissertation is split into three main chapters each discussing how to improve a different aspect of distance measurements to SNe Ia. In Chapter 2, we examine how instrumental calibration can affect these distance measurements, and discuss a new anomalous behaviour of CCD readout electronics related to the binary encoding of pixel values that affects most astronomical instruments currently in use. For the Nearby Supernova Factory, this anomaly introduces a dispersion in the measured B-band/U-band magnitudes of 0.11 mag/0.51 mag for the faintest 20% of measurements.

Another major source of systematic uncertainty in distance measurements to SNe Ia is intrinsic variation of the SNe Ia. In Chapter 3, we develop a new method of parametrizing SNe Ia using manifold learning to generate a non-linear decomposition of the intrinsic diversity of their spectra near maximum light. We identify regions of the parameter space of SNe Ia where previous standardization methods such as SALT2 have biases of up to 0.3 mag, and show how correlations between host galaxy properties and distance estimates are greatly reduced when standardizing SNe Ia using our new parametrization.

Finally, in Chapter 4, we discuss how upcoming surveys such as LSST will need to rely on photometric classification to identify the majority of the transients that they discover, which means that samples of SNe Ia used for cosmology will be contaminated with other types of transients. We developed a set of techniques for photometric classification to address the fact that spectroscopic subsamples used for training classifiers are typically highly biased compared to the full samples of transients and variables that will be discovered. Using these techniques, we built a photometric classifier that won the PLAsTiCC photometric classification challenge out of 1,094 competing teams.

    Cover page: Improving Cosmological Distance Measurements with Type Ia Supernovae: From Pixels to Dark Energy
    • Article
    • Peer Reviewed
    LBL Publications (2021)
    In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-m Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching rAB ≈ 22.5 mag. We run our simulations with the software SNMACHINE, a photometric classification pipeline using machine learning. The machine-learning algorithms struggle to classify supernovae when the training sample is magnitude limited, in contrast to representative training samples. Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint high-redshift supernovae observed from larger spectroscopic facilities; the algorithms’ range of average area under receiver operator characteristic curve (AUC) scores over 10 runs increases from 0.547–0.628 to 0.946–0.969 and purity of the classified sample reaches 95 per cent in all runs for two of the four algorithms. By creating new, artificial light curves using the augmentation software AVOCADO, we achieve a purity in our classified sample of 95 per cent in all 10 runs performed for all machine-learning algorithms considered. We also reach a highest average AUC score of 0.986 with the artificial neural network algorithm. Having ‘true’ faint supernovae to complement our magnitude-limited sample is a crucial requirement in optimization of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.
      Cover page: Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2018)
      Hot, dust-obscured galaxies, or "Hot DOGs," are a rare, dusty, hyperluminous galaxy population discovered by the WISE mission. Predominantly at redshifts 2-3, they include the most luminous known galaxies in the universe. Their high luminosities likely come from accretion onto highly obscured supermassive black holes (SMBHs). We have conducted a pilot survey to measure the SMBH masses of five z ∼ 2 Hot DOGs via broad Hα emission lines, using Keck/MOSFIRE and Gemini/FLAMINGOS-2. We detect broad Hα emission in all five Hot DOGs. We find substantial corresponding SMBH masses for these Hot DOGs (∼ 109 M⊙), and their derived Eddington ratios are close to unity. These z ∼ 2 Hot DOGs are the most luminous active galactic nuclei for their BH masses, suggesting that they are accreting at the maximum rates for their BHs. A similar property is found for known z ∼ 6 quasars. Our results are consistent with scenarios in which Hot DOGs represent a transitional, high-accretion phase between obscured and unobscured quasars. Hot DOGs may mark a special evolutionary stage before the red quasar and optical quasar phases, and they may be present at other cosmic epochs.
        Cover page: Eddington-limited Accretion in z ∼ 2 WISE-selected Hot, Dust-obscured Galaxies
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2021)
        The See Change survey was designed to make z > 1 cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope (HST) spanning the redshift range z = 1.13-1.75, discovering 57 likely transients and 27 likely SNe Ia at z ∼ 0.8-2.3. As in similar previous surveys, this proved to be a highly efficient use of HST for supernova observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star formation rates. We find that the resulting number of SNe Ia per orbit is a factor of ∼8 higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and supernova discoveries. Novel features include fully blinded supernova searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 mag of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.
          Cover page: The HST See Change Program. I. Survey Design, Pipeline, and Supernova Discoveries* *Based on observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555, under programs 13677, 14327.
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2015)
          The bacteriophage population is large, dynamic, ancient, and genetically diverse. Limited genomic information shows that phage genomes are mosaic, and the genetic architecture of phage populations remains ill-defined. To understand the population structure of phages infecting a single host strain, we isolated, sequenced, and compared 627 phages of Mycobacterium smegmatis. Their genetic diversity is considerable, and there are 28 distinct genomic types (clusters) with related nucleotide sequences. However, amino acid sequence comparisons show pervasive genomic mosaicism, and quantification of inter-cluster and intra-cluster relatedness reveals a continuum of genetic diversity, albeit with uneven representation of different phages. Furthermore, rarefaction analysis shows that the mycobacteriophage population is not closed, and there is a constant influx of genes from other sources. Phage isolation and analysis was performed by a large consortium of academic institutions, illustrating the substantial benefits of a disseminated, structured program involving large numbers of freshman undergraduates in scientific discovery.
            Cover page: Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity
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