# A UV census of the environments of stripped-envelope supernovae

Ning-Chen Sun1,2,3, Justyn R. Maund3 and Paul A. Crowther3
1 School of Astronomy and Space Science, University of Chinese Academy of
Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
2 National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun
Road, Chaoyang District, Beijing 100101, China
3 Department of Physics and Astronomy, University of Sheffield, Hicks
Building, Hounsfield Road, Sheffield S3 7RH, UK E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

This paper reports an environmental analysis of 41 uniformly-selected
stripped-envelope supernovae (SESNe) based on deep ultraviolet-optical images
acquired by the Hubble Space Telescope. Young stellar populations are detected
in most SN environments and their ages are derived with a hierarchical
Bayesian approach. The age distributions are indistinguishable between Type
IIb and Type Ib while that for Type Ic is systematically younger. This
suggests that the Type Ic SN progenitors are more massive while the Type IIb
and Type Ib SNe have very similar progenitor masses. Our result supports a
hybrid envelope-stripping mechanism, in which the hydrogen envelopes of the
SESN progenitors are stripped via a mass-insensitive process (e.g. binary
interaction) while the helium envelopes are stripped via a mass-sensitive
process (e.g. stellar wind of the post-binary interaction progenitor). We also
provide progenitor constraints for three Type Ibn SNe and two broad-lined Type
Ic SNe. All these results demonstrate the importance of the very diverse mass-
loss processes in the origins of SESNe.

###### keywords:

supernovae: general – stars: mass loss

††pubyear: 2022††pagerange: A UV census of the environments of stripped-
envelope supernovae–References

##  1 Introduction

Core-collapse supernovae (SNe) are the spectacular explosions of massive ($>$8
$M_{\odot}$) stars at the end of their lives. They disperse heavy elements
from stars into the space, drive powerful feedback to the interstellar medium,
and are associated with the formation of neutron stars or black holes (Heger
et al., 2003). They are also important phenomena in multi-messenger astronomy
as possible sources of neutrinos, cosmic rays, and gravitational waves
(Richardson et al., 2022).

The observational appearances of SNe are significantly affected by the various
mass-loss processes of their progenitors. Single stars of $M_{\rm ini}$ = 8–16
$M_{\odot}$ have relatively weak mass loss and will finally explode as the
hydrogen-rich Type II-P SNe (Smartt, 2009). If a star’s outer envelope is
removed by a strong wind [for Wolf-Rayet (WR) stars with $M_{\rm ini}>$ 25–30
$M_{\odot}$; Crowther 2007] and/or binary interaction (Podsiadlowski, Joss, &
Hsu, 1992), the supernova explosion will be of Type IIb (hydrogen lines
visible only at early times), Ib (no hydrogen) or Ic (no hydrogen/helium).
Some of the Type Ic SNe, referred to as the “broad-lined" Type Ic (Ic-BL111In
the following we shall use “Type Ic” to refer to the normal ones, not
including Type Ic-BL unless otherwise specified.) SNe or “hypernovae", show
very broad lines in their spectra owing to their extremely high explosion
energies (Taddia et al., 2019). For some SNe, dense circumstellar material
(CSM) can be formed if their progenitors undergo eruptive mass loss shortly
before explosion; the later ejecta-CSM interaction can produce strong and
narrow emission lines, and such a SN will be of Type IIn, Ibn or Icn depending
on the chemical composition of the progenitor and its CSM (Smith, 2014, 2017).

Arising from stripped progenitors, the hydrogen-poor SNe are also referred to
as the “stripped-envelope" (SE) SNe, including the above-mentioned Types IIb,
Ib, Ic, Ic-BL, Ibn and Icn. Compared with the Type II-P SNe, the search for
SESN progenitors on pre-explosion images has proven to be much more difficult.
Currently, the progenitors have only been detected for 5 Type IIb SNe (1993J,
2008ax, 2011dh, 2013df, 2016gkg; Aldering, Humphreys, & Richmond 1994;
Crockett et al. 2008; Maund et al. 2011; Van Dyk et al. 2014; Kilpatrick et
al. 2017; Tartaglia et al. 2017), 2 Type Ib SNe (iPTF13bvn and 2019yvr; Cao et
al. 2013; Eldridge et al. 2015; Kilpatrick et al. 2021; Sun et al. 2022) and 1
Type Ic SN (2017ein; Van Dyk et al. 2018; Xiang et al. 2019). Binary
companions have (possibly) been detected for the Type IIb SN 1993J (Maund et
al., 2004a), SN 2001ig (Ryder et al., 2018) and SN 2011dh (Maund, 2019), the
Type Ib SN 2013ge (Fox et al., 2022) and SN 2019yvr (Sun et al., 2022), and
the Type Ibn SN 2006jc (Maund et al., 2016; Sun et al., 2020). In addition,
host star clusters (which can be used to infer the progenitor properties) have
been found for the Type Ib SN 2014C (Milisavljevic et al., 2015; Sun, Maund, &
Crowther, 2020) and the Type Ic SN 2020oi (Gagliano et al., 2022).

The limited number of these direct detections poses a major challenge to our
understanding of the progenitors of SESNe and their pre-SN mass loss. This
motivates astronomers to explore alternative methods to constrain the
progenitors for SESNe, such as measuring the line strengths of heavy elements
in the nebular-phase spectra (e.g. Fang et al., 2019, 2022) and deriving the
ejecta masses by fitting light curves (e.g. Lyman et al., 2016; Taddia et al.,
2019). Environmental analysis is another powerful method to study SN
progenitors, since most massive stars form in groups and stars in each group
share very similar ages and metallicities (e.g. Anderson et al., 2012; Kangas
et al., 2013; Galbany et al., 2018; Kuncarayakti et al., 2018; Schady et al.,
2019; Xiao et al., 2019). Generally speaking, SNe from higher-mass progenitors
will more likely to be associated with tracers of recent star formation (e.g.
H$\alpha$ emission, dust, young stars, etc.), while those from lower-mass
progenitors may reside in much older environments.

Among the various techniques of environmental analysis, deep and high-spatial
resolution imaging conducted by the Hubble Space Telescope (HST) can directly
probe the stellar populations in the SN environments, and the stellar ages can
be derived by fitting stellar isochrones on the colour-magnitude diagrams
(CMDs) (e.g. Maund & Ramirez-Ruiz, 2016; Maund, 2017, 2018; Williams et al.,
2019; Sun et al., 2021, 2022). Compared with other techniques, this approach
provides (relatively) more accurate age measurements. The past works have,
however, largely relied on heterogeneous collections of archival observations
and could suffer from statistical biases that are difficult to assess.
Moreover, the age-dependance of the colours of the very young and massive
stars, often seen in SESN environments, is most sensitive at ultraviolet (UV)
wavelengths. For many SESNe, however, observations have been performed only at
optical wavelengths.

In this paper, we report a homogeneous set of deep UV-optical imaging
observations of a uniformly-selected sample of 41 SESNe in the Local Universe.
Taking advantage of the UV filter and the deep detection limits, we try to
probe the very young stellar populations in their environments and acquire an
accurate determination of their ages. We also make statistical comparisons
between the different SN types. Our aim is to reveal their possible progenitor
channels and the roles of the various mass-loss processes in their origins.

This paper is structured as follows: in Section 2 we describe the sample
selection, observation, photometry and analysis; Section 3 provides the
results and discussions for each individual SN types; and we finally close the
paper with a summary and our conclusions in Section 4.

##  2 Sample and analysis

Table 1: Sample of SESNe analyzed in this work Target | Type | Host galaxy | Distance | Inclination | Galactic extinction | References
---|---|---|---|---|---|---
|  |  |  $D$ (Mpc) |  $\theta$ (deg) |  $A_{V}^{\rm MW}$ (mag) |
1996cb | IIb | NGC 3510 | 16.8 (2.3) | 78.1 | 0.08 | Qiu et al. 1999
2003bg | IIb | ESO 420-g009 | 17.7 (1.2) | 41.7 | 0.06 | Hamuy et al. 2009; Mazzali et al. 2009
2004C | IIb | NGC 3683 | 32.1 (4.4) | 69.0 | 0.04 | Shivvers et al. 2017
2005ae | IIb | ESO 209-g9 | 13.6 (1.9) | 90.0 | 0.69 | Filippenko & Foley 2005
2008bo | IIb | NGC 6643 | 20.9 (2.9) | 62.7 | 0.16 | Navasardyan et al. 2008
2009dq | IIb | IC 2554 | 16.4 (1.1) | 70.8 | 0.55 | Green 2009
2009gj | IIb | NGC 134 | 19.2 (2.7) | 90.0 | 0.05 | Foley 2009
2009mk | IIb | ESO 293-g34 | 20.7 (1.4) | 74.6 | 0.04 | Chornock & Berger 2009
2010as | IIb | NGC 6000 | 31.0 (2.1) | 30.7 | 0.46 | Folatelli et al. 2014
2011hs | IIb | IC 5267 | 20.9 (4.8) | 47.8 | 0.03 | Milisavljevic et al. 2011
2013df | IIb | NGC 4414 | 17.9 (1.4) | 56.6 | 0.05 | Van Dyk et al. 2014
2015Y | IIb | NGC 2735 | 54.7 (11.3) | 74.2 | 0.10 | Childress et al. 2015
2016bas | IIb | ESO 163-g11 | 50.6 (9.3) | 70.9 | 0.40 | Hosseinzadeh et al. 2016a
1996N | Ib | NGC 1398 | 28.6 (5.9) | 47.5 | 0.04 | Sollerman, Leibundgut, & Spyromilio 1998
2000ds | Ib | NGC 2768 | 22.2 (2.9) | 90.0 | 0.12 | Filippenko & Chornock 2000
2001B | Ib | IC 391 | 25.2 (1.7) | 18.1 | 0.34 | Chornock & Filippenko 2001
2004ao | Ib | UGC 10862 | 29.6 (2.0) | 53.4 | 0.27 | Matheson et al. 2004b
iPTF13bvn | Ib | NGC 5806 | 27.0 (3.7) | 60.4 | 0.14 | Cao et al. 2013
2014C | Ib | NGC 7331 | 14.1 (1.0) | 70.0 | 0.24 | Kim et al. 2014; Milisavljevic et al. 2015
2014df | Ib | NGC 1448 | 17.2 (1.3) | 86.4 | 0.04 | Monard et al. 2014
2015Q | Ib | NGC 3888 | 39.3 (2.7) | 36.3 | 0.03 | Wiggins et al. 2015
2016cdd | Ib | ESO 218-g8 | 28.7 (2.0) | 90.0 | 0.49 | Hosseinzadeh et al. 2016b
1990W | Ic | NGC 6221 | 11.9 (1.6) | 50.9 | 0.44 | della Valle et al. 1990; Filippenko & McCarthy 1990
2000ew | Ic | NGC 3810 | 15.9 (2.2) | 48.2 | 0.12 | Filippenko, Chornock, & Modjaz 2000
2001ci | Ic | NGC 3079 | 20.6 (3.8) | 90.0 | 0.03 | Filippenko & Chornock 2001
2004bm | Ic | NGC 3437 | 24.2 (3.3) | 72.8 | 0.05 | Foley et al. 2004
2004gk | Ic | IC 3311 | 20.6 (2.8) | 90.0 | 0.08 | Quimby et al. 2004
2005at | Ic | NGC 6744 | 9.0 (0.7) | 53.5 | 0.11 | Kankare et al. 2014; Schmidt & Salvo 2005
2005aw | Ic | IC 4837a | 41.6 (2.9) | 90.0 | 0.16 | Morrell et al. 2005
2009em | Ic | NGC 157 | 12.1 (2.2) | 61.8 | 0.12 | Green 2009; Navasardyan & Benetti 2009
2012cw | Ic | NGC 3166 | 22.0 (1.5) | 56.2 | 0.08 | Itagaki et al. 2012
2012fh | Ic | NGC 3344 | 9.8 (0.9) | 18.7 | 0.09 | Nakano et al. 2012
1991N | Ib/Ic | NGC 3310 | 19.2 (1.3) | 16.1 | 0.06 | Filippenko & Korth 1991
1995F | Ib/Ic | NGC 2726 | 55.7 (10.3) | 90.0 | 0.10 | Lane et al. 1995
2005V | Ib/Ic | NGC 2146 | 16.7 (1.2) | 37.4 | 0.26 | Beswick et al. 2005; Taubenberger et al. 2005
2013ge | Ib/Ic | NGC 3287 | 14.6 (2.0) | 75.3 | 0.06 | Drout et al. 2016
2006jc | Ibn | UGC 4904 | 27.8 (1.9) | 52.2 | 0.05 | Foley et al. 2007; Pastorello et al. 2007
2015G | Ibn | NGC 6951 | 18.8 (1.7) | 50.8 | 0.99 | Shivvers et al. 2017
2015U | Ibn | NGC 2388 | 60.3 (11.1) | 52.6 | 0.15 | Shivvers et al. 2016
1997dq | Ic-BL | NGC 3810 | 15.9 (2.2) | 48.2 | 0.12 | Mazzali et al. 2004
2002ap | Ic-BL | NGC 628 | 9.8 (0.9) | 19.8 | 0.19 | Kinugasa et al. 2002
Table 2: Parameters derived from the stellar population fitting for the SN environments. $N_{\rm m}$: number of age components to fit the observed stars; $A_{V}^{\rm int}$ and d$A_{V}^{\rm int}$: the mean value and standard deviation of the internal extinctions from the host galaxy; log($t_{i}$/yr) and $w_{i}$ ($i$ = 1, 2, 3): mean stellar log-age and weight for the $i$-th age component; log($t_{\rm lim}$/yr): age limit older than which a stellar population will fall below the detection limits. The last column gives the numbers of data points used in the fitting, with $N_{\rm both}$, $N_{\rm F300X}$ and $N_{\rm F475X}$ corresponding to those detected in both bands, only in the F300X band and only in the F475X band, respectively. The errors are propagated from photometric uncertainties. Target | Type | $N_{\rm m}$ | $A_{V}^{\rm int}$ | ${\rm d}A_{V}^{\rm int}$ | log($t_{1}$/yr) | $w_{1}$ | log($t_{2}$/yr) | $w_{2}$ | log($t_{3}$/yr) | $w_{3}$ | log($t_{\rm lim}$/yr) |  $N_{\rm both}$, $N_{\rm F300X}$,
---|---|---|---|---|---|---|---|---|---|---|---|---
|  |  | (mag) | (mag) |  |  |  |  |  |  |  | $N_{\rm F475X}$
1996cb | IIb | 2 | 0.04${}_{-0.01}^{+0.02}$ | 0.01${}_{-0.00}^{+0.01}$ | 6.86${}_{-0.03}^{+0.03}$ | 0.64 | 7.21${}_{-0.03}^{+0.03}$ | 0.36 | … | … | 7.5 | 25, 23, 16
2003bg | IIb | 2 | 0.05${}_{-0.02}^{+0.03}$ | 0.02${}_{-0.00}^{+0.01}$ | 6.82${}_{-0.08}^{+0.04}$ | 0.72 | 7.31${}_{-0.11}^{+0.12}$ | 0.28 | … | … | 7.5 | 10, 10, 5
2004C | IIb | 1 | 0.77${}_{-0.12}^{+0.11}$ | 0.30${}_{-0.07}^{+0.06}$ | 6.74${}_{-0.02}^{+0.02}$ | 1.00 | … | … | … | … | 6.9 | 17, 2, 37
2005ae | IIb | 0 | … | … | … | … | … | … | … | … | 7.3 |
2008bo | IIb | 1 | 1.39${}_{-0.09}^{+0.09}$ | 0.67${}_{-0.06}^{+0.06}$ | 6.60${}_{-0.02}^{+0.03}$ | 1.00 | … | … | … | … | 6.8 | 43, 12, 38
2009dq | IIb | 1 | 1.09${}_{-0.05}^{+0.05}$ | 0.33${}_{-0.03}^{+0.03}$ | 6.72${}_{-0.01}^{+0.01}$ | 1.00 | … | … | … | … | 6.9 | 74, 5, 195
2009gj | IIb | 0 | … | … | … | … | … | … | … | … | 7.5 |
2009mk | IIb | 1 | 0.28${}_{-0.06}^{+0.06}$ | 0.05${}_{-0.05}^{+0.03}$ | 6.94${}_{-0.02}^{+0.02}$ | 1.00 | … | … | … | … | 7.3 | 22, 8, 21
2010as | IIb | 2 | 0.17${}_{-0.07}^{+0.10}$ | 0.03${}_{-0.02}^{+0.03}$ | 6.57${}_{-0.03}^{+0.03}$ | 0.38 | 6.76${}_{-0.01}^{+0.01}$ | 0.62 | … | … | 7.0 | 56, 5, 52
2011hs | IIb | 1 | 0.26${}_{-0.12}^{+0.14}$ | 0.05${}_{-0.05}^{+0.06}$ | 6.90${}_{-0.06}^{+0.12}$ | 1.00 | … | … | … | … | 7.4 | 3, 1, 4
2013df | IIb | 2 | 0.13${}_{-0.04}^{+0.05}$ | 0.03${}_{-0.02}^{+0.02}$ | 6.92${}_{-0.02}^{+0.02}$ | 0.62 | 7.14${}_{-0.04}^{+0.04}$ | 0.38 | … | … | 7.4 | 19, 24, 19
2015Y | IIb | 0 | … | … | … | … | … | … | … | … | 7.0 |
2016bas | IIb | 1 | 0.08${}_{-0.03}^{+0.04}$ | 0.02${}_{-0.01}^{+0.01}$ | 6.66${}_{-0.01}^{+0.01}$ | 1.00 | … | … | … | … | 6.8 | 25, 1, 13
1996N | Ib | 0 | … | … | … | … | … | … | … | … | 7.3 |
2000ds | Ib | 0 | … | … | … | … | … | … | … | … | 7.4 |
2001B | Ib | 1 | 0.06${}_{-0.02}^{+0.03}$ | 0.02${}_{-0.00}^{+0.01}$ | 6.83${}_{-0.01}^{+0.01}$ | 1.00 | … | … | … | … | 7.2 | 28, 8, 23
2004ao | Ib | 1 | 0.75${}_{-0.11}^{+0.11}$ | 0.47${}_{-0.08}^{+0.08}$ | 6.55${}_{-0.04}^{+0.04}$ | 1.00 | … | … | … | … | 6.9 | 19, 5, 6
iPTF13bvn | Ib | 1 | 0.78${}_{-0.13}^{+0.13}$ | 0.05${}_{-0.05}^{+0.06}$ | 6.71${}_{-0.04}^{+0.04}$ | 1.00 | … | … | … | … | 7.0 | 6, 0, 2
2014C | Ib | 1 | 0.62${}_{-0.04}^{+0.04}$ | 0.03${}_{-0.03}^{+0.03}$ | 6.93${}_{-0.01}^{+0.01}$ | 1.00 | … | … | … | … | 7.3 | 5, 3, 40
2014df | Ib | 1 | 0.55${}_{-0.06}^{+0.06}$ | 0.25${}_{-0.05}^{+0.04}$ | 6.91${}_{-0.02}^{+0.02}$ | 1.00 | … | … | … | … | 7.3 | 21, 14, 21
2015Q | Ib | 1 | 0.06${}_{-0.04}^{+0.07}$ | 0.02${}_{-0.01}^{+0.02}$ | 6.74${}_{-0.03}^{+0.03}$ | 1.00 | … | … | … | … | 7.1 | 4, 2, 1
2016cdd | Ib | 2 | 0.26${}_{-0.04}^{+0.04}$ | 0.14${}_{-0.03}^{+0.03}$ | 6.63${}_{-0.01}^{+0.01}$ | 0.75 | 6.76${}_{-0.03}^{+0.02}$ | 0.25 | … | … | 6.9 | 65, 7, 22
1990W | Ic | 2 | 0.78${}_{-0.04}^{+0.04}$ | 0.34${}_{-0.03}^{+0.03}$ | 6.62${}_{-0.02}^{+0.01}$ | 0.68 | 6.84${}_{-0.02}^{+0.02}$ | 0.32 | … | … | 7.2 | 176, 32, 60
2000ew | Ic | 2 | 0.78${}_{-0.07}^{+0.06}$ | 0.28${}_{-0.05}^{+0.04}$ | 6.61${}_{-0.02}^{+0.03}$ | 0.50 | 6.84${}_{-0.01}^{+0.01}$ | 0.50 | … | … | 7.2 | 128, 21, 57
2001ci | Ic | 0 | … | … | … | … | … | … | … | … | 7.4 |
2004bm | Ic | 2 | 1.20${}_{-0.06}^{+0.05}$ | 0.21${}_{-0.07}^{+0.05}$ | 6.56${}_{-0.03}^{+0.03}$ | 0.25 | 6.75${}_{-0.01}^{+0.01}$ | 0.75 | … | … | 6.9 | 34, 2, 76
2004gk | Ic | 1 | 0.62${}_{-0.16}^{+0.12}$ | 0.23${}_{-0.12}^{+0.07}$ | 6.82${}_{-0.03}^{+0.04}$ | 1.00 | … | … | … | … | 7.2 | 16, 8, 13
2005at | Ic | 2 | 0.61${}_{-0.07}^{+0.07}$ | 0.34${}_{-0.05}^{+0.05}$ | 6.59${}_{-0.11}^{+0.10}$ | 0.76 | 7.28${}_{-0.07}^{+0.05}$ | 0.24 | … | … | 7.6 | 57, 24, 13
2005aw | Ic | 1 | 0.58${}_{-0.13}^{+0.12}$ | 0.05${}_{-0.06}^{+0.06}$ | 6.75${}_{-0.02}^{+0.02}$ | 1.00 | … | … | … | … | 6.9 | 4, 0, 13
2009em | Ic | 3 | 0.39${}_{-0.04}^{+0.04}$ | 0.19${}_{-0.02}^{+0.03}$ | 6.72${}_{-0.04}^{+0.03}$ | 0.56 | 6.93${}_{-0.05}^{+0.04}$ | 0.30 | 7.18${}_{-0.04}^{+0.05}$ | 0.14 | 7.5 | 102, 37, 21
2012cw | Ic | 0 | … | … | … | … | … | … | … | … | 7.4 |
2012fh | Ic | 2 | 0.48${}_{-0.03}^{+0.03}$ | 0.24${}_{-0.02}^{+0.02}$ | 6.53${}_{-0.06}^{+0.05}$ | 0.85 | 7.49${}_{-0.03}^{+0.04}$ | 0.15 | … | … | 7.6 | 126, 39, 30
1991N | Ib/Ic | 2 | 0.31${}_{-0.02}^{+0.03}$ | 0.13${}_{-0.01}^{+0.01}$ | 6.58${}_{-0.01}^{+0.01}$ | 0.86 | 6.76${}_{-0.01}^{+0.01}$ | 0.14 | … | … | 7.1 | 345, 35, 34
1995F | Ib/Ic | 0 | … | … | … | … | … | … | … | … | 6.9 |
2005V | Ib/Ic | 0 | … | … | … | … | … | … | … | … | 7.3 |
2013ge | Ib/Ic | 1 | 1.06${}_{-0.26}^{+0.25}$ | 0.76${}_{-0.17}^{+0.12}$ | 6.59${}_{-0.22}^{+0.20}$ | 1.00 | … | … | … | … | 7.2 | 5, 5, 4
2006jc | Ibn | 0 | … | … | … | … | … | … | … | … | 7.3 |
2015G | Ibn | 0 | … | … | … | … | … | … | … | … | 7.2 |
2015U | Ibn | 0 | … | … | … | … | … | … | … | … | 6.9 |
1997dq | Ic-BL | 1 | 0.65${}_{-0.16}^{+0.17}$ | 0.40${}_{-0.13}^{+0.12}$ | 6.94${}_{-0.05}^{+0.05}$ | 1.00 | … | … | … | … | 7.3 | 6, 7, 9
2002ap | Ic-BL | 1 | 0.23${}_{-0.07}^{+0.10}$ | 0.08${}_{-0.10}^{+0.06}$ | 6.96${}_{-0.05}^{+0.04}$ | 1.00 | … | … | … | … | 7.6 | 7, 7, 2

Figure 1: CMDs and HST images of the Type IIb SN environments as listed in
Table 1. The HST images have sizes of 300 pc $\times$ 300 pc and are centred
on the SN positions (indicated by the crosshairs). The circular region we
defined for environmental analysis has a radius of 150 pc, i.e. a diameter
equal to the box size. In the CMDs, the grey-shaded regions show the average
detection limits for a random position in the SN environments, below which the
5$\sigma$ detection probability falls below 50%. Overlaid are parsec (v1.2S)
stellar isochrones; the colored ones correspond to the fitted model stellar
populations, with blue, red and green colours used for populations of
increasing ages, and, for each age component, we show 30 random realisations
derived from their age and extinction spreads; for reference, we also show
black isochrones with log-ages of 6.6, 6.8, 7.0, … in equal spacing of 0.2
dex, all reddened with the Galactic and mean internal extinction. The title
for each SN lists values of the Galactic extinction $A_{V}^{\rm MW}$, and if a
stellar population fitting is performed, the mean internal extinction
$A_{V}^{\rm int}$, internal extinction standard deviation d$A_{V}^{\rm int}$,
and the mean log-ages of the model stellar populations log($t_{i}$/yr) ($i$ =
1, 2, 3). The three arrows in the upper-left corners show the reddening
vectors with lengths corresponding to $A_{V}^{\rm MW}$ \+ $A_{V}^{\rm int}$
($\pm$ d$A_{V}^{\rm int}$).

Figure 1 – continued.

Figure 2: Same as Fig. 1 but for Type Ib SNe.

Figure 3: Same as Fig. 1 but for Type Ic SNe.

Figure 4: Same as Fig. 1 but for intermediate or ambiguous Type Ib/Ic SNe.

Figure 5: Same as Fig. 1 but for Type Ibn SNe.

Figure 6: Same as Fig. 1 but for Type Ic-BL SNe. Figure 7: Ages of the
resolved stellar populations in the SN environments. The shaded regions show
the age limits older than which a stellar population will fall below the
detection limits.

###  2.1 Target selection and observations

We selected Type IIb, Ib and Ic SNe from the IAU SN
list222http://www.cbat.eps.harvard.edu/lists/Supernovae.html, which occurred
no earlier than the year of 1990, for the HST SNAP program “A UV census of the
sites of core-collapse SNe" (PI: J. Maund; ID: 14762). The SNe were required
to have a named host galaxy whose recession velocity corresponds to a Hubble-
Flow distance of $\leq$25 Mpc (from the HyperLEDA database333http://leda.univ-
lyon1.fr and corrected for the infall toward Virgo; Makarov, et al. 2014).
Only 9 SNe were excluded from the selection since they already had deep UV
observations in the HST archive. Besides, we added some important SNe (which
had been heavily observed as part of photometric and spectroscopic campaigns)
to our sample with higher priority and relaxed distance constraints, such as
the Type Ibn and Type Ic-BL SNe. This strategy ensures that the targets were
selected in a uniform and unbiased way with a good coverage of SN types.

44 SNe were finally observed by the HST from 2016 Nov to 2018 May with the
ultraviolet-visible (UVIS) channel of the Wide Field Camera 3 (WFC3). The
extremely-wide UV filter F300X and optical filter F475X were used, with
exposure times of 1200 s and 350 s, respectively, for each target.
Observations in each filter were divided into two individual exposures with a
2-point dithering. The limiting magnitudes were typically 25.7 mag in F300X
and 26.6 mag in F475X (magnitudes are reported in the Vega system throughout
this paper). The choice of the extremely-wide filter F300X and the long
exposure times ensure that the young stellar populations can be probed down to
very deep limits.

Out of the observed SNe, we later found SN 2006oz is actually a superluminous
SN in a very distant dwarf galaxy (Leloudas et al., 2012) and SN 2008ha is a
peculiar Type Iax SN whose nature is not yet clear (Foley et al., 2009); these
two SNe were excluded in this work. SN 2005ar was also excluded since it is
located at more than 100 Mpc away according to more recent distance
measurements. The remaining 41 SESNE, listed in Table 1, were finally used for
the following analysis, including 13 IIb, 9 Ib, 10 Ic, 4 intermediate or
ambiguous Ib/Ic, 3 Ibn and 2 Ic-BL.

Table 1 also provides the Galactic extinctions and the host galaxy
inclinations and distances. Galactic extinctions are based on the estimates by
Schlafly & Finkbeiner (2011) and obtained from the NASA/IPAC IRSA Dust
Extinction Service444https://irsa.ipac.caltech.edu/applications/DUST. Host
galaxy inclinations are from the HyperLEDA database. Distances to the SN host
galaxies, when possible, are from the CosmicFlows
project555http://edd.ifa.hawaii.edu (Tully, et al., 2013; Tully, Courtois &
Sorce, 2016); for the other galaxies, we use Hubble-Flow distances adopting a
Hubble constant of $H_{0}$ = 73.0 $\pm$ 5 km s-1 Mpc-1. Note that, although we
aimed to select targets within 25 Mpc based on their recession velocities,
some of the observed SNe have larger distances according to the more accurate
CosmicFlows project.

We have not included any SESNe with archival HST data in order to ensure the
uniformity in source selection and observations. The archival data are from
various observing programs with different source selection criteria;
therefore, including them would introduce some nonuniformity which itself is
complicated and difficult to assess. For example, if the archival observations
were aimed at studying active galactic nuclei, the SNe within the images would
appear in the inner parts of the galaxies and those in the outer parts may
fall outside the fields of view. In addition, the archival data had a range of
filters and predominantly in the optical (there were only 9 SNe observed in
the UV before this program, although the number has increased recently). By
sticking with the extremely-wide F300X and F475X filters (to obtain high
throughputs, which are rarely used by other observations) and the same
exposure times for all targets, we try to acquire a uniform set of
observations.

###  2.2 Image processing and photometry

Images processed by the standard reduction pipeline (*_flc.fits and *_drc.fts)
were retrieved from the Mikulski Archive for Space Telescopes
666https://archive.stsci.edu/index.html. The images still contain a high level
of cosmic-ray residuals; therefore, we re-drizzled them with driz_cr_grow = 3
with the astrodrizzle package777http://drizzlepac.stsci.edu/, which can clean
the cosmic rays much more efficiently (all other parameters were unchanged).

We performed photometry with the dolphot package (Dolphin, 2000) by fitting
model point spread functions (PSFs) to the sources. The dolphot parameters
FitSky = 2 and Img_RAper = 3 were used, which perform better in crowded
regions. We turned off aperture corrections with ApCor = 0 since some images
have very few stars for this purpose, causing large uncertainties (note that
typical aperture corrections are only about several hundredths magnitude,
which is much smaller than the random errors). Force1 = 1 was used for better
modeling of close stars that may be blended with each other. All other
parameters were the same as those recommended in the dolphot user
manual888http://americano.dolphinsim.com/dolphot/dolphotWFC3.pdf.

A detection reported by dolphot is considered to be a good star if its quality
parameters meet the following criteria:

(1) type of source, TYPE = 1;

(2) signal-to-noise ratio, SNR $\geq$ 5;

(3) source sharpness, $-$0.5 $\leq$ SHARP $\leq$ 0.5;

(4) source crowding, CROWD $\leq$ 2;

(5) photometry quality flag, FLAG $\leq$ 3.

For stars detected in only one filter, we used artificial stars, inserted at
each source position, to estimate the detection limit in the other filter. An
artificial star was considered to be successfully recovered if it fell within
1 pixel of the inserted position and its dolphot quality parameters met all
the above criteria. The detection limits were derived independently for each
star since the sky background and source crowding may vary across the field.
We also used randomly positioned artificial stars to estimate additional
photometric uncertainties induced by source crowding and imperfect sky
subtraction.

We determined SN positions on the HST images with three different methods
depending on the availability of supporting observations: (1) relative
astrometry with previous HST observations, with typical uncertainties of $<$1
pixel; (2) relative astrometry with observations from other telescopes (e.g.
Swift/UVOT and Spitzer/IRAC), with typical uncertainties of $\sim$3 pixel; and
(3) using the reported SN R.A. and Dec. coordinates, with typical
uncertainties of $\sim$1". Sources within a (projected) distance of 150 pc
from the SN positions (excluding those within 3 pixels from the SNe) were
finally selected for the following analysis (we did not correct for the host
galaxies’ inclinations). Consistent with the previous studies (e.g. Maund,
2017, 2018), the size of the selection region was chosen to be compatible with
the typical scales of star-forming complexes, i.e. fundamental cells of star
formation within which the stars are born with similar ages (Efremov, 1995).
The SN positional errors are not important compared with the defined area.
Figures 1–6 show the images of the observed SN sites and the CMDs of the
detected sources in their environments.

###  2.3 Fitting of the resolved stellar populations

####  2.3.1 Method

In order to derive the stellar ages, we used a hierarchical Bayesian approach
to fit model stellar populations to the observed stars in the SN environments.
The method is described in detail in Maund & Ramirez-Ruiz (2016) and Sun et
al. (2021) and has been successfully applied in a number of studies (e.g.,
Maund 2017, 2018; Sun et al. 2020, 2021, 2022). In brief, the model
populations were simulated based on the parsec stellar isochrones (v1.2S;
Bressan et al. 2012) and have a Salpeter (1955) initial mass function, a 50%
binary fraction, and a flat distribution of primary-to-secondary mass ratio.
In addition to the Galactic extinction, the internal extinctions from the host
galaxy were assumed to have a Gaussian distribution $\mathcal{N}(A_{V}^{\rm
int},({\rm d}A_{V}^{\rm int})^{2})$ among the stars in each SN environment,
where $A_{V}^{\rm int}$ is the mean value and ${\rm d}A_{V}^{\rm int}$ is the
standard deviation; we used a flat prior for $A_{V}^{\rm int}$ and a
logarithmic prior for ${\rm d}A_{V}^{\rm int}$ over the range of 0 $\leq$
log(${\rm d}A_{V}^{\rm int}$/0.01 mag) $\leq$ 2, penalizing large values. In
each SN environment, the observed stars can be considered as arising from a
mixture of model stellar populations with different ages and each model
population corresponds to a short burst of star formation, in which the
stellar log-ages follow a narrow Gaussian distribution with a standard
deviation of 0.05 dex.

To determine the required number of model populations, we started the fitting
with one age component, and added more age components if the fitting was
significantly improved. We compared the fitting performances using the Bayes
factor $K_{n+1,n}$ = ($Z_{n+1}$/$(n+1)!$)/($Z_{n}$/$n!$) and the criterion of
Jeffreys (1961), where $Z_{n+1}$ and $Z_{n}$ are the Bayesian evidences for
the fitting with $n+1$ and $n$ components [note that factors of $n!$ and
$(n+1)!$ are needed to correct for overestimation since the labels for the
model populations are interchangeable with each other]. We stopped adding more
components if this did not improve the fitting significantly or if the number
of components reached a maximum value of 3. In practice, all SNe, except the
Type Ic SN 2009em, require fewer than 3 age components to model the detected
populations.

We did not perform any fitting when the SN environments contain no stars
detected in both filters or only a few stars marginally detected above the
detection limits. For each SN environment, we also estimated an age limit
older than which a stellar population will fall below the detection limits; in
doing this, we use the same extinction as derived from the population fitting,
or simply assume zero internal extinction if no population fitting is
performed for the SN (but this may overestimate the age limit).

####  2.3.2 Caveats

At the distances of the analyzed SNe, the linear size of a pixel correspond to
a few parsecs and the detected sources may or may not be single stars. In our
population modelling, we have considered the possibilities of each source
being a non-interacting binary system (Section 2.3.1; see also Maund &
Ramirez-Ruiz 2016 and Sun et al. 2021 for details), for which the composite
fluxes are the simple sums of the primary and secondary stars’ fluxes. This
approach also accounts for the visual binaries composed of stars in chance
alignment, thus source blending in crowded regions does not have a significant
influence on our results (the visual binaries slightly increase the effective
binary fraction, to which the population fitting is not very sensitive).
However, this effect may still lead to an age underestimation in particular if
the source lies in a region on the CMD where stars evolve very rapidly (and
thus drives the fitting most sensitively). We have not considered the
contribution from interacting binaries. The rejuvenated stars from binary mass
transfer and merger may appear much younger (Schneider et al., 2015);
therefore, our analysis tend to underestimate the stellar ages. It is,
however, not trivial to model the interacting binary populations; this
requires a detailed binary population synthesis and is beyond the scope of
this work.

Star clusters that appear more extended than the PSF have been excluded by the
selection criteria in Section 2.2 (extended sources have low sharpness values
reported by dolphot). We cannot rule out the possibility that the stellar
catalogues are still contaminated by very compact star clusters that are not
spatially resolved due to their small angular sizes. In some SN environments
some sources appear much brighter than the other sources and are more likely
to be star clusters; they were manually removed from the fitting. For the
ambiguous sources that are not easily identifiable by eye, our algorithm also
automatically assigns lower weights to the “outliers" in determining the model
populations’ parameters. For the low-mass star clusters whose brightnesses are
often dominated by one or a few massive stars (e.g. the Orion Nebula Cluster),
they can be fitted by our algorithm as if they are bright stars or binary
systems. Proper quantitative assessment would require a detailed modelling of
the star cluster population, which is beyond the scope of this work.

When available, we adopted metallicities for the model stellar populations
that follow the reported values for the SNe. For those without metallicity
estimates, we assume half-solar metallicity for the Type Ic-BL SNe (which
prefer low-metallicity environments; Modjaz et al. 2020) and solar metallicity
for the other types. We ignored the metallicity uncertainties/dispersions and
fixed their values in the fitting. This simplification greatly reduces the
computing time. However, this may introduce some systematic errors since the
stellar colours are metallicity-dependent.

Distances are fixed in our stellar population fitting. The typical
uncertainties in distance moduli are 0.15–0.40 dex with a median value of 0.2
dex. By manually fitting stellar isochrones while keeping the extinction
unchanged, we found that a 0.2-dex difference in the distance modulus roughly
corresponds to a difference of 0.03 dex in stellar log-age. We also carried
out a test by adding or subtracting 0.2 dex from the used distance modulus,
and the derived ages changed by a negligible amount (in particular for the
youngest population), since part of the distance difference has been absorbed
by the internal extinction as a free parameter.

It was found that in some SN environments (e.g. SN 2004dg and SN 2012P; Sun et
al. 2021) stars formed at different epochs may be located at different places
along the line of sight and have different extinction values. For simplicity,
however, our analysis assumes that $A_{V}^{\rm int}$ and ${\rm d}A_{V}^{\rm
int}$ are independent of stellar ages. While this may introduce some
systematic uncertainties, this simplification does not affect the youngest
populations (which we shall focus on) that dominate the observed stars and
drive the fitting in most circumstances. In the analysis we have used a
standard extinction law with $R_{V}$ = 3.1 (Fitzpatrick, 2004) for both the
Galactic and internal extinctions. We repeated the fitting with different
values of $R_{V}$ = 2.0 and 5.0 for the internal extinction; although the
derived extinctions may be different, the populations’ ages change very little
by $<$0.05 dex. Therefore, the reported ages are relatively insensitive to the
choice of the reddening law. The fitting results are also unaffected by the
uncertainties in the Galactic extinction, since any such errors will be
absorbed by the internal extinction, which is a free parameter to be fitted.

We have not considered the stochastic sampling effect when the number of data
points is very small. If the fitting is driven by only a few data points, the
nominal uncertainty propagated from the photometric errors could be
significantly underestimated. While it is difficult to make quantitative
measurement, we caution this effect and provide the numbers of data points
used in the fitting in the last column of Table 2.

Lastly, we have assumed zero uncertainty in the adopted stellar isochrones.
However, the evolution for young and massive stars is not well constrained and
different sets of models could predict different evolutionary tracks (Martins
& Palacios, 2013). The model uncertainty is another source of error in our
results.

##  3 Results and discussions

Figure 8: Cumulative age distributions of the detected youngest stellar
populations in the Types IIb (red), Ib (green) and Ic (blue) SN environments.
The total cumulative age distribution for the combined sample of Types IIb+Ib
SNe is shown in black. The thinner lines are 500 random realisations derived
from the age uncertainties; in the upper panel, the uncertainties are
propagated from the photometric uncertainties while in the lower panel, we
conservatively assume an additional uncertainty of 0.05 dex from sources
unaccounted for in our modelling (such as distance, extinction law, small
number statistics, etc.). The median log-ages are shown by the vertical dashed
lines with the shaded regions reflecting their uncertainties. Figure 9: A
hybrid envelope-stripping mechanism that dominates the origins of Types IIb,
Ib and Ic SNe.

The population fitting results are listed in Table 2 and also displayed in
Fig. 7. The quoted uncertainties are propagated from the photometric errors
and are estimated with the 68% equal-tailed credible intervals (we also
explore the effects of additional uncertainties in Section 3.1); therefore,
the uncertainties become larger when there is a significant degeneracy between
age and extinction with double-peaked posterior probability distributions. The
results show that very young stellar populations are present in the
environments of a large fraction of the SESNe. $\sim$80% of the observed Types
IIb, Ib and Ic SNe have stellar populations younger than 10 Myrs in their
vicinity (10/13 Type IIb, 7/9 Type Ib, 8/10 Type Ic, and 2/4 SNe that are of
intermediate or ambiguous Type Ib/Ic) and this fraction may be even higher if
some SN environments contain young stars from residual star formation that
have eluded our detection. For the Type Ic SN 1997dq and SN 2002ap, young
stellar populations are detected in their environments with ages of
log($t$/yr) = 6.94 and 6.96, respectively. In contrast, the Type Ibn SN 2006jc
and SN 2015G are located in very sparse areas without any obvious recent star
formation, and SN 2015U does not have any resolved stars in its vicinity that
are significantly detected in the UV filter.

It is worth noting that the age limits (older than which a stellar population
will not be detected in both filters) are not the same for all targets and are
strongly dependent on their distances and extinctions. Therefore, we shall
characterise the age of each SN environment not with all the detected
populations, but only with the youngest one, which is less affected by the
inhomogeneous age limit.

The presence of very young stellar populations in the environment is often
interpreted as the SNe having very massive progenitors with similarly young
ages (e.g. WR stars). However, it should be cautioned that the SN progenitors
may actually be much older and reside just in chance alignment. Also note that
star formation bursts on small scales may appear correlated if they are
controlled by physical processes on larger scales (e.g. spiral density waves
that sweep up and compress the up-stream low-density gas and leave behind a
trail of sequential star formation at the downstream, Sun et al. 2021; the
feedback from massive stars which can trigger new episodes of star formation
in their vicinity, Koenig et al. 2008; and the ubiquitous supersonic
turbulence that drives hierarchical patterns in the spatial distribution of
star formation, Sun et al. 2017a, b, 2018; Miller et al. 2022). The
relationship between a SN progenitor and the youngest stellar population in
its environment may, therefore, be very complicated: the progenitor may (1) be
a member of the youngest population and have the same age, (2) form at an
earlier epoch of star formation unrelated to the youngest population and
aligned just by chance, or (3) arise from an older stellar population in the
vicinity of the youngest population, whose formation is related to each other.

Statistically, however, it is still reasonable to assume that SNe with more
massive progenitors will have a higher probability to be associated with
younger stellar populations in their environments (and vice versa; since the
targets were uniformly selected, observed and analysed). Following this
assumption, we discuss the environments and progenitors for the SESN types in
the following subsections.

###  3.1 Types IIb, Ib and Ic

Figure 8 compares the cumulative age distributions for the youngest stellar
populations detected in the Types IIb, Ib and Ic SN environments. The
distributions are very similar between Type IIb and Type Ib SNe, while that
for Type Ic SNe is systematically much younger. We performed a two-sample
Anderson-Darling test999Unlike the Kolmogorov-Smirnov test, which uses only
the supremum distance between two cumulative distribution functions (CDFs),
the Anderson-Darling test uses the sum of all squared distances between two
CDFs to test the null hypothesis; therefore, it is more sensitive to CDF
differences in the tails. between Type Ic and Types IIb+Ib; the null
hypothesis that the two samples are drawn from the same distribution can be
rejected at a 6% significance level. The median log-ages of the detected
youngest populations are log($t$/yr) = 6.62 $\pm$ 0.03 for Type Ic SNe and
6.78 $\pm$ 0.02 for Types Ib+IIb SNe. This suggests that the Type Ic SN
progenitors are on average younger and more massive than those of Type IIb and
Type Ib SNe, while the Type IIb and Type Ib SNe have on average
indistinguishable progenitor masses.

In the upper panel of Fig. 8, the age uncertainties are propagated only from
the photometric uncertainties. As mentioned in Section 2.3.2, however, there
could be other error sources not included in our population fitting. While the
interacting binaries and ambiguous star clusters may lead to an age
underestimation, their effects are similar for different SN types since our
analysis is performed in a uniform way. For the other error sources
(distances, extinction law, small number statistics, etc.), we conservatively
assume that they contribute an additional uncertainty of 0.05 dex to the age
determination (see the discussion in Section 2.3.2). The updated age
distributions are displayed in the lower panel of Fig. 8; while the
distributions become broader, the difference between Type Ic and Types IIb+Ib
SNe is still significant. The uncertainties of the median log-ages increase by
0.01 dex.

We also use an alternative method without isochrone fitting to qualitatively
check the environmental difference for the different SN types; if the Type Ic
SNe tend to be associated with younger stellar populations, their environments
will have a larger number of bright stars than the Type IIb and Type Ib SNe.
We calculate the absolute magnitudes of the surrounding stellar populations
and correct their Galactic extinctions; apart from a few SNe with large
distances and/or high extinctions (Type IIb SN 2010as, SN 2015Y and SN2016bas;
Type Ib SN 2016cdd; and Type Ic SN 2005aw), the detections are complete down
to limits of $m_{F300X}$ = $-$7.5 mag and $m_{F475X}$ = $-$6.5 mag. The Type
IIb SN 2009dq is also excluded since its environment contains many sources
that are too bright to be single/binary stars but are more likely to be star
clusters (Fig. 1). We find that the Types IIb/Ib/Ic SN environments have on
average 5/8/15 stars brighter than $m_{F300X}$ = $-$7.5 mag, and 7/5/17 stars
brighter than $m_{F475X}$ = $-$6.5 mag, respectively. This is consistent with
the previous finding that Type Ic SN environments are on average younger than
the Type IIb and Type Ib SNe. In this analysis we have not corrected for the
internal extinctions within the host galaxies, but the stellar population
fitting shows that Type Ic SNe tend to have higher internal extinctions than
the Type IIb and Type Ib SNe (Table 2), so the number of bright stars could
potentially be even larger for the Type Ic SNe. All these results suggest that
the Type Ic SN environments and progenitors are significantly different from
those of Type IIb and Type Ib SNe.

####  3.1.1 A hybrid envelope-stripping mechanism?

Fang et al. (2019) analysed the late-time nebular spectra of 13 Type IIb, 16
Type Ib and 16 Type Ic SNe. They found that Type Ic SNe have larger [O i]/[Ca
ii] line ratios than Type IIb and Type Ib SNe, while those for Type IIb and
Type Ib SNe are very similar to each other. This result is later justified by
their more recent work (Fang et al., 2022) based on a larger sample. The [O
i]/[Ca ii] line ratio is a good indicator of the final carbon-oxygen core
mass, which is higher for more massive SN progenitors. Therefore, they have
reached the same conclusion as we draw from the above environmental analysis.

Our result therefore supports the hybrid envelope-stripping model proposed by
Fang et al. (2019, see also the schematic diagram in Fig. 9) for the origins
of SESNe. The indistinguishable progenitor masses between Type IIb and Type Ib
SNe suggest that the hydrogen envelope is stripped by a mass-insensitive
process, and binary interaction is the best candidate without major
difficulties. The outcome of binary interaction is strongly dependant on the
initial binary separation: a star in a relatively wide binary may end up with
a small residual hydrogen envelope and explode as a Type IIb SN, while a star
of the same initial mass in a closer binary can completely lose its hydrogen
envelope and explode as a Type Ib SN (e.g. Claeys et al., 2011). The effect of
binary interaction depends weakly on the progenitor mass, and we note that a
mass- and metallicity-dependant stellar wind may remove the residual hydrogen
envelope left after the binary interaction (e.g. Yoon, Dessart, & Clocchiatti,
2017).

The fact that Type Ic SNe appear to favour higher progenitor masses than Type
IIb and Type Ib SNe suggest that the stripping of the helium envelope is
caused by a mass-sensitive process. This may be the stellar wind of the
hydrogen-poor progenitor in the post-binary interaction phase, which is
stronger for more massive stars (Vink 2017; note that binary interaction alone
is very difficult to remove the entire helium envelope in self-consistent
models; Benvenuto & Bersten 2017). It is also possible that a very massive
progenitor can lose all of its hydrogen- and helium-rich layers solely via its
own stellar wind without the aid of binary interaction. Some Type Ic SNe may
still have modest He envelopes that are not completely stripped, but their
helium lines are not excited due to, e.g., the weak mixing between helium and
radioactive elements (Dessart et al., 2012).

The differences between Types IIb+Ib and Type Ic SN progenitors found by Fang
et al. (2019) and this work are only statistically meaningful instead of being
specific about individual progenitors. It is possible that a Type IIb or Type
Ib SN progenitor could be more massive than that for a Type Ic SN. Similarly,
the proposed hybrid envelope-stripping model may just account for most,
instead of all, of the observed SN populations, and there could be many other
progenitor channels for the origins of SESNe (e.g. Yoon, 2017; Yoon, Dessart,
& Clocchiatti, 2017; Hirai et al., 2020).

####  3.1.2 Comparison with previous works

The environments of SESNe have been studied in a number of previous works with
a variety of methods. For example, Galbany et al. (2018) and Kuncarayakti et
al. (2018) used the H$\alpha$ equivalent width to estimate the ages of the
ionising stellar populations, and Anderson et al. (2012) and Kangas et al.
(2013) analysed the SNe’ spatial correlation with H$\alpha$ emission (and
other tracers of star formation). Kangas et al. (2013) showed that Type Ic SNe
are most strongly correlated with recent star formation, while the difference
between Type II and Type Ib SNe is insignificant (consistent with our
results). On the other hand, however, some of the works found a trend that,
following Types IIb $\rightarrow$ Ib $\rightarrow$ Ic, the SNe occur in
progressively younger environments (e.g. Anderson et al., 2012; Kuncarayakti
et al., 2018).

We caution that the use of the H$\alpha$ equivalent width as a stellar age
indicator may suffer from large uncertainties. Its value is very sensitive to
the covering factor, the older stellar populations along the same line of
sight (which contribute to the stellar continuum), and whether the effect of
interacting binaries is taken into account. Recent works (e.g. Xiao, Stanway,
& Eldridge, 2018; Schady et al., 2019; Xiao et al., 2019) found that the
derived stellar ages can be very different if these effects were or were not
considered; Sun et al. (2021) showed that accurate stellar ages can only be
obtained by a detailed photoionisation calculation and a full modelling of the
nebular spectral lines. Large uncertainties may also affect the spatial
correlation analysis. As pointed out by Crowther (2013), the giant H ii
regions (rather than isolated and compact ones) in the vicinity of most SN
sites can undergo complicated and multi-generation evolution, and the observed
spatial association provides only weak constraints on the SN progenitor
masses. Moreover, some SNe apparently associated with H ii regions on the low-
spatial resolution images may actually have an offset between each other (e.g.
Maund & Ramirez-Ruiz, 2016).

Studying the resolved stellar populations allows a direct age estimate of the
SN environments. Maund (2018) analysed 23 SESNe and found the characteristic
ages for the environmental populations (approximately the mean ages for all
the detected populations for a given SN type, not just the youngest population
in the environment) are log($t$/yr) = 7.20, 7.05, and 6.57 for Types IIb, Ib
and Ic SNe, respectively. However, that work was based on a heterogeneous
dataset and the effect of selection bias is very difficult to estimate. In
addition, many of the analysed SNe lack UV observations; since the optical
colour is very insensitive to stellar age, the age determination may therefore
have relatively large uncertainties, especially for the youngest populations
of only a few Myrs.

In our analysis, the age distributions of the youngest environmental
populations are almost identical for the Type IIb and Type Ib SNe (Fig. 8). We
cannot rule out the possibility that this is due to the measurement
uncertainties and/or the limited sample size. Observations of larger samples
are required to further confirm this result.

###  3.2 Type Ibn

Type Ibn SNe are those which exhibit narrow helium lines with little evidence
for hydrogen. The narrow helium lines are a signature of strong interaction
between the SN ejecta and helium-rich CSM. The CSM was formed via eruptive
mass loss of their progenitors shortly before their explosions (Smith, 2017).
Early studies proposed that their progenitors are massive WR stars stripped by
their powerful winds (e.g. Foley et al., 2007; Pastorello et al., 2007). More
recently, Shivvers et al. (2017) ruled out a WR progenitor for SN 2015G by
examining its pre-explosion images. Sun et al. (2020) found a progenitor
initial mass of only $\lesssim$12 $M_{\odot}$ for SN 2006jc by analysing its
surviving binary companion. Modelling of the SN spectra (Dessart, Hillier, &
Kuncarayakti, 2022) showed that Type Ibn SN progenitors have final masses of
only $\lesssim$5 $M_{\odot}$, much lower than those for WR stars.

For the Type Ibn SN 2006jc and SN 2015G, our deep imaging did not detect any
obvious stellar populations in their environments younger than log($t$/yr) =
7.3 and 7.2, respectively (Table 2 and Fig. 7). The age limits were derived by
assuming zero internal extinctions within their host galaxies. If we adopt the
extinction estimates by Pastorello et al. (2007) and Shivvers et al. (2017),
the age limit changes to log($t$/yr) = 7.1 for SN 2015G while that for SN
2006jc is almost unchanged. The age limits correspond to progenitor masses of
$M_{\rm ini}$ $\lesssim$ 11.9 $M_{\odot}$ for SN 2006jc and $M_{\rm ini}$
$\lesssim$ 13.8 $M_{\odot}$ (assuming zero internal extinction) or 16.3
$M_{\odot}$ (using the extinction estimate from Shivvers et al. 2017) for SN
2015G. These constraints argue against massive WR progenitors, whose lifetimes
are only a few Myrs 101010Recently, Beasor et al. 2021 derived an older age of
$\sim$10 Myr for the Galactic cluster Westerlund 1, which host dozens of WR
stars, from its cool supergiant population. If the WR stars have the same age
as the cool supergiants, the age and initial mass limits for WR stars may have
to be soften to some extent, possibly owing to their binary evolutionary
channel. However, we also note that Westerlund 1 was formed in an extended
period of star formation of several Myrs, so the WR stars in this cluster may
still have very young ages consistent with previous constraints (Crowther,
2007).. Their progenitors are most likely stripped via binary interaction,
which then underwent eruptive mass loss shortly before their core collapse.
These two processes led to the stripping of their hydrogen envelopes and the
formation of the dense helium-rich CSM. Therefore, this result confirms that
violent eruptions, often seen in very massive ($>$25–30 $M_{\odot}$) luminous
blue variables, can also occur in much lower-mass stars stripped in binaries
(Fuller & Ro 2018; Wu & Fuller 2022; see also Sun et al. 2020 for a detailed
discussion).

For the more distant SN 2015U, the age limit [log($t$/yr) $>$ 6.9] is not
sufficient to robustly reject a WR progenitor. This limit is likely to be
overestimated since we assumed zero internal extinction but SN 2015U seems to
be in a dusty area. Moreover, there is a UV blob in its vicinity, which could
be an unresolved young star-forming complex. Therefore, the environment of SN
2015U is rather ambiguous.

###  3.3 Type Ic-BL

Type Ic-BL SNe are characterised by the broad lines in their spectra,
indicating extremely fast expansion velocities. Their explosion energies can
reach $\sim$1052 erg, almost an order of magnitude higher than the normal
core-collapse SNe. Lyman et al. (2016) found relatively low ejecta masses for
almost all Type Ic-BL SNe in their sample, suggesting moderately massive
progenitors of $M_{\rm init}$ = 8–20 $M_{\odot}$ stripped in binaries. In
contrast, Taddia et al. (2019) showed that at least 21% of the Type Ic-BL SNe
have very large ejecta masses of $>$5.5 $M_{\odot}$ and should arise from the
more massive WR progenitors with $M_{\rm ini}>$ 28 $M_{\odot}$.

In our sample, the Type Ic-BL SN 1997dq was poorly studied previously (see,
however, Mazzali et al., 2004) and SN 2002ap has received far more attention.
For SN 2002ap, the light curve analysis of Mazzali et al. (2002) derived a
carbon-oxygen core mass of 5 $M_{\odot}$, corresponding to an initial mass of
20–25 $M_{\odot}$ for the progenitor. Crockett et al. (2007) ruled out any
single progenitors that have not evolved into a WR phase based on detection
limits on the pre-explosion images; they suggested that the progenitor, if it
were indeed a single star, should have an initial mass of at least 30–40
$M_{\odot}$ and a higher-than-standard mass-loss rate. More recently, Zapartas
et al. (2017) constrained the progenitor initial mass to be $\lesssim$23
$M_{\odot}$ with binary population synthesis.

Maund (2018) detected only a few stars in the environment of SN 2002ap based
on optical images acquired by the High Resolution Channel (HRC) of HST’s
Advanced Camera for Surveys (ACS). Our new images, which use the extremely
wide F300X and F475X filters and reach deeper detection limits, clearly
reveals a young and resolved stellar population in the vicinity of SN 2002ap
(Fig. 6). This is consistent with the deep UV images acquired by Zapartas et
al. (2017, their Fig. 1) in the F275W and F336W filters with exposure times of
2778 s in each band. The derived age [log($t$/yr) = 6.96${}^{+0.04}_{-0.05}$]
corresponds to a progenitor initial mass of $M_{\rm ini}$ =
21.6${}^{+2.5}_{-1.8}$ $M_{\odot}$, assuming the progenitor is a member of and
has the same age as the detected population. This value is consistent with the
previous estimates as mentioned above. For SN 1997dq, the detected
environmental population suggests a progenitor mass of $M_{\rm ini}$ =
22.5${}^{+2.8}_{-2.3}$ $M_{\odot}$, very similar to that of SN 2002ap. It is
also possible, however, that the progenitor may have a lower initial mass if
it belongs to an undetected older stellar population. These results are
inconsistent with massive WR progenitors and agree with a binary progenitor
channel (but the envelope stripping may still be aided by its own stellar
wind). Note that Type Ic-BL SNe prefer low-metallicity environments (Modjaz et
al., 2020), where stars have weaker winds and the initial mass threshold can
be as high as $\sim$30 $M_{\odot}$ for a single star to evolve into the WR
phase (Crowther, 2007).

##  4 Summary and conclusions

This paper reports an environmental analysis of 41 SESNe. The targets were
selected and observed uniformly and unbiasedly, and deep UV-optical imaging
was conducted by the HST to probe the young stars in the SN environments. We
used a hierarchical Bayesian approach to fit resolved stellar populations on
the CMDs in order to derive their ages and extinctions. The high sensitivity
of UV-optical colour to stellar age/extinction allows us to make very precise
measurements, with typical uncertainties smaller than 0.1 dex in log-age and
0.1 mag in extinction in most cases.

As many as 80% of the observed Types IIb, Ib and Ic SNe have stellar
populations younger than 10 Myrs in their environments. The age distributions
for the youngest environmental populations for Type IIb and Type Ib SNe are
very similar to each other, while that for Type Ic SNe is systematically much
younger. This suggests that Type Ic SNe have more massive progenitors than
Type IIb and Type Ib SNe; in contrast, no compelling evidence is found for any
significant difference between Type IIb and Type Ib SN progenitor masses.

This result supports a hybrid model of envelope-stripping for the origins of
SESNe (as proposed by Fang et al. 2019). In this scenario, the stripping of
the hydrogen envelope is mostly caused by binary interaction as a mass-
insensitive process, while that for the helium envelope could be due to a
mass-sensitive mechanism, such as the wind of the hydrogen-poor progenitor at
the post-binary interaction phase.

For the Type Ibn SNe, our deep imaging excludes any young stellar populations
in the environments of SN 2006jc and SN 2015G that correspond to massive WR
progenitors. They most likely arise from moderately massive ($\lesssim$11.9
and $\lesssim$16.3 $M_{\odot}$, respectively) stars stripped in interacting
binaries. For the more distant SN 2015U, however, the age limit is not
sufficient to make any useful constraints. This result confirms that the very
violent eruptions, often seen in the very massive luminous blue variables, can
also occur in much lower-mass stars stripped in binaries, forming dense and
helium-rich CSM shortly before their final explosion.

For the Type Ic-BL SNe, we detect young stellar populations in the vicinity of
both SN 1997dq and SN 2002ap. Their progenitors would have initial masses of
$M_{\rm ini}$ = 22.5${}^{+2.8}_{-2.3}$ $M_{\odot}$ and 21.6${}^{+2.5}_{-1.8}$
$M_{\odot}$, respectively, if they are coeval with the detected populations.
This result also argues against WR progenitors for both SNe and favours an
interacting binary progenitor channel possibly aided by their own stellar
winds.

## Acknowledgements

The authors are very grateful to Prof. Keiichi Maeda, Mr. Qiliang Fang and the
anonymous referee for their very helpful comments on this paper. Research of
N-CS, JRM and PAC are funded by the Science and Technology Facilities Council
through grant ST/V000853/1. This paper is based on observations made with the
NASA/ESA Hubble Space Telescope.

## Data availability

Data used in this work are all publicly available from the Mikulski Archive
for Space Telescope (https://archive.stsci.edu).

## References

  * Aldering, Humphreys, & Richmond (1994) Aldering G., Humphreys R. M., Richmond M., 1994, AJ, 107, 662
  * Anderson et al. (2012) Anderson J. P., Habergham S. M., James P. A., Hamuy M., 2012, MNRAS, 424, 1372
  * Beasor et al. (2021) Beasor E. R., Davies B., Smith N., Gehrz R. D., Figer D. F., 2021, ApJ, 912, 16
  * Benvenuto & Bersten (2017) Benvenuto O. G., & Bersten M. C., 2017, Handbook of Supernovae. Springer International Publishing AG, Cham, p. 403
  * Beswick et al. (2005) Beswick R. J., Fenech D., Thrall H., Argo M. K., Muxlow T. W. B., Pedlar A., 2005, IAUC, 8572
  * Bressan et al. (2012) Bressan A., Marigo P., Girardi L., Salas nich B., Dal Cero C., Rubele S., Nanni A., 2012, MNRAS, 427, 127
  * Cao et al. (2013) Cao Y., Kasliwal M. M., Arcavi I., Horesh A., Hancock P., Valenti S., Cenko S. B., et al., 2013, ApJL, 775, L7
  * Childress et al. (2015) Childress M., Scalzo R., Yuan F., Zhang B., Ruiter A., Seitenzahl I., Schmidt B., et al., 2015, ATel, 7368
  * Chornock & Filippenko (2001) Chornock R., Filippenko A. V., 2001, IAUC, 7577
  * Chornock & Berger (2009) Chornock R., Berger E., 2009, CBET, 2086
  * Claeys et al. (2011) Claeys J. S. W., de Mink S. E., Pols O. R., Eldridge J. J., Baes M., 2011, A&A, 528, A131
  * Crockett et al. (2007) Crockett R. M., Smartt S. J., Eldridge J. J., Mattila S., Young D. R., Pastorello A., Maund J. R., et al., 2007, MNRAS, 381, 835
  * Crockett et al. (2008) Crockett R. M., Eldridge J. J., Smartt S. J., Pastorello A., Gal-Yam A., Fox D. B., Leonard D. C., et al., 2008, MNRAS, 391, L5
  * Crowther (2007) Crowther P. A., 2007, ARA&A, 45, 177
  * Crowther (2013) Crowther P. A., 2013, MNRAS, 428, 1927
  * della Valle et al. (1990) della Valle M., Pasquini L., Phillips M., McCarthy P., 1990, IAUC, 5079
  * Dessart et al. (2012) Dessart L., Hillier D. J., Li C., Woosley S., 2012, MNRAS, 424, 2139
  * Dessart, Hillier, & Kuncarayakti (2022) Dessart L., Hillier D. J., Kuncarayakti H., 2022, A&A, 658, A130
  * Dolphin (2000) Dolphin A. E., 2000, PASP, 112, 1383
  * Drout et al. (2016) Drout M. R., Milisavljevic D., Parrent J., Margutti R., Kamble A., Soderberg A. M., Challis P., et al., 2016, ApJ, 821, 57
  * Efremov (1995) Efremov Y. N., 1995, AJ, 110, 2757
  * Eldridge et al. (2015) Eldridge J. J., Fraser M., Maund J. R., Smartt S. J., 2015, MNRAS, 446, 2689
  * Fang et al. (2019) Fang Q., Maeda K., Kuncarayakti H., Sun F., Gal-Yam A., 2019, NatAs, 3, 434
  * Fang et al. (2022) Fang Q., Maeda K., Kuncarayakti H., Tanaka M., Kawabata K. S., Hattori T., Aoki K., et al., 2022, ApJ, 928, 151
  * Filippenko & McCarthy (1990) Filippenko A. V., McCarthy P., 1990, IAUC, 5090
  * Filippenko & Korth (1991) Filippenko A. V., Korth S., 1991, IAUC, 5234
  * Filippenko & Chornock (2000) Filippenko A. V., Chornock R., 2000, IAUC, 7511
  * Filippenko, Chornock, & Modjaz (2000) Filippenko A. V., Chornock R., Modjaz M., 2000, IAUC, 7547
  * Filippenko & Chornock (2001) Filippenko A. V., Chornock R., 2001, IAUC, 7638
  * Filippenko & Foley (2005) Filippenko A. V., Foley R. J., 2005, IAUC, 8486
  * Fitzpatrick (2004) Fitzpatrick E. L., 2004, ASPC, 309, 33, ASPC..309
  * Folatelli et al. (2014) Folatelli G., Bersten M. C., Kuncarayakti H., Olivares Estay F., Anderson J. P., Holmbo S., Maeda K., et al., 2014, ApJ, 792, 7
  * Foley et al. (2004) Foley R. J., Wong D. S., Ganeshalingam M., Filippenko A. V., Chornock R., 2004, IAUC, 8339
  * Foley et al. (2007) Foley R. J., Smith N., Ganeshalingam M., Li W., Chornock R., Filippenko A. V., 2007, ApJL, 657, L105
  * Foley et al. (2009) Foley R. J., Chornock R., Filippenko A. V., Ganeshalingam M., Kirshner R. P., Li W., Cenko S. B., et al., 2009, AJ, 138, 376
  * Foley (2009) Foley R. J., 2009, CBET, 1858
  * Fox et al. (2022) Fox O. D., Van Dyk S. D., Williams B. F., Drout M., Zapartas E., Smith N., Milisavljevic D., et al., 2022, ApJL, 929, L15
  * Fuller & Ro (2018) Fuller J., Ro S., 2018, MNRAS, 476, 1853
  * Gagliano et al. (2022) Gagliano A., Izzo L., Kilpatrick C. D., Mockler B., Jacobson-Galán W. V., Terreran G., Dimitriadis G., et al., 2022, ApJ, 924, 55
  * Galbany et al. (2018) Galbany L., Anderson J. P., Sánchez S. F., Kuncarayakti H., Pedraz S., González-Gaitán S., Stanishev V., et al., 2018, ApJ, 855, 107
  * Green (2009) Green D. W. E., 2009, CBET, 1789
  * Green (2009) Green D. W. E., 2009, CBET, 1807
  * Hamuy et al. (2009) Hamuy M., Deng J., Mazzali P. A., Morrell N. I., Phillips M. M., Roth M., Gonzalez S., et al., 2009, ApJ, 703, 1612
  * Heger et al. (2003) Heger A., Fryer C. L., Woosley S. E., Langer N., Hartmann D. H., 2003, ApJ, 591, 288
  * Hirai et al. (2020) Hirai R., Sato T., Podsiadlowski P., Vigna-Gómez A., Mandel I., 2020, MNRAS, 499, 1154
  * Hosseinzadeh et al. (2016a) Hosseinzadeh G., Arcavi I., Howell D. A., McCully C., Valenti S., 2016a, ATel, 8814
  * Hosseinzadeh et al. (2016b) Hosseinzadeh G., Arcavi I., Howell D. A., McCully C., Valenti S., 2016b, ATel, 9065
  * Itagaki et al. (2012) Itagaki K., Noguchi T., Nakano S., Yusa T., Wang X.-F., Liu Q., Zhang J.-J., et al., 2012, CBET, 3148
  * Jeffreys (1961) Jeffreys H., 1961, Theory of Probability, third edition. Oxford University Press, Oxford
  * Kangas et al. (2013) Kangas T., Mattila S., Kankare E., Kotilainen J. K., Väisänen P., Greimel R., Takalo A., 2013, MNRAS, 436, 3464
  * Kankare et al. (2014) Kankare E., Fraser M., Ryder S., Romero-Cañizales C., Mattila S., Kotak R., Laursen P., et al., 2014, A&A, 572, A75
  * Kim et al. (2014) Kim M., Zheng W., Li W., Filippenko A. V., Cenko S. B., Arbour R., Masi G., et al., 2014, CBET, 3777
  * Kilpatrick et al. (2017) Kilpatrick C. D., Foley R. J., Abramson L. E., Pan Y.-C., Lu C.-X., Williams P., Treu T., et al., 2017, MNRAS, 465, 4650
  * Kilpatrick et al. (2021) Kilpatrick C. D., Drout M. R., Auchettl K., Dimitriadis G., Foley R. J., Jones D. O., DeMarchi L., et al., 2021, MNRAS, 504, 2073
  * Kinugasa et al. (2002) Kinugasa K., Kawakita H., Ayani K., Kawabata T., Yamaoka H., Deng J. S., Mazzali P. A., et al., 2002, ApJL, 577, L97
  * Koenig et al. (2008) Koenig X. P., Allen L. E., Gutermuth R. A., Hora J. L., Brunt C. M., Muzerolle J., 2008, ApJ, 688, 1142
  * Kuncarayakti et al. (2018) Kuncarayakti H., Anderson J. P., Galbany L., Maeda K., Hamuy M., Aldering G., Arimoto N., et al., 2018, A&A, 613, A35
  * Lane et al. (1995) Lane D. J., Gray P., Fillipenko A. V., Barth A. J., 1995, IAUC, 6138
  * Leloudas et al. (2012) Leloudas G., Chatzopoulos E., Dilday B., Gorosabel J., Vinko J., Gallazzi A., Wheeler J. C., et al., 2012, A&A, 541, A129
  * Lyman et al. (2016) Lyman J. D., Bersier D., James P. A., Mazzali P. A., Eldridge J. J., Fraser M., Pian E., 2016, MNRAS, 457, 328
  * Makarov, et al. (2014) Makarov D., Prugniel P., Terekhova N., Courtois H., Vauglin I., 2014, A&A, 570, A13
  * Martins & Palacios (2013) Martins F., Palacios A., 2013, A&A, 560, A16
  * Maund et al. (2004a) Maund J. R., Smartt S. J., Kudritzki R. P., Podsiadlowski P., Gilmore G. F., 2004, Natur, 427, 129
  * Matheson et al. (2004b) Matheson T., Challis P., Kirshner R., Berlind P., 2004, IAUC, 8304
  * Maund et al. (2011) Maund J. R., Fraser M., Ergon M., Pastorello A., Smartt S. J., Sollerman J., Benetti S., et al., 2011, ApJL, 739, L37
  * Maund et al. (2016) Maund J. R., Pastorello A., Mattila S., Itagaki K., Boles T., 2016, ApJ, 833, 128
  * Maund & Ramirez-Ruiz (2016) Maund J. R., Ramirez-Ruiz E., 2016, MNRAS, 456, 3175
  * Maund (2017) Maund J. R., 2017, MNRAS, 469, 2202
  * Maund (2018) Maund J. R., 2018, MNRAS, 476, 2629
  * Maund (2019) Maund J. R., 2019, ApJ, 883, 86
  * Mazzali et al. (2002) Mazzali P. A., Deng J., Maeda K., Nomoto K., Umeda H., Hatano K., Iwamoto K., et al., 2002, ApJL, 572, L61
  * Mazzali et al. (2004) Mazzali P. A., Deng J., Maeda K., Nomoto K., Filippenko A. V., Matheson T., 2004, ApJ, 614, 858
  * Mazzali et al. (2004) Mazzali P. A., Deng J., Maeda K., Nomoto K., Filippenko A. V., Matheson T., 2004, ApJ, 614, 858
  * Mazzali et al. (2009) Mazzali P. A., Deng J., Hamuy M., Nomoto K., 2009, ApJ, 703, 1624
  * Milisavljevic et al. (2011) Milisavljevic D., Fesen R., Soderberg A., Pickering T., Kotze P., 2011, CBET, 2902
  * Milisavljevic et al. (2015) Milisavljevic D., Margutti R., Kamble A., Patnaude D. J., Raymond J. C., Eldridge J. J., Fong W., et al., 2015, ApJ, 815, 120
  * Miller et al. (2022) Miller A. E., Cioni M.-R. L., de Grijs R., Sun N.-C., Bell C. P. M., Choudhury S., Ivanov V. D., et al., 2022, MNRAS, 512, 1196.
  * Modjaz et al. (2005) Modjaz M., Kirshner R., Challis P., Matheson T., Foster J., 2005, IAUC, 8493
  * Modjaz et al. (2020) Modjaz M., Bianco F. B., Siwek M., Huang S., Perley D. A., Fierroz D., Liu Y.-Q., et al., 2020, ApJ, 892, 153
  * Monard et al. (2014) Monard L. A. G., Kneip R., Brimacombe J., Sato H., Childress M., Zhou G., Scalzo R., et al., 2014, CBET, 3977
  * Morrell et al. (2005) Morrell N., Hamuy M., Folatelli G., Roth M., 2005, IAUC, 8507
  * Nakano et al. (2012) Nakano S., Yusa T., Yoshimoto K., Tomasella L., Turatto M., Pastorello A., Ochner P., et al., 2012, CBET, 3263
  * Navasardyan et al. (2008) Navasardyan H., Benetti S., Harutyunyan A., Agnoletto I., Bufano F., Cappellaro E., Turatto M., 2008, CBET, 1325
  * Navasardyan & Benetti (2009) Navasardyan H., Benetti S., 2009, CBET, 1806
  * Pastorello et al. (2007) Pastorello A., Smartt S. J., Mattila S., Eldridge J. J., Young D., Itagaki K., Yamaoka H., et al., 2007, Natur, 447, 829
  * Podsiadlowski, Joss, & Hsu (1992) Podsiadlowski P., Joss P. C., Hsu J. J. L., 1992, ApJ, 391, 246
  * Qiu et al. (1999) Qiu Y., Li W., Qiao Q., Hu J., 1999, AJ, 117, 736
  * Quimby et al. (2004) Quimby R., Gerardy C., Hoeflich P., Wheeler J. C., Shetrone M., Riley V., McGaha J., et al., 2004, IAUC, 8446
  * Richardson et al. (2022) Richardson C. J., Zanolin M., Andresen H., Szczepańczyk M. J., Gill K., Wongwathanarat A., 2022, PhRvD, 105, 103008
  * Ryder et al. (2018) Ryder S. D., Van Dyk S. D., Fox O. D., Zapartas E., de Mink S. E., Smith N., Brunsden E., et al., 2018, ApJ, 856, 83
  * Salpeter (1955) Salpeter E. E., 1955, ApJ, 121, 161
  * Schady et al. (2019) Schady P., Eldridge J. J., Anderson J., Chen T.-W., Galbany L., Kuncarayakti H., Xiao L., 2019, MNRAS, 490, 4515
  * Schlafly & Finkbeiner (2011) Schlafly E. F., Finkbeiner D. P., 2011, ApJ, 737, 103
  * Schmidt & Salvo (2005) Schmidt B., Salvo M., 2005, IAUC, 8496
  * Schneider et al. (2015) Schneider F. R. N., Izzard R. G., Langer N., de Mink S. E., 2015, ApJ, 805, 20. doi:10.1088/0004-637X/805/1/20
  * Shivvers et al. (2017) Shivvers I., Modjaz M., Zheng W., Liu Y., Filippenko A. V., Silverman J. M., Matheson T., et al., 2017, PASP, 129, 054201
  * Shivvers et al. (2016) Shivvers I., Zheng W. K., Mauerhan J., Kleiser I. K. W., Van Dyk S. D., Silverman J. M., Graham M. L., et al., 2016, MNRAS, 461, 3057
  * Shivvers et al. (2017) Shivvers I., Zheng W., Van Dyk S. D., Mauerhan J., Filippenko A. V., Smith N., Foley R. J., et al., 2017, MNRAS, 471, 4381
  * Smartt (2009) Smartt S. J., 2009, ARA&A, 47, 63
  * Smith (2014) Smith N., 2014, ARA&A, 52, 487
  * Smith (2017) Smith N., 2017, Handbook of Supernovae, Springer International Publishing AG, p. 403
  * Sollerman, Leibundgut, & Spyromilio (1998) Sollerman J., Leibundgut B., Spyromilio J., 1998, A&A, 337, 207
  * Sun et al. (2017a) Sun N.-C., de Grijs R., Subramanian S., Cioni M.-R. L., Rubele S., Bekki K., Ivanov V. D., et al., 2017, ApJ, 835, 171
  * Sun et al. (2017b) Sun N.-C., de Grijs R., Subramanian S., Bekki K., Bell C. P. M., Cioni M.-R. L., Ivanov V. D., et al., 2017, ApJ, 849, 149
  * Sun et al. (2018) Sun N.-C., de Grijs R., Cioni M.-R. L., Rubele S., Subramanian S., van Loon J. T., Bekki K., et al., 2018, ApJ, 858, 31
  * Sun et al. (2020) Sun N.-C., Maund J. R., Hirai R., Crowther P. A., Podsiadlowski P., 2020, MNRAS, 491, 6000
  * Sun, Maund, & Crowther (2020) Sun N.-C., Maund J. R., Crowther P. A., 2020, MNRAS, 497, 5118
  * Sun et al. (2021) Sun N.-C., Maund J. R., Crowther P. A., Fang X., Zapartas E., 2021, MNRAS, 504, 2253
  * Sun et al. (2022) Sun N.-C., Maund J. R., Crowther P. A., Hirai R., Kashapov A., Liu J.-F., Liu L.-D., et al., 2022a, MNRAS, 510, 3701
  * Taddia et al. (2019) Taddia F., Sollerman J., Fremling C., Barbarino C., Karamehmetoglu E., Arcavi I., Cenko S. B., et al., 2019, A&A, 621, A71
  * Tartaglia et al. (2017) Tartaglia L., Fraser M., Sand D. J., Valenti S., Smartt S. J., McCully C., Anderson J. P., et al., 2017, ApJL, 836, L12
  * Taubenberger et al. (2005) Taubenberger S., Pastorello A., Benetti S., Aceituno J., 2005, IAUC, 8474
  * Tully, et al. (2013) Tully R. B., et al., 2013, AJ, 146, 86
  * Tully, Courtois & Sorce (2016) Tully R. B., Courtois H. M., Sorce J. G., 2016, AJ, 152, 50
  * Van Dyk et al. (2018) Van Dyk S. D., Zheng W., Brink T. G., Filippenko A. V., Milisavljevic D., Andrews J. E., Smith N., et al., 2018, ApJ, 860, 90
  * Van Dyk et al. (2014) Van Dyk S. D., Zheng W., Fox O. D., Cenko S. B., Clubb K. I., Filippenko A. V., Foley R. J., et al., 2014, AJ, 147, 37
  * Vink (2017) Vink J. S., 2017, A&A, 607, L8
  * Wiggins et al. (2015) Wiggins P., Masi G., Brimacombe J., Belligoli R., Castellani F., Marangoni C., Leonard D. C., et al., 2015, CBET, 4128
  * Williams et al. (2019) Williams B. F., Hillis T. J., Blair W. P., Long K. S., Murphy J. W., Dolphin A., Khan R., et al., 2019, ApJ, 881, 54
  * Wu & Fuller (2022) Wu S. C., Fuller J., 2022, ApJ, 930, 119
  * Xiang et al. (2019) Xiang D., Wang X., Mo J., Wang L., Smartt S., Fraser M., Ehgamberdiev S. A., et al., 2019, ApJ, 871, 176
  * Xiao, Stanway, & Eldridge (2018) Xiao L., Stanway E. R., Eldridge J. J., 2018, MNRAS, 477, 904
  * Xiao et al. (2019) Xiao L., Galbany L., Eldridge J. J., Stanway E. R., 2019, MNRAS, 482, 384
  * Yoon (2017) Yoon S.-C., 2017, MNRAS, 470, 3970
  * Yoon, Dessart, & Clocchiatti (2017) Yoon S.-C., Dessart L., Clocchiatti A., 2017, ApJ, 840, 10
  * Zapartas et al. (2017) Zapartas E., de Mink S. E., Van Dyk S. D., Fox O. D., Smith N., Bostroem K. A., de Koter A., et al., 2017, ApJ, 842, 125