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Search for host galaxies of unlocalized Fast Radio Bursts in the SDSS IV catalog

Luz Ángela García, Eduard Piratova-Moreno, Felipe González-Alarcón, Jhonier Rangel

TL;DR

This work develops and validates a DM–$z$ framework to localize Fast Radio Bursts (FRBs) with unknown redshift by matching them to SDSS-IV spectroscopic galaxies. It introduces four physically motivated DM–$z$ models (linear, log-parabolic, power-law, and a combined form), inverts them to obtain $z(DM)$, and uses an end-to-end pipeline that computes host probabilities for SDSS galaxies based on angular separation and redshift consistency. The methodology is calibrated on $112$ FRBs with confirmed redshifts, augmented with bootstrap and synthetic FRB realizations, and ranked using metrics including WAIC and a geometrical probability. When applied to CHIME/FRB events within the SDSS footprint, the approach yields high-probability host candidates, enabling automatic, scalable localization of unlocalized FRBs and guiding future surveys such as DESI and Euclid. Overall, the Combined model consistently provides the strongest predictive power, suggesting a viable path to rapid FRB host identification in large spectroscopic surveys.

Abstract

This theoretical work investigates different models to predict the redshift of Fast Radio Bursts (FRBs) from their observed dispersion measure (DM) and other reported properties. We performed an extensive revision of the FRBs with confirmed galaxy hosts in the literature and compiled an updated catalog. With this sample of FRBs, composed of 117 unique transients, we explore four physically motivated models that relate the DM and redshift ($z$): a linear trend (inspired by the Macquart relation), a log-parabolic function, a power-law, and a combined model from the above. We assess the success of these theoretical proposals by implementing different statistical metrics and ranking them. The DM-$z$ relations are also tested using 100 realizations of 500 simulated FRBs, which follow the observed DM trends. Relying on our theoretical modeling, we establish the probability of $\sim$1000 FRBs with unknown $z$ (from the latest CHIME data release) to be hosted by galaxies in the SDSS archival dataset. Our validation scheme allows us to predict the FRBs with a probability threshold of $\geq$0.95 to originate in these galaxies, using their 2D angular position in the sky, magnitude in the r-band, and redshift. This statistical proposal will be tested with upcoming data releases from DESI and new generations of galaxy surveys, such as Euclid, and it opens brilliant possibilities to localize these transients in an automatic pipeline.

Search for host galaxies of unlocalized Fast Radio Bursts in the SDSS IV catalog

TL;DR

This work develops and validates a DM– framework to localize Fast Radio Bursts (FRBs) with unknown redshift by matching them to SDSS-IV spectroscopic galaxies. It introduces four physically motivated DM– models (linear, log-parabolic, power-law, and a combined form), inverts them to obtain , and uses an end-to-end pipeline that computes host probabilities for SDSS galaxies based on angular separation and redshift consistency. The methodology is calibrated on FRBs with confirmed redshifts, augmented with bootstrap and synthetic FRB realizations, and ranked using metrics including WAIC and a geometrical probability. When applied to CHIME/FRB events within the SDSS footprint, the approach yields high-probability host candidates, enabling automatic, scalable localization of unlocalized FRBs and guiding future surveys such as DESI and Euclid. Overall, the Combined model consistently provides the strongest predictive power, suggesting a viable path to rapid FRB host identification in large spectroscopic surveys.

Abstract

This theoretical work investigates different models to predict the redshift of Fast Radio Bursts (FRBs) from their observed dispersion measure (DM) and other reported properties. We performed an extensive revision of the FRBs with confirmed galaxy hosts in the literature and compiled an updated catalog. With this sample of FRBs, composed of 117 unique transients, we explore four physically motivated models that relate the DM and redshift (): a linear trend (inspired by the Macquart relation), a log-parabolic function, a power-law, and a combined model from the above. We assess the success of these theoretical proposals by implementing different statistical metrics and ranking them. The DM- relations are also tested using 100 realizations of 500 simulated FRBs, which follow the observed DM trends. Relying on our theoretical modeling, we establish the probability of 1000 FRBs with unknown (from the latest CHIME data release) to be hosted by galaxies in the SDSS archival dataset. Our validation scheme allows us to predict the FRBs with a probability threshold of 0.95 to originate in these galaxies, using their 2D angular position in the sky, magnitude in the r-band, and redshift. This statistical proposal will be tested with upcoming data releases from DESI and new generations of galaxy surveys, such as Euclid, and it opens brilliant possibilities to localize these transients in an automatic pipeline.

Paper Structure

This paper contains 13 sections, 17 equations, 5 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Modeling of the DM-$z$
  4. Validation with localized FRBs
  5. Data augmentation for the validation dataset
  6. Best-fits for our DM-$z$ models based on confirmed FRBs
  7. Validation of FRBs in SDSS-IV
  8. Probability of FRBs with unknown $z$ to be hosted by a galaxy in the SDSS catalog
  9. Discussion and conclusions
  10. Appendix A. Best-fits for our DM-$z$ models based on confirmed FRBs at $z <$ 0.5
  11. Appendix B. Best-fits for our DM-$z$ models based on a synthetic sample of FRBs
  12. Appendix C. Best-fits for our DM-$z$ models based on a synthetic sample of FRBs at $z <$ 0.5
  13. Appendix D. FRBs with confirmed $z$ to date

Figures (5)

  • Figure 1: DM-$z$ relations best-fits adjusted and compared with our 112 confirmed FRBs. Panels A-D show the prediction of each model: blue solid lines show the result with the best-fit parameters displayed above, and the shadowed lighter regions present the error bands for the linear trend (A), log-parabolic function (B), power-law (C), and combined model (D).
  • Figure 2: Apparent magnitudes of the most likely transients' host galaxy as a function of the FRB's redshift. After running our validation scheme, we found 23 FRBs that match the SDSS footprint and presented them here.
  • Figure 3: DM-$z$ relations best-fits adjusted and compared with confirmed FRBs -with a redshift range of $z \leq$0.5 -. Panels A-D show the prediction of each model: blue solid lines show the result with the best-fit parameters displayed above, and the shadowed lighter regions present the error bands for the linear trend (A), log-parabolic function (B), power-law (C), and combined model (D).
  • Figure 4: DM-$z$ relations best-fits adjusted with 100 realizations of 500 synthetic FRBs, each. Panels A-D show the prediction of each model: blue solid lines show the result with the best-fit parameters displayed above, and the shadowed lighter regions present the error bands for the linear trend (A), log-parabolic function (B), power-law (C), and combined model (D).
  • Figure 5: DM-$z$ relations best-fits adjusted with 100 realizations of 500 synthetic FRBs, with a redshift range of $z \leq$0.5. Panels A-D show the prediction of each model: blue solid lines show the result with the best-fit parameters displayed above, and the shadowed lighter regions present the error bands for the linear trend (A), log-parabolic function (B), power-law (C), and combined model (D).