arXiv subjects

Marta Gonzalez

Publications and source records attributed to Marta Gonzalez.

11 recordsLinked to original sources

arXiv2024

Cloud structure and young star distribution in the Dragonfish complex

One way to shed some light on the star formation process this process is to analyse the relationship between the spatial distributions of gas and newly formed stars. In order to obtain robust results, it is necessary for this comparison to be made using quantitative and consistent descriptors applied to the same star-forming region. Here, we use fractal analysis to characterise and compare in a self-consistent way the structure of the cloud and the distribution of young stellar objects (YSO) in the Dragonfish star-forming complex. We used different emission maps of the Dragonfish Nebula and photometric information from the AllWISE catalogue to select a total of 1082 YSOs in the region, for some of which we derived physical properties from their spectral energy distributions (SEDs). For both datasets (cloud images and YSOs), the three-dimensional fractal dimension (Df) was calculated using previously developed and calibrated algorithms. The fractal dimension of the Dragonfish Nebula (Df = 2.6-2.7) agrees very well with values previously obtained for the Orion, Ophiuchus, and Perseus clouds. On the other hand, YSOs exhibit on average a significantly smaller value (Df = 1.9-2.0) that indicates a much more clumpy structure than the material from which they formed. This is a clear and direct evidence that the clustering degree of the newly born stars is significantly higher than that of the parent cloud from which they formed, but the physical mechanism behind this behaviour is still not clear. Additionally, younger Class I and Class II sources have smaller values (Df = 1.7 +/- 0.1) than more evolved Transition Disk objects (Df = 2.2 +/- 0.1), evidencing a certain evolutionary effect where an initially clumpy structure tends to gradually disappear over time.

astro-ph.GA↗

arXiv2023

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents. We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. Our results demonstrate that destination accessibility matters most overall, while low density and low connectivity also sharply increase CO$_2$ emissions. These general trends are similar across cities but we find idiosyncratic effects that can lead to substantially different recommendations. In more monocentric cities, we identify spatial corridors -- about 10--50 km from the city center -- where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.

cs.LG↗

arXiv2023

Exploring the Role of Explainability in AI-Assisted Embryo Selection

In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation, a process with large inter- and intra-clinician variability. Deep learning based methods are gaining attention, but their opaque nature compromises their acceptance in the clinical context, where transparency in the decision making is key. In this paper we analyze the current work in the explainability of AI-assisted embryo analysis models, identifying the limitations. We also discuss how these models could be integrated in the clinical context as decision support systems, considering the needs of clinicians and patients. Finally, we propose guidelines for the sake of increasing interpretability and trustworthiness, pushing this technology forward towards established clinical practice.

cs.CV↗

arXiv2021

High contrast and resolution near infrared photometry of the core of R136

We present the sharpest and deepest near infrared photometric analysis of the core of R136, a newly formed massive star cluster at the centre of the 30 Doradus star forming region in the Large Magellanic Cloud. We used the extreme adaptive optics of the SPHERE focal instrument implemented on the ESO Very Large Telescope and operated in its IRDIS imaging mode, for the second time with longer exposure time in the H- and K filters. Our aim was to (i) increase the number of resolved sources in the core of R136, and (ii) to compare with the first epoch to classify the properties of the detected common sources between the two epochs. Within the field of view (FOV) of 10.8"x12.1" (2.7pc x3.0pc), we detected 1499 sources in both H and K filters, for which 76% of these sources have visual companions closer than 0.2". The larger number of detected sources, enabled us to better sample the mass function (MF). The MF slopes are estimated at ages of 1, 1.5 and 2 Myr, at different radii, and for different mass ranges. The MF slopes for the mass range of 10-300 solar-mass are about 0.3 dex steeper than the mass range of 3-300 solar-mass, for the whole FOV and different radii. Comparing the JHK colours of 790 sources common in between the two epochs, 67% of detected sources in the outer region (r >3") are not consistent with evolutionary models at 1-2 Myr and with extinctions similar to the average cluster value, suggesting an origin from ongoing star formation within 30 Doradus, unrelated to R136.

astro-ph.SR↗

arXiv2020

The Spatial Evolution of Young Massive Clusters II. Looking for Imprints of Star Formation in NGC 2264 with Gaia DR2

Aims. To demonstrate that `INDICATE' is a powerful spatial analysis tool which when combined with kinematic data from Gaia DR2 can be used to robustly probe star formation history. Methods. We compared the dynamic & spatial distributions of young stellar objects (YSOs) at various evolutionary stages in NGC 2264 using Gaia DR2 proper motion data and INDICATE. Results. The dynamic & spatial behaviours of YSOs at different evolutionary stages are distinct. Dynamically, Class II YSOs predominately have non-random trajectories that are consistent with known substructures, whereas Class III YSOs have random trajectories with no clear expansion or contraction patterns. Spatially, there is a correlation between the evolutionary stage and source concentration: 69.4% of Class 0/I, 27.9% of Class II, and 7.7% of Class III objects are found to be clustered. The proportion of YSOs clustered with objects of the same class also follows this trend. Class 0/I objects are both found to be more tightly clustered with the general populous/objects of the same class than Class IIs and IIIs by a factor of 1.2/4.1 and 1.9/6.6, respectively. An exception to these findings is within 0.05deg of S Mon where Class III objects mimic the behaviours of Class II sources across the wider cluster region. Our results suggest (i) current YSOs distributions are a result of dynamical evolution, (ii) prolonged star formation has been occurring sequentially, and (iii) stellar feedback from S Mon is causing YSOs to appear as more evolved sources. Conclusions. Designed to provide a quantitative measure of clustering behaviours, INDICATE is a powerful tool with which to perform rigorous spatial analyses. Our findings are consistent with what is known about NGC 2264, effectively demonstrating that when combined with kinematic data from Gaia DR2 INDICATE can be used to robustly study the star formation history of a cluster.

astro-ph.GA↗

arXiv2019

Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined with the transportational features of a street. In such classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning has been adopted by major cities such as the city of San Francisco, the states of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning. We collect and label street imagery then train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87%, we then visualize samples from the embedding space of streets using the t-SNE method and apply class activation mapping methods to interpret the features in street imagery contributing to output classification from a model.

cs.CV↗

arXiv2019

Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting how changes in underlying factors at a given location affect urbanization outcomes at other locations, is currently not achievable at a large scale with traditional urban growth models, which are either too simplistic, or depend on detailed locally-collected socioeconomic data that is not available in most places. Here we develop a framework to estimate, purely from globally-available remote-sensing data and without parametric assumptions, the spatial sensitivity of the (\textit{static}) rate of change of urban sprawl to key macroeconomic development indicators. We formulate this spatial regression problem as an image-to-image translation task using conditional generative adversarial networks (GANs), where the gradients necessary for comparative static analysis are provided by the backpropagation algorithm used to train the model. This framework allows to naturally incorporate physical constraints, e.g., the inability to build over water bodies. To validate the spatial structure of model-generated built environment distributions, we use spatial statistics commonly used in urban form analysis. We apply our method to a novel dataset comprising of layers on the built environment, nightlighs measurements (a proxy for economic development and energy use), and population density for the world's most populous 15,000 cities.

cs.LG↗

arXiv2018

Modeling urbanization patterns with generative adversarial networks

In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

cs.LG↗

arXiv2017

Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale

Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.

cs.CV↗

arXiv2016

Phase-space structures and stellar populations in the star-forming region NGC~ 2264

In this work we analyse the structure of a subspace of the phase space of the star-forming region NGC~ 2264 using the Spectrum of Kinematic Groupings (SKG). We show that the SKG can be used to process a collection of star data to find substructure at different scales. We have found structure associated with the NGC~ 2264 region and also with the background area. In the NGC~ 2264 region, a hierarchical analysis shows substructure compatible with that found in previous specific studies of the area but with an objective, compact methodology that allows us to homogeneously compare the structure of different clusters and star-forming regions. Moreover, this structure is compatible with the different ages of the main NGC~ 2264 star-forming populations. The structure found in the field can be roughly associated with giant stars far in the background, dynamically decoupled from NGC~ 2264, which could be related either with the Outer Arm or Monoceros Ring. The results in this paper confirm the relationship between structure in the RV phase-space subspace and different kinds of populations, defined by other variables not necessarily analysed with the SKG, such as age or distance, showing the importance of detecting phase-space substructure in order to trace stellar populations in the broadest sense of the word.

astro-ph.GA↗

arXiv2015

Progmosis: Evaluating Risky Individual Behavior During Epidemics Using Mobile Network Data

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.

cs.SI↗