A reddening-free method to estimate the 56^{56}Ni mass of Type Ia supernovae

The increase in the number of Type Ia supernovae (SNe\,Ia) has demonstrated that the population shows larger diversity than has been assumed in the past. The reasons (e.g. parent population, explosion mechanism) for this diversity remain largely unknown. We have investigated a sample of SNe\,Ia near-infrared light curves and have correlated the phase of the second maximum with the bolometric peak luminosity. The peak bolometric luminosity is related to the time of the second maximum (relative to the {\it B} light curve maximum) as follows : $L_{max}(10^{43} erg s^{-1}) = (0.039 \pm 0.004) \times t_2(J)(days) + (0.013 \pm 0.106)$. $^{56}$Ni masses can be derived from the peak luminosity based on Arnett's rule, which states that the luminosity at maximum is equal to instantaneous energy generated by the nickel decay. We check this assumption against recent radiative-transfer calculations of Chandrasekhar-mass delayed detonation models and find this assumption is valid to within 10\% in recent radiative-transfer calculations of Chandrasekhar-mass delayed detonation models. The $L_{max}$ vs. $t_2$ relation is applied to a sample of 40 additional SNe\,Ia with significant reddening ($E(B-V) >$ 0.1 mag) and a reddening-free bolometric luminosity function of SNe~Ia is established. The method is tested with the $^{56}$Ni mass measurement from the direct observation of $\gamma-$rays in the heavily absorbed SN 2014J and found to be fully consistent. Super-Chandrasekhar-mass explosions, in particular SN\,2007if, do not follow the relations between peak luminosity and second IR maximum. This may point to an additional energy source contributing at maximum light. The luminosity function of SNe\,Ia is constructed and is shown to be asymmetric with a tail of low-luminosity objects and a rather sharp high-luminosity cutoff, although it might be influenced by selection effects.Comment: 9 pages, 3 figures, Accepted to A&

On Reflection of Shock Waves from Boundary Layers

Measurements of the reflection characteristics of shock waves from a flat surface with a laminar and turbulent boundary layer are presented. The investigations were carried out at Mach numbers from about 1.3 to 1.5 and a Reynolds number of 0.9 x 10^4. THe difference in the shock-wave interaction with laminar and turbulent boundary layers, first found in transonic flow is confirmed and ,investigated in detail for supersonic flow. The relative upstream influence of a shock wave impinging on a given boundary layer has been measured for both laminar and turbulent layers. The upstream influence of a shock wave in the laminar layer is found to be of the order of 50 bounday-layer thicknesses as compared with about 5 in the turbulent case. Separation almost always occurs in the laminar boundary layer. The separation is restricted to a region of finite extent upstream of the the shock wave. In the turbulent case no separation was found. A model of the flow near the point of impingement of the shock wave on the boundary layer is given for both cases. The difference between impulse-type and step-type shock waves is discussed and their interactions with the boundary layer are compared. Some general considerations on the experimental production of shock waves from wedges and cones are presented, as well as a discussion of boundary layer in supersonic flow. A few exampies of reflection of shock waves from supersonic shear layers are also presented

Limits on stable iron in Type \,Ia supernovae from NIR spectroscopy

We obtained optical and near-infrared spectra of Type$\,$Ia supernovae (SNe$\,$Ia) at epochs ranging from 224 to 496 days after the explosion. The spectra show emission lines from forbidden transitions of singly ionised iron and cobalt atoms. We used non-local thermodynamic equilibrium (NLTE) modelling of the first and second ionisation stages of iron, nickel, and cobalt to fit the spectra using a sampling algorithm allowing us to probe a broad parameter space. We derive velocity shifts, line widths, and abundance ratios for iron and cobalt. The measured line widths and velocity shifts of the singly ionised ions suggest a shared emitting region. Our data are fully compatible with radioactive $^{56}$Ni decay as the origin for cobalt and iron. We compare the measured abundance ratios of iron and cobalt to theoretical predictions of various SN$\,$Ia explosion models. These models include, in addition to $^{56}$Ni, different amounts of $^{57}$Ni and stable $^{54,56}$Fe. We can exclude models that produced only $^{54,56}$Fe or only $^{57}$Ni in addition to $^{56}$Ni. If we consider a model that has $^{56}$Ni, $^{57}$Ni, and $^{54,56}$Fe then our data imply that these ratios are $^{54,56}$Fe / $^{56}$Ni $=0.272\pm0.086$ and $^{57}$Ni / $^{56}$Ni $=0.032\pm0.011$.Comment: 10 pages, 7 figures, Accepted for publication in A&

Type Ia supernova constraints on compact object dark matter

The nature of dark matter (DM) is an open question in cosmology, despite its abundance in the universe. While elementary particles have been posited to explain DM, compact astrophysical objects such as black holes formed in the early universe offer a theoretically appealing alternate route. Here, we constrain the fraction of DM that can be made up of primordial black holes (PBHs) with masses $M \gtrsim 0.01 M_\odot$, using the Type Ia supernova Hubble diagram. Utilizing the Dyer-Roeder distance relation, where the homogeneous matter fraction is parameterized with $\eta$, we find a maximum fractional amount of DM in compact objects ($f_p$) of 0.50 at 95\% confidence level (C.L.), in the flat $\Lambda$CDM model and 0.49 when marginalising over a constant dark energy equation of state. These limits do not change when marginalising over cosmic curvature, demonstrating the robustness to the cosmological model. When allowing for the prior on $\eta$ to include $\eta > 1$, we derive $f_p < 0.32$ at 95$\%$ C.L., showing that the prior assumption of $\eta \leq 1$ gives a conservative upper limit on $f_p$. When including Cepheid calibrated supernovae, the 95\% C.L. constraints improve to $f_p < 0.25$. We find that the estimate for the Hubble constant in our inference is consistent with the homogeneous case, showing that inhomogeneities in the form of compact dark matter cannot account for the observed Hubble tension. In conclusion, we strongly exclude the possibility that PBHs with stellar masses and above form a dominant fraction of the dark matter.Comment: to be submitted to MNRAS Letter

iPTF16abc and the population of Type Ia supernovae: Comparing the photospheric, transitional and nebular phases

Key information about the progenitor system and the explosion mechanism of Type Ia supernovae (SNe~Ia) can be obtained from early observations, within a few days from explosion. iPTF16abc was discovered as a young SN~Ia with excellent early time data. Here, we present photometry and spectroscopy of the SN in the nebular phase. A comparison of the early time data with a sample of SNe~Ia shows distinct features, differing from normal SNe~Ia at early phases but similar to normal SNe~Ia at a few weeks after maximum light (i.e. the transitional phase) and well into the nebular phase. The transparency timescales ($t_0$) for this sample of SNe~Ia range between $\sim$ 25 and 41 days indicating a diversity in the ejecta masses. $t_0$ also weakly correlates with the peak bolometric luminosity, consistent with the interpretation that SNe with higher ejecta masses would produce more $^{56}$Ni. Comparing the $t_0$ and the maximum luminosity, L$_{max}$\, distribution of a sample of SNe~Ia to predictions from a wide range of explosion models we find an indication that the sub-Chandrasekhar mass models span the range of observed values. However, the bright end of the distribution can be better explained by Chandrasekhar mass delayed detonation models, hinting at multiple progenitor channels to explain the observed bolometric properties of SNe~Ia. iPTF16abc appears to be consistent with the predictions from the M$_{ch}$ models.Comment: 13 pages, 8 figures, accepted for publication in MNRA

Nebular spectroscopy of SN 2014J: Detection of stable nickel in near infrared spectra

We present near infrared (NIR) spectroscopy of the nearby supernova 2014J obtained $\sim$450 d after explosion. We detect the [Ni II] 1.939 $\mu$m line in the spectra indicating the presence of stable $^{58}$Ni in the ejecta. The stable nickel is not centrally concentrated but rather distributed as the iron. The spectra are dominated by forbidden [Fe II] and [Co II] lines. We use lines, in the NIR spectra, arising from the same upper energy levels to place constraints on the extinction from host galaxy dust. We find that that our data are in agreement with the high $A_V$ and low $R_V$ found in earlier studies from data near maximum light. Using a $^{56}$Ni mass prior from near maximum light $\gamma$-ray observations, we find $\sim$0.05 M$_\odot$ of stable nickel to be present in the ejecta. We find that the iron group features are redshifted from the host galaxy rest frame by $\sim$600 km s$^{-1}$.Comment: 6 pages, 4 figures, submitted to A&

Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17

The paper provides a quantitative and qualitative description of deep learning research using bibliometric indicators covering global research publications published during 14-year period 2004-17. Global deep learning research registered 106.76% high growth per annum, and averaged 7.99 citations per paper. Top 10 countries world- over dominate the research field with their 99.74% global publications share and more than 100% global citations share. China ranks the top with the highest (29.25%) global publications share, followed by USA (26.46%), U.K. (6.40%), etc. during the period. Canada tops in relative citation index (5.30). International collaboration has been a major driver of research in the subject with 14.96% to 53.76% of national-level share of top 10 countries output appeared as international collaborative publications. Computer Science is one of the most popular areas of research in deep learning research (76.85% share). The study identifies top 50 most productive organizations and 50 most productive authors and top 20 most productive journals reporting deep learning research and 118 highly cited papers with 100+ citations per paper