Searching for high-energy neutrinos from shock-interaction powered supernovae with the IceCube Neutrino Observatory

The sources of the astrophysical neutrino flux discovered by IceCube are for the most part unresolved. Extragalactic core-collapse supernovae (CCSNe) have been suggested as candidate multi-messenger sources. In interaction-powered supernovae, a shock propagates in a dense circumstellar medium (CSM), producing a bright optical emission and potentially accelerating particles to relativistic energies. Shock interaction is believed to be the main energy source for Type IIn supernovae (identified by narrow lines in the spectrum), hydrogen-rich superluminous supernovae and a subset of hydrogen-poor superluminous supernovae. Production of high-energy neutrinos is expected in collisions between the accelerated protons in the shocks and the cold CSM particles. We select a catalog of interaction-powered supernovae from the Bright Transient Survey of the Zwicky Transient Facility. We exploit a novel modeling effort that connects the time evolution of the optical emission to the properties of the ejecta and the CSM, allowing us to set predictions of the neutrino flux for each source. In this contribution, we describe a stacking search for high-energy neutrinos from this population of CCSNe with the IceCube Neutrino Observatory.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions. 8 pages, 4 figure

Searching for high-energy neutrinos from the most luminous supernovae with the IceCube Neutrino Observatory

The sources of the astrophysical neutrino flux discovered by IceCube remain for the most part unresolved. Extragalactic core-collapse supernovae (CCSNe) have been suggested as potentially able to produce high-energy neutrinos. In recent years, the Zwicky Transient Facility has discovered a population of exceptionally luminous supernovae, whose powering mechanisms have not yet been fully established. A fraction of these objects fall in the broader category of type IIn CCSNe, showing signs of interaction with a dense circumstellar medium. Theoretical models connect the supernova photometric properties to the dynamics of a shock-powered emission, predicting particle acceleration. In this contribution, we outline the plan for a search of high-energy neutrinos targeting the population of superluminous and type IIn supernovae with the IceCube Neutrino Observatory

Optically Informed Searches of High-Energy Neutrinos from Interaction-Powered Supernovae

The interaction between the ejecta of supernovae (SNe) of Type IIn and a dense circumstellar medium (CSM) can efficiently generate thermal UV/optical radiation and lead to the emission of neutrinos in the $1$-$10^{3}$ TeV range. We investigate the connection between the neutrino signal detectable at the IceCube Neutrino Observatory and the electromagnetic signal observable by optical wide-field, high-cadence surveys to outline the best strategy for upcoming follow-up searches. We outline a semi-analytical model that connects the optical lightcurve properties to the SN parameters and find that a large peak luminosity ($L_{\rm{peak}}\gtrsim 10^{43}$-$10^{44}$ erg) and an average rise time ($t_{\rm{rise}}\gtrsim 10$-$40$ days) are necessary for copious neutrino emission. Nevertheless, the most promising $L_{\rm{peak}}$ and $t_{\rm{rise}}$ are not sufficient to guarantee ideal conditions for neutrino detection. Comparable optical properties can be obtained for SN configurations that are not optimal for neutrino emission. Such ambiguous correspondence between the optical lightcurve properties and the number of IceCube neutrino events implies that relying on optical observations only, a range of expected neutrino events should be considered (e.g. the expected number of neutrino events can vary up to two orders of magnitude for some among the brightest SNe IIn observed by the Zwicky Transient Facility up to now, SN 2020usa and SN 2020in). In addition, the peak in the high-energy neutrino curve should be expected a few $t_{\rm{rise}}$ after the peak in the optical lightcurve. Our findings highlight that it is crucial to infer the SN properties from multi-wavelength observations rather than focusing on the optical band only to enhance upcoming neutrino searches.Comment: 20 pages, including 14 figures and 3 appendice

An improved infrastructure for the IceCube realtime system

The IceCube realtime alert system has been operating since 2016. It provides prompt alerts on high-energy neutrino events to the astroparticle physics community. The localization regions for the incoming direction of neutrinos are published through NASA's Gamma-ray Coordinate Network (GCN). The IceCube realtime system consists of infrastructure dedicated to the selection of alert events, the reconstruction of their topology and arrival direction, the calculation of directional uncertainty contours and the distribution of the event information through public alert networks. Using a message-based workflow management system, a dedicated software (SkyDriver) provides a representational state transfer (REST) interface to parallelized reconstruction algorithms. In this contribution, we outline the improvements of the internal infrastructure of the IceCube realtime system that aims to streamline the internal handling of neutrino events, their distribution to the SkyDriver interface, the collection of the reconstruction results as well as their conversion into human- and machine-readable alerts to be publicly distributed through different alert networks. An approach for the long-term storage and cataloging of alert events according to findability, accessibility, interoperability and reusability (FAIR) principles is outlined.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions. 8 pages, 3 figure

Neutrino follow-up with the Zwicky Transient Facility: Results from the first 24 campaigns

The Zwicky Transient Transient Facility (ZTF) performs a systematic neutrino follow-up program, searching for optical counterparts to high-energy neutrinos with dedicated Target-of-Opportunity (ToO) observations. Since first light in March 2018, ZTF has taken prompt observations for 24 high-quality neutrino alerts from the IceCube Neutrino Observatory, with a median latency of 12.2 hours from initial neutrino detection. From two of these campaigns, we have already reported tidal disruption event (TDE) AT2019dsg and likely TDE AT2019fdr as probable counterparts, suggesting that TDEs contribute >7.8% of the astrophysical neutrino flux. We here present the full results of our program through to December 2021. No additional candidate neutrino sources were identified by our program, allowing us to place the first constraints on the underlying optical luminosity function of astrophysical neutrino sources. Transients with optical absolutes magnitudes brighter that -21 can contribute no more than 87% of the total, while transients brighter than -22 can contribute no more than 58% of the total, neglecting the effect of extinction. These are the the first observational constraints on the neutrino emission of bright populations such as superluminous supernovae. None of the neutrinos were coincident with bright optical AGN flares comparable to that observed for TXS 0506+056/IC170922A, suggesting that most astrophysical neutrinos are not produced during such optical flares. We highlight the outlook for electromagnetic neutrino follow-up programs, including the expected potential for the Rubin Observatory.Comment: To be submitted to MNRAS, comments welcome

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Multi-messenger searches via IceCube’s high-energy neutrinos and gravitational-wave detections of LIGO/Virgo

We summarize initial results for high-energy neutrino counterpart searches coinciding with gravitational-wave events in LIGO/Virgo\u27s GWTC-2 catalog using IceCube\u27s neutrino triggers. We did not find any statistically significant high-energy neutrino counterpart and derived upper limits on the time-integrated neutrino emission on Earth as well as the isotropic equivalent energy emitted in high-energy neutrinos for each event