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During a failed core-collapse supernova, the protoneutron star eventually collapses under its own gravitational field and forms a black hole. This collapse happens quickly, on the dynamical time of the protoneutron star, ≲0.5 ms. During this collapse, barring any excessive rotation, the entire protoneutron star is accreted into the newly formed black hole. The main source of neutrinos is now removed and the signal abruptly shuts off over this formation timescale. However, while the source of neutrinos is turned off, the arrival times at an Earth-based detector will depend on the neutrino path. We show here that a modest amount of neutrinos, emitted just prior to the black hole forming, scatter on the infalling material into our line of sight and arrive after the formation of the black hole, up to 15 ms in our model. This neutrino echo, which we characterize with Monte Carlo simulations and analytic models, has a significantly higher average energy (upwards of ∼50 MeV) compared to the main neutrino signal, and for the canonical failed supernova explored here, is likely detectable in  (10 kT) supernova neutrino detectors for Galactic failed supernovae. The presence of this signal is important to consider if using black hole formation as a time post for triangulation or the post black hole timing profile for neutrino mass measurements. On its own, it can also be used to characterize or constrain the structure and nature of the accretion flow.

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SNEWS2.0 network of neutrino and dark matter detectors aims to use the relative arrival times of the neutrinos at the different experiments to point back to the supernova so as to facilitate follow-up observation. One of the simplest methods to estimate the CCSN direction is to use the first neutrino events detected through the inverse beta decay (IBD) process, \\(_e p e^+n\\). We will consider neutrino detectors sensitive to IBD interactions with low backgrounds. The difference in signal arrival times between a large and a small detector will be biased, however, with the first event at the smaller detector, on average, arriving later than that at the larger detector. This bias can be mitigated by using these first events in a data-driven approach without recourse to simulations or models. The resulting method requires, at minimum, only the times of the first events at most detectors, along with a longer time series of events from one larger detector to act as a reference lightcurve. In this article, we demonstrate this method and its uncertainty estimate using pairs of detectors of different sizes and with different supernova distances. Finally, we use this method to calculate probability skymaps using four detectors currently in operation (Super-Kamiokande, JUNO, LVD, and SNO+) and show that the calculated probabilities yield appropriate confidence intervals for all supernova directions. The area of the 68\\% confidence interval varies by distance and direction, but is expected to be a few thousand square degrees. The resulting skymaps should be useful for the multi-messenger community as a rapid, initial pointing to follow up on the SNEWS2.0 Galactic CCSN neutrino alert.

We compile a catalog of 578 highly probable and 62 likely red supergiants (RSGs) of the Milky Way, which represents the largest list of Galactic RSG candidates designed for continuous follow-up to date. We match distances measured by Gaia DR3, 2MASS photometry, and a 3D Galactic dust map to obtain luminous bright late-type stars. Determining the stars' bolometric luminosities and effective temperatures, we compare to Geneva stellar evolution tracks to determine likely RSG candidates, and quantify contamination using a catalog of Galactic AGB in the same luminosity-temperature space. We add details for common or interesting characteristics of RSG, such as multi-star system membership, variability, and classification as a runaway. As potential future core-collapse supernova (SN) progenitors, we study the ability of the catalog to inform the Supernova Early Warning System (SNEWS) coincidence network made to automate pointing, and show that for 3D position estimates made possible by neutrinos, the number of progenitor candidates can be significantly reduced, improving our ability to observe the progenitor pre-explosion and the early phases of the core-collapse supernova.

During a failed core-collapse supernova, the protoneutron star eventually collapses under its own gravitational field and forms a black hole. This collapse happens quickly, on the dynamical time of the protoneutron star, \\(\\)0.5 ms. During this collapse, barring any excessive rotation, the entire protoneutron star is accreted into the newly formed black hole. The main source of neutrinos is now removed and the signal abruptly shuts off over this formation timescale. However, while the source of neutrinos is turned off, the arrival times at an Earth-based detector will depend on the neutrino path. We show here that a modest amount of neutrinos, emitted just prior to the black hole forming, scatter on the infalling material into our line of sight and arrive after the formation of the black hole, up to 15 ms in our model. This neutrino echo, which we characterize with Monte Carlo simulations and analytic models, has a significantly higher average energy (upwards of \\(\\) 50 MeV) compared to the main neutrino signal, and for the canonical failed supernova explored here, is likely detectable in \\(O\\)(10 kT) supernova neutrino detectors for Galactic failed supernovae. The presence of this signal is important to consider if using black hole formation as a time post for triangulation or the post black hole timing profile for neutrino mass measurements. On its own, it can also be used to characterize or constrain the structure and nature of the accretion flow.

We investigate a new sequential recombination algorithm which effectively subtracts background as it reconstructs the jet. We examine the new algorithm's behavior in light of existing algorithms, and we find that in Monte Carlo comparisons, the new algorithm's robustness against collision backgrounds is comparable to that of other jet algorithms when the latter have been augmented by further background subtraction techniques.

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization. In this work, we rethink the set of 3D Gaussians as a random sample drawn from an underlying probability distribution describing the physical representation of the scene-in other words, Markov Chain Monte Carlo (MCMC) samples. Under this view, we show that the 3D Gaussian updates can be converted as Stochastic Gradient Langevin Dynamics (SGLD) updates by simply introducing noise. We then rewrite the densification and pruning strategies in 3D Gaussian Splatting as simply a deterministic state transition of MCMC samples, removing these heuristics from the framework. To do so, we revise the 'cloning' of Gaussians into a relocalization scheme that approximately preserves sample probability. To encourage efficient use of Gaussians, we introduce a regularizer that promotes the removal of unused Gaussians. On various standard evaluation scenes, we show that our method provides improved rendering quality, easy control over the number of Gaussians, and robustness to initialization.

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SNEWS2.0 network of neutrino and dark matter detectors aims to use the relative arrival times of the neutrinos at the different experiments to point back to the supernova so as to facilitate follow-up observation. One of the simplest methods to estimate the CCSN direction is to use the first neutrino events detected through the inverse beta decay (IBD) process, \\(_e p e^+n\\). We will consider neutrino detectors sensitive to IBD interactions with low backgrounds. The difference in signal arrival times between a large and a small detector will be biased, however, with the first event at the smaller detector, on average, arriving later than that at the larger detector. This bias can be mitigated by using these first events in a data-driven approach without recourse to simulations or models. The resulting method requires, at minimum, only the times of the first events at most detectors, along with a longer time series of events from one larger detector to act as a reference lightcurve. In this article, we demonstrate this method and its uncertainty estimate using pairs of detectors of different sizes and with different supernova distances. Finally, we use this method to calculate probability skymaps using four detectors currently in operation (Super-Kamiokande, JUNO, LVD, and SNO+) and show that the calculated probabilities yield appropriate confidence intervals for all supernova directions. The area of the 68\\% confidence interval varies by distance and direction, but is expected to be a few thousand square degrees. The resulting skymaps should be useful for the multi-messenger community as a rapid, initial pointing to follow up on the SNEWS2.0 Galactic CCSN neutrino alert.

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization. In this work, we rethink the set of 3D Gaussians as a random sample drawn from an underlying probability distribution describing the physical representation of the scene-in other words, Markov Chain Monte Carlo (MCMC) samples. Under this view, we show that the 3D Gaussian updates can be converted as Stochastic Gradient Langevin Dynamics (SGLD) updates by simply introducing noise. We then rewrite the densification and pruning strategies in 3D Gaussian Splatting as simply a deterministic state transition of MCMC samples, removing these heuristics from the framework. To do so, we revise the 'cloning' of Gaussians into a relocalization scheme that approximately preserves sample probability. To encourage efficient use of Gaussians, we introduce a regularizer that promotes the removal of unused Gaussians. On various standard evaluation scenes, we show that our method provides improved rendering quality, easy control over the number of Gaussians, and robustness to initialization.

Black hole formation in a core-collapse supernova is expected to lead to a distinctive, abrupt drop in neutrino luminosity due to the engulfment of the main neutrino-producing regions as well as the strong gravitational redshift of those remaining neutrinos which do escape. Previous analyses of the shape of the cut-off have focused on specific trajectories or simplified models of bulk neutrino transport. In this article, we integrate over simple \"ballistic\" geodesics to investigate potential effects on the cut-off profile of including all neutrino emission angles from a collapsing surface in the Schwarzschild metric, and from a contracting equatorial mass ring in the Kerr metric. We find that the non-radial geodesics contribute to a softening of the cut-off in both cases. In addition, extreme rotation introduces significant changes to the shape of the tail which may be observable in future neutrino detectors, or combinations of detectors.

Pixel detectors currently in use by high energy physics experiments such as ATLAS, CMS, LHCb, etc., are critical systems for tagging \\(B\\) hadrons within particle jets. However, the performance of standard tagging algorithms begins to fall in the case of highly boosted \\(B\\) hadrons (\\( = p/m >200\\)). This paper builds on the work of our previous study that uses the jump in hit multiplicity among the pixel layers when a \\(B\\) hadron decays within the detector volume. First, multiple \\(pp\\) interactions within a finite luminous region were found to have little effect. Second, the study has been extended to use the multivariant techniques of an artificial neural network (ANN). After training, the ANN shows significant improvements to the ability to reject light-quark and charm jets; thus increasing the expected significance of the technique.