26,082 research outputs found

    An investigation of entorhinal spatial representations in self-localisation behaviours

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    Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space. Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour. Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure. Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain

    TOPSEM, TwO Parameters Semi Empirical Model: Galaxy Evolution and Bulge/Disk Dicothomy from Two-Stage Halo Accretion

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    In recent years, increasing attention has been devoted to semi empirical, data-driven models to tackle some aspects of the complex and still largely debated topic of galaxy formation and evolution. We here present a new semi empirical model whose marking feature is simplicity: it relies on solely two assumptions, one initial condition and two free parameters. Galaxies are connected to evolving dark matter haloes through abundance matching between specific halo accretion rate (sHAR) and specific star formation rate (sSFR). Quenching is treated separately, in a fully empirical way, to marginalize over quiescent galaxies and test our assumption on the sSFR evolution without contaminations from passive objects. Our flexible and transparent model is able to reproduce the observed stellar mass functions up to z5z\sim 5, giving support to our hypothesis of a monotonic relation between sHAR and sSFR. We then exploit the model to test a hypothesis on morphological evolution of galaxies. We attempt to explain the bulge/disk bimodality in terms of the two halo accretion modes: fast and slow accretion. Specifically, we speculate that bulge/spheroidal components might form during the early phase of fast halo growth, while disks form during the later phase of slow accretion. We find excellent agreement with both the observational bulge and elliptical mass functions.Comment: 22 pages, 13 Figure

    Backsplash galaxies and their impact on galaxy evolution: a three-stage, four-type perspective

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    We study the population of backsplash galaxies at z=0z=0 in the outskirts of massive, isolated clusters of galaxies taken from the MDPL2-SAG semi-analytic catalogue. We consider four types of backsplash galaxies according to whether they are forming stars or passive at three stagesin their lifetimes: before entering the cluster, during their first incursion through the cluster, and after they exit the cluster. We analyse several geometric, dynamic, and astrophysical aspects of the four types at the three stages. Galaxies that form stars at all stages account for the majority of the backsplash population (58%58\%) and have stellar masses typically below M3×1010h1MM_\star\sim 3\times 10^{10} h^{-1}{\rm M}_\odot that avoid the innermost cluster's regions and are only mildly affected by it. In a similar mass range, galaxies that become passive after exiting the cluster (26%26\%) follow orbits characterised by small pericentric distance and a strong deflection by the cluster potential well while suffering a strong loss of both dark matter and gas content. Only a small fraction of our sample (4%4\%) become passive while orbiting inside the cluster. These galaxies have experienced heavy pre-processing and the cluster's tidal stripping and ram pressure provide the final blow to their star formation. Finally, galaxies that are passive before entering the cluster for the first time (12%12\%) are typically massive and are not affected significantly by the cluster. Using the bulge/total mass ratio as a proxy for morphology, we find that a single incursion through a cluster do not result in significant morphological changes in all four types.Comment: Accepted for publication in MNRAS. Comments are welcom

    Properties of star formation of the Large Magellanic Cloud as probed by young stellar objects

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    We perform a systematic study of evolutionary stages and stellar masses of young stellar objects (YSOs) in the Large Magellanic Cloud (LMC) to investigate properties of star formation of the galaxy. There are 4825 sources in our YSO sample, which are constructed by combining the previous studies identifying YSOs in the LMC. Spectral energy distributions of the YSOs from optical to infrared wavelengths were fitted with a model consisting of stellar, polycyclic aromatic hydrocarbon and dust emissions. We utilize the stellar-to-dust luminosity ratios thus derived to study the evolutionary stages of the sources; younger YSOs are expected to show lower stellar-to-dust luminosity ratios. We find that most of the YSOs are associated with the interstellar gas across the galaxy, which are younger with more gases, suggesting that more recent star formation is associated with larger amounts of the interstellar medium (ISM). N157 shows a hint of higher stellar-to-dust luminosity ratios between active star-forming regions in the LMC, suggesting that recent star formation in N157 is possibly in later evolutionary stages. We also find that the stellar mass function tends to be bottom-heavy in supergiant shells (SGSs), indicating that gas compression by SGSs may be ineffective in compressing the ISM enough to trigger massive star formation. There is no significant difference in the stellar mass function between YSOs likely associated with the interface between colliding SGSs and those with a single SGS, suggesting that gas compression by collisions between SGSs may also be ineffective for massive star formation.Comment: 26 pages, 16 figures, accepted for publication in Ap

    Smart Farm-Care using a Deep Learning Model on Mobile Phones

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    Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

    Multi-Epoch Machine Learning 2: Identifying physical drivers of galaxy properties in simulations

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    Using a novel machine learning method, we investigate the buildup of galaxy properties in different simulations, and in various environments within a single simulation. The aim of this work is to show the power of this approach at identifying the physical drivers of galaxy properties within simulations. We compare how the stellar mass is dependent on the value of other galaxy and halo properties at different points in time by examining the feature importance values of a machine learning model. By training the model on IllustrisTNG we show that stars are produced at earlier times in higher density regions of the universe than they are in low density regions. We also apply the technique to the Illustris, EAGLE, and CAMELS simulations. We find that stellar mass is built up in a similar way in EAGLE and IllustrisTNG, but significantly differently in the original Illustris, suggesting that subgrid model physics is more important than the choice of hydrodynamics method. These differences are driven by the efficiency of supernova feedback. Applying principal component analysis to the CAMELS simulations allows us to identify a component associated with the importance of a halo's gravitational potential and another component representing the time at which galaxies form. We discover that the speed of galactic winds is a more critical subgrid parameter than the total energy per unit star formation. Finally we find that the Simba black hole feedback model has a larger effect on galaxy formation than the IllustrisTNG black hole feedback model.Comment: 16 pages, 12 figures, accepted to MNRA

    The future of cosmology? A case for CMB spectral distortions

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    This thesis treats the topic of CMB Spectral Distortions (SDs), which represent any deviation from a pure black body shape of the CMB energy spectrum. As such, they can be used to probe the inflationary, expansion and thermal evolution of the universe both within Λ\LambdaCDM and beyond it. The currently missing observation of this rich probe of the universe makes of it an ideal target for future observational campaigns. In fact, while the Λ\LambdaCDM signal guarantees a discovery, the sensitivity to a wide variety of new physics opens the door to an enormous uncharted territory. In light of these considerations, the thesis opens by reviewing the topic of CMB SDs in a pedagogical and illustrative fashion, aimed at waking the interest of the broader community. This introductory premise sets the stage for the first main contribution of the thesis to the field of SDs: their implementation in the Boltzmann solver CLASS and the parameter inference code MontePython. The CLASS+MontePython pipeline is publicly available, fast, it includes all sources of SDs within Λ\LambdaCDM and many others beyond that, and allows to consistently account for any observational setup. By means of these numerical tools, the second main contribution of the thesis consists in showcasing the versatility and competitiveness of SDs for several cosmological models as well as for a number of different mission designs. Among others, the results cover features in the primordial power spectrum, primordial gravitational waves, non-standard dark matter properties, primordial black holes, primordial magnetic fields and Hubble tension. Finally, the manuscript is disseminated with (20) follow-up ideas that naturally extend the work carried out so far, highlighting how rich of unexplored possibilities the field of CMB SDs still is. The hope is that these suggestions will become a propeller for further interesting developments.Comment: PhD thesis. Pedagogical review of theory, experimental status and numerical tools (CLASS+MontePython) with broad overview of applications. Includes 20 original follow-up idea

    Machine learning approach towards predicting turbulent fluid flow using convolutional neural networks

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    Using convolutional neural networks, we present a novel method for predicting turbulent fluid flow through an array of obstacles in this thesis. In recent years, machine learning has exploded in popularity due to its ability to create accurate data driven models and the abundance of available data. In an attempt to understand the characteristics of turbulent fluid flow, we utilise a novel convolutional autoencoder neural network to predict the first ten POD modes of turbulent fluid flow. We find that the model is able to predict the first two POD modes well although and with less accuracy for the remaining eight POD modes. In addition, we find that the ML-predicted POD modes are accurate enough to be used to reconstruct turbulent flow that adequately captures the large-scale details of the original simulation

    Modeling the Galaxy Distribution in Clusters using Halo Cores

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    The galaxy distribution in dark matter-dominated halos is expected to approximately trace the details of the underlying dark matter substructure. In this paper we introduce halo `core-tracking' as a way to efficiently follow the small-scale substructure in cosmological simulations and apply the technique to model the galaxy distribution in observed clusters. The method relies on explicitly tracking the set of particles identified as belonging to a halo's central density core, once a halo has attained a certain threshold mass. The halo cores are then followed throughout the entire evolution of the simulation. The aim of core-tracking is to simplify substructure analysis tasks by avoiding the use of subhalos and, at the same time, to more easily account for the so-called ``orphan'' galaxies, which have lost substantial dark mass due to tidal stripping. We show that simple models based on halo cores can reproduce the number and spatial distribution of galaxies found in optically-selected clusters in the Sloan Digital Sky Survey. We also discuss future applications of the core-tracking methodology in studying the galaxy-halo connection.Comment: 17 pages, 20 figures, 1 Appendix; version accepted by OJ

    A New Sample of Warm Extreme Debris Disks from the ALLWISE Catalog

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    Extreme debris disks (EDDs) are rare systems with peculiarly large amounts of warm dust that may stem from recent giant impacts between planetary embryos during the final phases of terrestrial planet growth. Here we report on the identification and characterization of six new EDDs. These disks surround F5-G9 type main-sequence stars with ages >100 Myr, have dust temperatures higher than 300 K, and fractional luminosities between 0.01 and 0.07. Using time-domain photometric data at 3.4 and 4.6 μm from the WISE all-sky surveys, we conclude that four of these disks exhibited variable mid-infrared (IR) emission between 2010 and 2019. Analyzing the sample of all known EDDs, now expanded to 17 objects, we find that 14 of them showed changes at 3-5 μm over the past decade, suggesting that mid-IR variability is an inherent characteristic of EDDs. We also report that wide-orbit pairs are significantly more common in EDD systems than in the normal stellar population. While current models of rocky planet formation predict that the majority of giant collisions occur in the first 100 Myr, we find that the sample of EDDs is dominated by systems older than this age. This raises the possibility that the era of giant impacts may be longer than we think, or that some other mechanism(s) can also produce EDDs. We examine a scenario where the observed warm dust stems from the disruption and/or collisions of comets delivered from an outer reservoir into the inner regions, and explore what role the wide companions could play in this process
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