rameter for a continuous jamming transition. The quantity Δ z $^{2}$N would then be analogous to a sus- ceptibility, χ ∼ p − $^{γ}$, diverging with an exponent γ = 0 . 7. Our results are different than those of Henkes and Chakraborty, 37 where fluctuations of z are found to be independent of pressure, Δ z ∼ p $^{0}$. Note, how- ever, the subtle difference in ensemble. These au- thors study a pressure-ensemble, in which states are generated by quenching random particle configura- tions to the local minimum of the potential energy landscape (similar to the procedure in Ref. $^{16}$). One may view these states as the inherent structures of a high temperature liquid. Our ensemble then corre- sponds to the inherent structures of a driven glassy material. We fix volume-fraction and sample only states that are connected by the trajectory of the system in phase-space. The ensemble therefore re- flects the dynamics of the system and the region of phase-space where it is guided to. 3.1.2 Yield properties We now go beyond the properties of the elastic states and discuss aspects related to their failure during the plastic events. As indicated in the in- troduction, plastic events can be viewed as bifurca- tions in energy-landscape. A local energy minimum vanishes and the system has to search for a new minimum at lower energy and stress. In quasistatic dynamics this process is instantaneous. The asso- ciated stress-drop is therefore visible as a vertical line in the stress-strain relation (Fig. 1). In the following we characterize the amount of dissipation during a plastic event by the associated stress-drop, Δ σ . The frequency of plastic events is discussed in terms of the length of elastic branches, Δ γ$_{avg}$ . Just like the probability distributions within the elastic states, the functions P (Δ σ ) and P (Δ γ$_{avg}$ ) (Figs. 5 and 6) are universal and can be rescaled on a single master-curve. Here, it is sufficient to use the first moment of the distribution, i.e. the ensemble-averaged stress drops and elastic-branch lengths, respectively. As the logarithmic scale in the inset in Fig. 5 shows, the collapse for the stress- drop distribution is quite good for large as well as for small stress drops. The black line repre- sents a fit of the form Δ σ $_{−}$1 exp( − Δ σ/σ$_{L}$ ) with the stress-scale σ$_{L}$ ≈ 5Δ σ$_{avg}$ . The intermediate 6 Figure 5: (Top) Distribution of stress-drops nor- malized with average values Δ σ$_{avg}$ . Inset shows the same figure in a log-log representation. (Bottom) Scaling of Δ σ$_{avg}$ with pressure p . power-law behaviour P ∼ Δ σ $_{−}$1 reflects the lack of scale related to a typical event size. The only rele- vant scale is the exponential cut-off at σ$_{L}$ . Tewari et al. 38 have reported an exponent of − 0 . 7 in the energy-drop distribution at finite-strain rates. The simulated systems are somewhat smaller, however. Kabla et al. 39 have found an exponent of − 1 . 5 in a vertex model for foams, in agreement with renor- malization group arguments. 40 The exponential tail has been observed in sev- eral different studies 8,11,11,41,42 in two and in three spatial dimensions. Tsamados et al. 11 have further- more related this feature of the stress-drop distri- bution to the diversity of local flow-defects causing the plastic event. A similar universality has been observed by Mal- oney and Lemaˆıtre. 8 Their simulations are con- ducted with three different interaction potentials but without changing the density, which is set to high values far away from the rigidity transition. 10 -4 10 -2 10 0  0  5  10  15  20  25  30 avg P avg 0.01 1 ~x $^{-1}$exp(-x/x$_{0}$ ) 10 -5 10 -4 10 -3 10 -2 avg p N = 900 = 1600 = 2500