SNITCH:seeking a simple, informative star formation history inference tool

Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in PYTHON, developed to quickly (2 min) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionization. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy, Gunn & White (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching, and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customizable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user defined analytic SFH model for specific science cases. Herein, we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCHis sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra

Is Acceleration Used for Ocular Pursuit and Spatial Estimation during Prediction Motion?

Here we examined ocular pursuit and spatial estimation in a linear prediction motion task that emphasized extrapolation of occluded accelerative object motion. Results from the ocular response up to occlusion showed that there was evidence in the eye position, velocity and acceleration data that participants were attempting to pursue the moving object in accord with the veridical motion properties. They then attempted to maintain ocular pursuit of the randomly-ordered accelerative object motion during occlusion but this was not ideal, and resulted in undershoot of eye position and velocity at the moment of object reappearance. In spatial estimation there was a general bias, with participants less likely to report object reappearance being behind than ahead of the expected position. In addition, participants’ spatial estimation did not take into account the effects of object acceleration. Logistic regression indicated that spatial estimation was best predicted for the majority of participants by the difference between actual object reappearance position and an extrapolation based on pre-occlusion velocity. In combination, and in light of previous work, we interpret these findings as showing that eye movements are scaled in accord with the effects of object acceleration but do not directly specify information for accurate spatial estimation in prediction motion

Temporal estimation with two moving objects: overt and covert pursuit

The current study examined temporal estimation in a prediction motion task where participants were cued to overtly pursue one of two moving objects, which could either arrive first, i.e., shortest [time to contact (TTC)] or second (i.e., longest TTC) after a period of occlusion. Participants were instructed to estimate TTC of the first-arriving object only, thus making it necessary to overtly pursue the cued object while at the same time covertly pursuing the other (non-cued) object. A control (baseline) condition was also included in which participants had to estimate TTC of a single, overtly pursued object. Results showed that participants were able to estimate the arrival order of the two objects with very high accuracy irrespective of whether they had overtly or covertly pursued the first-arriving object. However, compared to the single-object baseline, participants’ temporal estimation of the covert object was impaired when it arrived 500 ms before the overtly pursued object. In terms of eye movements, participants exhibited significantly more switches in gaze location during occlusion from the cued to the non-cued object but only when the latter arrived first. Still, comparison of trials with and without a switch in gaze location when the non-cued object arrived first indicated no advantage for temporal estimation. Taken together, our results indicate that overt pursuit is sufficient but not necessary for accurate temporal estimation. Covert pursuit can enable representation of a moving object’s trajectory and thereby accurate temporal estimation providing the object moves close to the overt attentional focus

Development of Secondary Woodland in Oak Wood Pastures Reduces the Richness of Rare Epiphytic Lichens

Wooded pastures with ancient trees were formerly abundant throughout Europe, but during the last century, grazing has largely been abandoned often resulting in dense forests. Ancient trees constitute habitat for many declining and threatened species, but the effects of secondary woodland on the biodiversity associated with these trees are largely unknown. We tested for difference in species richness, occurrence, and abundance of a set of nationally and regionally red-listed epiphytic lichens between ancient oaks located in secondary woodland and ancient oaks located in open conditions. We refined the test of the effect of secondary woodland by also including other explanatory variables. Species occurrence and abundance were modelled jointly using overdispersed zero-inflated Poisson models. The richness of the red-listed lichens on ancient oaks in secondary woodland was half of that compared with oaks growing in open conditions. The species-level analyses revealed that this was mainly the result of lower occupancy of two of the study species. The tree-level abundance of one species was also lower in secondary woodland. Potential explanations for this pattern are that the study lichens are adapted to desiccating conditions enhancing their population persistence by low competition or that open, windy conditions enhance their colonisation rate. This means that the development of secondary woodland is a threat to red-listed epiphytic lichens. We therefore suggest that woody vegetation is cleared and grazing resumed in abandoned oak pastures. Importantly, this will also benefit the vitality of the oaks

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data