arXiv subjects

Masao Sako

Publications and source records attributed to Masao Sako.

At least 37 records · Page 2Linked to original sources

arXiv2022

The Pantheon+ Analysis: Cosmological Constraints

We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from $z=0.001$ to 2.26. This work features an increased sample size, increased redshift span, and improved treatment of systematic uncertainties in comparison to the original Pantheon analysis and results in a factor of two improvement in cosmological constraining power. For a Flat$Λ$CDM model, we find $Ω_M=0.334\pm0.018$ from SNe Ia alone. For a Flat$w_0$CDM model, we measure $w_0=-0.90\pm0.14$ from SNe Ia alone, H$_0=73.5\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including the Cepheid host distances and covariance (SH0ES), and $w_0=-0.978^{+0.024}_{-0.031}$ when combining the SN likelihood with constraints from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO); both $w_0$ values are consistent with a cosmological constant. We also present the most precise measurements to date on the evolution of dark energy in a Flat$w_0w_a$CDM universe, and measure $w_a=-0.1^{+0.9}_{-2.0}$ from Pantheon+ alone, H$_0=73.3\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including SH0ES, and $w_a=-0.65^{+0.28}_{-0.32}$ when combining Pantheon+ with CMB and BAO data. Finally, we find that systematic uncertainties in the use of SNe Ia along the distance ladder comprise less than one third of the total uncertainty in the measurement of H$_0$ and cannot explain the present "Hubble tension" between local measurements and early-Universe predictions from the cosmological model.

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arXiv2022

A Convolutional Neural Network Approach to Supernova Time-Series Classification

One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically unfeasible. Thus, the ability to identify SNe by type using time-series image data alone is crucial, especially in light of the increasing breadth and depth of upcoming telescopes. We present a convolutional neural network method for fast supernova time-series classification, with observed brightness data smoothed in both the wavelength and time directions with Gaussian process regression. We apply this method to full duration and truncated SN time-series, to simulate retrospective as well as real-time classification performance. Retrospective classification is used to differentiate cosmologically useful Type Ia SNe from other SN types, and this method achieves >99% accuracy on this task. We are also able to differentiate between 6 SN types with 60% accuracy given only two nights of data and 98% accuracy retrospectively.

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arXiv2022

SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO-Virgo Event GW190814

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.

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arXiv2021

Aperture-corrected spectroscopic type Ia supernova host galaxy properties

We use type Ia supernovae (SNe Ia) data obtained by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II/SNe) in combination with the publicly available SDSS DR16 fiber spectroscopy of their host galaxies to correlate SNe Ia light-curve parameters and Hubble residuals to several host galaxy properties. Fixed-aperture fiber spectroscopy suffers from aperture effects: the fraction of the galaxy covered by the fiber varies depending on its projected size on the sky, thus measured properties are not representative of the whole galaxy. The advent of Integral Field Spectroscopy has provided a way for correcting the missing light, by studying how these galaxy parameters change with the aperture size. Here we study how the standard SN host galaxy relations change once global host galaxy parameters are corrected for aperture effects. We recover previous trends on SN Hubble residuals with host galaxy properties, but we find that discarding objects with poor fiber coverage instead of correcting for aperture loss introduces biases in the sample that affect SN host galaxy relations. The net effect of applying the commonly used $g$-band fraction criterion is discarding intrinsically faint \mbox{SNe~Ia} in high-mass galaxies, thus artificially increasing the height of the mass step by 0.02 mag and its significance. Current and next generation of fixed-aperture fiber spectroscopy surveys, such as DES, DESI or TiDES in 4MOST, that aim at study SN and galaxy correlations must consider, and correct for, these effects.

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arXiv2021

Photometric Classification of Early-Time Supernova Lightcurves with SCONE

In this work, we present classification results on early supernova lightcurves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using lightcurve data. SCONE is able to identify SN types from lightcurves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe lightcurves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength-time heatmaps. SCONE uses these heatmaps to perform 6-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r<20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% 5 days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package located at github.com/helenqu/scone.

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arXiv2021

A search of the full six years of the Dark Energy Survey for outer Solar System objects

We present the results of a search for outer Solar System objects in the full six years of data (Y6) from the Dark Energy Survey (DES). The DES covered a contiguous $5000$ deg$^2$ of the southern sky with $\approx 80,000$ $3$ deg$^2$ exposures in the $grizY$ optical/IR filters between 2013 and 2019. This search yielded 815 trans-Neptunian objects (TNOs), one Centaur and one Oort cloud comet, with 461 objects reported for the first time in this paper. We present methodology that builds upon our previous search carried out on the first four years of data. Here, all DES images were reprocessed with an improved detection pipeline that leads to an average completeness gain of 0.47 mag per exposure, as well as an improved transient catalog production and optimized algorithms for linkage of detections into orbits. All objects were verified by visual inspection and by computing the sub-threshold significance, the total signal-to-noise ratio in the stack of images in which the object's presence is indicated by the orbit fit, but no detection was reported. This yields a highly pure catalog of TNOs complete to $r \approx 23.8$ mag and distances $29 150$ au, $q>30$ au) are consistent with the null hypothesis of azimuthal isotropy; and non-resonant TNOs with $q>38$ au, $a>50$ au show a highly significant tendency to be sunward of the major mean motion resonances, whereas this tendency is not present for $q<38$ au.

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arXiv2021

SCONE: Supernova Classification with a Convolutional Neural Network

We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for accurate redshift data. Photometric data is pre-processed via 2D Gaussian process regression into two-dimensional images created from flux values at each location in wavelength-time space. These "flux heatmaps" of each supernova detection, along with "uncertainty heatmaps" of the Gaussian process uncertainty, constitute the dataset for our model. This preprocessing step not only smooths over irregular sampling rates between filters but also allows SCONE to be independent of the filter set on which it was trained. Our model has achieved impressive performance without redshift on the in-distribution SNIa classification problem: $99.73 \pm 0.26$% test accuracy with no over/underfitting on a subset of supernovae from PLAsTiCC's unblinded test dataset. We have also achieved $98.18 \pm 0.3$% test accuracy performing 6-way classification of supernovae by type. The out-of-distribution performance does not fully match the in-distribution results, suggesting that the detailed characteristics of the training sample in comparison to the test sample have a big impact on the performance. We discuss the implication and directions for future work. All of the data processing and model code developed for this paper can be found in the SCONE software package located at github.com/helenqu/scone.

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arXiv2021

Going Forward with the Nancy Grace Roman Space Telescope Transient Survey: Validation of Precision Forward-Modeling Photometry for Undersampled Imaging

The Nancy Grace Roman Space Telescope (Roman) is an observatory for both wide-field observations and coronagraphy that is scheduled for launch in the mid 2020's. Part of the planned survey is a deep, cadenced field or fields that enable cosmological measurements with type Ia supernovae (SNe Ia). With a pixel scale of 0".11, the Wide Field Instrument will be undersampled, presenting a difficulty for precisely subtracting the galaxy light underneath the SNe. We use simulated data to validate the ability of a forward-model code (such codes are frequently also called "scene-modeling" codes) to perform precision supernova photometry for the Nancy Grace Roman Space Telescope SN survey. Our simulation includes over 760,000 image cutouts around SNe Ia or host galaxies (~ 10% of a full-scale survey). To have a realistic 2D distribution of underlying galaxy light, we use the VELA simulated high-resolution images of galaxies. We run each set of cutouts through our forward-modeling code which automatically measures time-dependent SN fluxes. Given our assumed inputs of a perfect model of the instrument PSFs and calibration, we find biases at the millimagnitude level from this method in four red filters (Y106, J129, H158, and F184), easily meeting the 0.5% Roman inter-filter calibration requirement for a cutting-edge measurement of cosmological parameters using SNe Ia. Simulated data in the bluer Z087 filter shows larger ~ 2--3 millimagnitude biases, also meeting this requirement, but with more room for improvement. Our forward-model code has been released on Zenodo.

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arXiv2020

Testing the isotropy of the Dark Energy Survey's extreme trans-Neptunian objects

We test whether the population of "extreme" trans-Neptunian objects (eTNOs) detected in the Y4 Dark Energy Survey (DES) data exhibit azimuthal asymmetries which might be evidence of gravitational perturbations from an unseen super-Earth in a distant orbit. By rotating the orbits of the detected eTNOs, we construct a synthetic population which, when subject to the DES selection function, reproduces the detected distribution of eTNOs in the orbital elements $a,e,$ and $i$ as well as absolute magnitude $H$, but has uniform distributions in mean anomaly $M$, longitude of ascending node $Ω,$ and argument of perihelion $ω.$ We then compare the detected distributions in each of $Ω, ω,$ and $\varpi\equivΩ+ω$ to those expected from the isotropic population, using Kuiper's variant of the Kolmogorov-Smirnov test. The three angles are tested for each of 4 definitions of the eTNO population, choosing among $a>(150,250)$ AU and perihelion $q>(30,37)$ AU. These choices yield 3--7 eTNOs in the DES Y4 sample. Among the twelve total tests, two have the likelihood of drawing the observed angles from the isotropic population at $p<0.05.$ The 3 detections at $a>250, q>37$ AU, and the 4 detections at $a>250, q>30$ AU, have $Ω$ distribution with $p=0.03$ of coming from the isotropic construction, but this is not strong evidence of anisotropy given the 12 different tests. The DES data taken on their own are thus consistent with azimuthal isotropy and do not require a "Planet 9" hypothesis. The limited sky coverage and object count mean, however, that the DES data by no means falsify this hypothesis.

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arXiv2020

Trans-Neptunian objects found in the first four years of the Dark Energy Survey

We present a catalog of 316 trans-Neptunian bodies detected by the Dark Energy Survey (DES). These objects include 245 discoveries by DES (139 not previously published) detected in $\approx 60,000$ exposures from the first four seasons of the survey ("Y4" data). The survey covers a contiguous 5000 deg$^2$ of the southern sky in the $grizY$ optical/NIR filter set, with a typical TNO in this part of the sky being targeted by $25-30$ Y4 exposures. We describe the processes for detection of transient sources and the linkage into TNO orbits, which are made challenging by the absence of the few-hour repeat observations employed by TNO-optimized surveys. We also describe the procedures for determining detection efficiencies vs. magnitude and estimating rates of false-positive linkages. This work presents all TNOs which were detected on $\ge 6$ unique nights in the Y4 data and pass a "sub-threshold confirmation" test wherein we demand the the object be detectable in a stack of the individual images in which the orbit indicates an object should be present, but was not detected. This eliminates false positives and yields TNO detections complete to $r\lesssim 23.3$ mag with virtually no dependence on orbital properties for bound TNOs at distance $30\,{\rm AU} 0.3$ mag more depth, and arcs of $>4$ years for nearly all detections.

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arXiv2019

The Hyper Suprime-Cam SSP Transient Survey in COSMOS: Overview

We present an overview of a deep transient survey of the COSMOS field with the Subaru Hyper Suprime-Cam (HSC). The survey was performed for the 1.77 deg$^2$ ultra-deep layer and 5.78 deg$^2$ deep layer in the Subaru Strategic Program over 6- and 4-month periods from 2016 to 2017, respectively. The ultra-deep layer shows a median depth per epoch of 26.4, 26.3, 26.0, 25.6, and 24.6 mag in $g$, $r$, $i$, $z$, and $y$ bands, respectively; the deep layer is $\sim0.6$ mag shallower. In total, 1,824 supernova candidates were identified. Based on light curve fitting and derived light curve shape parameter, we classified 433 objects as Type Ia supernovae (SNe); among these candidates, 129 objects have spectroscopic or COSMOS2015 photometric redshifts and 58 objects are located at $z > 1$. Our unique dataset doubles the number of Type Ia SNe at $z > 1$ and enables various time-domain analyses of Type II SNe, high redshift superluminous SNe, variable stars, and active galactic nuclei.

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arXiv2019

The Next Generation of Cosmological Measurements with Type Ia Supernovae

While Type Ia Supernovae (SNe Ia) are one of the most mature cosmological probes, the next era promises to be extremely exciting in the number of different ways SNe Ia are used to measure various cosmological parameters. Here we review the experiments in the 2020s that will yield orders of magnitudes more SNe Ia, and the new understandings and capabilities to constrain systematic uncertainties at a level to match these statistics. We then discuss five different cosmological probes with SNe Ia: the conventional Hubble diagram for measuring dark energy properties, the distance ladder for measuring the Hubble constant, peculiar velocities and weak lensing for measuring sigma8 and strong-lens measurements of H0 and other cosmological parameters. For each of these probes, we discuss the experiments that will provide the best measurements and also the SN Ia-related systematics that affect each one.

astro-ph.CO↗

arXiv2019

Gravitational wave cosmology and astrophysics with large spectroscopic galaxy surveys

During the next decade, gravitational waves will be observed from hundreds of binary inspiral events. When the redshifts of the host galaxies are known, these events can be used as `standard sirens', sensitive to the expansion rate of the Universe. Measurements of the Hubble constant $H_0$ from standard sirens can be done independently of other cosmological probes, and events occurring at $z<0.1$ will allow us to infer $H_0$ independently of cosmological models. The next generation of spectroscopic galaxy surveys will play a crucial role in reducing systematic uncertainties in $H_0$ from standard sirens, particularly for the numerous `dark sirens' which do not have an electromagnetic counterpart. In combination with large spectroscopic data sets, standard sirens with an EM counterpart are expected to constrain $H_0$ to $\sim 1-2\%$ precision within the next decade. This is competitive with the best estimates of $H_0$ obtained to date and will help illuminate the current tension between existing measurements. Information on the galaxies that host the gravitational wave events will also shed light on the origin and evolution of compact object binaries.

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arXiv2018

Evidence for Color Dichotomy in the Primordial Neptunian Trojan Population

In the current model of early Solar System evolution, the stable members of the Jovian and Neptunian Trojan populations were captured into resonance from the leftover reservoir of planetesimals during the outward migration of the giant planets. As a result, both Jovian and Neptunian Trojans share a common origin with the primordial disk population, whose other surviving members constitute today's trans-Neptunian object (TNO) populations. The cold classical TNOs are ultra-red, while the dynamically excited "hot" population of TNOs contains a mixture of ultra-red and blue objects. In contrast, Jovian and Neptunian Trojans are observed to be blue. While the absence of ultra-red Jovian Trojans can be readily explained by the sublimation of volatile material from their surfaces due to the high flux of solar radiation at 5AU, the lack of ultra-red Neptunian Trojans presents both a puzzle and a challenge to formation models. In this work we report the discovery by the Dark Energy Survey (DES) of two new dynamically stable L4 Neptunian Trojans,2013 VX30 and 2014 UU240, both with inclinations i >30 degrees, making them the highest-inclination known stable Neptunian Trojans. We have measured the colors of these and three other dynamically stable Neptunian Trojans previously observed by DES, and find that 2013 VX30 is ultra-red, the first such Neptunian Trojan in its class. As such, 2013 VX30 may be a "missing link" between the Trojan and TNO populations. Using a simulation of the DES TNO detection efficiency, we find that there are 162 +/- 73 Trojans with Hr < 10 at the L4 Lagrange point of Neptune. Moreover, the blue-to-red Neptunian Trojan population ratio should be higher than 17:1. Based on this result, we discuss the possible origin of the ultra-red Neptunian Trojan population and its implications for the formation history of Neptunian Trojans.

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arXiv2017

Discovery and Physical Characterization of a Large Scattered Disk Object at 92 AU

We report the observation and physical characterization of the possible dwarf planet \UZ\ ("DeeDee"), a dynamically detached trans-Neptunian object discovered at 92 AU. This object is currently the second-most distant known trans-Neptunian object with reported orbital elements, surpassed in distance only by the dwarf planet Eris. The object was discovered with an $r$-band magnitude of 23.0 in data collected by the Dark Energy Survey between 2014 and 2016. Its 1140-year orbit has $(a,e,i) = (109~\mathrm{AU}, 0.65, 26.8^{\circ})$. It will reach its perihelion distance of 38 AU in the year 2142. Integrations of its orbit show it to be dynamically stable on Gyr timescales, with only weak interactions with Neptune. We have performed followup observations with ALMA, using 3 hours of on-source integration time to measure the object's thermal emission in the Rayleigh-Jeans tail. The signal is detected at 7$σ$ significance, from which we determine a $V$-band albedo of $13.1^{+3.3}_{-2.4}\mathrm{(stat)}^{+2.0}_{-1.4}\mathrm{(sys)}$ percent and a diameter of $635^{+57}_{-61}\mathrm{(stat)}^{+32}_{-39}\mathrm{(sys)}$~km, assuming a spherical body with uniform surface properties.

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arXiv2016

A new approach for obtaining cosmological constraints from Type Ia Supernovae using Approximate Bayesian Computation

Cosmological parameter estimation techniques that robustly account for systematic measurement uncertainties will be crucial for the next generation of cosmological surveys. We present a new analysis method, superABC, for obtaining cosmological constraints from Type Ia supernova (SN Ia) light curves using Approximate Bayesian Computation (ABC) without any likelihood assumptions. The ABC method works by using a forward model simulation of the data where systematic uncertainties can be simulated and marginalized over. A key feature of the method presented here is the use of two distinct metrics, the `Tripp' and `Light Curve' metrics, which allow us to compare the simulated data to the observed data set. The Tripp metric takes as input the parameters of models fit to each light curve with the SALT-II method, whereas the Light Curve metric uses the measured fluxes directly without model fitting. We apply the superABC sampler to a simulated data set of $\sim$1000 SNe corresponding to the first season of the Dark Energy Survey Supernova Program. Varying $Ω_m, w_0, α$ and $β$ and a magnitude offset parameter, with no systematics we obtain $Δ(w_0) = w_0^{\rm true} - w_0^{\rm best \, fit} = -0.036\pm0.109$ (a $\sim11$% 1$σ$ uncertainty) using the Tripp metric and $Δ(w_0) = -0.055\pm0.068$ (a $\sim7$% 1$σ$ uncertainty) using the Light Curve metric. Including 1% calibration uncertainties in four passbands, adding 4 more parameters, we obtain $Δ(w_0) = -0.062\pm0.132$ (a $\sim14$% 1$σ$ uncertainty) using the Tripp metric. Overall we find a $17$% increase in the uncertainty on $w_0$ with systematics compared to without. We contrast this with a MCMC approach where systematic effects are approximately included. We find that the MCMC method slightly underestimates the impact of calibration uncertainties for this simulated data set.

astro-ph.CO↗

arXiv2016

Host Galaxy Identification for Supernova Surveys

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

astro-ph.CO↗

arXiv2016

SDSS-II Supernova Survey: An Analysis of the Largest Sample of Type Ia Supernovae and Correlations with Host-Galaxy Spectral Properties

Using the largest single-survey sample of Type Ia supernovae (SNe Ia) to date, we study the relationship between properties of SNe Ia and those of their host galaxies, focusing primarily on correlations with Hubble residuals (HR). Our sample consists of 345 photometrically-classified or spectroscopically-confirmed SNeIa discovered as part of the SDSS-II Supernova Survey (SDSS-SNS). This analysis utilizes host-galaxy spectroscopy obtained during the SDSS-I/II spectroscopic survey and from an ancillary program on the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) that obtained spectra for nearly all host galaxies of SDSS-II SN candidates. In addition, we use photometric host-galaxy properties from the SDSS-SNS data release (Sako et al. 2014) such as host stellar mass and star-formation rate. We confirm the well-known relation between HR and host-galaxy mass and find a 3.6σ significance of a non-zero linear slope. We also recover correlations between HR and host-galaxy gas-phase metallicity and specific star-formation rate as they are reported in the literature. With our large dataset, we examine correlations between HR and multiple host-galaxy properties simultaneously and find no evidence of a significant correlation. We also independently analyze our spectroscopically-confirmed and photometrically-classified SNe Ia and comment on the significance of similar combined datasets for future surveys.

astro-ph.CO↗