Line Profiles from Discrete Kinematic Data

We develop a method to extract the shape information of line profiles from discrete kinematic data. The Gauss-Hermite expansion, which is widely used to describe the line of sight velocity distributions extracted from absorption spectra of elliptical galaxies, is not readily applicable to samples of discrete stellar velocity measurements, accompanied by individual measurement errors and probabilities of membership. We introduce two parameter families of probability distributions describing symmetric and asymmetric distortions of the line profiles from Gaussianity. These are used as the basis of a maximum likelihood estimator to quantify the shape of the line profiles. Tests show that the method outperforms a Gauss-Hermite expansion for discrete data, with a lower limit for the relative gain of approx 2 for sample sizes N approx 800. To ensure that our methods can give reliable descriptions of the shape, we develop an efficient test to assess the statistical quality of the obtained fit. As an application, we turn our attention to the discrete velocity datasets of the dwarf spheroidals of the Milky Way. In Sculptor, Carina and Sextans the symmetric deviations are consistent with velocity distributions more peaked than Gaussian. In Fornax, instead, there is an evolution in the symmetric deviations of the line profile from a peakier to more flat-topped distribution on moving outwards. These results suggest a radially biased orbital structure for the outer parts of Sculptor, Carina and Sextans. On the other hand, tangential anisotropy is favoured in Fornax. This is all consistent with a picture in which Fornax may have had a different evolutionary history to Sculptor, Carina and Sextans.Comment: MNRAS, accepted for publication, minor change

Neural field model for measuring and reproducing time intervals

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016, the project ”Neurofield” (POCI-01-0145-FEDER-031393) and the research centre CMAT within the project UID/MAT/00013/2013

Equation of state for Universe from similarity symmetries

In this paper we proposed to use the group of analysis of symmetries of the dynamical system to describe the evolution of the Universe. This methods is used in searching for the unknown equation of state. It is shown that group of symmetries enforce the form of the equation of state for noninteracting scaling multifluids. We showed that symmetries give rise the equation of state in the form $p=-\Lambda+w_{1}\rho(a)+w_{2}a^{\beta}+0$ and energy density $\rho=\Lambda+\rho_{01}a^{-3(1+w)}+\rho_{02}a^{\beta}+\rho_{03}a^{-3}$, which is commonly used in cosmology. The FRW model filled with scaling fluid (called homological) is confronted with the observations of distant type Ia supernovae. We found the class of model parameters admissible by the statistical analysis of SNIa data. We showed that the model with scaling fluid fits well to supernovae data. We found that $\Omega_{\text{m},0} \simeq 0.4$ and $n \simeq -1$ ($\beta = -3n$), which can correspond to (hyper) phantom fluid, and to a high density universe. However if we assume prior that $\Omega_{\text{m},0}=0.3$ then the favoured model is close to concordance $\Lambda$CDM model. Our results predict that in the considered model with scaling fluids distant type Ia supernovae should be brighter than in $\Lambda$CDM model, while intermediate distant SNIa should be fainter than in $\Lambda$CDM model. We also investigate whether the model with scaling fluid is actually preferred by data over $\Lambda$CDM model. As a result we find from the Akaike model selection criterion prefers the model with noninteracting scaling fluid.Comment: accepted for publication versio

A combined measurement of cosmic growth and expansion from clusters of galaxies, the CMB and galaxy clustering

Combining galaxy cluster data from the ROSAT All-Sky Survey and the Chandra X-ray Observatory, cosmic microwave background data from the Wilkinson Microwave Anisotropy Probe, and galaxy clustering data from the WiggleZ Dark Energy Survey, the 6-degree Field Galaxy Survey and the Sloan Digital Sky Survey III, we test for consistency the cosmic growth of structure predicted by General Relativity (GR) and the cosmic expansion history predicted by the cosmological constant plus cold dark matter paradigm (LCDM). The combination of these three independent, well studied measurements of the evolution of the mean energy density and its fluctuations is able to break strong degeneracies between model parameters. We model the key properties of cosmic growth with the normalization of the matter power spectrum, sigma_8, and the cosmic growth index, gamma, and those of cosmic expansion with the mean matter density, Omega_m, the Hubble constant, H_0, and a kinematical parameter equivalent to that for the dark energy equation of state, w. For a spatially flat geometry, w=-1, and allowing for systematic uncertainties, we obtain sigma_8=0.785+-0.019 and gamma=0.570+0.064-0.063 (at the 68.3 per cent confidence level). Allowing both w and gamma to vary we find w=-0.950+0.069-0.070 and gamma=0.533+-0.080. To further tighten the constraints on the expansion parameters, we also include supernova, Cepheid variable and baryon acoustic oscillation data. For w=-1, we have gamma=0.616+-0.061. For our most general model with a free w, we measure Omega_m=0.278+0.012-0.011, H_0=70.0+-1.3 km s^-1 Mpc^-1 and w=-0.987+0.054-0.053 for the expansion parameters, and sigma_8=0.789+-0.019 and gamma=0.604+-0.078 for the growth parameters. These results are in excellent agreement with GR+LCDM (gamma~0.55; w=-1) and represent the tightest and most robust simultaneous constraint on cosmic growth and expansion to date.Comment: 14 pages, 5 figures, 1 table. Matches the accepted version for MNRAS. New sections 3 and 6 added, containing 2 new figures. Table extended. The results including BAO data have been slightly modified due to an updated BAO analysis. Conclusions unchange

Dark energy problem: from phantom theory to modified Gauss-Bonnet gravity

The solution of dark energy problem in the models without scalars is presented. It is shown that late-time accelerating cosmology may be generated by the ideal fluid with some implicit equation of state. The universe evolution within modified Gauss-Bonnet gravity is considered. It is demonstrated that such gravitational approach may predict the (quintessential, cosmological constant or transient phantom) acceleration of the late-time universe with natural transiton from deceleration to acceleration (or from non-phantom to phantom era in the last case).Comment: LaTeX 8 pages, prepared for the Proceedings of QFEXT'05, minor correctons, references adde

SN 2021hpr and its two siblings in the Cepheid calibrator galaxy NGC 3147: A hierarchical BayeSN analysis of a Type Ia supernova trio, and a Hubble constant constraint

To improve Type Ia supernova (SN Ia) standardisability, the consistency of distance estimates to siblings -- SNe in the same host galaxy -- should be investigated. We present Young Supernova Experiment Pan-STARRS-1 $grizy$ photometry of SN 2021hpr, the third spectroscopically confirmed SN Ia in the high-stellar-mass Cepheid-calibrator galaxy NGC 3147. We analyse NGC 3147's trio of SN Ia siblings: SNe 1997bq, 2008fv and 2021hpr, using a new version of the BayeSN model of SN Ia spectral-energy distributions, retrained simultaneously using optical-NIR $BgVrizYJH$ (0.35--1.8 $\mu$m) data. The distance estimates to each sibling are consistent, with a sample standard deviation $\lesssim$0.01 mag, much smaller than the total intrinsic scatter in the training sample: $\sigma_0\approx0.09$ mag. Fitting normal SN Ia siblings in three additional galaxies, we estimate a $\approx$90% probability that the siblings' intrinsic scatter is smaller than $\sigma_0$. We build a new hierarchical model that fits light curves of siblings in a single galaxy simultaneously; this yields more precise estimates of the common distance and the dust parameters. Fitting the trio for a common dust law shape yields $R_V=2.69\pm0.52$. Our work motivates future hierarchical modelling of more siblings, to tightly constrain their intrinsic scatter, and better understand SN-host correlations. Finally, we estimate the Hubble constant, using a Cepheid distance to NGC 3147, the siblings trio, and 109 Hubble flow ($0.01 < z_{\rm{CMB}} < 0.08$) SNe Ia; marginalising over the siblings' and population's intrinsic scatters, and the peculiar velocity dispersion, yields $H_0=77.9\pm6.5 \text{ km s}^{-1}\text{Mpc}^{-1}$.Comment: Submitted to MNRAS; 30 pages, 22 figure

The stellar and sub-stellar IMF of simple and composite populations

The current knowledge on the stellar IMF is documented. It appears to become top-heavy when the star-formation rate density surpasses about 0.1Msun/(yr pc^3) on a pc scale and it may become increasingly bottom-heavy with increasing metallicity and in increasingly massive early-type galaxies. It declines quite steeply below about 0.07Msun with brown dwarfs (BDs) and very low mass stars having their own IMF. The most massive star of mass mmax formed in an embedded cluster with stellar mass Mecl correlates strongly with Mecl being a result of gravitation-driven but resource-limited growth and fragmentation induced starvation. There is no convincing evidence whatsoever that massive stars do form in isolation. Various methods of discretising a stellar population are introduced: optimal sampling leads to a mass distribution that perfectly represents the exact form of the desired IMF and the mmax-to-Mecl relation, while random sampling results in statistical variations of the shape of the IMF. The observed mmax-to-Mecl correlation and the small spread of IMF power-law indices together suggest that optimally sampling the IMF may be the more realistic description of star formation than random sampling from a universal IMF with a constant upper mass limit. Composite populations on galaxy scales, which are formed from many pc scale star formation events, need to be described by the integrated galactic IMF. This IGIMF varies systematically from top-light to top-heavy in dependence of galaxy type and star formation rate, with dramatic implications for theories of galaxy formation and evolution.Comment: 167 pages, 37 figures, 3 tables, published in Stellar Systems and Galactic Structure, Vol.5, Springer. This revised version is consistent with the published version and includes additional references and minor additions to the text as well as a recomputed Table 1. ISBN 978-90-481-8817-

A neural integrator model for planning and value-based decision making of a robotics assistant

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.The work received financial support from FCT through the PhD fellowships PD/BD/128183/2016 and SFRH/BD/124912/2016, the project “Neurofield” (PTDC/MAT-APL/31393/2017) and the research centre CMAT within the project UID/MAT/00013/2013

LensWatch: I. Resolved HST Observations and Constraints on the Strongly-Lensed Type Ia Supernova 2022qmx ("SN Zwicky")

Supernovae (SNe) that have been multiply-imaged by gravitational lensing are rare and powerful probes for cosmology. Each detection is an opportunity to develop the critical tools and methodologies needed as the sample of lensed SNe increases by orders of magnitude with the upcoming Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope. The latest such discovery is of the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky"; Goobar et al. 2022) at z = 0.3544. SN Zwicky was discovered by the Zwicky Transient Facility (ZTF) in spatially unresolved data. Here we present follow-up Hubble Space Telescope observations of SN Zwicky, the first from the multi-cycle "LensWatch" program (www.lenswatch.org). We measure photometry for each of the four images of SN Zwicky, which are resolved in three WFC3/UVIS filters (F475W, F625W, F814W) but unresolved with WFC3/IR F160W, and produce an analysis of the lensing system using a variety of independent lens modeling methods. We find consistency between time delays estimated with the single epoch of HST photometry and the lens model predictions constrained through the multiple image positions, with both inferring time delays of <1 day. Our lens models converge to an Einstein radius of (0.168+0.009-0.005)", the smallest yet seen in a lensed SN. The "standard candle" nature of SN Zwicky provides magnification estimates independent of the lens modeling that are brighter by ~1.5 mag and ~0.8 mag for two of the four images, suggesting significant microlensing and/or additional substructure beyond the flexibility of our image-position mass models

The Young Supernova Experiment: Survey Goals, Overview, and Operations

Time domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, including SNe within days or hours of explosion and faint, red transients, are just beginning to be explored. Here, we present the Young Supernova Experiment (YSE), a novel optical time-domain survey on the Pan-STARRS telescopes. Our survey is designed to obtain well-sampled $griz$ light curves for thousands of transient events up to $z \approx 0.2$. This large sample of transients with 4-band light curves will lay the foundation for the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, providing a critical training set in similar filters and a well-calibrated low-redshift anchor of cosmologically useful SNe Ia to benefit dark energy science. As the name suggests, YSE complements and extends other ongoing time-domain surveys by discovering fast-rising SNe within a few hours to days of explosion. YSE is the only current four-band time-domain survey and is able to discover transients as faint $\sim$21.5 mag in $gri$ and $\sim$20.5 mag in $z$, depths that allow us to probe the earliest epochs of stellar explosions. YSE is currently observing approximately 750 square degrees of sky every three days and we plan to increase the area to 1500 square degrees in the near future. When operating at full capacity, survey simulations show that YSE will find $\sim$5000 new SNe per year and at least two SNe within three days of explosion per month. To date, YSE has discovered or observed 8.3% of the transient candidates reported to the International Astronomical Union in 2020. We present an overview of YSE, including science goals, survey characteristics and a summary of our transient discoveries to date.Comment: ApJ, in press; more information at https://yse.ucsc.edu