Population synthesis of Galactic pulsars with machine learning
Michele Ronchi




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Doctoral Thesis
 


 

 

 
 
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 Tutor: 

Prof. Lluís Font Guiteras 




 


A thesis submitted for the degree of 
 
 



 
 



Barcelona, December 2023


 










The research reported in this thesis was carried out at the Institute of Space Sciences (ICE – CSIC), within the framework of the doctoral program in Physics at the Autonomous University of Barcelona (UAB). It was supported by the ERC via the Consolidator Grant “MAGNESIA” (No. 817661, PI: Nanda Rea), by the program Unidad de Excelencia María de Maeztu CEX2020-001058-M and partially by the grant SGR2021-01269 (PI: Vanessa Graber). 




Cover image © by Olena Shmahalo 














 


 





















 


The recent advancements in data availability and computational power have allowed the development of new machine-learning algorithms and data-analysis techniques that have opened up new avenues to study the Galactic neutron-star population.

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 of a simulated population of neutron stars from density maps storing the information on their sky position and proper motion. 
We then explore a simulation-based inference approach to constrain our physical models from the observed radio pulsar population. 
We employ a neural density estimator to predict the posterior distribution of the parameters describing the birth magnetic-field and spin-period distributions and the late-time magnetic-field decay from density maps containing information on the spin period and spin-period derivative. 
Our results for the initial 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. 

Besides the development of the population-synthesis framework, 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.

Overall this thesis work represents the first step towards developing a simulation framework able to model the different neutron star classes in a unified scenario and constraining the properties of the neutron-star population as a whole from multi-wavelength observations through machine learning.  



 §.§ This thesis is based on the following publications:
 


	

  * <cit.> 

“Analyzing the Galactic Pulsar Distribution with Machine Learning” 

Published in: ApJ 916.2, 100, p. 100


  * <cit.> 

“Long-period Pulsars as Possible Outcomes of Supernova Fallback Accretion” 

Published in: ApJ 934.2, 184, p. 184


  * <cit.> 

“Constraining the Nature of the 18 min Periodic Radio Transient GLEAM-X J162759.5-523504.3 via Multiwavelength Observations and Magneto-thermal Simulations” 
 
Published in: ApJ 940.1, 72, p. 72


  * <cit.> 

“A long-period radio transient active for three decades: population study in the neutron star and white dwarf rotating dipole scenarios” 
 
Accepted for publication in ApJ


  * <cit.> 

“Simulation-based inference for pulsar population synthesis” 

Submitted to ApJ










 
These past four years, besides being challenging, have been very enjoyable, stimulating and rewarding in a lot of aspects and I am very grateful to all the people that I met and who shared this part of my life with me.

First of all I would like to deeply thank my supervisors Nanda and Vanessa. Your guidance, exceptional expertise, enthusiasm, support and understanding have been fundamental during these years. I learnt a lot from you both from a scientific and a human point of view. Thanks to you I became more aware of both the positive aspects and drawbacks of academia and your advices are going to be incredibly valuable for choosing my future path.

People who have previously been and are currently part of the MAGNESIA group are all fantastic. The atmosphere has always been very friendly and supportive. Thanks to the entire Italian crew (Alice, Alessio, Francesco, Stefano, Daniele, Arianna and the new-comers Davide, Thomas and Anna) who always made me feel like at home and to all the people coming from Spain and other parts of the world (Albert I and Albert II, Celsa, Clara, Abu, Emilie, Kostas, Christine and Raj) who opened up my horizons. I feel enriched of having met so many interesting people with their diverse perspectives, vast knowledge and experience. I learned a lot from all of you. 
Special thanks to my PhD mates, Abu for your kindness and wisdom in every situation, Clara for your incredible determination and Celsa, also team-mate (and Spanish teacher), for your contagious enthusiasm and authenticity in everything you do. Together with Vanessa, the harmony and synergy in our population-synthesis group was fantastic and this thesis is only possible thanks to such an amazing team-work.

I extend my gratitude to all the people I met at ICE, a very international and stimulating environment with a lot of PhD students sharing the same journey and people at PIC, for all the helpful conversations on the computing side.
I would further like to thank my examiners, Ben Stappers, Alessandro Patruno and Helena Dominguez Sanchez, who took their time to read this thesis and give constructive feedback that helped me to improve this work.

Lastly, I would like to thank my family, my mum, dad, brother and my grandmother and all my friends back home for their support, encouragement and understanding in all these years.
 

















                
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XMM–Newton
Chandra
NuSTAR
ROSAT
Swift
NICER
Insight-HXMT
Parkes







SGR 1935+2154
XTE J1810-197

M_⊙ 

ATNF[ATNF]Australia Telescope National Facility
PHL[PHL]Parkes High-Latitude
PMB[PMB]Parkes Multi-Beam
SMB[SMB]Swinburne Intermediate-latitude
MLP[MLP]multi-layer perceptron
CNN[CNN]convolutional neural network
MDN[MDN]mixture density network
MAF[MAF]masked autoregressive flow
NPE[NPE]neural posterior estimation
SBC[SBC]simulation-based calibration
DM[DM]dispersion measure
SNR[SNR]Supernova Remnant
GRBs[GRBs]Gamma-Ray Bursts
FRBs[FRBs]Fast Radio Bursts
ANNs[ANNs]artificial neural networks
Adam[Adam]Adaptive Moment Estimation
RMSE[RMSE]root-mean-square error
MRE[MRE]mean relative error
SKA[SKA]Square Kilometer Array
ML[ML]machine learning
RPPs[RPPs]rotation-powered pulsars
SGRs[SGRs]soft gamma-ray repeaters
AXPs[AXPs]anomalous X-ray pulsars
RRATs[RRATs]rotating radio transients
CCOs[CCOs]central compact objects
XDINSs[XDINSs]X-ray dim isolated neutron stars
CCSN[CCSN]core-collapse supernova
ReLU[ReLU]rectified linear unit
MWA[MWA]Murchison Widefield Array
ATCA[ATCA]Australia Telescope Compact Array
ASKAP[ASKAP]Australian Square Kilometre Array Pathfinder
VLA[VLA]Very Large Array
VLITE[VLITE]Low Band Ionospheric and Transient Experiment GMRT[GMRT]Giant Metrewave Radio Telescope
PMPS[PMPS]Parkes Multibeam Pulsar Survey
SMPS[SMPS]Swinburne Intermediate-latitude Pulsar Survey
HTRU[HTRU]High Time Resolution Universe
ISM[ISM]interstellar medium
CNN[CNN]convolutional neural network
MDN[MDN]mixture density network
GMM[GMM]Gaussian-mixture model
ReLU[ReLU]rectified linear unit
SBI[SBI]simulation-based inference
MCMC[MCMC]Markov chain Monte Carlo
NPE[NPE]neural posterior estimation
NLE[NLE]neural likelihood estimation
NRE[NRE]neural ratio estimation
CI[CI]credible interval
HDR[HDR]highest-density region














lr@=l 



Speed of light in a vacuum     c     [2.99792458 × 10^10]cm   s^-1 

Gravitational constant     G     [6.67259 × 10^-8]cm^3   g^-1  s^-2 

Planck constant     h     [6.6260755 × 10^-27]erg   s 

Electron charge     e     [4.8032068 × 10^-10]esu 

Electron mass     m_e     [9.1093897 × 10^-28]g 

Proton mass     m_p     [1.6726231 × 10^-24]g 

Neutron mass     m_n     [1.6749286 × 10^-24]g 

Boltzmann constant     k_ B     [1.380658 × 10^-16]erg   K^-1 

Thomson cross-section     σ_ T     [6.65245 × 10^-25]cm^-2  
 



Solar mass     M_⊙     [1.989 × 10^33]g 

Parsec     pc     [3.0856 × 10^18]cm 

Jansky     Jy     [1.0 × 10^-23]erg   cm^-2  s^-1  Hz^-1 

Year     yr     [3.1557 × 10^7]s 


































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