{"id":"7a579101-78ef-4992-9e62-8b366735964c","arxiv_id":"2601.18114","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"An IIR-based liveness-aware trigger preserves coherence memory through detector deadtime and sustains higher simulated event-recovery efficiency than binary coincidence logic.","lead":"This paper proposes a liveness-aware trigger that uses a slowly decaying memory filter to keep dead channels from breaking coincidence chains in neutrino-array data acquisition. A simulation comparison claims it recovers trigger efficiency under high deadtime where conventional coincidence triggers collapse.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Efficiency gain is confounded: proposed IIR baseline adds temporal filtering, not just liveness persistence.","rationale":"The paper's mathematical construction (Eq. 3) is stable and hardware-plausible, and the authors honestly list validation gaps in Sec. 4.3. However, the central claim is that preserving state through non-liveness intervals is what delays efficiency loss. The reported comparison against generic binary coincidence logic does not isolate this mechanism because the proposed system also includes IIR temporal smoothing. A hard-reset IIR control would directly test whether the efficiency advantage survives when the only difference is liveness-driven persistence. This is an internal-validity concern rather than an external-model concern, so it differs from the reader's weakest assumption about deadtime realism, though both point to under-specified validation. The verdict remains CONDITIONAL: the idea is promising, but the causal contribution of liveness awareness is not yet demonstrated. No change to the reader's conditional verdict is needed, but the revision should include the control experiment.","tokens_in":14880,"tokens_out":9242,"duration_ms":106325,"concrete_test":"Add a control trigger to the pipeline: use Eq. (3) with the same k=0.9 but set Psi_eff_i[n]=0 whenever L_i[n]=0 (hard-reset IIR), and keep the same coherence score and threshold calibration at Pdead=0. Recompute Fig. 8 for this control, the proposed trigger, and the binary coincidence baseline; also report noise-only false-trigger rates at each Pdead. If the hard-reset IIR curve matches the proposed curve within statistical error, the claimed liveness-specific benefit is not established.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central comparison (Fig. 8, Secs. 2.9 and 3.2) does not isolate the liveness mechanism. The proposed trigger uses a first-order IIR (Eq. 3) with k=0.9; the 'baseline coincidence' uses instantaneous thresholded counts (Eqs. 7-8) with no temporal smoothing, then gates out dead samples. These two systems differ in two independent ways: (i) the IIR's low-pass filtering/integration and (ii) persistence of state during non-liveness. Thresholds are calibrated only at Pdead=0, so the observed efficiency margin at elevated Pdead could reflect the IIR's ability to accumulate energy over a sliding time window, not liveness awareness. A control with the same IIR but with hard reset during non-liveness (replace Eq. 4 with Psi_eff=0 when Li=0) is never reported. Without that control, the causal claim 'preserving coherence across short non-liveness intervals' is underdetermined. This is load-bearing because the contribution is specifically the liveness-aware update law.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a liveness-aware trigger for distributed optical arrays in high-energy neutrino detectors. The central idea is a first-order IIR update law (Eq. 3) that constructs a per-channel effective observable which, during deadtime intervals characterized by a binary liveness function L_i[n], decays smoothly instead of being hard-gated to zero. This observable is aggregated into an energy-like coherence score (Eq. 5) and compared against a conventional multiplicity coincidence trigger (Eqs. 7–8) under synthetically injected deadtime. The authors report that the proposed trigger maintains higher trigger efficiency, higher SNR, and lower MSE as deadtime probability grows (Figs. 8, 11, 12), and they argue that the benefit stems from preserving coherence across short non-liveness intervals. Validation uses event topologies from IceCube Open Data converted to waveforms with a parametric PMT/digitizer model and controlled deadtime injection. The paper concludes with FPGA-readiness claims based on the O(1) recursion and fixed-point compatibility.","tokens_in":15215,"tokens_out":3870,"duration_ms":43385,"significance":"If the central claim holds, the liveness-aware update law is a simple, hardware-feasible mechanism for distinguishing measurement unavailability from true silence, and it could be relevant to next-generation DAQ triggers in large-scale optical arrays. The paper's strengths are its clearly posed filter formulation (a single stable pole for 0<k<1), its explicit separation of the continuous measurement layer from the discrete decision layer, and a validation design that uses publicly available event topologies rather than fully synthetic signals. The threshold calibration procedure at P_dead=0, with a stated false-trigger target and 2×10^7 Monte Carlo trials, is a good practice, as is the parameter sensitivity study for k. However, the comparison that supports the headline claim is not clean: the baseline and proposed systems differ in two independent respects—temporal low-pass filtering and liveness persistence—and the paper does not isolate the liveness mechanism. Also, the statistical and methodological reporting in the validation is under-specified in several places. These issues are load-bearing because the paper's contribution is specifically the liveness-aware update law, not t","major_comments":[{"comment":"The hidden note is that the improvement could be a temporal-filtering artifact, which is a serious concern. The control experiment is feasible in the simulation framework and should be included.","section":"Sec. 3.2, Fig. 8; Eqs. (3), (7)–(8)"},{"comment":"This is load-bearing because the quantitative claim of improved efficiency rests entirely on this figure.","section":"Sec. 3.2, Fig. 8; Sec. 2.6"},{"comment":"This is a specific testable concern; it does not require model changes, only an additional simulation.","section":"Sec. 2.4, Table 2, Sec. 3.2"},{"comment":"A minor-to-moderate issue, but it affects the interpretation of a secondary claim.","section":"Sec. 3.6, Fig. 13"}],"minor_comments":[{"comment":"The notation '∆t = ∆t (corresponding to fs = fs)' is a placeholder and should be filled with actual values. Elsewhere, Fig. 4 states fs=100 MHz while Table 2 states fs=60 MSPS; please resolve this inconsistency.","section":"Sec. 2.4, Eq. (2) area"},{"comment":"Table 1 lists many parameters as 'representative' or 'order' values without concrete numbers. Since the simulation results depend on these, give the exact values used in the reported runs, or provide a separate table of the nominal simulation configuration.","section":"Table 1"},{"comment":"Fig. 3 labels the shared stage as 'Eqs. (iir) – (decision)' with placeholder equation references; use actual equation numbers (3)–(8).","section":"Fig. 3"},{"comment":"The label 'baseline=median' is unclear; specify what the baseline curve represents and how the median is computed.","section":"Fig. 4"},{"comment":"The y-axis scale '1e-12' is odd for a score that is thresholded around 2.66; check the units or normalization and make the axis readable.","section":"Fig. 5"},{"comment":"The statement that the energy-like statistic 'approximates a log-likelihood ratio' is not justified and is not used in the paper; either add a derivation or remove the claim.","section":"Sec. 2.7"}],"recommendation":"major_revision","confidential_remarks":"This is a reasonable engineering-oriented paper with a clear, simple filter law, but the central claim needs a decisive control experiment: same IIR with and without liveness persistence. Also, the paper would benefit from stronger statistical reporting (error bars, event counts, false-trigger rates versus P_dead). The authors should also be encouraged to cite the actual IceCube trigger literature more thoroughly; the current reference to [9] (a self-citation) is not a substitute. I recommend major revision rather than rejection because the shortcomings are addressable within the simulation framework and the core filter idea is not fundamentally flawed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a well-written proof-of-design for treating deadtime as an explicit state in a trigger pipeline. The IIR update law is simple and the hardware story is plausible. But the central comparison in Fig. 8 doesn't test what the abstract claims. The proposed trigger uses a first-order IIR with k=0.9; the baseline uses instantaneous thresholded hits. Two things differ at once: the temporal smoothing and the persistence of memory through non-liveness. The paper never runs the obvious control—the same IIR but with a hard reset to zero when a channel is non-live. Without that, the efficiency margin at elevated Pdead could just be the low-pass filter accumulating energy over a window, not liveness awareness. That is load-bearing because the contribution is specifically the liveness-aware update.\n\nThe paper does some things well. The pipeline diagram is clear, the separation of measurement and decision is a sensible design principle, and the stability argument for the recursion is correct. The limitations section is honest: the authors admit the deadtime injection is a controlled model, not a full electronics simulation, and that a hardware-in-the-loop test is still needed. That honesty earns credit.\n\nThe validation is under-specified on smaller points too. Section 2.6 doesn't say how IceCube event topologies were extracted from open data. Table 1 lists several 'representative' values without giving numbers. Figure 8's caption mentions error bars 'where shown' but none appear. No code or data release is mentioned. None of these are fatal, but together they make the results hard to build on.\n\nThe novelty is modest—an IIR with a liveness gate is standard DSP—but the framing around coherence triggers for neutrino DAQ is a reasonable new application. The math checks out. The citation pattern is fine, including the self-cited Synchromodulametry framework, which does seem directly relevant.\n\nBottom line: this deserves a serious referee, but it needs major revision. The missing control should be run and reported, the parameter values need to be concrete, and either error bars or a clear statement about statistical uncertainty should be explicit. If the control shows the gain mostly disappears, the paper becomes a minor note about a practical trick; if it survives, the liveness story is real. I'd push for review rather than desk rejection.","headline":"Plausible liveness-aware trigger idea, but the headline efficiency result is confounded: the baseline lacks temporal filtering, so the liveness-persistence benefit isn't isolated.","tokens_in":15618,"tokens_out":2478,"would_cite":false,"duration_ms":27903,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A liveness-aware IIR trigger delays efficiency loss under detector deadtime.","keywords":["deadtime","liveness-aware trigger","IIR filter","coincidence trigger","neutrino detector","FPGA","trigger efficiency","optical sensor array"],"falsifier":"A hardware-in-the-loop test that injects measured front-end deadtime (with rate-dependent recovery and clipping) into an FPGA prototype: if the liveness-aware trigger's efficiency advantage over coincidence logic shrinks to statistical noise under real deadtime patterns, the central claim fails. Alternatively, measure the false-trigger rate under noise-only data with high deadtime probability; if the IIR memory raises spurious triggers beyond the calibrated 10^-3 proxy, the persistence prior is too strong.","tokens_in":14826,"feed_emoji":"🧊","tokens_out":4252,"duration_ms":43040,"temperature":0.7,"pith_summary":"The paper argues that conventional coincidence triggers in optical neutrino detectors fail during deadtime because they treat a temporarily non-live channel as evidence of physical silence. To fix this, it introduces a liveness-aware IIR filter at each sensor: when a channel goes non-live, the filter's output decays smoothly instead of collapsing, preserving the memory of recent light. The authors show by simulation that this continuity keeps the network-level coherence score high enough to trigger on events that a binary coincidence logic would fragment. A reader should care because deadtime is unavoidable in large photomultiplier arrays, and the proposed update law is simple enough for FPGA implementation.","feed_headline":"IIR filter keeps neutrino triggers alive through deadtime gaps","feed_subtitle":"A liveness-aware update lets optical sensors remember signals while non-live, delaying efficiency loss.","key_machinery":"The key mechanism is the liveness-aware IIR update law (Eq. 3), a one-pole recursive filter with a liveness gate: each sensor keeps a state Ψeff that tracks the instantaneous signal while live and decays smoothly as exp(−α Δt) when non-live. This is paired with an energy-like coherence score G[n]=Σ w_i (Ψeff_i)^2 computed over a sliding window, and a threshold Γ calibrated to a fixed false-trigger rate. The combination turns the trigger problem from a fragile all-or-nothing coincidence requirement into continuous coherence tracking, with O(1) per-sample cost per channel and a single tunable parameter k that sets persistence.","core_discovery":"The central claim is that a first-order recursive IIR update law, Ψeff[n] = k Ψeff[n−1] + (1−k) Ψ[n] L[n], separates measurement construction from trigger decision, so a channel that is temporarily non-live is marked as unavailable rather than silent. When L[n]=0, the observable decays exponentially with factor k; this preserves phase and amplitude information accumulated before deadtime. Aggregating these observables into an energy-like coherence score, the trigger sustains higher event-recovery efficiency at elevated deadtime probability than a calibrated multiplicity coincidence trigger, while maintaining a 10^-3 false-trigger proxy. The authors also report higher SNR and lower reconstruc","pith_inferences":["The continuity-persistence assumption is a prior that the physical signal stays correlated on timescales comparable to deadtime; if real saturation events are followed by baseline shifts or nonlinear recovery, the optimal k may need to be time-varying rather than fixed.","A testable extension is to compare the IIR trigger against a coincidence trigger on real streamed waveforms with hardware-in-the-loop; the predicted benefit should appear as an increased fraction of recovered events under measured deadtime episodes.","The idea could transfer to any sparse sensor network (e.g., distributed acoustic or radio arrays) where per-channel deadtime is common and true silence is not the same as unavailability.","At very high deadtime probability both methods degrade; the claim is about delaying the onset of efficiency loss, not eliminating deadtime information loss."],"forward_implications":["If deadtime is intermittent, even brief non-liveness in a subset of channels no longer breaks valid coincidence chains.","The trigger can be deployed in low-power FPGAs because the filter uses one multiply-accumulate per sample per channel and no global buffering.","Calibrating thresholds at zero deadtime and holding them fixed across sweeps means the efficiency gain is structural, not a retuning artifact.","The parameter k provides a clear operating region (roughly 0.70–0.95) where efficiency is high and the false-trigger proxy stays negligible.","The same measurement/decision separation can be extended to covariance or low-rank coherence metrics without changing the update law."],"fun_headline_variants":["Liveness-aware trigger sustains neutrino detection during deadtime","IIR trigger mitigates deadtime loss in neutrino arrays","IIR filter preserves neutrino signal through deadtime gaps","Recursive IIR update keeps neutrino trigger alive in deadtime"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that real detector deadtime behaves like a binary liveness gate with fixed or distributed recovery windows; if actual non-liveness involves rate-dependent recovery, baseline shifts, or corrupted samples rather than clean removal, the simulated efficiency gain may be overstated.","fun_headline_variants_meta":{"raw":{"variants":["Liveness-aware trigger sustains neutrino detection during deadtime","IIR trigger mitigates deadtime loss in neutrino arrays","IIR filter preserves neutrino signal through deadtime gaps","Recursive IIR update keeps neutrino trigger alive in deadtime"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000268,"raw_usage":{"total_tokens":1431,"prompt_tokens":697,"completion_tokens":734,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":441,"completion_tokens_details":{"reasoning_tokens":667}},"tokens_in":441,"tokens_out":734,"duration_ms":8192,"temperature":1.0,"reasoning_tokens":667,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-03T08:05:46.674536+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A hardware-in-the-loop test that injects measured front-end deadtime (with rate-dependent recovery and clipping) into an FPGA prototype: if the liveness-aware trigger's efficiency advantage over coincidence logic shrinks to statistical noise under real deadtime patterns, the central claim fails. Alternatively, measure the false-trigger rate under noise-only data with high deadtime probability; if the IIR memory raises spurious triggers beyond the calibrated 10^-3 proxy, the persistence prior is too strong.","supporting_citations":[],"review_version":1}