Nucleosynthesis in outflows of compact objects and detection prospects of associated kilonovae

We perform a comparative analysis of nucleosynthesis yields from binary neutron star (BNS) mergers, black hole-neutron star (BHNS) mergers, and core-collapse supernovae (CCSNe) with the goal of determining which are the most dominant sources of r-process enrichment observed in stars. We find that BNS and BHNS binaries may eject similar mass distributions of robust r-process nuclei post merger (up to 3rd peak and actinides, $A\sim200-240$), after accounting for the volumetric event rates. Magnetorotational (MR) CCSNe likely undergo a weak r-process (up to $A\sim140$) and contribute to the production of light element primary process (LEPP) nuclei, whereas typical thermal, neutrino-driven CCSNe only synthesize up to 1st r-process peak nuclei ($A\sim80-90$). We also find that the upper limit to the rate of MR CCSNe is $\lesssim1\%$ the rate of typical thermal CCSNe; if the rate was higher, then weak r-process nuclei would be overproduced. Although the largest uncertainty is from the volumetric event rate, the prospects are encouraging for confirming these rates in the next few years with upcoming surveys. Using a simple model to estimate the resulting kilonova light curve from mergers and our set of fiducial merger parameters, we predict that $\sim7$ BNS and $\sim2$ BHNS events will be detectable per year by the Vera C. Rubin Observatory (LSST), with prior gravitational wave (GW) triggers.Comment: 14 pages, 5+3 figures, 1 table. Comments welcom

Diffuse neutrino background from past core-collapse supernovae

Core-collapse supernovae are among the most powerful explosions in the universe, emitting thermal neutrinos that carry away the majority of the gravitational binding energy released. These neutrinos create a diffuse supernova neutrino background (DSNB), one of the largest energy budgets among all radiation backgrounds. Detecting the DSNB is a crucial goal of modern high-energy astrophysics and particle physics, providing valuable insights in both core-collapse modeling, neutrino physics, and cosmic supernova rate history. In this review, we discuss the key ingredients of DSNB calculation and what we can learn from future detections, including black-hole formation and non-standard neutrino interactions. Additionally, we provide an overview of the latest updates in neutrino experiments, which could lead to the detection of the DSNB in the next decade. With the promise of this breakthrough discovery on the horizon, the study of DSNB holds enormous potential for advancing our understanding of the Universe.Comment: 21 pages, 8 figures. Invited review article submitted to Proceedings of the Japan Academy, Series B. Figures are made using the numerical codes that accompany this paper; see https://github.com/shinichiroando/PyDSNB/tree/mai

Diffuse supernova neutrino background with up-to-date star formation rate measurements and long-term multi-dimensional supernova simulations

The sensitivity of current and future neutrino detectors like Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to allow for the detection of the diffuse supernova neutrino background (DSNB). However, the DSNB model ingredients like the core-collapse supernova (CCSN) rate, neutrino emission spectra, and the fraction of failed supernovae are not precisely known. We quantify the uncertainty on each of these ingredients by (i) compiling a large database of recent star formation rate density measurements, (ii) combining neutrino emission from long-term axisymmetric CCSNe simulations and strategies for estimating the emission from the protoneutron star cooling phase, and (iii) assuming different models of failed supernovae. Finally, we calculate the fluxes and event rates at multiple experiments and perform a simplified statistical estimate of the time required to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO. Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$ events per year, where the errors represent our uncertainty from star formation rate density measurements, uncertainty in neutrino emission, and uncertainty in the failed-supernova scenario. In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial.Comment: 19 pages, 9 figures, 3+2 tables. Comments welcom

Impact of late-time neutrino emission on the Diffuse Supernova Neutrino Background

In the absence of high-statistics supernova neutrino measurements, estimates of the diffuse supernova neutrino background (DSNB) hinge on the precision of simulations of core-collapse supernovae (CCSNe). Understanding the cooling phase of protoneutron star (PNS) evolution ($\gtrsim1\,{\rm s}$ after core bounce) is crucial, since approximately 50% of the energy liberated by neutrinos is emitted during the cooling phase. We model the cooling phase with a hybrid method, by combining the neutrino emission predicted by 3D hydrodynamic simulations with several cooling phase estimates, including a novel two-parameter correlation depending on the final baryonic PNS mass and the time of shock revival. We find that the predicted DSNB event rate at Super-Kamiokande can vary by a factor of $\sim2-3$ depending on the cooling phase treatment. We also find that except for one cooling estimate, the range in predicted DSNB events is largely driven by the uncertainty in the neutrino mean energy. With a good understanding of the late time neutrino emission, more precise DSNB estimates can be made for the next generation of DSNB searches.Comment: 13 pages, 6+4 figures, 5 tables. Comments welcom