Search

The properties of the entire neutron star population can be inferred by modeling their evolution, from birth to the present, through pulsar population synthesis. This involves simulating a mock population, applying observational filters, and comparing the resulting sources to the limited subset of detected pulsars. We specifically focus on the magneto-rotational properties of Galactic isolated neutron stars and provide new insights into the intrinsic radio luminosity law by combining pulsar population synthesis with a simulation-based inference (SBI) technique called truncated sequential neural posterior estimation (TSNPE). We employ TSNPE to train a neural density estimator on simulated pulsar populations to approximate the posterior distribution of the underlying parameters. This technique efficiently explores the parameter space by concentrating on regions that are most likely to match the observed data thus allowing a significant reduction in training dataset size. We demonstrate the efficiency of TSNPE over standard neural posterior estimation (NPE), achieving robust inferences of magneto-rotational parameters consistent with previous studies using only around 4% of the simulations required by NPE approaches. Moreover, for the first time, we incorporate data from the Thousand Pulsar Array (TPA) program on MeerKAT, the largest unified sample of neutron stars with consistent fluxes measurement to date, to help constrain the stars' intrinsic radio luminosity. We find that adding flux information as an input to the neural network largely improves the constraints on the pulsars' radio luminosity, as well as improving the estimates on other input parameters.

This thesis work represents the first efforts to combine population synthesis studies of the Galactic isolated neutron stars with deep-learning techniques with the aim of better understanding neutron-star birth properties and evolution. In particular, we develop a flexible population-synthesis framework to model the dynamical and magneto-rotational evolution of neutron stars, their emission in radio and their detection with radio telescopes. We first study the feasibility of using deep neural networks to infer the dynamical properties at birth and then explore a simulation-based inference approach to predict the birth magnetic-field and spin-period distributions and the late-time magnetic-field decay for the observed radio pulsar population. Our results for the birth magneto-rotational properties agree with the findings of previous works while we constrain the late-time evolution of the magnetic field in neutron stars for the first time. Moreover, this thesis also studies possible scenarios to explain the puzzling nature of recently discovered periodic radio sources with very long periods of the order of thousands of seconds. In particular, by assuming a neutron-star origin, we study the spin-period evolution of a newborn neutron star interacting with a supernova fallback disk and find that the combination of strong, magnetar-like magnetic fields and moderate accretion rates can lead to very large spin periods on timescales of ten thousands of years. Moreover, we perform population synthesis studies to assess the possibility for these sources to be either neutron stars or magnetic white dwarfs emitting coherently through magnetic dipolar losses. These discoveries have opened up a new perspective on the neutron-star population and have started to question our current understanding of how coherent radio emission is produced in pulsar magnetospheres., Comment: PhD thesis defended in the Autonomous University of Barcelona (UAB), 8 Februay 2024. Advisors: Nanda Rea, Vanessa Graber

We combine pulsar population synthesis with simulation-based inference (SBI) to constrain the magnetorotational properties of isolated Galactic radio pulsars. We first develop a framework to model neutron star birth properties and their dynamical and magnetorotational evolution. We specifically sample initial magnetic field strengths, $B$, and spin periods, $P$, from lognormal distributions and capture the late-time magnetic field decay with a power law. Each lognormal is described by a mean, $\mu_{\log B}, \mu_{\log P}$, and standard deviation, $\sigma_{\log B}, \sigma_{\log P}$, while the power law is characterized by the index, $a_{\rm late}$. We subsequently model the stars' radio emission and observational biases to mimic detections with three radio surveys, and we produce a large database of synthetic $P$--$\dot{P}$ diagrams by varying our five magnetorotational input parameters. We then follow an SBI approach that focuses on neural posterior estimation and train deep neural networks to infer the parameters' posterior distributions. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain $\mu_{\log B} = 13.10^{+0.08}_{-0.10}$, $\sigma_{\log B} = 0.45^{+0.05}_{-0.05}$ and $\mu_{\log P} = -1.00^{+0.26}_{-0.21}$, $\sigma_{\log P} = 0.38^{+0.33}_{-0.18}$ for the lognormal distributions and $a_{\rm late} = -1.80^{+0.65}_{-0.61}$ for the power law at the $95\%$ credible interval. We contrast our results with previous studies and highlight uncertainties of the inferred $a_{\rm late}$ value. Our approach represents a crucial step toward robust statistical inference for complex population synthesis frameworks and forms the basis for future multiwavelength analyses of Galactic pulsars., Comment: 31 pages, 16 figures, 5 tables, 2 appendices; published version

The nature of two recently discovered radio emitters with unusually long periods of 18min (GLEAM-X J1627-52) and 21min (GPM J1839-10) is highly debated. Their bright radio emission resembles that of radio magnetars, but their long periodicities and lack of detection at other wavelengths challenge the neutron-star interpretation. In contrast, long rotational periods are common in white dwarfs but, although predicted, dipolar radio emission from isolated magnetic white dwarfs has never been unambiguously observed. In this work, we investigate these long-period objects as potential isolated neutron-star or white-dwarf dipolar radio emitters and find that both scenarios pose significant challenges to our understanding of radio emission via pair production in dipolar magnetospheres. We also perform population-synthesis simulations based on dipolar spin-down in both pictures, assuming different initial-period distributions, masses, radii, beaming fractions, and magnetic-field prescriptions, to assess their impact on the ultra-long pulsar population. In the neutron-star scenario, we do not expect a large number of ultra-long period pulsars under any physically motivated (or even extreme) assumptions for the period evolution. On the other hand, in the white-dwarf scenario, we can easily accommodate a large population of long-period radio emitters. However, no mechanism can easily explain the production of such bright coherent radio emission in either scenarios., Comment: 10 pages, 4 figures; ApJ in press

We report the discovery of a highly circularly polarized, variable, steep-spectrum pulsar in the Australian Square Kilometre Array Pathfinder (ASKAP) Variables and Slow Transients (VAST) survey. The pulsar is located about $1^\circ$ from the center of the Large Magellanic Cloud, and has a significant fractional circular polarization of $\sim$20%. We discovered pulsations with a period of 322.5 ms, dispersion measure (DM) of 157.5 pc cm$^{-3}$, and rotation measure (RM) of $+456$ rad m$^{-2}$ using observations from the MeerKAT and the Parkes telescopes. This DM firmly places the source, PSR J0523$-$7125, in the Large Magellanic Cloud (LMC). This RM is extreme compared to other pulsars in the LMC (more than twice that of the largest previously reported one). The average flux density of $\sim$1 mJy at 1400 MHz and $\sim$25 mJy at 400 MHz places it among the most luminous radio pulsars known. It likely evaded previous discovery because of its very steep radio spectrum (spectral index $\alpha \approx -3$, where $S_\nu \propto \nu^\alpha$) and broad pulse profile (duty cycle $\gtrsim35$%). We discuss implications for searches for unusual radio sources in continuum images, as well as extragalactic pulsars in the Magellanic Clouds and beyond. Our result highlighted the possibility of identifying pulsars, especially extreme pulsars, from radio continuum images. Future large-scale radio surveys will give us an unprecedented opportunity to discover more pulsars and potentially the most distant pulsars beyond the Magellanic Clouds., Comment: 17 pages, 7 figures; accepted by ApJ

For about half a century the radio pulsar population was observed to spin in the ~0.002-12s range, with different pulsar classes having a spin-period evolution that differs substantially depending on their magnetic fields or past accretion history. The recent detection of several slowly rotating pulsars has re-opened the long-standing question of the exact physics, and observational biases, driving the upper bound of the period range of the pulsar population. In this work, we perform a parameter study of the spin-period evolution of pulsars interacting with supernova fallback matter and specifically look at the fallback accretion disk scenario. Depending on the initial conditions at formation, this evolution can differ substantially from the typical dipolar spin-down, resulting in pulsars that show spin periods longer than their coeval peers. By using general assumptions for the pulsar spin period and magnetic field at birth, initial fallback accretion rates and including magnetic field decay, we find that very long spin periods (>100s) can be reached in the presence of strong, magnetar-like magnetic fields (>10^{14}G) and moderate initial fallback accretion rates (~10^{22-27} g s^{-1}). In addition, we study the cases of two recently discovered periodic radio sources, the pulsar PSR J0901-4046 (P = 75.9 s) and the radio transient GLEAM-X\,J162759.5-523504.3 (P = 1091 s), in light of our model. We conclude that the supernova fallback scenario could represent a viable channel to produce a population of long-period isolated pulsars that only recent observation campaigns are starting to unveil., Comment: 13 pages, 6 figures; ApJ in press

We explore the possibility of inferring the properties of the Galactic neutron star population through machine learning. In particular, in this paper we focus on their dynamical characteristics and show that an artificial neural network is able to estimate with high accuracy the parameters which control the current positions of a mock population of pulsars. For this purpose, we implement a simplified population-synthesis framework (where selection biases are neglected at this stage) and concentrate on the natal kick-velocity distribution and the distribution of birth distances from the Galactic plane. By varying these and evolving the pulsar trajectories in time, we generate a series of simulations that are used to train and validate a suitably structured convolutional neural network. We demonstrate that our network is able to recover the parameters governing the kick-velocity and Galactic height distribution with a mean relative error of about $10^{-2}$. We discuss the limitations of our idealized approach and study a toy problem to introduce selection effects in a phenomenological way by incorporating the observed proper motions of 216 isolated pulsars. Our analysis highlights that increasing the sample of pulsars with accurate proper motion measurements by a factor of $\sim$10, one of the future breakthroughs of the Square Kilometer Array, we might succeed in constraining the birth spatial and kick-velocity distribution of the neutron stars in the Milky Way with high precision through machine learning., Comment: 34 pages, including 12 pages appendices, 15 figures, 5 tables

We perform the luminosity--volume test, also known as $\langle V/V_{MAX}\rangle$, to Fast Radio Bursts (FRBs). We compare the 23 FRBs, recently discovered by ASKAP, with 20 of the FRBs found by Parkes. These samples have different flux limits and correspond to different explored volumes. We assume that their dispersion measure indicates their redshift and apply the appropriate cosmological corrections to the spectrum and rate in order to compute the $\langle V/V_{MAX}\rangle$ for the ASKAP and Parkes samples. For a radio spectrum of FRBs $F_\nu \propto \nu^{-1.6}$, we find $\langle V/V_{MAX}\rangle=0.66\pm 0.05$ for the ASKAP sample, that includes FRBs up to $z=0.7$, and 0.52$\pm 0.04$ for Parkes, that extends up to $z=2.1$. The ASKAP value suggests that the population of FRB progenitors evolves faster than the star formation rate, while the Parkes value is consistent with it. Even a delayed (as a power law or gaussian) star formation rate cannot reproduce the $\langle V/V_{MAX}\rangle$ of both samples. If FRBs do not evolve in luminosity, the $\langle V/V_{MAX}\rangle$ values of ASKAP and Parkes sample are consistent with a population of progenitors whose density strongly evolves with redshift as $\sim z^{2.8}$ up to $z \sim 0.7$. We discuss possible scenarios accounting for our results., Comment: 10 pages, 5 figures, 3 tables, accepted by A&A on 2019/04/02