Search

In this paper we use level set-based topology optimisation to design three-dimensional periodic piezoelectric materials with enhanced properties. Our methodology is fully memory-distributed and written in Julia using the package GridapTopOpt. We compare and assess several existing iterative solvers with respect to their weak scalability and find that an approximate Schur complement preconditioned GMRES method demonstrates the best performance and scalability for solving the piezoelectric homogenisation equations. We use the developed techniques to computationally design high-resolution piezoelectric metamaterials with enhanced stiffness and piezoelectric properties that yield new insights into material design for sensor, hydrophone, and actuator applications. We suggest two robust structures with simple geometric features that exhibit enhanced piezoelectric properties several times larger than those of the base material. We find that level set-based topology optimisation is well suited to problems involving piezoelectricity and has the advantage of avoiding large regions of intermediate density material.

The capability of the global, electromagnetic gyrokinetic GENE code interfaced with the transport Tango solver is exploited to address the impact of fusion alpha particles (in their dual role of fast particles and heating source) on plasma profiles and performance at JET in the discharges with the highest quasi-stationary peak fusion power during the DTE2 experimental campaigns. Employing radially global nonlinear electromagnetic GENE-Tango simulations, we compare results with/without alpha particles and alpha heating. Our findings reveal that alpha particles have a negligible impact on turbulent transport, with GENE-Tango converging to similar plasma profiles regardless of their inclusion as a kinetic species in GENE. On the other hand, alpha heating is found to contribute to the peaking of the electron temperature profiles, leading to a 1keV drop on the on-axis electron temperature when alpha heating is neglected in Tango. The minimal impact of alpha particles on turbulent transport in this JET discharge - despite this being the shot with the highest fusion output - is attributed to the low content of fusion alpha in this discharge. To assess the potential impact of alpha particles on turbulent transport in regimes with higher alpha particle density, as expected in ITER and fusion reactors, we artificially increased the alpha particle concentration to levels expected for ITER. By performing global nonlinear GENE standalone simulations, we found that increasing the alpha particle density beyond five times the nominal value lead to significant overall turbulence destabilization.

In this paper we present GridapTopOpt, an extendable framework for level set-based topology optimisation that can be readily distributed across a personal computer or high-performance computing cluster. The package is written in Julia and uses the Gridap package ecosystem for parallel finite element assembly from arbitrary weak formulations of partial differential equation (PDEs) along with the scalable solvers from the Portable and Extendable Toolkit for Scientific Computing (PETSc). The resulting user interface is intuitive and easy-to-use, allowing for the implementation of a wide range of topology optimisation problems with a syntax that is near one-to-one with the mathematical notation. Furthermore, we implement automatic differentiation to help mitigate the bottleneck associated with the analytic derivation of sensitivities for complex problems. GridapTopOpt is capable of solving a range of benchmark and research topology optimisation problems with large numbers of degrees of freedom. This educational article demonstrates the usability and versatility of the package by describing the formulation and step-by-step implementation of several distinct topology optimisation problems. The driver scripts for these problems are provided and the package source code is available at https://github$.$com/zjwegert/GridapTopOpt.jl.

Supernova (SN) 1987A offers a unique opportunity to study how a spatially resolved SN evolves into a young supernova remnant (SNR). We present and analyze Hubble Space Telescope (HST) imaging observations of SN 1987A obtained in 2022 and compare them with HST observations from 2009 to 2021. These observations allow us to follow the evolution of the equatorial ring (ER), the rapidly expanding ejecta, and emission from the center over a wide range in wavelength from 2000 to 11 000 AA. The ER has continued to fade since it reached its maximum ~8200 days after the explosion. In contrast, the ejecta brightened until day ~11000 before their emission levelled off; the west side brightened more than the east side, which we attribute to the stronger X-ray emission by the ER on that side. The asymmetric ejecta expand homologously in all filters, which are dominated by various emission lines from hydrogen, calcium, and iron. From this overall similarity, we infer the ejecta are chemically well-mixed on large scales. The exception is the diffuse morphology observed in the UV filters dominated by emission from the Mg II resonance lines that get scattered before escaping. The 2022 observations do not show any sign of the compact object that was inferred from highly-ionized emission near the remnant's center observed with JWST. We determine an upper limit on the flux from a compact central source in the [O III] HST image. The non-detection of this line indicates that the S and Ar lines observed with JWST originate from the O free inner Si - S - Ar rich zone and/or that the observed [O III] flux is strongly affected by dust scattering.

We use rest-frame optical and near-infrared (NIR) observations of 42 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project at low-$z$ and 37 from the RAISIN Survey at high-$z$ to investigate correlations between SN Ia host galaxy dust, host mass, and redshift. This is the first time the SN Ia host galaxy dust extinction law at high-$z$ has been estimated using combined optical and rest-frame NIR data ($YJ$-band). We use the BayeSN hierarchical model to leverage the data's wide rest-frame wavelength range (extending to $\sim$1.0-1.2 microns for the RAISIN sample at $0.2\lesssim z\lesssim0.6$). By contrasting the RAISIN and CSP data, we constrain the population distributions of the host dust $R_V$ parameter for both redshift ranges. We place a limit on the difference in population mean $R_V$ between RAISIN and CSP of $-1.16<\Delta\mu(R_V)<1.38$ with 95% posterior probability. For RAISIN we estimate $\mu(R_V)=2.58\pm0.57$, and constrain the population standard deviation to $\sigma(R_V)<0.90~[2.42]$ at the 68 [95]% level. Given that we are only able to constrain the size of the low- to high-$z$ shift in $\mu(R_V)$ to $\lesssim1.4$ - which could still propagate to a substantial bias in the equation of state parameter $w$ - these and other recent results motivate continued effort to obtain rest-frame NIR data at low and high redshifts (e.g. using the Roman Space Telescope)., Comment: 15 pages, 13 figures, 1 table. Accepted for publication in MNRAS

The propagation of signalling molecules within cellular networks is affected by network topology, but also by the spatial arrangement of cells in the networks. Understanding the collective reaction--diffusion behaviour in space of signals propagating through cellular networks is an important consideration for example for regenerative signals that convey positional information. In this work, we consider stochastic and deterministic versions of random walk models of signalling molecules propagating and reacting within one-dimensional spatial networks with arbitrary node placement and connectivity. By taking a continuum limit of the random walk models, we derive an inhomogeneous reaction--diffusion--advection equation, where diffusivity and advective velocity depend on local node density and connectivity within the network. Our results show that large spatial variations of molecule concentrations can be induced by heterogeneous node distributions. Furthermore, we find that noise within the stochastic random walk model is directly influenced by node density. We apply our models to consider signal propagation within the osteocyte network of bone, where signals propagating to the bone surface regulate bone formation and resorption processes. We investigate signal-to-noise ratios for different damage detection scenarios and show that the location of perturbations to the network can be detected by signals received at the network boundaries., Comment: 29 pages, 11 figures; v2: Minor revisions for clarification, including explicit mention of spatial and time scales in Section 3.3; v3: Correction of the expression for wave speed in Appendix A

Natural language understanding includes the tasks of intent detection (identifying a user's objectives) and slot filling (extracting the entities relevant to those objectives). Prior slot filling methods assume that each intent type cannot occur more than once within a message, however this is often not a valid assumption for real-world settings. In this work, we generalize slot filling by removing the constraint of unique intents in a message. We cast this as a JSON generation task and approach it using a language model. We create a pre-training dataset by combining DBpedia and existing slot filling datasets that we convert for JSON generation. We also generate an in-domain dataset using GPT-3. We train T5 models for this task (with and without exemplars in the prompt) and find that both training datasets improve performance, and that the model is able to generalize to intent types not seen during training.

We present an extension of the projection method proposed by Challis et al. (Int J Solids Struct 45(14$\unicode{x2013}$15):4130$\unicode{x2013}$4146, 2008) for constrained level set-based topology optimisation that harnesses the Hilbertian velocity extension-regularisation framework. Our Hilbertian projection method chooses a normal velocity for the level set function as a linear combination of (1) an orthogonal projection operator applied to the extended optimisation objective shape sensitivity and (2) a weighted sum of orthogonal basis functions for the extended constraint shape sensitivities. This combination aims for the best possible first-order improvement of the optimisation objective in addition to first-order improvement of the constraints. Our formulation utilising basis orthogonalisation naturally handles linearly dependent constraint shape sensitivities. Furthermore, use of the Hilbertian extension-regularisation framework ensures that the resulting normal velocity is extended away from the boundary and enriched with additional regularity. Our approach is generally applicable to any topology optimisation problem to be solved in the level set framework. We consider several benchmark constrained microstructure optimisation problems and demonstrate that our method is effective with little-to-no parameter tuning. We also find that our method performs well when compared to a Hilbertian sequential linear programming method., Comment: 23 pages, 8 figures

There is growing evidence that Type Ia supernovae (SNe Ia) may originate from multiple explosion channels. Previous studies have indicated that the ejecta velocity of SNe Ia is one powerful tool to discriminate between different channels. In this work, we study ~400 confirmed SNe Ia discovered by the Pan-STARRS1 Medium Deep Survey (PS1-MDS), and obtain a sample of ~50 SNe Ia that have near-peak Si II 6355 velocity (Vsi) measurements. We investigate the relationships between Vsi and various parameters, including SN light-curve width, color, host-galaxy properties, and redshift. No significant trends are identified between Vsi and light-curve parameters. Regarding the host-galaxy properties, we see a significant trend that high-velocity (HV) SNe Ia (Vsi > 12000 km/s) tend to reside in more massive galaxies compared to normal-velocity (NV) SNe Ia (Vsi < 12000 km/s) when combining both the PS1-MDS dataset and those from previous low-z studies. While we do not see a significant trend between Vsi and redshift, HV SNe Ia appear to be more prevalent in low-z samples than in high-z samples. We discuss several possibilities that could potentially contribute to this trend. Furthermore, we investigate the potential bias on SN Ia distances and find no significant difference in Hubble residuals between HV and NV subgroups., Comment: 15 pages, 13 figures, 3 tables, accepted for publication in MNRAS

Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory. We propose a novel image-based method to estimate three key components for stability computation: Center of Mass (CoM), Base of Support (BoS), and Center of Pressure (CoP). Furthermore, we quantitatively validate our image-based methods for computing two classic stability measures, CoMtoCoP and CoMtoBoS distances, against values generated directly from laboratory-based sensor output (ground truth) using a publicly available, multi-modality (mocap, foot pressure, two-view videos), ten-subject human motion dataset. Using Leave One Subject Out (LOSO) cross-validation, experimental results show: 1) our image-based CoM estimation method (CoMNet) consistently outperforms state-of-the-art inertial sensor-based CoM estimation techniques; 2) stability computed by our image-based method combined with insole foot pressure sensor data produces consistent, strong, and statistically significant correlation with ground truth stability measures (CoMtoCoP r = 0.79 p < 0.001, CoMtoBoS r = 0.75 p < 0.001); 3) our fully image-based estimation of stability produces consistent, positive, and statistically significant correlation on the two stability metrics (CoMtoCoP r = 0.31 p < 0.001, CoMtoBoS r = 0.22 p < 0.043). Our study provides promising quantitative evidence for the feasibility of image-based stability evaluation in natural environments.

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012-2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SN Ia ($0.2 \lesssim z \lesssim 0.6$) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-$z$ HST data with 42 SN Ia at $z<0.1$ observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical data) to pursue a unique avenue to constrain the dark energy equation of state parameter, $w$. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size $\sim$0.06-0.1~mag with 1.5- to 2.5-$\sigma$ significance depending on the method and step location. Combining our NIR sample with CMB constraints, we find $1+w=-0.17\pm0.12$ (stat$+$syst). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measure $H_0=75.9\pm 2.2$ km s$^{-1}$ Mpc$^{-1}$ from stars with geometric distance calibration in the hosts of 8 SNe Ia observed in the NIR versus $H_0=71.2\pm3.8$ km s$^{-1}$ Mpc$^{-1}$ using an inverse distance ladder approach tied to Planck. Using optical data we find $1+w=-0.10\pm0.09$ and with optical and NIR data combined, we find $1+w=-0.06\pm0.07$; these shifts of up to 0.11 in $w$ could point to inconsistency in optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-$z$ samples, new light-curve models, calibration improvements, and by building high-$z$ samples from the Roman Space Telescope., Comment: Updated cosmology constraints and text to match published version, and with data release at https://github.com/djones1040/RAISIN_DataRelease

We present the 30-min cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1-day cadence DECam observations within the first $\sim$5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe~Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly-evolving early colors in a narrow range ($g-i\approx -0.20\pm0.20$ mag) within the first $\sim 10$ days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc's evolution is similar to normal SNe Ia without excess in $g-i$. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical non-degenerate companion undergoing Roche-lobe overflow at viewing angles smaller than $45^{\circ}$., Comment: 20 pages, 14 figures, 5 tables. Published in ApJ

The classification of supernovae (SNe) and its impact on our understanding of the explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification, connecting SN light curves back to their spectroscopically defined classes. Here we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class., Comment: Updated to match published version in ApJ. Companion paper to Villar et al. (2020). Full machine-readable tables available as ancillary files at right

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies. Here we present the complete sample of 5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters and depth, making this a useful training set for the community. Using this dataset, we train a novel semi-supervised machine learning algorithm to photometrically classify 2,315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of a random forest supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I). We find the highest accuracy rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Our complete spectroscopically- and photometrically-classified samples break down into: 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects). Finally, we discuss how this algorithm can be modified for online LSST data streams., Comment: Submitted to ApJ; Companion paper to Hosseinzadeh et al.; Tables 1 and 2 available on ashleyvillar.com prior to publication

The interaction of a supernova with a circumstellar medium (CSM) can dramatically increase the emitted luminosity by converting kinetic energy to thermal energy. In 'superluminous' supernovae (SLSNe) of Type IIn -- named for narrow hydrogen lines in their spectra -- the integrated emission can reach $\sim 10^{51}$ erg, attainable by thermalising most of the kinetic energy of a conventional SN. A few transients in the centres of active galaxies have shown similar spectra and even larger energies, but are difficult to distinguish from accretion onto the supermassive black hole. Here we present a new event, SN2016aps, offset from the centre of a low-mass galaxy, that radiated $\gtrsim 5 \times 10^{51}$ erg, necessitating a hyper-energetic supernova explosion. We find a total (SN ejecta $+$ CSM) mass likely exceeding 50-100 M$_\odot$, with energy $\gtrsim 10^{52}$ erg, consistent with some models of pair-instability supernovae (PISNe) or pulsational PISNe -- theoretically-predicted thermonuclear explosions from helium cores $>50$ M$_\odot$. Independent of the explosion mechanism, this event demonstrates the existence of extremely energetic stellar explosions, detectable at very high redshifts, and provides insight into dense CSM formation in the most massive stars., Comment: Published in Nature Astronomy, 13 April 2020

To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame. A unique video and foot pressure data set of 813,050 synchronized pairs, composed of 5-minute long choreographed Taiji movement sequences of 6 subjects, is collected and used for leaving-one-subject-out cross validation. Our initial experimental results demonstrate reliable and repeatable foot pressure prediction from a single image, setting the first baseline for such a complex cross modality mapping problem in computer vision. Furthermore, we compute and quantitatively validate the Center of Pressure (CoP) and Base of Support (BoS) from predicted foot pressure distribution, obtaining key components in pose stability analysis from images with potential applications in kinesiology, medicine, sports and robotics.

The nearby SN 1987A offers a spatially resolved view of the evolution of a young supernova remnant. Here we precent recent Hubble Space Telescope imaging observations of SN 1987A, which we use to study the evolution of the ejecta, the circumstellar equatorial ring (ER) and the increasing emission from material outside the ER. We find that the inner ejecta have been brightening at a gradually slower rate and that the western side has been brighter than the eastern side since ~7000 days. This is expected given that the X-rays from the ER are most likely powering the ejecta emission. At the same time the optical emission from the ER continues to fade linearly with time. The ER is expanding at 680\pm 50 km/s, which reflects the typical velocity of transmitted shocks in the dense hotspots. A dozen spots and a rim of diffuse H-alpha emission have appeared outside the ER since 9500 days. The new spots are more than an order of magnitude fainter than the spots in the ER and also fade faster. We show that the spots and diffuse emission outside the ER may be explained by fast ejecta interacting with high-latitude material that extends from the ER toward the outer rings. Further observations of this emission will make it possible to determine the detailed geometry of the high-latitude material and provide insight into the formation of the rings and the mass-loss history of the progenitor., Comment: Accepted for publication in ApJ

We present comprehensive observations and analysis of the energetic H-stripped SN 2016coi (a.k.a. ASASSN-16fp), spanning the $\gamma$-ray through optical and radio wavelengths, acquired within the first hours to $\sim$420 days post explosion. Our campaign confirms the identification of He in the SN ejecta, which we interpret to be caused by a larger mixing of Ni into the outer ejecta layers. From the modeling of the broad bolometric light curve we derive a large ejecta mass to kinetic energy ratio ($M_{\rm{ej}}\sim 4-7\,\rm{M_{\odot}}$, $E_{\rm{k}}\sim 7-8\times 10^{51}\,\rm{erg}$). The small [\ion{Ca}{ii}] \lam\lam7291,7324 to [\ion{O}{i}] \lam\lam6300,6364 ratio ($\sim$0.2) observed in our late-time optical spectra is suggestive of a large progenitor core mass at the time of collapse. We find that SN 2016coi is a luminous source of X-rays ($L_{X}>10^{39}\,\rm{erg\,s^{-1}}$ in the first $\sim100$ days post explosion) and radio emission ($L_{8.5\,GHz}\sim7\times 10^{27}\,\rm{erg\,s^{-1}Hz^{-1}}$ at peak). These values are in line with those of relativistic SNe (2009bb, 2012ap). However, for SN 2016coi we infer substantial pre-explosion progenitor mass-loss with rate $\dot M \sim (1-2)\times 10^{-4}\,\rm{M_{\odot}yr^{-1}}$ and a sub-relativistic shock velocity $v_{sh}\sim0.15c$, in stark contrast with relativistic SNe and similar to normal SNe. Finally, we find no evidence for a SN-associated shock breakout $\gamma$-ray pulse with energy $E_{\gamma}>2\times 10^{46}\,\rm{erg}$. While we cannot exclude the presence of a companion in a binary system, taken together, our findings are consistent with a massive single star progenitor that experienced large mass loss in the years leading up to core-collapse, but was unable to achieve complete stripping of its outer layers before explosion., Comment: Submitted to ApJ. Main text: 21 pages; Appendix: 15 pages; 12 figures

We present and analyse a new tidal disruption event (TDE), AT2017eqx at redshift z=0.1089, discovered by Pan-STARRS and ATLAS. The position of the transient is consistent with the nucleus of its host galaxy; it peaks at a luminosity of $L \approx 10^{44}$ erg s$^{-1}$; and the spectrum shows a persistent blackbody temperature $T \gtrsim 20,000$ K with broad H I and He II emission. The lines are initially centered at zero velocity, but by 100 days the H I lines disappear while the He II develops a blueshift of $\gtrsim 5,000$ km s$^{-1}$. Both the early- and late-time morphologies have been seen in other TDEs, but the complete transition between them is unprecedented. The evolution can be explained by combining an extended atmosphere, undergoing slow contraction, with a wind in the polar direction becoming visible at late times. Our observations confirm that a lack of hydrogen a TDE spectrum does not indicate a stripped star, while the proposed model implies that much of the diversity in TDEs may be due to the observer viewing angle. Modelling the light curve suggests AT2017eqx resulted from the complete disruption of a solar-mass star by a black hole of $\sim 10^{6.3} M_\odot$. The host is another quiescent, Balmer-strong galaxy, though fainter and less centrally concentrated than most TDE hosts. Radio limits rule out a relativistic jet, while X-ray limits at 500 days are among the deepest for a TDE at this phase., Comment: Updated to match published version

We analyze a set of 89 Type Ia supernovae (SN Ia) that have both optical and near-infrared (NIR) photometry to derive distances and construct low redshift ($z < 0.04$) Hubble diagrams. We construct mean light curve (LC) templates using a hierarchical Bayesian model. We explore both Gaussian process (GP) and template methods for fitting the LCs and estimating distances, while including peculiar velocity and photometric uncertainties. For the 56 SN Ia with both optical and NIR observations near maximum light, the GP method yields a NIR-only Hubble-diagram with a RMS of $0.117 \pm 0.014$ mag when referenced to the NIR maxima. For each NIR band, a comparable GP method RMS is obtained when referencing to NIR-max or B-max. Using NIR LC templates referenced to B-max yields a larger RMS value of $0.138 \pm 0.014$ mag. Fitting the corresponding optical data using standard LC fitters that use LC shape and color corrections yields larger RMS values of $0.179 \pm 0.018$ mag with SALT2 and $0.174 \pm 0.021$ mag with SNooPy. Applying our GP method to subsets of SN Ia NIR LCs at NIR maximum light, even without corrections for LC shape, color, or host-galaxy dust reddening, provides smaller RMS in the inferred distances, at the $\sim 2.3 - 4.1\sigma$ level, than standard optical methods that do correct for those effects. Our ongoing RAISIN program on the Hubble Space Telescope will exploit this promising infrared approach to limit systematic errors when measuring the expansion history of the universe to constrain dark energy., Comment: 37 pages, 13 figures, 15 tables

Pose stability analysis is the key to understanding locomotion and control of body equilibrium, with applications in numerous fields such as kinesiology, medicine, and robotics. In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait. We propose and validate a novel approach to learn CoP from pose of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure heatmaps, and hence the CoP locations, from 2D human pose derived from video. We have collected a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named PressNET. Cross-subject validation results show promising performance of PressNET, significantly outperforming the baseline method of K-Nearest Neighbors. Furthermore, we demonstrate that our computation of center of pressure (CoP) from PressNET is not only significantly more accurate than those obtained from the baseline approach but also meets the expectations of corresponding lab-based measurements of stability studies in kinesiology.

We present spectroscopy from the first three seasons of the Dark Energy Survey Supernova Program (DES-SN). We describe the supernova spectroscopic program in full: strategy, observations, data reduction, and classification. We have spectroscopically confirmed 307 supernovae, including 251 type Ia supernovae (SNe Ia) over a redshift range of $0.017 < z < 0.85$. We determine the effective spectroscopic selection function for our sample, and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. We also provide a full overview of the strategy, observations, and data products of DES-SN, which has discovered 12,015 likely supernovae during these first three seasons. The data presented here are used for the first cosmology analysis by DES-SN ('DES-SN3YR'), the results of which are given in DES Collaboration (2018a)., Comment: 25 pages, 14 figures, 8 tables. Submitted to AJ

Measurements of the dark energy equation-of-state parameter, $w$, have been limited by uncertainty in the selection effects and photometric calibration of $z<0.1$ Type Ia supernovae (SNe Ia). The Foundation Supernova Survey is designed to lower these uncertainties by creating a new sample of $z<0.1$ SNe Ia observed on the Pan-STARRS system. Here, we combine the Foundation sample with SNe from the Pan-STARRS Medium Deep Survey and measure cosmological parameters with 1,338 SNe from a single telescope and a single, well-calibrated photometric system. For the first time, both the low-$z$ and high-$z$ data are predominantly discovered by surveys that do not target pre-selected galaxies, reducing selection bias uncertainties. The $z>0.1$ data include 875 SNe without spectroscopic classifications and we show that we can robustly marginalize over CC SN contamination. We measure Foundation Hubble residuals to be fainter than the pre-existing low-$z$ Hubble residuals by $0.046 \pm 0.027$ mag (stat+sys). By combining the SN Ia data with cosmic microwave background constraints, we find $w=-0.938 \pm 0.053$, consistent with $\Lambda$CDM. With 463 spectroscopically classified SNe Ia alone, we measure $w=-0.933\pm0.061$. Using the more homogeneous and better-characterized Foundation sample gives a 55% reduction in the systematic uncertainty attributed to SN Ia sample selection biases. Although use of just a single photometric system at low and high redshift increases the impact of photometric calibration uncertainties in this analysis, previous low-$z$ samples may have correlated calibration uncertainties that were neglected in past studies. The full Foundation sample will observe up to 800 SNe to anchor the LSST and WFIRST Hubble diagrams., Comment: 30 pages, 17 figures, accepted by ApJ

We present the analysis underpinning the measurement of cosmological parameters from 207 spectroscopically classified type Ia supernovae (SNe Ia) from the first three years of the Dark Energy Survey Supernova Program (DES-SN), spanning a redshift range of 0.017<$z$<0.849. We combine the DES-SN sample with an external sample of 122 low-redshift ($z$<0.1) SNe Ia, resulting in a "DES-SN3YR" sample of 329 SNe Ia. Our cosmological analyses are blinded: after combining our DES-SN3YR distances with constraints from the Cosmic Microwave Background (CMB; Planck Collaboration 2016), our uncertainties in the measurement of the dark energy equation-of-state parameter, $w$, are .042 (stat) and .059 (stat+syst) at 68% confidence. We provide a detailed systematic uncertainty budget, which has nearly equal contributions from photometric calibration, astrophysical bias corrections, and instrumental bias corrections. We also include several new sources of systematic uncertainty. While our sample is <1/3 the size of the Pantheon sample, our constraints on $w$ are only larger by 1.4$\times$, showing the impact of the DES SN Ia light curve quality. We find that the traditional stretch and color standardization parameters of the DES SNe Ia are in agreement with earlier SN Ia samples such as Pan-STARRS1 and the Supernova Legacy Survey. However, we find smaller intrinsic scatter about the Hubble diagram (0.077 mag). Interestingly, we find no evidence for a Hubble residual step ( 0.007 $\pm$ 0.018 mag) as a function of host galaxy mass for the DES subset, in 2.4$\sigma$ tension with previous measurements. We also present novel validation methods of our sample using simulated SNe Ia inserted in DECam images and using large catalog-level simulations to test for biases in our analysis pipelines., Comment: 30 Pages, 18 Figures, 12 Tables. Submitted to ApJ. Comments welcome

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Omega_m = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Omega_m = 0.321 +_ 0.018. For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN~Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN~Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05~mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4~mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

The combination of multiple observational probes has long been advocated as a powerful technique to constrain cosmological parameters, in particular dark energy. The Dark Energy Survey has measured 207 spectroscopically--confirmed Type Ia supernova lightcurves; the baryon acoustic oscillation feature; weak gravitational lensing; and galaxy clustering. Here we present combined results from these probes, deriving constraints on the equation of state, $w$, of dark energy and its energy density in the Universe. Independently of other experiments, such as those that measure the cosmic microwave background, the probes from this single photometric survey rule out a Universe with no dark energy, finding $w=-0.80^{+0.09}_{-0.11}$. The geometry is shown to be consistent with a spatially flat Universe, and we obtain a constraint on the baryon density of $\Omega_b=0.069^{+0.009}_{-0.012}$ that is independent of early Universe measurements. These results demonstrate the potential power of large multi-probe photometric surveys and pave the way for order of magnitude advances in our constraints on properties of dark energy and cosmology over the next decade., Comment: 8 pages, 2 figures; v3 matches version accepted by PRL