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Swift Satellite and AI unravel the distance of the farthest Gamma-Ray Bursts

Illustration: Schematic of the formation, propagation and detection on Earth of a distant gamma burst, together with a description of the determination of its distance by machine learning algorithms. . Source: Team publication.

International team of astrophysicists has developed a novel machine learning technique to measure the distance to gamma-ray bursts, the most powerful explosions in the universe. The result was published in the Astrophysical Journal Letters (ApJL) and the Astrophysical Journal Supplement Series (ApJSS). The lead authors of these works are Dr. Maria Dainotti (Japan, NAOJ) and Mr. Aditya Narendra from the Astronomical Observatory of the Jagiellonian University, Poland.

Gamma-Ray Bursts (GRBs) are the most explosive phenomena in the Universe occurring after the Big Bang. In a few seconds, they release the same amount of energy our Sun releases in its entire lifetime. Because they are so explosive and bright, they can be observed at the edge of the visible Universe up to the time it was just 500 million years old. Looking at the Universe at such large distances opens the possibility of chasing the oldest stars that are extremely rare to observe and are thought to be the progenitors of GRBs.

GRBs are believed to occur in different ways. One is when a massive star, more than 30 times heavier than our Sun, reaches the end of its life, and explodes in a spectacular supernova. These are some of the brightest events in the known universe, and have been observed to give rise to a particular category of GRBs called Long GRBs. The second type of event which causes GRBs is called mergers. This is when the corpses of dead stars, such as neutron stars, come together gravitationally and collide with each other, releasing a huge amount of energy in a very short time. These events have been observed to lead to Short GRBs.

RBs are not only observed far away but also at close distances, and this allows us to understand how the stars evolve over time and how many GRBs can occur in a given space and in a given time. However, measuring the distance of GRBs is a very challenging task due to the paucity of Telescopes pointing to these serendipitous events and the limitations of other facilities. Thus, we need an indirect measurement of the distance, and this is where the Neil Gehrels Swift satellite data and machine learning come to help us.

An international team, led by Dr. Maria Dainotti, Assistant Professor at the National Astronomical Observatory of Japan, and Mr. Aditya Narendra, final year doctoral student at Jagiellonian University in Kraków, Poland, has developed a well-crafted machine learning methodology for inferring the distance to GRBs completely based on GRB properties which are not directly dependent on the distance.

The novelty of this approach is that, instead of using one machine learning model, they gather several methods together to improve the predictive power of these tools. This method is called Superlearner and assigns to each algorithm a weight whose values range from 0 to 1, and each weight corresponds to the predictive power of that singular method - says Dr. Dainotti. The advantage of the Superlearner is that the final prediction is always more performant than the singular models. Superlearner is also used to discard the algorithms which are the least predictive, for example, we set a threshold for accepting a model into our ensemble. This threshold is 0.05.

This increases the chances that the method can determine more precisely the distance of GRBs.

Because the SuperLearner can combine any machine learning model for creating the final ensemble, we can adapt the methodology depending on the data we are working with - explains Mr. Narendra. A crucial component of training the machine learning models is cross validation. This is a technique which mimics the real-life performance of a machine learning algorithm and thus gives us an accurate estimate of its performance.

Indeed, the team showed that the GRB rate does not follow the star formation rate at small distances, thus opening the possibility that the long duration GRBs at small distances may be generated not by a collapse of massive stars (with masses 30 times larger than the mass of the Sun) as it was previously assumed, but rather by the merger of very dense objects (neutron stars). This claim has been developed based on the Swift X-ray data by Dr. Vahe’ Petrosian, Professor at Stanford University and Dr. Dainotti, while the current paper confirms this for the optical data as well.

This research pushes forward the frontier in both Gamma-Ray astronomy and machine learning. Follow-up research and innovation can help astrophysicists achieve more precise results and, in the future, even address cosmological problems.

Illustration: Gamma-Ray Bursts flow-chart scheme. Credit: The Authors.


Original publication

Maria Giovanna Dainotti, Aditya Narendra, Agnieszka Pollo, Vahé Petrosian, Małgorzata Bogdan, Kazunari Iwasaki, Jason Xavier Prochaska, Enrico Rinaldi, David Zhou, Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach, ApJL 967 L30 (2024).

The findings described are part of a study conducted in the Department of Stellar and Extragalactic Astronomy of the Jagiellonian University Astronomical Observatory in Kraków. A similar X-rays analysis using data by Swift is also published: Dainotti, M. G. et al. "Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model." The Astrophysical Journal Supplement Series 271.1 (2024): 22, DOI: 10.3847/1538-4365/ad1aaf

Contact

Aditya Narendra
Astronomical Observatory
Jagiellonian University
Aditya.Narendra [@] doctoral.uj.edu.pl

Chaos and Einstein-Rosen Gravitational Waves

Illustration: The parameters on the axes (P and rho) define the particle's initial state, and the color of the corresponding dot is associated with its final position obtained after some time. The figure shows that test particles initially in similar initial states can have entirely different final states. Credit: The Authors.

In 2015, the first detection of gravitational waves was made. Now, nine years later, gravitational wave astronomy is a rapidly growing field. We have learned to observe the Universe in a previously unknown way.

TThe success achieved in 2015 did not come out of nowhere. The construction and refinement of the first gravitational wave detector took several decades. The intellectual effort of theorists studying the predictions of Einstein's general relativity had been ongoing since its discovery in 1915. Several generations of researchers over the course of 100 years tried to figure out how to mathematically describe such waves, whether their existence is consistent with the structure of the theory, what properties they have, and how such waves can interact with matter. A key step was the 1937 paper by Albert Einstein and Nathan Rosen. In the original version of this article, the authors erroneously argued that the existence of gravitational waves is inconsistent with the structure of the theory. When their error was pointed out, their article turned into proof of the existence of gravitational waves. The class of mathematical solutions to Einstein's equations found by Einstein and Rosen did not describe waves originating from astrophysical sources. The equations were solved under the simplifying assumption of cylindrical symmetry. Although we know that the universe does not resemble a giant tube, the solutions discovered proved that gravitational waves are admissible in general relativitiy and revealed some of their properties. In their article, Einstein and Rosen mention that the class of solutions they found includes so-called standing waves. This is a special case where the gravitational wave does not effectively move at the speed of light but corresponds to the oscillation of the structure of spacetime like a struck surface of a drum.

Although standing gravitational waves may have appeared in the early universe, there is currently no convincing evidence of their existence beyond the hypothetical possibility arising from the structure of Einstein's equations. Many years ago, experiments were planned in which such waves could be produced in the laboratory and indirectly detected through their interaction with the electromagnetic field. Unfortunately, to this day, these experiments remain beyond our technical capabilities, and the standing waves themselves are the subject of exclusively mathematical research. Although their detection is a matter of the distant future, some of their properties make them truly extraordinary. Stationary vibrations of spacetime in some way resemble particles: localized energy nodes interacting with the environment. If we remember that a gravitational wave is a disturbance of spacetime (time and space), the matter becomes astonishing: time and space, vibrating, effectively behave like an object with mass. In other words, something can be made out of nothing because how else to describe objects made only of time and space? These objects differ from John Wheeler's geons in that they always appear in many copies, like the ridges of small waves on the surface of a drum membrane.

As part of research conducted at the Department of Relativistic Astrophysics and Cosmology, the behavior of test particles in the spacetime of a standing gravitational wave belonging to the Einstein-Rosen class of solutions was examined. Test particles are understood as particles small enough that the spacetime curvature resulting from their energy is negligibly small. Test particles, without disturbing spacetime vibrations, move to their rhythm like grains of sand bouncing on the surface of a drum. The research revealed that the behavior of such particles could be incredibly complex. Deterministic chaos was discovered: although the equations clearly predict the movement of particles, in practice, their final position is extremely sensitive to the initial position. The famous butterfly effect occurs. A detailed analysis of the dynamics of test particles revealed the existence of a complex structure in the solution space known as the heteroclinic network. The butterfly effect for the studied system can be visualized using a fractal. The parameters on the axes (P and rho) define the particle's initial state, and the color of the corresponding dot is associated with its final position obtained after some time. The figure shows that test particles initially in similar initial states can have entirely different final states.

Illustration: The fractal. The parameters on the axes (P and rho) define the particle's initial state, and the color of the corresponding dot is associated with its final position obtained after some time. The figure shows that test particles initially in similar initial states can have entirely different final states. Credit: The Authors.


Original publication

Sebastian J. Szybka, Syed U. Naqvi, “Chaos and Einstein-Rosen gravitational waves”, Phys. Rev. D108, L081501 (2023).

The findings described are part of a study conducted in the Department of Relativistic Astrophysics and Cosmology of the Jagiellonian University Astronomical Observatory in Kraków.

Contact

Sebastian Szybka
Astronomical Observatory
Jagiellonian University
S.Szybka [@] uj.edu.pl

Multi-wavelength diagnostics of the Molecular Cloud, DC 314.8-5.1

Figure 1: DC 314.8--5.1 region as seen at different wavelengths: (top left) Spitzer MIPS 24 micron log-scaled intensity mosaic map; (top right) Spitzer IRAC 5.8 micron log-scaled intensity mosaic map; (bottom left) DSS red linear scaled image (700 nm); (bottom right) Swift UVOT M2 – 2250 Angstrom band log-scaled map. In each panel, the white dashed ellipse denotes the globule with the central position marked by a white``x''. The green ellipses mark UVOT detected sources with HD 130079 marked on the left and TYC 9015-926-1 marked near the northern boundary of the globule. ``C1'' marks the YSO candidate identified by Whittet (2007). ``C2'' and ``C3'' mark the potential YSO candidates identified in this work. The X-ray source detected with Swift-XRT is indicated by ``S'' with a cross.

An international team of astronomers led by researchers at the Jagiellonian University Astronomical Observatory, study the multi-wavelength characteristics of the interstellar medium, probing the stellar populations within the dark cloud, DC 314.8-5.1. The team utilized data from the Gaia optical, 2MASS near-infrared, and WISE mid-infrared surveys, dedicated imaging with the Spitzer Space Telescope, and X-ray data obtained with the Swift-XRT Telescope (XRT).

The physical state of molecular clouds at a given evolutionary stage is strongly dependent on the development of star formation within such systems. Stars form when the dense cores of molecular clouds collapse, with the infall of material resulting in the gravitational potential energy heating the material and increasing its density to the point of gravitational collapse and consequently a star forming. The main effects of stellar formation are the processing of the dust within the cloud, the disruption of the cloud structure, and heating of the cloud material. These processes continue as the system is altered and disrupted by the evolving young star.

It follows that there in studying clouds prior to the onset of star formation, to study the conditions there. Through the use of multi-wavelength observations, we can probe the conditions in such clouds, as seen in Figure 1. Using observations in optical bands (bottom-left panel) shows the absorption of stellar light by the dust, in infrared we can begin to see the stars hidden behind the cloud (top-right), and even see the emission of the dust itself (top-left), additionally we can see nearby objects using UV observations (bottom-left).

Each wavelength can show different aspects of the system with mid to far infrared observations showing dust emission with cooler dust becoming more prevalent with millimeter observations. Using broadband observations one can inspect the temperature profile of a molecular cloud as seen in Figure 2.
 
Figure 2: (top) SED of the DC 314.8-5.1 system, based on observations with Planck (filled black circles), IRAS (red crosses), and WISE (open red circles). Dark red solid and dashed curves represent modified blackbody models for the emission of cold (14 K) and warm (160 K) gas within or on the surface of the cloud, respectively; black solid curve denotes the superposition of the two. (bottom) SED of HD 130079 from ground-based telescopes and Gaia survey (small blue stars), WISE (open red circles) and finally with the Swift UVOT (big blue star). Dark blue dot-dashed curve corresponds to the intrinsic emission of the field star HD 130079, modeled as a blackbody with the temperature 10,500 K and the total luminosity of ; dark blue solid curve illustrates this intrinsic emission subjected to the interstellar reddening.
Figure 2: (top) SED of the DC 314.8-5.1 system, based on observations with Planck (filled black circles), IRAS (red crosses), and WISE (open red circles). Dark red solid and dashed curves represent modified blackbody models for the emission of cold (14 K) and warm (160 K) gas within or on the surface of the cloud, respectively; black solid curve denotes the superposition of the two. (bottom) SED of HD 130079 from ground-based telescopes and Gaia survey (small blue stars), WISE (open red circles) and finally with the Swift UVOT (big blue star). Dark blue dot-dashed curve corresponds to the intrinsic emission of the field star HD 130079, modeled as a blackbody with the temperature 10,500 K and the total luminosity of ; dark blue solid curve illustrates this intrinsic emission subjected to the interstellar reddening.

 


In this research we further discuss the multi-wavelength properties of the dark globule, DC 314.8-5.1, through dedicated observations with the Spitzer Space Telescope and the Swift-XRT and UVOT instruments, supplemented by the archival Planck, IRAS, WISE, 2MASS, and Gaia data. We identified it as an ideal candidate for deeper observations, particularly in high-energy X-ray and gamma-ray.

Figure 1: DC 314.8--5.1 region as seen at different wavelengths: (top left) Spitzer MIPS 24 micron log-scaled intensity mosaic map; (top right) Spitzer IRAC 5.8 micron log-scaled intensity mosaic map; (bottom left) DSS red linear scaled image (700 nm); (bottom right) Swift UVOT M2 – 2250 Angstrom band log-scaled map. In each panel, the white dashed ellipse denotes the globule with the central position marked by a white``x''. The green ellipses mark UVOT detected sources with HD 130079 marked on the left and TYC 9015-926-1 marked near the northern boundary of the globule. ``C1'' marks the YSO candidate identified by Whittet (2007). ``C2'' and ``C3'' mark the potential YSO candidates identified in this work. The X-ray source detected with Swift-XRT is indicated by ``S'' with a cross.


Original publication

E. Kosmaczewski, Ł. Stawarz et al. “Multiwavelength Study of Dark Globule DC 314.8-5.1: Point Source Identification and Diffuse Emission Characterization”. In: ApJ 959.1 (Dec. 2023), p. 37, DOI 10.3847/1538-4357/ad077a. arXiv: 2209.02372 [astro-ph.GA].

The findings described are part of a study conducted in the Department of High Energy Astrophysics of the Jagiellonian University Astronomical Observatory in Kraków.

Contact

Łukasz Stawarz
Astronomical Observatory
Jagiellonian University
L.Stawarz [@] uj.edu.pl

Classification of galactic mergers with convolutional neural networks

Na ilustracji: Układ Arp 87, czyli NGC 3808A i NGC 3808B: zderzające się ze sobą galaktyki sfotografowane teleskopem Hubble'a. Źródło: APOD / NASA, ESA, Harshwardhan Pathak. Lambda-CDM cosmology assumes that galaxies form by hierarchical mergers of smaller structures, so mergers of galaxies provide astronomers with crucial information about the evolution of galaxies over time.

The merging process takes place differently depending on the properties of the colliding galaxies. Major mergers, whose components have a mass ratio of up to 1:4, when colliding at the appropriate speed and angle, merge their structures, which leads to a modification of their morphology. Then the specific features associated with the collision of galaxies - bridges, double nuclei and other tidal features, including tails and plumes of different shapes and sizes - can become apparent. In addition, major mergers can cause phenomena of rapid formation of new stars and activation of nuclei of active galaxies. However, the influence of mergers on these phenomena is still under debate, as it is not clear exactly what role they play compared to other physical processes, such as smooth gas accretion.

When the mass of one of the galaxies involved in a collision is significantly greater than that of the other galaxy, the merger process is smoother, and the smaller galaxy is usually absorbed by the larger one, leaving it almost untouched. Galaxy mergers are a significant factor in the study of the evolution of the Universe. It is estimated that mergers account for less than 10% of low redshift galaxies, with the percentage rising to 20% for those in the range of 2 to 3.

A major challenge in the study of galaxy collisions is detecting them with sufficient efficiency and completeness. Due to the wide range of morphological features of galaxy collisions, their visual classification is difficult in terms of consistent, reproducible application. The use of morphological parameters provides a way to determine morphology in a reliable and invariant way. They describe the shapes and concentration of light in the images. However, obtaining these parameters requires images with sufficiently high resolution. Another method - so-called close pairs - is more direct. It involves finding pairs of galaxies that are close together in the sky and at the same redshift. However, it requires expensive, long-term spectroscopic observations.

The result challenges the previous knowledge of pulsars. The traditional scheme, according to which particles are accelerated along magnetic field lines inside or slightly outside the magnetosphere, cannot explain the new observations well. We may be observing particle acceleration through a so-called magnetic reconnection process outside the light cylinder that somehow preserves the rotation pattern, but even this scenario faces difficulties in explaining how radiation of such extreme energy is produced.

With the upcoming new sky surveys providing astronomers with large amounts of data, including Euclid and LSST, it will be crucial to create algorithms to automate time-consuming and repetitive tasks, specifically such as identifying galaxy mergers. Recent studies have shown that it is possible to use convolutional neural networks (CNNs) to solve the problem of visual classification of galaxy mergers.

A recent comparative study of machine learning-based galaxy collision detection methods by an international team of astronomers aims to understand the relative performance of different machine learning methods within the same system (Margalef-Bentabol 2024 et al.). A total of six machine learning methods were tested, based on the same cosmological, gravomagnetohydrodynamic IllustrisTNG simulations. These allowed the generation of images that mimicked the real data. All of the grids that have not been pre-trained on galaxy images yield similar results, despite being built on different architectures. This may indicate that preprocessing the training data is a more important factor than choosing the parameters of the neural networks.

This aspect is now also being studied by astronomers from Jagiellonian University Astronomical Observatory, testing and comparing the performance of convolutional networks trained on original and processed data. They match Sersic profiles - the dependence of light intensity on distance from the galactic center - to synthetic images of galaxies, and then subtract them from the original ones. This process creates so-called residual images, where anything that doesn't fit the matched profile remains. This allows subtle features of galactic mergers, such as diffusion structures or tidal features, to be highlighted. As a result, it is possible to train three different networks with the same architecture on three different data sets - the original images, the fitted Sersic models and the residual images.

The results show that the network trained on the original data performs best. Its overall accuracy - the number of correctly classified images - is 74%. The network performs better at identifying non-colliding galaxies. 82% of them are correctly identified, while for colliding galaxies this number drops to 64%. The network trained on the Sersic models has similar performance in recognizing non-mergers: 80% of them are correctly classified. In contrast, it performs much worse at identifying mergers, correctly predicting only 56% of them. The network trained on residual images shows different characteristics - it correctly classifies 67% of images showing intact galaxies and 66% of images of colliding galaxies, making it the most effective network in correctly classifying mergers. The last two networks in general have similar accuracy, classifying correctly about 69% of all images.

By applying machine learning methods, it was possible to determine that the classification of galaxies into mergers and non-mergers is possible using both the weak diffusion structures present in the residual images and the spatial information contained in the Sersic profiles. The next step will be to adapt networks trained on synthetic images to real astronomical data, as it turns out that networks trained on simulation inputs perform much worse in evaluating real images. Techniques that focus, among other things, on finding common features in the domain of the images used for training and evaluation appear to be key in this aspect.
 
 
Illustration: Arp 87 system, or NGC 3808A and NGC 3808B: colliding galaxies photographed with the Hubble telescope. Source: APOD / NASA, ESA, Harshwardhan Pathak.



Original publication: in preparation.

The findings described are part of a study conducted in the Department of Stellar and Extragalactic Astronomy of the Jagiellonian University Astronomical Observatory in Kraków.
 

Contact


Dawid Chudy
Astronomical Observatory
Jagiellonian University
Dawid.Chudy [@] doctoral.uj.edu.pl
 

Pulsar and gamma rays with record energy

Illustration: It is believed that the energies of infrared light photons from the pulsar's poles are amplified to gamma-ray (blue) energy by ultrarelativistic electrons. (Science Communication Lab for DESY)

Pulsars are fast-rotating neutron stars endowed with very strong magnetic fields. Although it has been more than 55 years since their discovery, the mechanism responsible for their radiation is still mysterious.

Astrophysicists at the H.E.S.S. Observatory in Namibia have discovered the highest energy gamma rays ever from the Vela pulsar. The energy of the photons of this radiation reaches 20 teraelectronvolts, which is as much as ten trillion times the energy of photons in visible light. As reported by the international team, which included astronomers from Jagiellonian University in Kraków, these observational results are difficult to reconcile with the current theory of the formation of high energy gamma rays in pulsars.

Pulsars are the left-over corpses of stars that spectacularly exploded in a supernova. The explosions leave behind a tiny, dead star with a diameter of just some 20 kilometres, rotating extremely fast and endowed with an enormous magnetic field. Such a star is made up almost entirely of neurons and is also extremely dense: a teaspoon of its matter has a mass of more than five billion tons, or roughly 900 times the mass of the Great Pyramid of Giza.

Pulsars also emit rotating beams of electromagnetic radiation. They can be compared to cosmic lighthouses. If their beam sweeps across our solar system, we see flashes of radiation at regular time intervals. These flashes, also called pulses of radiation, can be searched for in different energy bands of the electromagnetic spectrum. Their source is probably the fast electrons created and accelerated in the pulsar's magnetosphere as it travels toward its periphery. The magnetosphere is made up of plasma and electromagnetic fields that surround and co-rotate with the star. Heading outward from it, the electrons gain energy and then release it in the form of observed regular beams of radiation.

The Vela pulsar (PSR J0835-4510) located in the Southern sky in the constellation Vela is the brightest pulsar in the radio band of the electromagnetic spectrum and the brightest persistent source of cosmic gamma rays in the gigaelectronvolts (GeV) range. It rotates about eleven times per second. Above few GeV, however, its radiation ends abruptly. Scientists assume that the electrons reach the end of the pulsar’s magnetosphere and escape from it. Using long-term observations with the H.E.S.S. telescopes, a new radiation component at even higher energies has been discovered, with energies of up to tens of teraelectronvolts (TeV). This very high-energy component appears at the same phase intervals as the one observed in the GeV range. To attain these energies, the electrons might have to travel even farther than the magnetosphere, yet the rotational emission pattern needs to remain intact.

The result challenges the previous knowledge of pulsars. The traditional scheme, according to which particles are accelerated along magnetic field lines inside or slightly outside the magnetosphere, cannot explain the new observations well. We may be observing particle acceleration through a so-called magnetic reconnection process outside the light cylinder that somehow preserves the rotation pattern, but even this scenario faces difficulties in explaining how radiation of such extreme energy is produced.

The Vela pulsar, apart from its other superlatives, holds the record as the pulsar with the highest-energy gamma rays discovered to date.
 
 
Illustration: It is believed that the energies of infrared light photons from the pulsar's poles are amplified to gamma-ray (blue) energy by ultrarelativistic electrons. (Science Communication Lab for DESY)

 

Original publication

The H.E.S.S. collaboration, Discovery of a Radiation Component from the Vela Pulsar Reaching 20 Teraelectronvolts, Nature Astronomy (2023).

The findings described are part of a study conducted in the Department of High Energy Astrophysics of the Jagiellonian University Astronomical Observatory in Kraków. The participation of Polish scientists in the H.E.S.S. project was co-financed from the program of the Minister of Education and Science "Support for participation of Polish scientific teams in international research infrastructure projects" under agreement no. 2021/WK/06.

Contact

Łukasz Stawarz
Astronomical Observatory
Jagiellonian University
Łukasz.Stawarz [@] uj.edu.pl

PSR B0809+74 – a drifting subpulse pulsar reveals the secrets of the emission mechanism

 Illustration 2: Examples of the series of pulses observed in PSR B0809+74 with the LOFAR PL611 radio telescope in Łazy near Kraków. The observations were made at 150 MHz with a bandwidth of 72 MHz. The series of pulses reproduces the appearance of the shapes of successive consecutive pulses (from bottom to top), showing their varying brightness on a color scale. The series on the left shows the typical drift phenomenon -- in successive pulses the emission shifts to the left (it occurs progressively earlier in the pulse phase), creating characteristic and regular "drift bands." The middle image shows radiation decay (so-called nulling) -- the pulsar's emission has decayed over several pulses. In the right image, in addition to the pulse decay, there was also a change in the mode of radiation, as manifested by the disruption of the regularity of the drift bands. Source: Team publication.

 

Pulsars are fast-rotating neutron stars endowed with a very strong magnetic field. While more than 55 years have passed since their discovery, the mechanism responsible for their radiation still remains mysterious.

We know that radiate electrons accelerated along twisted magnetic field lines, which, along with the z pulsar rotation creates a "lighthouse" effect, but the details of the mechanism of radio emission formation remain largely unknown. One way to study this mechanism are observations of single pulses coming from pulsars, in which the phenomenon of "drifting pulses" is sometimes seen: with each successive pulse, the emission clearly shifts in phase, which is best visible in images of a series of pulses, revealing characteristic "drift bands".

Some pulsars also show other effects related to the emission mechanism, such as pulse nulling and radiation mode changes (moding). One such object is PSR B0809+74, which is regularly observed by a radio telescope belonging to the Jagiellonian University and being part of the pan-European LOFAR network in Łazy near Kraków. Observations of such objects are a key element in our attempts to understand the physical conditions in the magnetosphere of pulsars and the phenomena occurring there that lead to their radio emissions.

 
Illustration: Examples of the series of pulses observed in PSR B0809+74 with the LOFAR PL611 radio telescope in Łazy near Kraków. The observations were made at 150 MHz with a bandwidth of 72 MHz. The series of pulses reproduces the appearance of the shapes of successive consecutive pulses (from bottom to top), showing their varying brightness on a color scale. The series on the left shows the typical drift phenomenon -- in successive pulses the emission shifts to the left (it occurs progressively earlier in the pulse phase), creating characteristic and regular "drift bands." The middle image shows radiation decay (so-called nulling) -- the pulsar's emission has decayed over several pulses. In the right image, in addition to the pulse decay, there was also a change in the mode of radiation, as manifested by the disruption of the regularity of the drift bands. Source: Team publication.


Original publication

Rahul Basu, Wojciech Lewandowski, Jarosław Kijak, Bartosz Śmierciak, Marian Soida, Leszek Błaszkiewicz, Andrzej Krankowski, Single pulse emission from PSR B0809+74 at 150 MHz using Polish LOFAR station, Monthly Notices of the Royal Astronomical Society, Vol 526, Issue 1, pp.691-699 (2023).

The publication was prepared as part of the POLFAR consortium (Polish consortium of the LOFAR network), with the participation of the Prof. Janusz Gil Institute of Astronomy at the University of Zielona Góra, the Astronomical Observatory of the Jagiellonian University in Kraków, and the Space Environment Radio Diagnostics Center belonging to the University of Warmia and Mazury in Olsztyn. The work received funding from NCN grant no. 2020/37/B/ST9/02215. Polish participation in the activities of the LOFAR network is funded by the Ministry of Science and Higher Education (LOFAR2. 0 upgrade, decision number: 2021/WK/2), which also funds the maintenance of the LOFAR PL-610 Borówiec, LOFAR PL-611 Łazy, and LOFAR PL-612 Bałdy radio telescopes (decisions nos. 30/530252/SPUB/SP/2022, 29/530358/SPUB/SP/2022, and 28/530020/SPUB/SP/2022).
 
Contact

Marian Soida
Astronomical Observatory
Jagiellonian University
Marian.Soida [@] uj.edu.pl