Анализ нейтринных взаимодействий для поиска сигналов от сверхновой тема диссертации и автореферата по ВАК РФ 00.00.00, кандидат наук Шешуков Андрей Сергеевич

• Шешуков Андрей Сергеевич
• кандидат наук
• 2024

Оглавление диссертации кандидат наук Шешуков Андрей Сергеевич

Contents

Introduction

1 Core-collapse supernova process

1.1 Pre-collapse stellar evolution

1.1.1 Burning phases

1.1.2 Pre-supernova neutrino emission

1.2 Core collapse

1.2.1 Initial phase of collapse

1.2.2 Neutrino trapping

1.2.3 Core bounce and shock formation

1.2.4 Shock breakout

1.2.5 Shock stagnation and neutrino heating

1.2.6 Neutrino cooling

1.3 Supernova neutrino signal

1.3.1 Neutrino flavor transitions

1.3.2 Signal models used in this work

2 Supernova neutrinos in NOvA detectors

2.1 NOvA detectors

2.1.1 Detector composition

2.1.2 Data Acquisition

2.1.3 Data Driven Trigger system

2.2 Detection channels

2.2.1 Inverse beta decay

2.2.2 Elastic scattering of neutrinos on electrons

2.2.3 Neutral current scattering on carbon

3 Selection of supernova neutrino interactions in NOvA

3.1 Simulation chain

3.1.1 GenieSNova

3.1.2 Detector Simulation

3.1.3 Background data overlays

3.2 Background rejection

3.2.1 Clustering algorithm

3.2.2 Candidate selection

3.2.3 Removing time-correlated candidate groups

3.3 Selection results

3.4 Summary

4 Shape analysis method

4.1 Hypothesis test

4.2 Choice of the test statistic function

4.2.1 Counting analysis (CA)

4.2.2 Shape analysis (SA)

4.3 Test statistic distribution

4.3.1 Combining measurements

4.3.2 Rate uncertainty

4.4 Implementation

4.5 Example

4.6 Summary

5 Supernova neutrino triggering system in NOvA

5.1 Supernova trigger infrastructure

5.1.1 Cross-detector triggering

5.1.2 Sending trigger signal to SNEWS

5.2 Triggering delay

5.3 Trigger system commissioning

5.4 Summary

6 The NOvA experiment's sensitivity to supernova

6.1 NOvA triggering system sensitivity

6.1.1 Triggering threshold

6.1.2 Detection significance

6.1.3 Probability of detecting the next galactic supernova

6.2 NOvA sensitivity for Near+Far detectors combination

6.2.1 Counting vs. shape analysis

6.2.2 Expected signal model dependency

6.2.3 Estimating supernova signal start time

6.2.4 Results

6.3 Summary

7 Presupernova neutrino signal

7.1 Pre-supernova neutrino detection

7.1.1 Detectors

7.1.2 Expected signals and backgrounds

7.2 Counting vs. shape analysis

7.3 Expected detection efficiency

7.4 Summary

Summary

Acronyms

Bibliography

List of Figures

List of Tables

Введение диссертации (часть автореферата) на тему «Анализ нейтринных взаимодействий для поиска сигналов от сверхновой»

Introduction

The evolution of a massive (M > 8M0) main sequence star is expected to end in a gravitational collapse of the inner stellar core, leading to a violent supernova explosion of the remains of the outer stellar shells and the formation of a neutron star or a black hole. The physics of the core-collapse supernova (SN) is not yet fully understood despite many theoretical advancements. The modeling and understanding of the stellar dynamics involved in core-collapse supernovae events requires knowledge of the complex interplay between different physics processes including relativistic stellar magnetohydrodynamics, nuclear processes, and particle physics, which transition rapidly during the initial collapse of the star and the explosive expansion phases of the event. However it is expected that about 99 % of the gravitational collapse energy is emitted in a short burst of neutrinos, produced in the inner regions of the collapsing core within several seconds after the collapse. These neutrinos interact with the outer stellar shells driving the explosion phase and then propagate outside of the star, and can be observed on Earth in the neutrino detectors, providing the unique information about both supernova physics and neutrino physics.

Up to now the only measurement of the neutrino burst from supernova was the SN1987a event — a supernova in the Large Magellanic Cloud at 51 kpc distance from the Earth — when three neutrino experiments observed a total of 25 antineutrino interactions within 13 seconds [1, 2, 3]. This observation confirmed and constrained various supernova models, however the limited statistics didn't allow a thorough measurement. The optical signal of SN1987a explosion became visible only several hours after the neutrino burst detection, because the outer stellar shells are opaque for the photons but transparent for neutrinos.

This means that a detection of a future supernova neutrino signal can be used for two main purposes.

The energy spectrum, flavor composition, and time profile of this neutrino

signal depend on the physics processes in the supernova as well as the neutrino properties, providing a unique opportunity to probe models and study effects that would otherwise be experimentally inaccessible [4]. Thus a measurement of the neutrino energy spectrum and time evolution of the neutrino flux are central to our understanding of these processes and will allow for more detailed models of the stellar dynamics to be evaluated.

On the other hand, the expected neutrino signals have common features across various models, allowing a robust supernova burst indication in real time. So the neutrino signal can be used as an early warning and pointing to supernova for additional measurements and analyses. It will allow astronomical observations of the early stages of the supernova, providing valuable information about the explosion process. Also such warning, if issued with low latency, can be used as a trigger for other detectors (including neutrino) to save their data for further joint analysis. Detected supernova neutrino signal can be used in multimessenger analysis: starting time and other parameters of the signal can enhance the efficiency of the template matching for gravitational wave searches.

Additionally, the nuclear burning processes in the late stages of stellar evolution (in particular the silicon burning phase) will produce a neutrino flux increasing in time for about a week prior to the core collapse. This so called "presuper-nova neutrino signal", if detected, can be used as an even earlier warning of the future supernova.

In order to achieve these goals many neutrino and dark matter detectors, potentially sensitive to neutrino signals from supernovae, implement dedicated supernova detection systems, designed to identify the presence of the supernova signal based on detected neutrino interaction candidates in one or many detectors. Such systems need to be stable and operate with low latency to stay in constant readiness for the next galactic supernova.

However the definition of "supernova signal observation" depends greatly on the experiment conditions and detection channels. A statistical analysis of the detected neutrino interactions is required to determine whether the observed portion of data contains supernova neutrino signal or is composed of the fluctuation of background events. Many neutrino experiments [5, 6, 7] use a Counting Analysis (CA) — a simple significance evaluation using Poisson distribution based on the number of observed interactions in a sliding time window. While fast and robust,

this method is suboptimal for the case of high background, because it doesn't take into account the features of the expected signal.

Goal

The goal of this dissertation is to develop a system for detection, selection and analysis of neutrino interactions in the neutrino detectors in search for signals from a core-collapse supernova, implement and deploy such system for the NOvA experiment, extend this approach for a combined analysis of data from several detectors within a SuperNova Early Warning System (SNEWS), and for the search of neutrino signals from final stages of the stellar evolution, prior to core-collapse supernova (presupernova neutrino signals).

In order to achieve this goal, the following tasks were completed:

1. Study the expected response of NOvA detectors for the main neutrino interaction channels from supernovae in the scintillator, taking into account the time and energy dependence of the neutrino flux. This required modifying the standard NOvA simulation chain (used for neutrino beam analysis) to simulate low-energy neutrino interactions, while preserving information about the time structure of the signal.

2. Build an algorithm for the reconstruction and selection of neutrino interactions from supernova explosions within the NOvA triggering system. Requirements for this algorithm are:

• Processing speed: Data is processed in real time in the trigger system. This requirement limits the complexity of algorithms and imposes the use of the simplest selection methods.

• Stability of operation in variable detector background conditions: neutrino beam switching, temperature changes, and noisy electron channels should not cause false alarms of the system.

• The ability to process different fragments (time slices) of the data from the detectors independently of each other in parallel running processes.

3. Create an infrastructure to run the neutrino interaction selection algorithm and collect its results. The infrastructure uses existing subsystems of the

experiment: a system of software triggers (Data-Driven Triggers, DDT), performing basic reconstruction of events in real time, and a central trigger node (Global Trigger). Infrastructure requirements:

• Stability and reliability of data transfer from thousands of parallel DDT processes to the single GlobalTriger node.

• Sorting of the received data in the GlobalTrigger buffer for further statistical analysis.

• Handling the cases of data loss, unstable background conditions, etc.

4. Develop a statistical analysis method to determine the presence of a signal from a supernova in the data stream from the detector, as well as to determine the starting time and the significance of this signal. The method should ensure the sensitivity of the NOvA detector to a supernova at a distance of 10 kiloparsecs (the approximate distance to the center of the Milky Way) with an average false alarm rate of about 1 in 7 days (as one of the conditions for subsequent use within the SNEWS system). The task is complicated by the high background level in the NOvA far detector, due to its location on the surface and its exposure to the cosmic rays, so the method of counting neutrino interactions in a fixed time window (Counting Analysis) used by other experiments is unsuitable.

5. Analyze the sensitivity of the NOvA experiment to the neutrino signal from the supernova explosions using the event selection algorithms and the statistical method of data processing developed in the previous tasks. Estimate the fraction of supernova candidate stars, the signal from which can be detected in the NOvA experiment.

6. Deploy the developed supernova detection system on the NOvA detectors, and test the stability of its operation. Since the resulting system is closely related to the complex process of detector data acquisition for various physical measurements, the stability requirements are particularly important: a failure in the supernova detection system could potentially disrupt the acquisition of the neutrino beam data for the main NOvA oscillation analyses.

7. Prepare the NOvA supernova detection system for integration into the SNEWS global network:

• Separate the statistical analysis part into an independent software process. This would allow modifying and restarting the algorithms without stopping the main system of experiment data acquisition.

• Provide a mechanism to send an alert to SNEWS when a supernova signal of sufficiently high significance is detected.

8. Apply the developed statistical method for the search of signals from the final stage of star evolution a few days before the supernova (presupernova neutrino signals). Since NOvA detectors are insensitive to this signal, the experiments with low background and sensitivity to low-energy neutrinos are considered: Borexino, KamLAND, SK-Gd. It is necessary to:

• Estimate the signal detection range and time before the supernova for these detectors individually and in the case of detectors combinations, using the developed statistical method, which takes into account the signal shape.

• Compare the results of the method with the standard approaches used in the analyses of these experiments.

Scientific novelty

1. The developed algorithms and the system for supernova neutrino detection in the NOvA experiment were created for the first time. Previously, the NOvA experiment did not have a procedure and a framework for analyzing interactions of low-energy neutrinos.

2. The proposed method of statistical processing of neutrino events to search for a signal with a given time profile has not been previously formulated and has not been used to search for neutrinos from supernovae.

3. For the first time, the sensitivity of the NOvA experiment to the neutrino signal from a supernova was calculated with consideration of the measured background level and the efficiency of signal selection. Previous estimates were based only on the expected number of neutrino interactions.

4. The proposed statistical method was applied to the case of a presupernova neutrino signal for the first time. Also, for the first time the sensitivity for a combined network of experiments detecting such a signal was evaluated.

5. The detection system launched on the NOvA detectors has been connected to the SNEWS global network for searching neutrinos from supernovae. Previously, the NOvA experiment could only receive alerts from this network; now the results of the NOvA supernova search can be automatically sent to SNEWS to combine with data from other neutrino experiments and provide an early warning to the astronomical community about a supernova burst.

Practical relevance

1. The NOvA experiment plans to collect data until 2026. In the event of a supernova explosion in our galaxy, the developed system will allow the data corresponding to the neutrino signal from the supernova to be recorded for further thorough analysis.

2. The data selected and stored by the developed system are used for additional analyses, namely, the search for anomalies coincident in time with the gravitational wave signals detected by the LIGO/Virgo collaboration [8, 9].

3. The modules for data analysis and background suppression developed and running in the NOvA software trigger environment can also be applied to other NOvA tasks: monitoring the readout electronics channels condition, extracting different components of the background activity in the detector. Application of these modules, for example, to the task of searching magnetic monopoles in NOvA [10] will reduce the background level and increase the speed and efficiency of real-time event processing.

4. The developed method of statistical analysis and combination of signals from different detectors is universal and applicable for the real-time data processing. This method can be applied in other experiments separately as well as for the joint analysis within the SNEWS2.0 network.

5. Incorporating NOvA into the SNEWS2.0 network will increase the likelihood of detecting a neutrino signal from a galactic supernova in the future.

Main points of the defense

1. A procedure for reconstruction and selection of neutrino interactions from supernovae in the Far and Near detectors of NOvA experiment has been developed.

This procedure allowed to reduce the background level from from about 75 x 106 hits/s to 2500cands/s for the Far Detector and from about 7 x 105 hits/s to 0.52cands/s for the Near Detector, leading to signal to noise ratio of 1 : 29(Far Detector) and 2.5 : 1(Near Detector) for the first second of the signal from 9.6 M0 progenitor supernova at the distance of 10kpc.

2. A dedicated statistical Shape Analysis method was developed and applied for detecting neutrino signals from a supernova.

The method makes is applicable both for individual detectors and for the mode of joint detection in several detectors or experiments in real time or with minimal delay.

For the NOvA case, the method increases the maximum range of supernova detection by 1-1.5 kpc (for different supernova models) compared to the standard Counting Analysis approach. The combined mode of near and far detectors will increase the detection range by another 1-1.5 kpc, compared to the individual detectors more.

The advantages over the standard event counting method are retained even when a simplified analytical waveform is used.

The software package that implements the developed statistical method is publicly available and is ready to be used in other experiments [11].

3. A supernova detection system based on NOvA detector data was created and launched, based on the developed reconstruction and selection procedures and statistical processing method.

NOvA is sensitive to the neutrino signal from a supernova at up to 6.2 kpc

for a star with a mass of 9.6 M0 and up to 11.2 kpc for a star with a mass

of 27Mq. The system has a maximum signal detection latency of 60s. The system has been running on the NOvA near and far detectors since November 1, 2017. The triggering events of the system have been studied and are in line with the expected false alarm rate due to statistical background fluctuations.

4. Integration of the NOvA experiment into the global supernova search system SNEWS. The NOvA experiment is a full member of the network and is capable of sending supernova alerts to the SNEWS network. The existing infrastructure is optimized for future modifications that will be required in the development of SNEWSv2.0. The low latency of the NOvA supernova triggering system reduces the overall latency of SNEWS network for detecting the supernova signal.

5. The developed statistical method has been applied to search for the pre-supernova neutrino signal. The sensitivity to such a signal for detectors KamLAND, Borexino and SK-Gd and their combinations is estimated.

Shape analysis method gives advantages over the standard method of counting events in the time window: in the range of detection and in the time from the detection of the neutrino signal to the beginning of the collapse of the supernova core.

For the KamLAND experiment and the significance threshold of supernova detection at 5sigma: the maximum detection range increases by 20-60 pc and the time from detection to supernova outburst at 200 pc increases by 30-120 minutes, depending on the signal model.

The feasibility of using a combined analysis for several experiments was shown: the overall sensitivity of the system increases even when adding an experiment with relatively low sensitivity. For example, for one of the considered signal models, the time from detection to supernova flare for the KamLAND+Borexino system is 500 min, significantly larger than the 239 min (KamLAND) and 21 min (Borexino) for these experiments separately.

Reliability

The reliability of the results obtained is supported by the following:

1. The developed method of statistical analysis taking into account the signal shape is equivalent to the standard method of counting events in the time window, if we use a constant value within a given window as the signal shape. In this case the statistical distributions and the obtained significance of the signal observation fully coincide with the expected one described by the Poisson distribution for the number of signal and background events.

2. The NOvA supernova detection system operation was analyzed for the period from October 1, 2018 to August 15, 2019, in order to study the system stability. During this period, three bursts of false positives associated with detector malfunctions were detected. The remaining 47 system alarms during this time correspond to statistical background fluctuations and are in accordance with the designed false alarm rate (1 event per week).

Approbation of the work

The main results of this work were reported in the international conferences, workshops and seminars:

1. "Supernova neutrino detection in NOvA experiment" (poster), 27th International Conference on Neutrino Physics and Astrophysics (Neutrino 2016), London, Unighted Kingdom, July 2016

2. "Detection of the galactic supernova neutrino signal in NOvA experiment" (poster), 35th International Cosmic Ray Conference (ICRC 2017), Busan, South Korea, July 2017

3. "Trigger system and detection of Supernova in the NOvA experiment" (talk), 26th Symposium on Nuclear Electronics and Computing (NEC 2017), Budva, Montenegro, September 2017

4. "Detection of Galactic Supernova Neutrinos at the NOvA Experiment" (poster), 28th International Conference on Neutrino Physics and Astrophysics (Neutrino 2018), June 2018

5. "Supernova neutrino signal detection in the NOvA experiment" (talk), Workshop on Statistical Issues in Experimental Neutrino Physics (PHYSTAT-nu 2019), CERN, Switzerland, January 2019

6. "Supernova triggering and signals combination for the NOvA detectors" (talk), SNEWS 2.0 workshop: Supernova Neutrinos in the Multi-Messenger Era, Sudbury, Canada, 2019

7. "Detecting neutrinos from the next galactic supernova in the NOvA detectors" (talk), Conference on Neutrino and Nuclear Physics 2020 (CNNP2020), Cape Town, South Africa, Febaruary 2020

8. "Galactic Supernova Neutrino Detection with the NOvA Detectors" (poster), 29th International Conference on Neutrino Physics and Astrophysics (Neutrino 2020), online, June 2020

9. "NOvA in 10 minutes" (talk), Conference for young researchers in the Fer-milab community (New Perspectives 2020), online, July 2020

10. "SuperNova Early Warning System v2.0" (poster), 6th International Conference on Particle Physics and Astrophysics (ICPPA 2022), November 2022

Personal contribution

The author of the thesis directly performed the work described in the thesis: development of methods, construction of software architecture and implementation, the deployment and maintenance of the system on the NOvA detectors; and obtained the results presented for the defense. The content of the thesis and the main statements presented for the defense reflect the author's fundamental personal contribution to the published works.

Publications

The main results of this thesis are presented in 5 publications, three of which [12, 13, 14] are the papers published in the journals indexed by Scopus, Web of Science, and RSCI, and two [15, 16] are conference proceedings.

Structure of the thesis

The dissertation consists of introduction, 7 chapters, conclusion, bibliography, lists of figures and tables. It contains 123 pages with 24 figures and 10 tables. Bibliography contains a list of 70 entries.

Заключение диссертации по теме «Другие cпециальности», Шешуков Андрей Сергеевич

Summary

The main results of this work are the following:

1. A procedure for reconstruction and selection of neutrino interactions from supernovae in the Far and Near detectors of NOvA experiment has been developed.

This procedure includes several stages of background suppression, monitoring the state of the electronic channels of the detector, the procedure of combining the signals in different detector channels to determine the interaction parameters.

2. A new statistical Shape Analysis method was developed for detecting signals from a supernova.

It is shown that for the NOvA case, the developed method increases the maximum range of supernova detection by 1-1.5 kpc (for different supernova models) compared to the standard Counting Analysis approach, which does not take into account the temporal structure of the signal.

The method makes it possible to calculate the significance of a supernova observation both for individual detectors and for the mode of joint detection in several detectors or experiments in real time or with minimal delay. It is shown that for NOvA, the combined analysis of data from near and far detectors will increase the maximum detection range by another 1-1.5 kpc (for different supernova models).

Despite the dependence of the method on the expected signal model, its advantages over the standard event counting method are retained when a simplified analytical waveform is used.

The software package that implements the developed statistical method is publicly available and is ready to be used in other experiments.

3. Using the developed reconstruction and selection procedures and statistical processing method, a supernova detection system based on NOvA detector data was created and launched.

This system allows NOvA to be sensitive to the neutrino signal from a supernova at up to 6.2 kpc for a star with a mass of 9.6 M0 and up to 11.2 kpc for a star with a mass of 27 M0.

The system analyzes data in real time, with a maximum signal detection delay of 60s from a supernova.

The system has been running on the NOvA near and far detectors since November 1, 2017, and has been updated several times to improve stability. The triggering events of the system have been studied and are in line with the expected false alarm rate due to statistical background fluctuations.

4. Integration of the NOvA experiment into the global supernova search system SNEWS2.0. The NOvA experiment is a full member of the network and is sending supernova alerts to the SNEWS network. The existing infrastructure is optimized for future modifications that will be required in the development of SNEWS. The low latency of the NOvA supernova triggering system reduces the overall latency of SNEWS network for detecting the supernova signal.

5. The developed statistical method has been applied to search for the neutrino signal preceding the supernova explosion. The sensitivity to such a signal for detectors and their combinations is estimated, in particular:

It is shown that taking into account the shape of the signal gives advantages over the standard method of counting events in the time window: in the range of detection and in the time from the detection of the neutrino signal to the beginning of the collapse of the supernova nucleus.

For the KamLAND experiment and the significance threshold of supernova detection at 5 sigma: the maximum detection range increases by 20-60 pc and the time from detection to supernova outburst at 200 pc increases by 30-120 minutes, depending on the signal model.

The feasibility of using a combined analysis for several experiments is shown: the overall sensitivity of the system increases even when adding an experi-

ment with relatively low sensitivity. For example, for one signal model, the time from detection to supernova flare for the KamLAND+Borexino system is 500 min, significantly more than the 239 min (KamLAND) and 21 min (Borexino) for these experiments separately.

In conclusion I would like to express my deep gratitude to my supervisors Alec Habig and Oleg Samoylov for their guidance and help at all stages of this work. This thesis couldn't be completed without their insightful suggestions, expertise and constant support and encouragement.

I would like to thank NOvA collaboration for a friendly and constructive working atmosphere, making each idea and contribution welcome. I am grateful to A. Olshevskiy and the NOvA group in JINR for the opportunity to conduct this research in a highly professional team. I thank my friends and colleagues in JINR for helpful discussions and genuine interest.

I am deeply grateful to my wife Alina Vishneva for her support, help and inspiration, and many useful suggestions during my work on this thesis. And a huge thank you to my friends and family who never stopped supporting and beleiving in me during these years.

This work was supported by the Russian Science Foundation under grant 1812-00271.

Список литературы диссертационного исследования кандидат наук Шешуков Андрей Сергеевич, 2024 год

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