Supernova Progenitors From Stellar Populations And Supernova Remnants (STSCI/AURA/UIUC) M33 (LGGS) Sumit K. Sarbadhicary PITT-PACC, University of Pittsburgh Team - Carles Badenes (Pitt), Laura Chomiuk, Daniel Huizenga, Jessica Maldonado (MSU), Damiano Caprioli (U. Chicago)

What is the nature of the binary companion in Type Ia SNe What is the nature of the binary companion in Type Ia SNe? Do all massive stars produce Core-collapse SNe? [Refs: Wang & Han ‘12, Maoz ‘14, Kochanek ‘08, Smartt ‘09, Smartt ’15]

Delay-time distribution (DTD) = the timescales on which stars become supernova Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) = the timescales on which stars become supernova Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) = the timescales on which stars become supernova Star Formation History Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) = the timescales on which stars become supernova Star Formation History SN Rate today = (DTD*SFH)today Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) = the timescales on which stars become supernova Star Formation History Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) = the timescales on which stars become supernova Star Formation History SN Rate today = (DTD*SFH)today Illustration: S. Sarbadhicary/C Badenes

Delay-time distribution (DTD) is a direct constraint on stellar evolution models Type Ia SN Measured DTDs Predicted DTDs Illustration: C Badenes/ L Chomiuk Time [Gyr]

Delay-time distribution (DTD) is a direct constraint on stellar evolution models Core Collapse SN Predicted DTDs No measured CC DTD yet Core collapse SN per 106 M (Zapartas 2017) Time [Myrs]

Limitation of DTD from SN surveys – luminosity -weighted stellar ages Illustration: C Badenes

The alternative? Star formation histories from resolved stellar populations HST, PHAT (Dalcanton 2012, Lewis 2015)

Treat supernova remnant surveys as ‘effective’ SN surveys (Magnier ‘95, Gordon ‘98, ‘99, Badenes ‘10, Long ’10 Braun ‘12)

First DTD in the Local Group measured by Maoz & Badenes (2010)

But we must understand supernova remnant surveys Most importantly…. Visibility Times Selection Effects

Sarbadhicary et. al (2017) – a data constrained, physical model of visibility times So this is the main plot, and we see how visibility times scale with ISM density. And its not a trivial scenario. We have a lot of scatter in visibility times, because SNRs evolve with different kinetic energies and ambient densities. Sarbadhicary ‘17

Sarbadhicary et. al (2017) – a data constrained, physical model of visibility times OBSERVATIONAL CONSTRAINTS Radio SNR surveys [Gordon 98, 99, Chomiuk 09] ISM maps [Braun ‘12] Stellar brightness maps [Massey 06, , Gil de Paz 07, Morissey 07, Dale 09] Radio light curve model So this is the main plot, and we see how visibility times scale with ISM density. And its not a trivial scenario. We have a lot of scatter in visibility times, because SNRs evolve with different kinetic energies and ambient densities. Sarbadhicary ‘17

Detection-Limited lifetimes Sarbadhicary et. al (2017) – a data constrained, physical model of visibility times Sedov lifetimes The visibility time is decided by two factors. One is the physics of the remnant evolution. SNRs in denser ISM will produce radio emission for shorter period of time, and so you get a nice scaling relation for the visibility times. However, about a third of the SNRs will have radio emission below the detection limit of the survey, and so their visibility times will be shorter than whats predicted by the scaling relation. So this model will allow us to quantify the bias from selection effects in SNR surveys. Also it seems the visibility times are decided by two main factors – one is the Sedov-Taylor lifetime, which is what Carlos and Dan predicted. As the SNR shock transitions to radiative, the shock velocity is very low. At this point, we assume the synchrotron mechanism becomes inefficient – there may not be enough field amplification to produce GeV electrons, and the shock will soon become close to turbulent ISM fluctuations. The other factor is the detection limit of the radio survey – these SNR slips below the noise sensitivity of the radio survey. Its only 30%, but we can reduce this population by current generation radio telescopes. So we have a model that allows us to quantify the SNR visibility times by looking at their constrained radio light curves. Detection-Limited lifetimes Sarbadhicary ‘17

A systematic search of supernova remnants from deep radio observations Jessica Maldonado (MSU)

(Sarbadhicary et al, in prep) DTD can test stellar evolution scenarios for a wide variety of stellar objects Planetary Nebula (Badenes ‘15) Pulsating Variables (Sarbadhicary et al, in prep) Pl. Nebula per 106 M Var. Stars per M Time [Myr] Time [Myr]

SN Delay-Time Distribution SUMMARY Star-formation histories SNR Population Model Radio SNR surveys Jessica Maldonado’s Poster SN Delay-Time Distribution in the Local Group Directly constrain progenitor models for a population of SNe

Radio light curves are simple to model, can explore parameter space [Sarbadhicary ’17]

Sarbadhicary et. al (2017) – a data constrained, physical model of visibility times So this is the main plot, and we see how visibility times scale with ISM density. And its not a trivial scenario. We have a lot of scatter in visibility times, because SNRs evolve with different kinetic energies and ambient densities. Sarbadhicary ‘17