How does a smoke detector detect fire, and why do some go off when you cook?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   Smoke detector (Wikipedia)

   WikipediaPublished Oct 7, 2026Checked Oct 17, 2026

   “A smoke detector is a device that senses smoke, typically as an indicator of fire. Smoke detectors/alarms are usually housed in plastic enclosures, typically shaped like a disk about 125 millimetres (5 in) in diameter and 25 millimetres (1 in) thick, but shape and size vary. Smoke can be detected either optically (photoelectric) or by physical process (ionization). Detectors may use one or both sensing methods. Sensitive detectors can be used to detect and deter smoking in banned areas. Smoke detectors in large commercial and industrial buildings are usually connected to a central fire alarm system. Household smoke detectors, also known as smoke alarms, generally issue an audible or visual alarm from the detector itself or several detectors if there are multiple devices interconnected. Household smoke detectors range from individual battery-powered units to several interlinked units with battery backup. With interlinked units, if any unit detects smoke, alarms will trigger all of the units. This happens even if household power has gone out. Residential smoke alarms are usually powered with a 9-volt battery, a non-removable "10 Year Sealed" battery, or by mains electricity.”

2. 2

   Aspirating smoke detector (Wikipedia)

   WikipediaPublished Sep 29, 2026Checked Oct 17, 2026

   “An aspirating smoke detector (ASD) is a system used in active fire protection, consisting of a central detection unit which draws air through a network of pipes to detect smoke. The sampling chamber is based on a nephelometer that detects the presence of smoke particles suspended in air by detecting the light scattered by them in the chamber. ASDs can typically detect smoke before it is visible to the naked eye. In most cases aspirating smoke detectors require a fan unit to draw in a sample of air from the protected area through its network of pipes.”

3. 3

   A multi-wavelength multi-task learning framework for risk-aware fire source classification and smoke density prediction.

   Scientific reports (Ahn et al.)Published May 4, 2026Checked Oct 17, 2026

   “This study proposes a novel multi-task learning (MTL) framework that simultaneously performs fire source classification and smoke density prediction using multi-wavelength optical sensing at 460, 530, 660, and 940 nm. The system was validated under UL 268 standard fire scenarios, including smoldering fires (filter paper and wood), flaming fires (polyurethane foam), and cooking nuisance sources (hamburger patties). Among the evaluated architectures, the CNN-LSTM model achieved the best performance, with a classification accuracy of 97% and a smoke density prediction error of 0.668 MSE. Compared to single-task learning approaches, the MTL framework reduced memory usage by 45% and achieved inference times 82% faster, demonstrating its suitability for real-time edge-based fire detection systems. Ablation studies further revealed that the 940 nm near-infrared wavelength provides discriminative features for distinguishing fire smoke from representative nuisance aerosols. Overall, the proposed approach enables reliable early detection with reduced false alarms in indoor environments.”

4. 4

   LED array-based multi-angle light scattering for aspirating smoke detection and classification.

   Scientific reports (Kim et al.)Published Jul 16, 2025Checked Oct 17, 2026

   “In particular, most smoke detection systems based on Mie scattering, which rely on single-scattering measurements, may not perform effectively in real-world environments where multiple scattering occurs. We present an advanced smoke detection instrument for aspirating smoke detection and classification based on multiple scattering. Multi-angle light scattering with an LED array instead of angle-positioned PDs was measured, and the unique optical property ratios of fire and non-fire aerosols were calculated. The feasibility of smoke detection and classification was verified by evaluating the classification performance of 10 types of fire and non-fire aerosols using general supervised learning algorithms. The advanced smoke detection instrument features a simple design, making it cost-effective and compact. In addition to reducing false fire alarms, it is expected to contribute to choosing appropriate fire extinguishers based on fire class and advancing research of complex fire detection.”

5. 5

   Invisible Smoke: Analyzing and Enhancing the Security of Smoke Detectors Under Intentional Electromagnetic Interference.

   Sensors (Basel, Switzerland) (Liu et al.)Published Jul 23, 2026Checked Oct 17, 2026

   “Smoke detectors are pivotal sensors in fire protection systems, serving as the primary trigger for alarm mechanisms and structural fire-response strategies. Their security is fundamental to the stable operation of the entire fire-protection infrastructure. This paper investigates the manipulation of commercial smoke detectors through the injection of Intentional Electromagnetic Interference (IEMI), which can induce false alarms or suppress alarms even in the presence of smoke. Our core insight is that, despite rigorous Electromagnetic Compatibility (EMC) testing, the button and amplification circuits in the detector architectures evaluated in this study can remain susceptible to coupling with IEMI attack signals. We validated these threats on nine gas sensors and seven off-the-shelf commercial smoke detectors, achieving an attack distance of over 3 m with a total power maintained within 50 W. We further observed similar IEMI-induced effects in the additional gas-detection devices evaluated in this study, including an alcohol detector, a toxic gas detector, and an air-quality detector. Finally, we propose both hardware- and software-based countermeasures to mitigate these risks.”