Periodic Reporting for period 1 - FASTIDIoUS (Fully Autonomous Search Tool to Investigate Directly Images and mOdeling of Unexplored Strong-lenses)
Período documentado: 2023-12-01 hasta 2025-11-30
The effect of gravitational lensing, which means that light of a background source is deflected by a massive object such as a galaxy cluster, enables the possibility to probe the universe in multiple aspects, including the study of magnified high-redshift objects, novel cosmological measurements of the Hubble constant, and of the dark matter and dark energy density parameter values, as well as investigations on the formation and evolution of galaxy clusters and their member galaxies in an overdense environment. The project aims to develop a new lens search tools and analyze of a subset of them in detail. The objectives of the project are 1) to increase the sample of known strong lensing clusters using deep learning techniques, 2) to develop and model with an automated pipeline multiple galaxy-scale lenses within clusters to study their formation and evolution, and 3) to expand one model to the full cluster to make the first comparison between a state-of-the-art model based on image positions only and a model based on the image pixel values directly, yielding insight in the effect of constraining the mass parameters through the pixel values and allowing a source surface brightness reconstruction, as so far only performed with galaxy-scale lenses. The project that is carried out is an innovative project built to impact several areas within the galaxy evolution and strong lensing fields.
The Marie Curie Fellow exploited so far a neural network to detect new galaxies strongly lensed by galaxies using public images observed by the Hyper Supreme Cam. By increasing the sample from a previous work, color-images of more than 137 million objects got analyzed by the convolutional neural network. The candidates with the highest scores got finally visually inspected by multiple experts to increase purity. This led to the public release of several hundred new strong lensing candidates. For the first time, this also included an analysis of the lens environment to identify candidates in highly overdense environment (see next section for more details). The results are published with open access. A spectroscopic confirmation of the most interesting systems is planned, enabling their detailed study to gain more insights into galaxy evolution and the dark matter distribution.
In addition, the Marie Curie Fellow build an enhanced total mass model of a previously known strong lensing cluster, hosting one of the known strongly lensed supernova. This model upgrade consists of implementing significantly more spectroscopic data, leading to nearly doubling the redshift range of strongly lensed background sources. The new model got publicly released and the corresponding article published. This new model is the basis for a total mass model going beyond the standard by using the multi-color images to reconstruct the supernova host directly, which work is currently carried out. This novel model will then be exploited for cosmography and the measure the expansion rate of the universe.
The Marie Curie Fellow is additionally involved in the analysis of all the other known supernovae strongly lensed by a galaxy cluster, and got recently selected as one of the three galaxy-scale lens Key Project coordinators carried out within the Euclid consortium, exploiting the data observed by the European space telescope Euclid launched 2024.
In addition, the Marie Curie Fellow build an enhanced total mass model of a previously known strong lensing cluster, hosting one of the known strongly lensed supernova. This model upgrade consists of implementing significantly more spectroscopic data, leading to nearly doubling the redshift range of strongly lensed background sources. The new model got publicly released and the corresponding article published. This new model is the basis for a total mass model going beyond the standard by using the multi-color images to reconstruct the supernova host directly, which work is currently carried out. This novel model will then be exploited for cosmography and the measure the expansion rate of the universe.
The Marie Curie Fellow is additionally involved in the analysis of all the other known supernovae strongly lensed by a galaxy cluster, and got recently selected as one of the three galaxy-scale lens Key Project coordinators carried out within the Euclid consortium, exploiting the data observed by the European space telescope Euclid launched 2024.
While lens search with convolutional neural networks became the state of the art technique, the work carried out by the Marie Curie Fellow goes beyond this in multiple aspects. First, normally a stringent pre-selection is done before applying the convolutional neural network. The published work is the first that analyses more than 100 million images without any stringent cut beforehand. This is crucial for the ongoing and upcoming wide filed surveys, such as the Euclid space mission of Europe, delivering billions of images that need to be analyzed. Second, the work includes for the first time an environment analysis. Tested against visual inspection and known non-lensing galaxy clusters, a new method was developed using photometrically determined redshifts. These redshifts were obtained by combining results from three different techniques to increase their precision, of which one algorithm, based on deep learning, was previously developed by the Fellow himself. Third, the publication includes a first total mass model for all identified lens candidates, obtained in a fully automated way, making it to the larges sample ever modeled in a uniform way. Although these rough mass model is not enough for a detailed analysis and comparable with the model required for the lensed supernovae, automated and fast detection, environment analysis, and modeling will be crucial in the upcoming decade as the European space telescope is expected to deliver on the order of 100 000 galaxy scale lenses.
On the other hand, improving the total cluster mass model for their details analysis is the other task tackled by the Marie Curie Fellow. Including the observed pixel values of the lensed supernova host and reconstructing its unlensed light distribution increases the constraints by around three orders of magnitude, leading to a new generation of mass models with unprecedented local accuracy. However, this increase of data results in significant computational challenges.
On the other hand, improving the total cluster mass model for their details analysis is the other task tackled by the Marie Curie Fellow. Including the observed pixel values of the lensed supernova host and reconstructing its unlensed light distribution increases the constraints by around three orders of magnitude, leading to a new generation of mass models with unprecedented local accuracy. However, this increase of data results in significant computational challenges.