Supervisor: dr hab. Paweł Bielewicz, prof. NCBJ

Co-supervisor: prof. dr hab. Marek Biesiada

Application deadline: 2024-08-05

Start Date: 2024-10

Location: Pasteura 7, 02-093 Warsaw

Description:

Gravitational lensing of the cosmic microwave background (CMB) is a relativistic effect caused by the gravitational interaction of the CMB photons with matter inhomogeneities encountered during their travel from the last scattering surface to an observer. Reconstructed from correlated CMB ansiotropy gravitational potential of the lensing structures projected along the line-of-sight gives a unique image of the formation of the large scale structure at high redshifts and enables testing cosmological models at large scales. On the other hand, generated by the lensing effect divergence-free component of CMB polarisation has to be precisely estimated and corrected to be able to detect primordial gravitational waves produced during the inflationary epoch.

This PhD project will involve research on different aspects of the CMB gravitational lensing effect including developing and implementation of algorithms for estimation of the gravitational lensing potential, cross-correlations with galaxy and radio surveys, correcting CMB maps for the lensing effect and constraining amplitude of the primordial gravitational waves and sum of neutrino masses. The PhD student will analyse publicly available data from the Planck satellite and other ongoing and near future CMB surveys. The cross-correlation studies will be realized within the framework of the Rubin Observatory Legacy Survey of Space and Time survey. We seek strongly motivated PhD candidates with interest in cosmology who have experience in programming and numerical methods.

Funding: NCBJ

For the official announcement CLICK HERE

Supervisor: dr Katarzyna Małek, prof. NCBJ

Co-supervisor: dr Pratik Dabhade

Application deadline: 2024-08-05

Start Date: 2024-10

Location: Pasteura 7, 02-093 Warsaw

Description:

In the era of large, precise radio observatories such as LOFAR or SKA, and large optical surveys such as LSST, combining optical and radio data seems to be the best way to overcome the sometimes unavailability of infrared data. Although radio and infrared wavelengths probe different physical properties of galaxies, they still provide important information about the star formation rate of galaxies and hence the dust attenuation.

This project aims to combine deep radio and deep optical surveys to link proxies for dust attenuation, and hence stellar mass and star formation rate properties, of normal star-forming galaxies, but also of the low surface brightness galaxies that will be discovered in large numbers in the LSST era. The work will start with data from the North Ecliptic Pole field, where the low surface brightness galaxies have already been selected from the LSST-like deep optical data, and which is covered by the LOFAR deep field.

Funding: NCBJ

For the official announcement CLICK HERE

Supervisor: dr Katarzyna Małek, prof. NCBJ

Co-supervisor: dr William Pearson

Application deadline: 2024-08-05

Start Date: 2024-10

Location: Pasteura 7, 02-093 Warsaw

Description:

With ongoing and upcoming large surveys, such as the Legacy Survey of Space and Time (LSST), that will observe billions of galaxies, we need quick, efficient, and reproducible methods to identify different types of galaxies, including galaxy mergers. We currently have a number of different methods to identify galaxy mergers, such as visual identification, morphological statistics and machine learning. However, we do not have a clear picture of which method is the best. These methods are also not easily transferable between different surveys.

In this PhD project, the successful applicant will compare existing merger identification methods. The student will compare machine learning methods with the more classical morphological parameter selection. This will show, for the first time, which of these methods is the most reliable. The PhD student will also develop their own methods to identify galaxy mergers, which can consistently identify mergers in different survey data. This project will use state of the art observations from the Hyper Suprime-Cam and Euclid as well as data from the latest large cosmological simulations. As our team is an active member of LSST, the PhD student will be perfectly placed to apply their methods to create the first merger identifications for LSST.

Our team is one of the leading global research groups in galaxy mergers. We also have wide ranging expertise in morphological classifications and machine learning. The team also has a deep understanding of the physics that galaxies in general, and galaxy mergers in specific. As a result, this PhD project offers a unique opportunity for the student to gain expertise in all of these areas.

With highly accessible techniques that will be developed, modern technologies, and modern and near- future data sets, the results of this PhD project will be highly impactful and will influence future galaxy merger research.

For more information, contact: dr William Pearson, e-mail: william.pearson@ncbj.gov.pl

Funding: NCN

For the official announcement CLICK HERE

Supervisor: dr hab. Katarzyna Małek, prof. NCBJ

Co-supervisor: prof. dr hab. Agnieszka Pollo

Application deadline: 2024-08-05

Start Date: 2024-10

Location: Pasteura 7, 02-093 Warsaw

Description:

The upcoming Legacy Survey of Space and Time (LSST) will be the benchmark for the next decade, providing unprecedented depth and quality optical data on millions of galaxies, including low and high surface brightness galaxies, LSBs and HSBs, respectively. This PhD project focuses on the identification and analysis of LSBs, which are very diffuse and fainter than the typical night sky, using the LSST data. The PhD student will investigate intermediate galaxies between LSBs and HSBs to understand if there is an intrinsic separation or continuity between the two populations.

In the framework of this project, the PhD candidate will perform morphological analysis on these galaxies to estimate their properties such as size, surface brightness, and concentration. Several widely used tools like Galfit, Photutils, Autoprof, and machine learning techniques will be used to compare and evaluate the robustness of different tools for optimal morphological estimation of faint galaxies. The student will also carry out a multi-wavelength analysis of these galaxies by compiling ancillary data from the literature (e.g., GALEX, Spitzer, JWST, Herschel). The CIGALE tool will be used to perform Spectral Energy Distribution (SED) fitting techniques to estimate the stellar mass, star-formation rate, and dust attenuation of the galaxies.

Our team is an active member of the LSST collaboration with access to early data releases that will be available at the beginning of the 2024 year. We also have experts in the field of LSBs, galaxy morphology, as well as SED fitting. Therefore, this PhD project offers a unique opportunity for the student to gain expertise in all these aspects. The results of this research will provide essential insights into the nature of very faint galaxies and their role in galaxy formation and evolution scenarios.

Funding: NCN

For the official announcement CLICK HERE

Supervisor: prof. dr hab. Agnieszka Pollo

Co-supervisor: dr Junais

Application deadline: 2024-08-05

Start Date: 2024-10

Location: Pasteura 7, 02-093 Warsaw

Description:

The upcoming Legacy Survey of Space and Time (LSST) will be the benchmark for the next decade, providing unprecedented depth and quality optical data on millions of galaxies, including low and high surface brightness galaxies (LSBGs and HSBGs, respectively). This PhD project focuses on the identification and classification of LSBGs, i.e. galaxies that are very diffuse and fainter than the typical night sky, using the LSST data. The successful PhD candidate will develop and apply methods (in particular, but not only, methods based on different machine-learning-approaches) to identify LSBGs in optical data, starting from the existing survey data, with the aim to apply the developed methodologies to the coming LSST survey. The next task will be to classify so-obtained catalogs of LSBGs into sub- populations and analyze the properties of these populations with the aim of understanding the physical reasons behind their diversity.

This PhD project will be conducted as part of the NCN MAESTRO project “ Barely Visible: Low Surface Brightness Universe in the LSST era,” led by Prof. Agnieszka Pollo.

Our team is an active member of the LSST collaboration and will have access to early data releases at the beginning of 2027. We have experts in LSBGs, galaxy morphology, SED fitting, and machine learning. Therefore, this PhD project offers a unique opportunity to gain expertise in all these aspects. The results of this research will provide essential insights into the nature of very faint galaxies and their role in galaxy formation and evolution scenarios.

A successful candidate is expected to be already skilled in programming, with a particular emphasis on machine learning applications.

Funding: NCN

For the official announcement CLICK HERE