arXiv subjects

A. Vafaei Sadr

Publications and source records attributed to A. Vafaei Sadr.

10 recordsLinked to original sources

arXiv2023

SKA Science Data Challenge 2: analysis and results

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000~h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, `reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy -- which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance -- underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets.

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arXiv2021

Planck Limits on Cosmic String Tension Using Machine Learning

We develop two parallel machine-learning pipelines to estimate the contribution of cosmic strings (CSs), conveniently encoded in their tension ($Gμ$), to the anisotropies of the cosmic microwave background radiation observed by {\it Planck}. The first approach is tree-based and feeds on certain map features derived by image processing and statistical tools. The second uses convolutional neural network with the goal to explore possible non-trivial features of the CS imprints. The two pipelines are trained on {\it Planck} simulations and when applied to {\it Planck} \texttt{SMICA} map yield the $3σ$ upper bound of $Gμ\lesssim 8.6\times 10^{-7}$. We also train and apply the pipelines to make forecasts for futuristic CMB-S4-like surveys and conservatively find their minimum detectable tension to be $Gμ_{\rm min}\sim 1.9\times 10^{-7}$.

astro-ph.CO↗

arXiv2021

Clustering of Local Extrema in Planck CMB maps

The clustering of local extrema will be exploited to examine Gaussianity, asymmetry, and the footprint of the cosmic-string network on the CMB observed by Planck. The number density of local extrema ($n_{\rm pk}$ for peak and $n_{\rm tr}$ for trough) and sharp clipping ($n_{\rm pix}$) statistics support the Gaussianity hypothesis for all component separations. However, the pixel at the threshold reveals a more consistent treatment with respect to end-to-end simulations. A very tiny deviation from associated simulations in the context of trough density, in the threshold range $θ\in [-2-0]$ for NILC and CR component separations, are detected. The unweighted two-point correlation function, of the local extrema, illustrates good consistency between different component separations and corresponding Gaussian simulations for almost all available thresholds. However, for high thresholds, a small deficit in the clustering of peaks is observed with respect to the Planck fiducial $Λ$CDM model. To put a significant constraint on the amplitude of the mass function based on the value of $Ψ$ around the Doppler peak ($θ\approx 70-75$ arcmin), we should consider $\vartheta\lesssim 0.0$. The scale-independent bias factors for the peak above a threshold for large separation angle and high threshold level are in agreement with the value expected for a pure Gaussian CMB. Applying the $n_{\rm pk}$, $n_{\rm tr}$, $Ψ_{\rm pk-pk}$ and $Ψ_{\rm tr-tr}$ measures on the tessellated CMB map with patches of $7.5^2$ deg$^2$ size prove statistical isotropy in the Planck maps. The peak clustering analysis puts the upper bound on the cosmic-string tension, $Gμ^{(\rm up)} \lesssim 5.59\times 10^{-7}$, in SMICA.

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arXiv2020

Dynamical Dark Energy Properties Hidden in the Dark Matter Halos and Voids

In this paper, we analysed the halos and voids properties of a GR-based N-body simulation carried out at redshifts z= 0.0 and z= 0.8 as differences between dynamical dark energy models (namely PL and CPL) with respect to LCDM. Analysing the halos demonstrates that both models, PL and CPL, behave like LCDM, despite the velocity dispersion of halos was more sensitive to the dynamical dark energy model. In addition, a void finder was developed to extract the properties of voids from simulated data. Further statistical model on voids confirms that the PL model produces larger voids. In summary, our novel simulation demonstrates void properties are better than halo properties in discriminating between dark energy models. Hence, the results suggest to make more use of the properties of voids in future studies of discriminating dynamical dark energy models.

astro-ph.CO↗

arXiv2020

Square Kilometre Array Science Data Challenge 1: analysis and results

As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

astro-ph.IM↗

arXiv2020

IMDb data from Two Generations, from 1979 to 2019; Part one, Dataset Introduction and Preliminary Analysis

"IMDb" as a user-regulating and one the most-visited portal has provided an opportunity to create an enormous database. Analysis of the information on Internet Movie Database - IMDb, either those related to the movie or provided by users would help to reveal the determinative factors in the route of success for each movie. As the lack of a comprehensive dataset was felt, we determined to do create a compendious dataset for the later analysis using the statistical methods and machine learning models; It comprises of various information provided on IMDb such as rating data, genre, cast and crew, MPAA rating certificate, parental guide details, related movie information, posters, etc, for over 79k titles which is the largest dataset by this date. The present paper is the first paper in a series of papers aiming at the mentioned goals, by a description of the created dataset and a preliminary analysis including some trend in data, demographic analysis of IMDb scores and their relation of genre MPAA rating certificate has been investigated.

cs.CY↗

arXiv2018

Eigen-reconstruction of Perturbations to the Primordial Tensor Power Spectrum

We explore the potential of the B-mode anisotropies of the Cosmic Microwave Background radiation (CMB) to constrain the shape of the primordial tensor power spectrum in a model-independent way. We expand possible perturbations to the power-law primordial tensor spectrum (predicted by the simplest single-field slow-roll inflationary models) using various sets of localized and nonlocalized basis functions and construct the Fisher matrix for their amplitudes. The eigen-analysis of the Fisher matrix would then yield a hierarchy of uncorrelated perturbation patterns (called tensor eigenmodes or TeMs) which are rank-ordered according to their measurability by data. We find that the first three TeMs are expected to be constrainable within a few percent by the next generation of B-mode experiments. We discuss how the method can be iteratively used to reconstruct the observable part of any general deviation from the fiducial power spectrum.

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arXiv2018

DeepSource: Point Source Detection using Deep Learning

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images.

astro-ph.IM↗

arXiv2018

Cosmic String Detection with Tree-Based Machine Learning

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of processed CMB maps that boost the cosmic string detectability. Our proposed classifiers, after training, give results improved over or similar to the claimed detectability levels of the existing methods for string tension, $Gμ$. They can make $3σ$ detection of strings with $Gμ\gtrsim 2.1\times 10^{-10}$ for noise-free, $0.9'$-resolution CMB observations. The minimum detectable tension increases to $Gμ\gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB S4-like (II) strategy, still a significant improvement over the previous results.

astro-ph.CO↗

arXiv2017

Multi-Scale Pipeline for the Search of String-Induced CMB Anisotropies

We propose a multi-scale edge-detection algorithm to search for the Gott-Kaiser-Stebbins imprints of a cosmic string (CS) network on the Cosmic Microwave Background (CMB) anisotropies. Curvelet decomposition and extended Canny algorithm are used to enhance the string detectability. Various statistical tools are then applied to quantify the deviation of CMB maps having a cosmic string contribution with respect to pure Gaussian anisotropies of inflationary origin. These statistical measures include the one-point probability density function, the weighted two-point correlation function (TPCF) of the anisotropies, the unweighted TPCF of the peaks and of the up-crossing map, as well as their cross-correlation. We use this algorithm on a hundred of simulated Nambu-Goto CMB flat sky maps, covering approximately $10\%$ of the sky, and for different string tensions $Gμ$. On noiseless sky maps with an angular resolution of $0.9'$, we show that our pipeline detects CSs with $Gμ$ as low as $Gμ\gtrsim 4.3\times 10^{-10}$. At the same resolution, but with a noise level typical to a CMB-S4 phase II experiment, the detection threshold would be to $Gμ\gtrsim 1.2 \times 10^{-7}$.

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