Probing the distance and morphology of the Large Magellanic Cloud with RR Lyrae stars

We present a Bayesian analysis of the distances to 15,040 Large Magellanic Cloud (LMC) RR Lyrae stars using $V$- and $I$-band light curves from the Optical Gravitational Lensing Experiment, in combination with new $z$-band observations from the Dark Energy Camera. Our median individual RR Lyrae distance statistical error is 1.89 kpc (fractional distance error of 3.76 per cent). We present three-dimensional contour plots of the number density of LMC RR Lyrae stars and measure a distance to the core LMC RR Lyrae centre of ${50.2482\pm0.0546 {\rm(statistical)} \pm0.4628 {\rm(systematic)} {\rm kpc}}$, equivalently ${\mu_{\rm LMC}=18.5056\pm0.0024 {\rm(statistical)} \pm0.02 {\rm(systematic)}}$. This finding is statistically consistent with and four times more precise than the canonical value determined by a recent meta-analysis of 233 separate LMC distance determinations. We also measure a maximum tilt angle of $11.84^{\circ}\pm0.80^{\circ}$ at a position angle of $62^\circ$, and report highly precise constraints on the $V$, $I$, and $z$ RR Lyrae period--magnitude relations. The full dataset of observed mean-flux magnitudes, derived colour excess ${E(V-I)}$ values, and fitted distances for the 15,040 RR Lyrae stars produced through this work is made available through the publication's associated online data.Comment: 7 pages, 8 figure

A Bayesian Approach to Calibrating Period-Luminosity Relations of RR Lyrae Stars in the Mid-Infrared

A Bayesian approach to calibrating period-luminosity (PL) relations has substantial benefits over generic least-squares fits. In particular, the Bayesian approach takes into account the full prior distribution of the model parameters, such as the a priori distances, and refits these parameters as part of the process of settling on the most highly-constrained final fit. Additionally, the Bayesian approach can naturally ingest data from multiple wavebands and simultaneously fit the parameters of PL relations for each waveband in a procedure that constrains the parameter posterior distributions so as to minimize the scatter of the final fits appropriately in all wavebands. Here we describe the generalized approach to Bayesian model fitting and then specialize to a detailed description of applying Bayesian linear model fitting to the mid-infrared PL relations of RR Lyrae variable stars. For this example application we quantify the improvement afforded by using a Bayesian model fit. We also compare distances previously predicted in our example application to recently published parallax distances measured with the Hubble Space Telescope and find their agreement to be a vindication of our methodology. Our intent with this article is to spread awareness of the benefits and applicability of this Bayesian approach and encourage future PL relation investigations to consider employing this powerful analysis method.Comment: 6 pages, 1 figure. Accepted for publication in Astrophysics & Space Science. Following a presentation at the conference The Fundamental Cosmic Distance Scale: State of the Art and the Gaia Perspective, Naples, May 201

GAGrank: Software for Glycosaminoglycan Sequence Ranking using a Bipartite Graph Model

The Sulfated Glycosaminoglycans (GAGs) Are Long, Linear Polysaccharide Chains that Are Typically Found as the Glycan Portion of Proteoglycans. These GAGs Are Characterized by Repeating Disaccharide Units with Variable Sulfation and Acetylation Patterns Along the Chain. GAG Length and Modification Patterns Have Profound Impacts on Growth Factor Signaling Mechanisms Central to Numerous Physiological Processes. Electron Activated Dissociation Tandem Mass Spectrometry is a Very Effective Technique for Assigning the Structures of GAG Saccharides; However, Manual Interpretation of the Resulting Complex Tandem Mass Spectra is a Difficult and Time-Consuming Process that Drives the Development of Computational Methods for Accurate and Efficient Sequencing. We Have Recently Published GAGfinder, the First Peak Picking and Elemental Composition Assignment Algorithm Specifically Designed for GAG Tandem Mass Spectra. Here, We Present GAGrank, a Novel Network-Based Method for Determining GAG Structure using Information Extracted from Tandem Mass Spectra using GAGfinder. GAGrank is based on Google\u27s PageRank Algorithm for Ranking Websites for Search Engine Output. in Particular, It is an Implementation of BiRank, an Extension of PageRank for Bipartite Networks. in Our Implementation, the Two Partitions Comprise Every Possible Sequence for a Given GAG Composition and the Tandem MS Fragments Found using GAGfinder. Sequences Are Given a Higher Ranking If They Link to Many Important Fragments. using the Simulated Annealing Probabilistic Optimization Technique, We Optimized GAGrank\u27s Parameters on Ten Training Sequences. We Then Validated GAGrank\u27s Performance on Three Validation Sequences. We Also Demonstrated GAGrank\u27s Ability to Sequence Isomeric Mixtures using Two Mixtures at Five Different Ratios

NFPA Fluid Powered Vehicle Challenge 2023

This report includes the design process undergone by Team Shifty in designing a vehicle for the NFPA’s Fluid Powered Vehicle challenge. The report covers the background of the competition, research done by the team, engineering specifications for the design, preliminary and final designs, the manufacturing plan and process, project management details, and several recommendations for future teams participating in the challenge. The National Fluid Power Association, NFPA, is a trade association with the goal of connecting fluid power companies and advancing fluid power. With the goal of advancement in mind, NFPA hosts an annual Fluid Powered Vehicle Challenge (FPVC). Since before the NFPA took over this challenge, Cal Poly has produced a team to compete. Team Shifty completed research into past Cal Poly teams as well as other competing university teams to define the engineering specifications for the new vehicle and decide the design directions. The final design includes a new frame to address issues with the last teams frame, a new hydraulic circuit design and selection of new components to improve the circuits performance in the FPVC events and reduce losses, and the addition of gear shifting to the vehicle. With respect to hydraulics, a new manifold was sourced to accommodate the simplified fluid circuit, along with a larger motor to allow the vehicle to operate at higher torque. The prior team’s pneumatic system was completely replaced by a pneumatic front gear shifting system. The electronics implemented was the same system as the previous year, including an STM microcontroller, Nextion touch screen display, and Hydraforce valve operator with only two solenoid valves. Working together, these components allowed the rider to toggle between three unique drive modes, including: direct, regen, and sprint. To produce a functional vehicle, research and planning was put into manufacturing and assembly processes as detailed in the manufacturing plan. The final product failed to perform as proposed in Team Shifty’s Scope of Work, as the vehicle’s rear chain consistently fell off during operation at the competition. This resulted in the vehicle not placing during a few of the challenges, including the Sprint and Endurance races. The cause of this failure was a function of the frame flexing under dynamic loading due to insufficient torsional stiffness, as well as the rear chain being too small to handle the large output torque of the upsized rear motor

Non-parametric belief propagation for mobile mapping sensor fusion

© 2016 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Many different forms of sensor fusion have been proposed each with its own niche. We propose a method of fusing multiple different sensor types. Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional (3D) point clouds. We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors. This technique allows continuous variables to be used, is trivially parallel making it suitable for modern many-core processors, and easily accommodates varying types and combinations of sensors. By defining the relationships between common sensors, a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors. This allows the use of unreliable sensors which firstly, may start and stop providing data at any time and secondly, the integration of new sensor types simply by defining their relationship with existing sensors. These features allow a flexible framework to be developed which is suitable for many tasks. Using an abstract algorithm, we can instead focus on the relationships between sensors. Where possible we use the existing relationships between sensors rather than developing new ones. These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network. In this paper, we present the initial results from this technique and the intended course for future work

On Kinks and Bound States in the Gross-Neveu Model

We investigate static space dependent \sigx=\lag\bar\psi\psi\rag saddle point configurations in the two dimensional Gross-Neveu model in the large N limit. We solve the saddle point condition for \sigx explicitly by employing supersymmetric quantum mechanics and using simple properties of the diagonal resolvent of one dimensional Schr\"odinger operators rather than inverse scattering techniques. The resulting solutions in the sector of unbroken supersymmetry are the Callan-Coleman-Gross-Zee kink configurations. We thus provide a direct and clean construction of these kinks. In the sector of broken supersymmetry we derive the DHN saddle point configurations. Our method of finding such non-trivial static configurations may be applied also in other two dimensional field theories.Comment: Revised version. A new section added with derivation of the DHN static configurations in the sector of broken supersymmetry. Some references added as well. 25 pp, latex, e-mail [email protected]

Dynamical Generation of Extended Objects in a 1+11+1 Dimensional Chiral Field Theory: Non-Perturbative Dirac Operator Resolvent Analysis

We analyze the $1+1$ dimensional Nambu-Jona-Lasinio model non-perturbatively. In addition to its simple ground state saddle points, the effective action of this model has a rich collection of non-trivial saddle points in which the composite fields \sigx=\lag\bar\psi\psi\rag and \pix=\lag\bar\psi i\gam_5\psi\rag form static space dependent configurations because of non-trivial dynamics. These configurations may be viewed as one dimensional chiral bags that trap the original fermions (``quarks") into stable extended entities (``hadrons"). We provide explicit expressions for the profiles of these objects and calculate their masses. Our analysis of these saddle points is based on an explicit representation we find for the diagonal resolvent of the Dirac operator in a \{\sigx, \pix\} background which produces a prescribed number of bound states. We analyse in detail the cases of a single as well as two bound states. We find that bags that trap $N$ fermions are the most stable ones, because they release all the fermion rest mass as binding energy and become massless. Our explicit construction of the diagonal resolvent is based on elementary Sturm-Liouville theory and simple dimensional analysis and does not depend on the large $N$ approximation. These facts make it, in our view, simpler and more direct than the calculations previously done by Shei, using the inverse scattering method following Dashen, Hasslacher, and Neveu. Our method of finding such non-trivial static configurations may be applied to other $1+1$ dimensional field theories

CfAIR2: Near Infrared Light Curves of 94 Type Ia Supernovae

CfAIR2 is a large homogeneously reduced set of near-infrared (NIR) light curves for Type Ia supernovae (SN Ia) obtained with the 1.3m Peters Automated InfraRed Imaging TELescope (PAIRITEL). This data set includes 4607 measurements of 94 SN Ia and 4 additional SN Iax observed from 2005-2011 at the Fred Lawrence Whipple Observatory on Mount Hopkins, Arizona. CfAIR2 includes JHKs photometric measurements for 88 normal and 6 spectroscopically peculiar SN Ia in the nearby universe, with a median redshift of z~0.021 for the normal SN Ia. CfAIR2 data span the range from -13 days to +127 days from B-band maximum. More than half of the light curves begin before the time of maximum and the coverage typically contains ~13-18 epochs of observation, depending on the filter. We present extensive tests that verify the fidelity of the CfAIR2 data pipeline, including comparison to the excellent data of the Carnegie Supernova Project. CfAIR2 contributes to a firm local anchor for supernova cosmology studies in the NIR. Because SN Ia are more nearly standard candles in the NIR and are less vulnerable to the vexing problems of extinction by dust, CfAIR2 will help the supernova cosmology community develop more precise and accurate extragalactic distance probes to improve our knowledge of cosmological parameters, including dark energy and its potential time variation.Comment: 31 pages, 15 figures, 10 tables. Accepted to ApJS. v2 modified to more closely match journal versio