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At least 109 records · Page 6

The young and bright type ia supernova ASASSN-14lP: discovery, early-time observations, first-light time, distance to NGC 4666, and progenitor constraints

On 2014 December 9.61, the All-sky Automated Survey for SuperNovae (ASAS-SN or “Assassin”) discovered ASASSN-14lp just ∼2 days after first light using a global array of 14 cm diameter telescopes. ASASSN-14lp went on to become a bright supernova (V = 11.94 mag), second only to SN 2014J for the year. We present prediscovery photometry (with a detection less than a day after first light) and ultraviolet through near-infrared photometric and spectroscopic data covering the rise and fall of ASASSN-14lp for more than 100 days. We find that ASASSN-14lp had a broad light curve (Δm{sub 15}(B)=0.80±0.05), a B-band maximum at 2457015.82 ± 0.03, a rise time of 16.94{sub −0.10}{sup +0.11} days, and moderate host-galaxy extinction (E(B−V){sub host}=0.33±0.06). Using ASASSN-14lp, we derive a distance modulus for NGC 4666 of μ=30.8±0.2, corresponding to a distance of 14.7 ± 1.5 Mpc. However, adding ASASSN-14lp to the calibrating sample of Type Ia supernovae still requires an independent distance to the host galaxy. Finally, using our early-time photometric and spectroscopic observations, we rule out red giant secondaries and, assuming a favorable viewing angle and explosion time, any nondegenerate companion larger than 0.34 R{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗

Discovery of Extended Tidal Tails around the Globular Cluster Palomar 13

We use photometry from the DECam Legacy Survey to detect candidate tidal tails extending ~5° on either side of the Palomar 13 globular cluster. The tails are aligned with the proper motion of Palomar 13 and are consistent with its old, metal-poor stellar population. We identify three RR Lyrae stars (RRLs) that are plausibly associated with the tails, in addition to four previously known in the cluster. From these RRLs, we find that the mean distance to the cluster and tails is 23.6 ± 0.2 kpc and estimate the total (initial) luminosity of the cluster to be , consistent with previous claims that its initial luminosity was higher than its current luminosity. Combined with previously determined proper motion and radial velocity measurements of the cluster, we find that Palomar 13 is on a highly eccentric orbit (e ~ 0.8) with a pericenter of ~9 kpc and an apocenter of ~69 kpc, and a recent pericentric passage of the cluster ~75 Myr ago. We note a prominent linear structure in the interstellar dust map that runs parallel to the candidate tidal features, but conclude that reddening due to dust is unlikely to account for the structure that we observe. If confirmed, the Palomar 13 stellar stream would be one of very few streams with a known progenitor system, making it uniquely powerful for studying the disruption of globular clusters, the formation of the stellar halo, and the distribution of matter within our Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗

Exploring Halo Substructure with Giant Stars. XV. Discovery of a Connection between the Monoceros Ring and the Triangulum–Andromeda Overdensity?

Thanks to modern sky surveys, over twenty stellar streams and overdensity structures have been discovered in the halo of the Milky Way. Here, in this paper, we present an analysis of spectroscopic observations of individual stars from one such structure, "A13", first identified as an overdensity using the M giant catalog from the Two Micron All-Sky Survey. Our spectroscopic observations show that stars identified with A13 have a velocity dispersion of $\lesssim$ 40 $\mathrm{km~s^{-1}}$, implying that it is a genuine coherent structure rather than a chance super-position of random halo stars. From its position on the sky, distance ($\sim$15 kpc heliocentric), and kinematical properties, A13 is likely to be an extension of another low Galactic latitude substructure -- the Galactic Anticenter Stellar Structure (also known as the Monoceros Ring) -- towards smaller Galactic longitude and farther distance. Furthermore, the kinematics of A13 also connect it with another structure in the southern Galactic hemisphere -- the Triangulum-Andromeda overdensity. Finally, we discuss these three connected structures within the context of a previously proposed scenario that one or all of these features originate from the disk of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

Real-time Recovery Efficiencies and Performance of the Palomar Transient Factory's Transient Discovery Pipeline

In this paper, we present the transient source detection efficiencies of the Palomar Transient Factory (PTF), parameterizing the number of transients that PTF found versus the number of similar transients that occurred over the same period in the survey search area but were missed. PTF was an optical sky survey carried out with the Palomar 48 inch telescope over 2009–2012, observing more than 8000 square degrees of sky with cadences of between one and five days, locating around 50,000 non-moving transient sources, and spectroscopically confirming around 1900 supernovae. We assess the effectiveness with which PTF detected transient sources, by inserting $\simeq 7$ million artificial point sources into real PTF data. We then study the efficiency with which the PTF real-time pipeline recovered these sources as a function of the source magnitude, host galaxy surface brightness, and various observing conditions (using proxies for seeing, sky brightness, and transparency). The product of this study is a multi-dimensional recovery efficiency grid appropriate for the range of observing conditions that PTF experienced and that can then be used for studies of the rates, environments, and luminosity functions of different transient types using detailed Monte Carlo simulations. Finally, we illustrate the technique using the observationally well-understood class of type Ia supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗

Discovery of a Plains Caldera Complex and Extinct Lava Lake in Arabia Terra, Mars: Implications for the Discovery of Additional Highland Volcanic Source Regions

Several irregularly shaped topographic depressions occur near the dichotomy boundary in northern Arabia Terra, Mars. The geomorphology of these features suggests that they formed by collapse, opposed to meteor impact. At least one depression (approx.55 by 85 km) displays geologic features indicating a complex, multi-stage collapse history. Features within and around the collapse structure indicate volcanic processes. The complex occurs within Hesperian ridged plains of likely volcanic origin and displays no crater rim or evidence for ejecta. Instead the depression consists of a series of circumferential graben and down-dropped blocks which also display upper surfaces similar to ridged plain lavas. Large blocks within the depression are tilted towards the crater center, and display graben that appear to have originally been linked with circumferential graben outside of the complex related to earlier collapse events. A nearly 700 m high mound exists along a graben within the complex that might be a vent. The deepest depression displays two sets of nearly continuous terraces, which we interpret as high-stands of a drained lava lake. These features appear similar to the black ledge described during the Kilauea Iki eruption in 1959. A lacustrine origin for the terraces seems unlikely because of the paucity of channels found in or around the depression that could be linked to aqueous surface processes. In addition, there is no obvious evidence for lacustrine sediments within the basin. Together with the presence of significant faulting that is indicative of collapse we conclude that this crater complex represents a large caldera formed in the Late Noachian to Early Hesperian. Other linear and irregular depressions in the region also might be linked to ancient volcanism. If that hypothesis is correct, it suggests that northern Arabia Terra could contain a large, previously unrecognized highland igneous province. Evacuation of magma via explosive and effusive activity produced localized collapse, might have contributed to nearby ridged plains, and pyroclastic materials erupted from these vents might have supplied sediments in fretted terrain and other deposits. The recognition of volcanoes within Arabia Terra expands the known extent of Noachian-Hesperian volcanism to cover much of the preserved martian highland crust.

Bleacher, Jacob↗

k-merSNP discovery: Software for alignment-and reference-free scalable SNP discovery, phylogenetics, and annotation for hundreds of microbial genomes

With the flood of whole genome finished and draft microbial sequences, we need faster, more scalable bioinformatics tools for sequence comparison. An algorithm is described to find single nucleotide polymorphisms (SNPs) in whole genome data. It scales to hundreds of bacterial or viral genomes, and can be used for finished and/or draft genomes available as unassembled contigs or raw, unassembled reads. The method is fast to compute, finding SNPs and building a SNP phylogeny in minutes to hours, depending on the size and diversity of the input sequences. The SNP-based trees that result are consistent with known taxonomy and trees determined in other studies. The approach we describe can handle many gigabases of sequence in a single run. The algorithm is based on k-mer analysis.

Source record↗

Near-Earth asteroid discovery rate review

Fifteen to twenty years ago the discovery of 1 or 2 Near Earth Asteroids (NEAs) per year was typical from one systematic search program, Palomar Planet Crossing Asteroid Survey (PCAS), and the incidental discovery from a variety of other astronomical program. Sky coverage and magnitude were both limited by slower emulsions, requiring longer exposures. The 1970's sky coverage of 15,000 to 25,000 sq. deg. per year led to about 1 NEA discovery every 13,000 sq. deg. Looking at the years from 1987 through 1990, it was found that by comparing 1987/1988 and 1989/1990, the world discovery rate of NEAs went from 20 to 43. More specifically, PCAS' results when grouped into the two year periods, show an increase from 5 discoveries in the 1st period to 20 in the 2nd period, a fourfold increase. Also, the discoveries went from representing about 25 pct. of the world total to about 50 pct. of discoveries worldwide. The surge of discoveries enjoyed by PCAS in particular is attributed to new fine grain sensitive emulsions, film hypering, more uniformity in the quality of the photograph, more equitable scheduling, better weather, and coordination of efforts. The maximum discoveries seem to have been attained at Palomar Schmidt.

Helin, Eleanor F.↗

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Nichol, J. Jake [Univ. of New Mexico, Albuquerque,↗

Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists’ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

97 MATHEMATICS AND COMPUTING↗

A perspective on Bayesian methods applied to materials discovery and design

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.

36 MATERIALS SCIENCE↗

Purposive discovery of operations

The Generate, Prune & Prove (GPP) methodology for discovering definitions of mathematical operators is introduced. GPP is a task within the IL exploration discovery system. We developed GPP for use in the discovery of mathematical operators with a wider class of representations than was possible with the previous methods by Lenat and by Shen. GPP utilizes the purpose for which an operator is created to prune the possible definitions. The relevant search spaces are immense and there exists insufficient information for a complete evaluation of the purpose constraint, so it is necessary to perform a partial evaluation of the purpose (i.e., pruning) constraint. The constraint is first transformed so that it is operational with respect to the partial information, and then it is applied to examples in order to test the generated candidates for an operator's definition. In the GPP process, once a candidate definition survives this empirical prune, it is passed on to a theorem prover for formal verification. We describe the application of this methodology to the (re)discovery of the definition of multiplication for Conway numbers, a discovery which is difficult for human mathematicians. We successfully model this discovery process utilizing information which was reasonably available at the time of Conway's original discovery. As part of this discovery process, we reduce the size of the search space from a computationally intractable size to 3468 elements.

Sims, Michael H.↗

STS-114: Discovery Day 3 Mission Status Briefing

Paul Hill, STS-114 Lead Shuttle Flight Director and John Shannon, Space Shuttle Flight Operations and Integration Manager are seen in this STS-114 flight day three Mission Status Briefing. Hill expresses that the Discovery Orbiter is performing beautifully and that STS-114 crew performed a picture perfect rendezvous. He also shows a video of the Rendezvous Pitch Maneuver (RPM) where Commander Eileen Collins slowly flips Discovery in a circle to give the Expedition 11 crew a view of Discovery's heat resistant tiles. He talks about the removal of the Multi-Purpose Logistics Module (MPLM) from Discovery's payload bay using the ISS Robotic Arm. Shannon discusses two objectives that his team is fulfilling on this mission. They include: 1) Understanding the health of Space Shuttle Discovery; and 2) Assessing the performance of the External Tank. Questions from the news media pertaining to the life expectancy of the Space Shuttle Discovery, battery lifetime on the wing leading edge impact sensors, foam loss, PAL ramps, and the safety of the Space Shuttle Discovery are addressed.

Source record↗

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗

Research towards a systematic signature discovery process

In its most general form, a signature is a unique or distinguishing measurement, pattern, or collection of data that identifies a phenomenon (object, action, or behavior) of interest. The discovery of signatures is an important aspect of a wide range of disciplines from basic science to national security for the rapid and efficient detection and/or prediction of phenomena. Current practice in signature discovery is typically accomplished by asking domain experts to characterize and/or model individual phenomena to identify what might compose a useful signature. What is lacking is an approach that can be applied across a broad spectrum of domains and information sources to efficiently and robustly construct candidate signatures, validate their reliability, measure their quality, and overcome the challenge of detection -- all in the face of dynamic conditions, measurement obfuscation and noisy data environments. Our research has focused on the identification of common elements of signature discovery across application domains and the synthesis of those elements into a systematic process for more robust and efficient signature development. In this way, a systematic signature discovery process lays the groundwork for leveraging knowledge obtained from signatures to a particular domain or problem area, and, more generally, to problems outside that domain. This paper presents the initial results of this research by discussing a mathematical framework for representing signatures and placing that framework in the context of a systematic signature discovery process. Additionally, the basic steps of this process are described with details about the methods available to support the different stages of signature discovery, development, and deployment.

signature discovery↗

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry

Current proteomics approaches are comprised of both broad discovery measurements as well as more quantitative targeted measurements. These two different measurement types are used to initially identify potentially important proteins (e.g., candidate biomarkers) and then enable improved quantification for a limited number of selected proteins. However, both approaches suffer from limitations, particularly the lower sensitivity, accuracy, and quantitation precision for discovery approaches compared to targeted approaches, and the limited proteome coverage provided by targeted approaches. Herein, we describe a new proteomics approach that allows both discovery and targeted monitoring (DTM) in a single analysis using liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled peptides for target ions are spiked into tryptic digests and both the labeled and unlabeled peptides are broadly detected using LC-IMS-MS instrumentation, allowing the benefits of discovery and targeted approaches. To understand the possible improvement of the DTM approach, it was compared to LC-MS broad measurements using an accurate mass and time tag database and selected reaction monitoring (SRM) targeted measurements. The DTM results yielded greater peptide/protein coverage and a significant improvement in the detection of lower abundance species compared to LC-MS discovery measurements. DTM was also observed to have similar detection limits as SRM for the targeted measurements indicating its potential for combining the discovery and targeted approaches.

Environmental Molecular Sciences Laboratory↗

GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns

The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. In order to address these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.

97 MATHEMATICS AND COMPUTING↗