Fitting Supernova Spectral Parameters with DUNE Erin Conley On - PowerPoint PPT Presentation

Fitting Supernova Spectral Parameters with DUNE Erin Conley On behalf of the DUNE Collaboration April 14, 2019

Outline Introduction • – The Deep Underground Neutrino Experiment (DUNE) – Supernova neutrinos Modeling supernova neutrinos in DUNE • – SNOwGLoBES – MARLEY – Pinched-thermal flux model Parameter fitting algorithm • – Studying incorrect detector performance assumptions Summary • 2

International experiment for neutrino science (1100+ collaborators!) • Neutrino oscillation physics, supernova physics , nucleon decay – Two detectors: • – Near detector on-site at Fermilab Far detector at Sanford Underground Research Facility (SURF) in South Dakota – Far detector: world’s largest liquid argon time-projection chamber • (40 kton fiducial mass) www.dunescience.org Ionization electrons drift – due to high-voltage electric field Parallel wire planes – create 3D images of particle tracks 3

Supernova Neutrinos in DUNE Expect ~3000 neutrino • Number of SN interactions expected to be seen in DUNE detector interaction events in DUNE detector for a 10 kpc SN – Neutrinos of all flavors carry 99% of core collapse energy – LAr is sensitive to ! " (versus water/scintillator which are sensitive to ̅ ! " ) DUNE devotes much time • into studying theory, event simulation, reconstruction algorithms, etc. related to supernova physics 4

Simulating Supernova Neutrino Signals • SNOwGLoBES: SuperNova Observatories with GLoBES – GLoBES: General Long Baseline Experiment Simulator • Open source event rate calculation tool http://phy.duke.edu/~schol/snowglobes/ 5

Supernova Flux Model Supernova neutrino spectrum AKA • “pinched-thermal form”: & " # " # ! " # = % exp − + + 1 " # " # – " # : Neutrino energy – % : Normalization constant (related to luminosity, . ) " # : Mean neutrino energy – – + : Pinching parameter; large + corresponds to more pinched spectrum Parameters of interest: . , " # , + • Pinched-thermal for a 10kpc supernova (K. Scholberg) Note: Fluence refers to a time-integrated flux. 6

MARLEY: Model of Argon Reaction Low-Energy Yields MARLEY models low-energy • ! " CC neutrino interactions More sophisticated modeling • of final state particles $ % = 16.3 MeV S. Gardiner (http://www.marleygen.org/) 7

Measuring the Flux Parameters Use pinched-thermal flux + • MARLEY modeling to simulate event rates in DUNE detector Flux parameters play • significant role in ! " event rates Develop algorithm to • measure, constrain flux parameters based on SNOwGLoBES event rates 8

Parameter Fitting Algorithm Algorithm uses the • 1) Test Spectrum ! " , $ % " , & " following tools: – “Test spectrum” with given set of pinching parameters ! " , $ % " , & " – Grid of energy spectra containing combinations of ( ) ( ! , $ % , & ) … 2) Grid with many different combinations of (!, ⟨$ % ⟩, &) Compute ' ( value between • test spectrum and all grid spectra; determine best-fit grid element, “sensitivity regions” that constrain parameters 9

Studying Biases due to Incorrect Detector Assumptions • Test spectrum: data from supernova as observed by DUNE • Grids: different DUNE detector performance assumptions • Change assumptions for test spectrum, and for grids, to study effect of mismatched assumptions about detector performance – Study parameter biases introduced by incorrect assumptions using fractional difference from truth: Frac. Diff. = * − * , * , 10

Studying Effect of Detector Performance Knowledge on Bias: Each box corresponds to a unique combination of test • Test Spectrum Resolution (Percent) spectrum and grid; diagonal boxes correspond to correct assumptions • Color scale indicates best-fit parameter fractional difference from truth As assumptions get farther from truth, biases • increase; +30% shift in assumed energy resolution yields ±20% bias on ' Grid Spectra Resolution (Percent) 11

Summary • DUNE is preparing to observe supernova neutrinos and extract as much information as possible • Parameter fitting algorithm used to understand DUNE’s ability to constrain supernova flux parameters – 2D fractional difference plots show bias results from imperfect knowledge of detector parameters; helps quantify how well we need to know these parameters 12

Backup Slides

Liquid Argon Time Projection Chamber Neutrino-argon interaction: argon is • ionized by charged secondary particles Scintillation light detected by photon – detectors provides timing information Charged particles drift toward induction • planes, deposit charge on collection plane wires Charge deposited on wire planes • Reconstructed wire objects (signals for – specific particles) Reconstructed 2D hits (single ionized – particles) Reconstructed 2D clusters (ionization of – multiple particles) Reconstructed 3D objects like tracks, – showers, space points LArTPC Schematic 14

Forward Fitting: “Sensitivity” Example ( ) Map Use SNOwGLoBES to generate • binned energy spectra for a given set of pinched-thermal parameters " , & " → “test spectrum” ! " , $ % Determine ( ) values for all elements • in grid with many combinations of ( ! , $ % , & ) Minimize ( ) while profiling over 1 or • 2 model parameters Form sensitivity regions using cut • on ( ) values 15

Energy Resolution: Introduction Determine how smearing • affects parameter measurements – what if our resolution assumptions are incorrect? Smearing matrices: true • deposited energy from MARLEY + LArSoft; smeared with Gaussian resolution from 0 − 30% 16

Examples of Sensitivity Regions Notes: • Here we see superimposed ! sensitivity regions + best-fit parameters for one test spectrum input into different grids • We can see how " (10 &' ergs) the areas change and also how the bias in our best-fit measurements change! ! - . (MeV) 17